New technologies such as smart sensors improve rehabilitation processes and thereby increase older adults’ capabilities to participate in social life, leading to direct physical and mental health benefits. Wearable smart sensors for home use have the additional advantage of monitoring day-to-day activities and thereby identifying rehabilitation progress and needs. However, identifying and selecting rehabilitation priorities is ethically challenging because physicians, therapists, and caregivers may impose their own personal values leading to paternalism. Therefore, we develop a discussion template consisting of a (...) series of adaptable questions for the patient–physician encounter based on the capability approach. The goal is to improve geriatric rehabilitation and thereby increase participation in social life and well-being. To achieve this goal, we first analyzed what is considered important for participation on basis of the capability approach, human rights, and ethics of care. Second, we conducted an ethical analysis of each of the four identified dimensions of participation: political, economic, socio-cultural, and care. To improve compliance with rehabilitation measures, health professionals must align rehabilitation measures in an open dialogue with the patient’s aspiration for participation in each dimension. A discussion template based on the capability approach allows for a proactive approach in patient information and stimulates a critical assessment of treatment alternatives while reducing the risk of imposing personal values. (shrink)

In contemporary bioethics, the autonomy of the patient has assumed considerable importance. Progressing from a more limited notion of informed consent, shared decision making calls upon patients to voice the desires and preferences of their authentic self, engaging in choice among alternatives as a way to exercise deeply held values. One influential opinion in Jewish bioethics holds that Jewish law, in contradistinction to secular bioethics, limits the patient's exercise of autonomy only in those instances in which treatment choices are sensitive (...) to preferences. Here, we analyze a discussion in the Mishna, a foundational text of rabbinic Judaism, regarding patient autonomy in the setting of religiously mandated fasting, and commentaries in the Babylonian and Palestinian Talmuds, finding both a more expansive notion of such autonomy and a potential metaphysical grounding for it in the importance of patient self-knowledge. (shrink)

Disclosure in clinical practice is aimed at promoting patient autonomy, usually culminating in patient choice (e.g., to consent to an operation or not, or between different medications). In medical ethics, there is an implicit background assumption that knowing more about (X) automatically translates to greater, or more genuine, autonomy with respect to one's choices involving (X). I challenge this assumption by arguing that in rare cases, withholding information can promote a patient's autonomy (understood as the capacity for rational choice (...) in alignment with one's values and goals). (shrink)

Brain-related diseases are among the most difficult diseases due to their sensitivity, the difficulty of performing operations, and their high costs. In contrast, the operation is not necessary to succeed, as the results of the operation may be unsuccessful. One of the most common diseases that affect the brain is Alzheimer’s disease, which affects adults, a disease that leads to memory loss and forgetting information in varying degrees. According to the condition of each patient. For these reasons, it is (...) important to classify memory loss and to know the patient at what level and his assessment of Alzheimer's disease through CT scans of the brain. In this thesis, we review ways and techniques to use deep learning classification to classifying the Alzheimer's Disease The proposed method used to improve patient care, reduce costs, and allow fast and reliable analysis in large studies. The model will be designed using Python language for implementing the system, which is very useful for doctors, classifying the Alzheimer's Disease, was used. The model used 70% from image for training and 30% from image for validation, our trained model achieved an accuracy of 100% on a held-out test set. (shrink)

Abstract:Respect for persons protects patients regarding their own healthcare decisions. Patient informed choice for altruism (PICA) is a proposed means for a fully autonomous patient with decisionmaking capacity to limit his or her own treatment for altruistic reasons. An altruistic decision could bond the patient with others at the end of life. We contend that PICA can also be an advance directive option. The proxy, family, and physicians must be reminded that a patient’s altruistic treatment refusal should be respected.

Scientists have the ability to influence policy in important ways through how they present their results. Surprisingly, existing codes of scientific ethics have little to say about such choices. I propose that we can arrive at a set of ethical guidelines to govern scientists’ presentation of information to policymakers by looking to bioethics: roughly, just as a clinician should aim to promote informed decision-making by patients, a scientist should aim to promote informed decision-making by policymakers. Though this may sound (...) like a natural proposal, I show it offers guidance that conflicts with standard scientific practices. I conclude by considering one cost of the proposal: that it would prevent scientists from acting as advocates in a way that is currently common in certain fields. I accept that the proposal would restrict scientists’ political advocacy rights, but argue that the benefits of adopting it — promoting democratic governance — justify the restriction. (shrink)

Background: Mental health professionals working in correctional contexts engage a double role to care and control. This dual loyalty conflict has repeatedly been criticized to impede the development of a high-quality alliance. As therapeutic alliance is a robust predictor of outcome measures of psychotherapy, it is essential to investigate the effects of this ethical dilemma. Methods: This qualitative interview study investigates patients’ perceptions of their therapists’ dual role conflict in court-mandated treatment settings. We interviewed 41 older incarcerated persons using a (...) semi-structured interview guide, the interviews were subsequently analyzed following thematic analysis. Results: We first present the patients’ perceptions of their treating psychotherapist’s dual loyalty conflict, which was linked to their overall treatment experience. In a second step, we outline the study participants’ reasons for this judgment, which were most commonly linked to feelings of trust or betrayal. More specifically, they named certain therapist characteristics and activities that enabled them to develop a trustful therapeutic alliance, which we grouped into four topics: respecting the patient’s pace and perceived coercion; patient health needs to be first priority; clarity in roles and responsibilities; and the art of communication – between transparency and unchecked information sharing. Discussion: Developing a high quality alliance in mandatory offender treatment is central due to its relationship with recovery and desistance. Our findings show that some therapists’ characteristics and activities attenuate the negative impact of their double role on the development and maintenance of the alliance. To increase the effectiveness of court-mandated treatments, we need to support clinicians in dealing with their dual role to allow the formation of a high quality therapeutic alliance. Our qualitative interview study contributed to this much-needed empirical research on therapist’ characteristics promoting a trustful relationship in correctional settings. (shrink)

Vertigo is a common symptom that can result from various underlying diseases and conditions, ranging from benign to severe. Accurate and timely diagnosis of the cause of vertigo is crucial for appropriate management and treatment. In this research, we propose the development of an expert system for vertigo diseases diagnosis, utilizing artificial intelligence (AI) and the proposed Expert System which was produced to help assist healthcare professionals in diagnosing the cause of vertigo based on a patient's symptoms, medical history, and (...) other relevant clinical information. The proposed expert system presents an overview about vertigo diseases are given, the cause of disease is outlined and the treatment or recommendation of disease whenever possible is given out. CLIPS language was used for designing and implementing the proposed expert system. The potential of the proposed expert system lies in its ability to enhance the accuracy and efficiency of vertigo diagnosis, as well as assist in the proper referral and management of patients. (shrink)

Diagnosing flatulence involves a thorough assessment of an individual's symptoms, medical history, and, if necessary, the use of diagnostic tests. Healthcare providers gather information about the patient's medical background and conduct a physical examination to identify any signs of gastrointestinal issues. Dietary habits are evaluated, and potential triggers are identified through an elimination diet. Diagnostic tests such as breath tests, stool analysis, or imaging studies may be performed to further investigate the underlying causes of excessive flatulence. Accurate diagnosis is (...) crucial for effective management and treatment of flatulence, enabling individuals to gain control over their symptoms and address any underlying conditions. (shrink)

Owing to its grappling with a motley of intricate socioeconomic, as well as medico-legal, crises, Haiti has found itself bereft of some of its people, many of whom have had to leave the Caribbean country in search of improved lives elsewhere. Receiving some of the Haitian refugees fleeing abject poverty, unemployment, and other harms and barriers has been the United States, one of Haiti's northern neighbors and a country that has played an outcome-determinative, if not outsized, role in steering the (...) country toward its presently hobbled state. Drawing on the U.S.’s recent treatment of Haitian refugees, this paper argues that U.S. reception of Haitian immigrants rubs salt in the wound of a long history of dehumanizing and oppressive abuses endured by Haitians. Furthermore, and more importantly, this paper posits that U.S. failure to wholly embrace its legal obligation to accept Haitian refugees under international law needs to be understood in the light of the specific horrors inflicted by science, in pre-independence Haiti, on non-consenting, Afro-Haitian experimental subjects. And by extension, such a contextually-nuanced understanding is crucial in shaping the delivery of healthcare services to Haitian refugees fortunate enough to remain in the U.S. —as an awareness and appreciation of the socio-historical context of patients' lived experiences, i.e., their complete social history, can furnish important clues vis-à-vis the presence and etiologies of disease, influence the foci of physical exams, and generally pave the way for the provision of cost-efficient and evidence-based care. (shrink)

Migraine is a complex neurological disorder characterized by recurrent moderate to severe headaches, accompanied by additional symptoms such as nausea, sensitivity to light and sound, and visual disturbances. Accurate and timely diagnosis of migraines is crucial for effective management and treatment. However, the diverse range of symptoms and overlapping characteristics with other headache disorders pose challenges in the diagnostic process. In this research, we propose the development of an expert system for migraine diagnosis using artificial intelligence and the CLIPS (C (...) Language Integrated Production System) framework. The expert system utilizes a rule-based inference engine to analyze patient-reported symptoms and provide reliable diagnoses or probability scores indicating the likelihood of migraine. The knowledge base of the expert system is designed based on expert knowledge obtained from medical professionals specializing in migraines. The collected knowledge is translated into a structured format suitable for the CLIPS inference engine, incorporating rules and facts to represent the diagnostic criteria and associated symptoms. The system prompts users to provide relevant information about their symptoms, medical history, and potential triggers. It applies the defined rules and facts to evaluate the likelihood of migraine and generate accurate diagnoses or probability scores. Preliminary evaluation results demonstrate the potential of the expert system as a valuable tool for diagnosing migraines. A dataset of anonymized patient records with confirmed migraine cases was used to test the system. The diagnoses generated by the expert system were compared against the known diagnoses, and a high level of accuracy was observed, with 90% of cases correctly diagnosed as migraines. These results highlight the effectiveness and reliability of the system in assisting medical professionals in the diagnosis of migraines. The proposed expert system offers several advantages for migraine diagnosis. It leverages the collective knowledge and expertise of experienced migraine specialists, providing a standardized and consistent approach to diagnosis. The system can handle large amounts of patient data and effectively analyse complex relationships between symptoms, risk factors, and diagnostic criteria. Furthermore, it offers real-time feedback and recommendations, supporting medical professionals in their clinical decision-making process. Future work involves refining the expert system based on feedback from medical experts, expanding the knowledge base to encompass a wider range of symptoms and risk factors, and conducting further evaluations to enhance its accuracy and applicability in clinical settings. The development of an expert system for migraine diagnosis has the potential to improve the diagnostic process, leading to more effective management and treatment strategies for individuals suffering from migraines. (shrink)

This abstract is a synopsis of the paper "An Expert System for Diagnosing Whooping Cough Using CLIPS." The bacterium Bordetella pertussis causes whooping cough, a highly infectious respiratory ailment with several phases of symptoms. An accurate and timely diagnosis is critical for effective treatment and the avoidance of future transmission. The construction of an expert system for detecting whooping cough using the CLIPS (C Language Integrated Production System) architecture is highlighted in this abstract. The expert system provides standardized and systematic (...) evaluation, which reduces the chance of misdiagnosis and improves patient outcomes. Maintenance and upgrades are required to keep the system current with growing medical knowledge. More study in this area has the potential to advance expert systems in identifying and managing a variety of medical disorders. (shrink)

Heart palpitations, while often benign, can sometimes be indicative of severe underlying conditions requiring immediate intervention. Accurate and swift diagnosis thus remains a clinical priority. "A CLIPS-Based Expert System for Heart Palpitations Diagnosis" represents a novel approach to addressing this challenge, harnessing the power of artificial intelligence and rule-based expert systems. Specifically, this system applies a suite of 7 if-then rules to evaluate potential heart palpitations causes and assign one of three outcomes: 1) A confirmed diagnosis of heart palpitations, 2) (...) A suspected link to cardiovascular diseases, and 3) A possible association with anxiety or stress disorders. The expert system offers an intuitive user interface, allowing for seamless symptom input and instant diagnosis based on user-provided information. This paper explores the various phases of this expert system's lifecycle, including design, implementation, and evaluation. Furthermore, the study situates the system within the broader discourse on rule-based expert systems for heart palpitations diagnosis, critically analyzing their efficiency, potential pitfalls, and ongoing challenges. Through this research, the value of integrating rule-based expert systems in clinical diagnostic processes is highlighted, illustrating its capacity to enhance diagnostic accuracy and patient outcomes. (shrink)

Abstract: Colon cancer is a prevalent and life-threatening disease, necessitating accurate and timely diagnosis for effective treatment and improved patient outcomes. This research paper presents the development of a knowledge-based system for diagnosing colon cancer using the CLIPS language. Knowledge-based systems offer the potential to assist healthcare professionals in making informed diagnoses by leveraging expert knowledge and reasoning mechanisms. The methodology involves acquiring and structuring medical knowledge specific to colon cancer, followed by the implementation of a knowledge- based system using (...) CLIPS. The system's design integrates relevant medical databases and employs advanced reasoning mechanisms to facilitate accurate diagnosis. Evaluation results demonstrate the system's effectiveness, with high accuracy and sensitivity rates compared to existing methods. The findings highlight the potential of knowledge-based systems in improving clinical practice and patient care. The research contributes to the growing field of medical informatics by demonstrating the feasibility and effectiveness of CLIPS-based knowledge systems for colon cancer diagnosis. Future research can focus on enhancing system performance and expanding its scope to encompass other types of cancer diagnoses. (shrink)

The study aimed at identifying responsive flexibility and its role in improving service quality, from the point of view of the internal beneficiary in non-governmental hospitals in Gaza Strip. The study relied on the descriptive and analytical approach, and the questionnaire was designed as a tool to collect data, and the researchers used the comprehensive survey method, and the number of the study population was (536) single, where (434) questionnaires were retrieved, and the recovery rate was (80.97%). The study showed (...) many results, the most important of which were: The presence of moderate degree of approval by the study sample on the responsive flexibility, as it was evident through the response flexibility field obtaining a relative weight (60.50%), and the results of the study indicated that there is a high degree of approval by the members of the study sample. On the quality of service, as it was evident through the field of service quality obtaining a relative weight (79.90%). The results of the study revealed a statistically significant relationship between responsive flexibility and service quality in non-governmental hospitals in Gaza Strip, with a correlation coefficient of 0.418, and the results of the study revealed a statistically significant effect between responsive flexibility and service quality in non-governmental hospitals in Gaza Strip. The study reached many recommendations, the most important of which were: Finding mechanisms by which stored information can be used to enhance the decision-making process, and setting up an effective system to receive patients' complaints in a manner that ensures rapid response and treatment, to achieve continuous communication between patients and the hospital management, and to notify patients of abuse With the complaints that they submit, and working to provide all medical and health specialties in the hospitals under study, by making use of the medical delegations that visit Gaza Strip, involving them in the treatment processes, bringing in doctors and specialists from abroad, and updating the standards related to measuring the services provided to patients continuously, based on On patients' suggestions and complaints, developing facilities in hospitals, as well as updating medical devices and equipment used in hospitals periodically. (shrink)

In this paper an Artificial Neural Network (ANN) model, for predicting the category of a tumor was developed and tested. Taking patients’ tests, a number of information gained that influence the classification of the tumor. Such information as age, sex, histologic-type, degree-of-diffe, status of bone, bone-marrow, lung, pleura, peritoneum, liver, brain, skin, neck, supraclavicular, axillar, mediastinum, and abdominal. They were used as input variables for the ANN model. A model based on the Multilayer Perceptron Topology was established and (...) trained using data set which its title is “primary tumor” and was obtained from the University Medical Centre, Institute of Oncology, Ljubljana, Yugoslavia Test data evaluation shows that the ANN model is able to correctly predict the tumor category with 76.67 % accuracy. (shrink)

Patients with serious illnesses or injuries may decide to quit their medical treatment if they think paying the fees will put their families into destitution. Without treatment, it is likely that fatal outcomes will soon follow. We call this phenomenon “near-suicide”. This study attempted to explore this phenomenon by examining how the seriousness of the patient’s illness or injury and the subjective evaluation of the patient’s and family’s financial situation after paying treatment fees affect the final decision on (...) the treatment process. Bayesian Mindsponge Framework (BMF) analytics were employed to analyze a dataset of 1042 Vietnamese patients. We found that the more serious the illnesses or injuries of patients were, the more likely they were to choose to quit treatment if they perceived that paying the treatment fees heavily affected their families’ financial status. Particularly, only one in four patients with the most serious health issues who thought that continuing the treatment would push themselves and their families into destitution would decide to continue the treatment. Considering the information-filtering mechanism using subjective cost–benefit judgments, these patients likely chose the financial well-being and future of their family members over their individual suffering and inevitable death. Our study also demonstrates that mindsponge-based reasoning and BMF analytics can be effective in designing and processing health data for studying extreme psychosocial phenomena. Moreover, we suggest that policymakers implement and adjust their policies (e.g., health insurance) following scientific evidence to mitigate patients’ likelihood of making “near-suicide” decisions and improve social equality in the healthcare system. (shrink)

Background: Arthritis is very common but is not well understood. Actually, “arthritis” is not a single disease; it is an informal way of referring to joint pain or joint disease. There are more than 100 different types of arthritis and related conditions. People of all ages, sexes and races can and do have arthritis, and it is the leading cause of disability in America. More than 50 million adults and 300,000 children have some type of arthritis. It is most common (...) among women and occurs more frequently as people get older. Common arthritis joint symptoms include swelling, pain, stiffness and decreased range of motion. Symptoms may come and go. They can be mild, moderate or severe. They may stay about the same for years, but may progress or get worse over time. Severe arthritis can result in chronic pain, inability to do daily activities and make it difficult to walk or climb stairs. Arthritis can cause permanent joint changes. These changes may be visible, such as knobby finger joints, but often the damage can only be seen on X-ray. Some types of arthritis also affect the heart, eyes, lungs, kidneys and skin as well as the joints. Objectives: The main goal of this expert system is to get the appropriate diagnosis of disease and the correct treatment and give the appropriate method of treatment through several tips that concern the disease and how to treat it and we will see it through the application on the expert system. Methods: in this paper the design of the proposed Expert System which was produced to help Orthopedist in diagnosing Arthritis disease through its symptoms such as: pain on pressure in a joint , Inflammation indicated by joint swelling, Stiffness especially in the morning , Loss of flexibility of joint, Limited, joint movement, Deformity of the joints , Weight loss and fatigue , Non-specific fever and Crepitus. The proposed expert system presents an overview about Arthritis disease is given, the cause of diseases is outlined and the treatment of disease whenever possible is given out. SL5 Object Expert System language was used for designing and implementing the proposed expert system. Results: The proposed Arthritis disease diagnosis expert system was evaluated by Orthopedics students and they were satisfied with its performance. Conclusions: The Proposed expert system is very useful for Orthopedist, patients with arthritis and newly graduated Orthopedics students. (shrink)

Background: Dengue Disease is a mosquito-borne tropical disease caused by the dengue virus, symptoms typically begin three to fourteen days after infection. This may include a high fever, headache, vomiting, muscle and joint pains, and a characteristic skin rash. Dengue serology is applied in different settings, such as for surveillance, in health care facilities in endemic areas and in travel clinics in non-endemic areas. The applicability and quality of serological tests in dengue endemic regions has to be judged against a (...) background of potential cross reactivity with other flavi-viruses, difficulties in distinguishing primary from secondary infections and technological problems related to the fact that most dengue endemic regions are relatively poor of resources .Objectives: to help doctors and patients in diagnosing Dengue Disease and give them the information of how to prevent Dengue Disease and to be able to understand the signs and symptoms of Dengue Disease. Methods: We collected all relevant material for Dengue Disease. Then we designed and implemented a knowledge based system for diagnosing Dengue Disease using SL5 Object Language. Results: The knowledge based system was evaluated by a group of Patients and specialized doctors and they found it very friendly and easy to use. (shrink)

Subjective rights enjoy limited import in Kelsenian theory for whereas the concept of duty underlies every legal norm, that of rights is merely possible and only emerges when the imposition of the sanction attached to the breach of the duty is made dependent upon a subject's will to bring legal action. The presence of secondary norms establishing certain duties of medical professionals on informed consent displays the existence of correlative reflex rights of patients. Yet, together with secondary norms, Western legal (...) systems typically institute norms of competence enabling patients to take legal action when those duties correlative to their reflex right to informed consent have been infringed. Thus, informed consent constitutes a set of subjective legal positions of patients and medical professionals. Despite its excessive formalism and questionable neutrality, the Kelsenian approach permits to define and clearly distinguish patients' genuine rights from mere aspirations devoid of legal basis. (shrink)

This work presents the design of an expert system that aims to provide the patient with background for suitable diagnosis of some of the eye diseases. The eye has always been viewed as a tunnel to the inner workings of the body. There are many disease states that may produce symptoms from the eye. CLIPS language is used as a tool for designing our expert system. An initial evaluation of the expert system was carried out and a positive feedback was (...) received from the users. (shrink)

Antimicrobial resistance (AMR) is a global public health disaster driven largely by antibiotic use in human health care. Doctors considering whether to prescribe antibiotics face an ethical conflict between upholding individual patient health and advancing public health aims. Existing literature mainly examines whether patients awaiting consultations desire or expect to receive antibiotic prescriptions, but does not report views of the wider public regarding conditions under which doctors should prescribe antibiotics. It also does not explore the ethical significance of public views (...) or their sensitivity to awareness of AMR risks or the standpoint (self-interested or impartial) taken by participants. Methods: An online survey was conducted with a sample of the U.S. public (n = 158). Participants were asked to indicate what relative priority should be given to individual patients and society-at-large from various standpoints and in various contexts, including antibiotic prescription. Results: Of the participants, 50.3% thought that doctors should generally prioritize individual patients over society, whereas 32.0% prioritized society over individual patients. When asked in the context of AMR, 39.2% prioritized individuals whereas 45.5% prioritized society. Participants were significantly less willing to prioritize society over individuals when they themselves were the patient, both in general (p = .001) and in relation to AMR specifically (p = .006). Conclusions: Participants’ attitudes were more oriented to society and sensitive to collective responsibility when informed about the social costs of antibiotic use and when considered from a third-person rather than first-person perspective. That is, as participants came closer to taking the perspective of an informed and impartial “ideal observer,” their support for prioritizing society increased. Our findings suggest that, insofar as antibiotic policies and practices should be informed by attitudes that are impartial and well-informed, there is significant support for prioritizing society. (shrink)

Abstract: The study aimed to determine the strategic flexibility and its relationship to the level of quality of services provided, from the viewpoint of the internal beneficiary in non-governmental hospitals in Gaza Strip. The study relied on the descriptive and analytical approach, and the questionnaire was designed as a tool to collect data and consisted of (39) items, and the researchers used the comprehensive survey method, and the number of the study population was (536) individuals, where (434) questionnaires were retrieved, (...) and the recovery rate was (80.97%). The study revealed many results, the most important of which were: the existence of a moderate degree of approval by the study sample individuals on strategic flexibility, as it was evident through the area of strategic flexibility as a whole having a relative weight (60.44%). The study is on the quality of services, as it became clear through the field of service quality obtaining a relative weight (79.90%). The results of the study revealed a statistically significant relationship between strategic flexibility and the quality of services in non-governmental hospitals in Gaza Strip, with a correlation coefficient of 0.490. The study reached many recommendations, the most important of which were: the need to work on appointing young people and those with potentials, because jobs are vacant in the hospitals under study, and the need to seek the help of an administrative staff with scientific and practical qualifications, and to work on updating information systems, archiving and networks through which data and information are transferred between departments And the creation of mechanisms by which stored information can be used to enhance the decision-making process, and an effective system to receive patients' complaints in a manner that ensures rapid response and treatment, to achieve continuous communication between patients and the hospital management, and to notify patients of dealing with the complaints they submit, and work to provide all Medical and health specialties in the hospitals under study, by making use of the medical delegations that visit Gaza Strip, involving them in the treatment processes, bringing in doctors and specialists from abroad, updating the standards related to measuring the services provided to patients on an ongoing basis, based on the suggestions and complaints of patients, and developing facilities in hospitals As well as updating the medical devices and equipment used in hospitals periodically. (shrink)

Cardiovascular diseases, including heart disease, pose a significant global health challenge, contributing to a substantial burden on healthcare systems and individuals. Early detection and accurate prediction of heart disease are crucial for timely intervention and improved patient outcomes. This research explores the potential of neural networks in predicting heart disease using a dataset collected from Kaggle, consisting of 1025 samples with 14 distinct features. The study's primary objective is to develop an effective neural network model for binary classification, identifying the (...) presence or absence of heart disease. The neural network architecture includes an input layer, a hidden layer, and an output layer, designed to capture intricate relationships within the dataset. Rigorous training and validation processes, accompanied by data preprocessing steps, ensure the model's robustness and generalization capabilities. The results demonstrate promising performance, with an accuracy of 92% and an average error of 0.062. Moreover, an analysis of feature importance highlights key predictors, including "oldpeak," "thalach," "trestbps," "ca," "thal," "cp," "chol," "sex," "restecg," "age," "slope," "fbs," and "exang." This research contributes to the field of predictive healthcare by leveraging neural networks to enhance heart disease prediction. The developed model offers the potential for early identification of individuals at risk, facilitating timely medical interventions and ultimately improving public health. Further exploration of machine learning techniques in healthcare promises to reshape disease prediction and prevention strategies. (shrink)

Abstract Heart Attack is the Cardiovascular Disease (CVD) which causes the most deaths among CVDs. We collected a dataset from Kaggle website. In this paper, we propose an ANN model for the predicting whether a patient has a heart attack or not that. The dataset set consists of 9 features with 1000 samples. We split the dataset into training, validation, and testing. After training and validating the proposed model, we tested it with testing dataset. The proposed model reached an accuracy (...) of 98.01% on the Heart Disease Dataset. (shrink)

Abstract: The Knowledge-Based System for Diagnosing Breast Cancer aims to support medical students in enhancing their education regarding diagnosis and counseling. The system facilitates the analysis of biopsy images under a microscope, determination of tumor type, selection of appropriate treatment methods, and identification of disease-related questions. According to the Ministry of Health's annual report in Gaza, there were 7,069 cases of breast cancer between 2009 and 2014, with 1,502 cases reported in 2014. In an era dominated by visual information, (...) where 65% of the population are visual learners, the Knowledge-Based System serves as an effective tool to aid long-term information retention by utilizing visual and textual resources. The system features a user-friendly interface, enabling students to diagnose the disease and expand their understanding. Furthermore, it can be accessed via smartphones. The Knowledge-Based System comprises three primary components: Firstly, it provides specific patient-oriented questions to familiarize students with the questioning process. Secondly, it facilitates the analysis of biopsy sample images to determine their integrity and identify potential infections. Lastly, the system incorporates animated videos illustrating various approaches for treating breast cancer. (shrink)

Mouth ulcers, also known as canker sores, are a common oral health issue affecting a significant portion of the population. Early and accurate diagnosis of mouth ulcers is crucial for effective treatment and prevention of complications. This paper presents an expert system developed using CLIPS (C Language Integrated Production System) to diagnose mouth ulcer disease. The expert system utilizes a rule-based approach, incorporating a comprehensive knowledge base consisting of symptoms, risk factors, and medical literature related to mouth ulcers. By employing (...) an inference engine, the system evaluates patient input against the rules and generates a diagnosis based on the matching symptoms and relevant factors. The system also provides detailed explanations and recommendations for each diagnosis, aiding healthcare professionals and patients in understanding the condition and selecting appropriate treatment options. The development and validation of the expert system involved a multi-step process, including knowledge acquisition, rule formulation, and testing against a diverse dataset of mouth ulcer cases. The system's performance was evaluated by comparing its diagnoses with those made by medical professionals, demonstrating a high level of accuracy and consistency. The implementation of an expert system for diagnosing mouth ulcer disease offers several advantages, including the ability to provide timely and accessible healthcare services, reduce misdiagnosis rates, and assist healthcare professionals in decision-making processes. Moreover, it empowers individuals to make informed decisions about their health and seek appropriate medical intervention. This paper contributes to the field of medical expert systems by showcasing the practical application of CLIPS in the diagnosis of mouth ulcer disease. The results demonstrate the potential of expert systems as valuable tools for healthcare professionals and patients alike, enhancing the efficiency and accuracy of diagnosis in oral health care. (shrink)

Abstract: Heart diseases are increasing daily at a rapid rate and it is alarming and vital to predict heart diseases early. The diagnosis of heart diseases is a challenging task i.e. it must be done accurately and proficiently. The aim of this study is to determine which patient is more likely to have heart disease based on a number of medical features. We organized a heart disease prediction model to identify whether the person is likely to be diagnosed with a (...) heart disease or not using the medical features of the person. We used many different algorithms of machine learning such as Gaussian Mixture, Nearest Centroid, MultinomialNB, Logistic RegressionCV, Linear SVC, Linear Discriminant Analysis, SGD Classifier, Extra Tree Classifier, Calibrated ClassifierCV, Quadratic Discriminant Analysis, GaussianNB, Random Forest Classifier, ComplementNB, MLP Classifier, BernoulliNB, Bagging Classifier, LGBM Classifier, Ada Boost Classifier, K Neighbors Classifier, Logistic Regression, Gradient Boosting Classifier, Decision Tree Classifier, and Deep Learning to predict and classify the patient with heart disease. A quite helpful approach was used to regulate how the model can be used to improve the accuracy of prediction of heart diseases in any person. The strength of the proposed model was very satisfying and was able to predict evidence of having a heart disease in a particular person by using Deep Learning and Random Forest Classifier which showed a good accuracy in comparison to the other used classifiers. The proposed heart disease prediction model will enhances medical care and reduces the cost. This study gives us significant knowledge that can help us predict the person with heart disease. The dataset was collected from Kaggle depository and the model is implemented using python. (shrink)

The Knowledge Based System for Diagnosing Breast Cancer is used to assist medical students to improve their education on diagnosis and counseling the process of analyzing the biopsy image of the microscope, determining the type of tumor and the treatment method for each case and identifying the disease related questions. According to the Ministry of Health in its annual report in Gaza, between 2009 and 2014 there are 7069 cases of breast cancer, and in 2014 there are 1502 cases of (...) breast cancer . We are now in the age of visual information where 65% of the populations are visual learners; the Knowledge Based System is the easiest way to ensure students remember the information in the long term, using visual and textual information. The Knowledge Based System has an easy-to-use interface to help students diagnose the disease and enhance their information about the disease; the system can be used on smart phones. This Knowledge Based System can be divided into three main parts: The first part is specific questions for the patient, to help student to know questions type. The second part is the process of analyzing the biopsy sample image, to see if the biopsy image is intact or infected. The last part is the video shows ways to treat breast cancer in the form of animation. (shrink)

Purpose: The method of diagnosing diabetic retinopathy (DR) through artificial intelligence (AI)-based systems has been commercially available since 2018. This introduces new ethical challenges with regard to obtaining informed consent from patients. The purpose of this work is to develop a checklist of items to be disclosed when diagnosing DR with AI systems in a primary care setting. -/- Methods: Two systematic literature searches were conducted in PubMed and Web of Science databases: a narrow search focusing on DR and a (...) broad search on general issues of AI-based diagnosis. An ethics content analysis was conducted inductively to extract two features of included publications: (1) novel information content for AI-aided diagnosis and (2) the ethical justification for its disclosure. -/- Results: The narrow search yielded n = 537 records of which n = 4 met the inclusion criteria. The information process was scarcely addressed for primary care setting. The broad search yielded n = 60 records of which n = 11 were included. In total, eight novel elements were identified to be included in the information process for ethical reasons, all of which stem from the technical specifics of medical AI. -/- Conclusions: Implications for the general practitioner are two-fold: First, doctors need to be better informed about the ethical implications of novel technologies and must understand them to properly inform patients. Second, patient's overconfidence or fears can be countered by communicating the risks, limitations, and potential benefits of diagnostic AI systems. If patients accept and are aware of the limitations of AI-aided diagnosis, they increase their chances of being diagnosed and treated in time. (shrink)

Chest pain is the pain felt in the chest by infants, children and adolescents. In most cases the pain is not associated with the heart. It is mainly recognized by the observance or report of pain by the infant, child or adolescent by reports of distress by parents or care givers. Chest pain is not unusual in children. Lots of children are seen in ambulatory clinics, emergency rooms and hospitals and cardiology clinics. Usually there is a benign cause for the (...) pain for utmost children. Certain patients have conditions that are serious and perhaps life-threatening. Chest pain in pediatric patients needs careful physical examination and a detailed history that would point to the possibility of a serious cause. Researches of pediatric chest pain are scarce. It has been difficult to create evidence-based guidelines for evaluation. In this paper we propose an expert system to help doctors and parents, and care giver in diagnosing chest pain in infants and children. This expert System is design and implemented in SL5 Object language. (shrink)

Abstract: Heart attacks, or myocardial infarctions, are a leading cause of mortality worldwide. Early prediction and accurate analysis of potential risk factors play a crucial role in preventing heart attacks and improving patient outcomes. In this study, we conduct a comprehensive review of datasets related to heart attack analysis and prediction. We begin by examining the various types of datasets available for heart attack research, encompassing clinical, demographic, and physiological data. These datasets originate from diverse sources, including hospitals, research institutions, (...) and public health agencies. Our analysis aims to identify common features, data quality, and potential biases in these datasets. Next, we explore the predictive modeling techniques employed in heart attack prediction. Machine learning algorithms, such as decision trees, support vector machines, and deep neural networks, have gained prominence in predicting heart attacks. We discuss the strengths and limitations of these methods and highlight recent advancements in predictive modeling. Furthermore, we delve into the critical risk factors associated with heart attacks. Factors such as age, gender, hypertension, diabetes, and cholesterol levels are examined for their significance in predicting cardiac events. We also investigate the role of lifestyle factors, including smoking, diet, and physical activity, in heart attack risk assessment. Additionally, this review addresses the importance of data preprocessing and feature engineering in improving prediction accuracy. Feature selection methods, missing data handling, and data scaling techniques are discussed to enhance the robustness of heart attack prediction models. In conclusion, this comprehensive dataset review provides valuable insights into the state of heart attack analysis and prediction. It serves as a resource for researchers and healthcare professionals seeking to better understand the datasets available for heart attack research and the methods employed for accurate prediction. Ultimately, our efforts in dataset analysis and predictive modeling contribute to the advancement of preventive cardiology and the reduction of heart attack-related morbidity and mortality. (shrink)

Kidney stones pose a significant health concern, and early detection can lead to timely intervention and improved patient outcomes. This research endeavours to predict the presence of kidney stones based on urine analysis, utilizing a neural network model. A dataset of 552 urine specimens, comprising six essential physical characteristics (specific gravity, pH, osmolarity, conductivity, urea concentration, and calcium concentration), was collected and prepared. Our proposed neural network architecture, featuring three layers (input, hidden, output), was trained and validated, achieving an impressive (...) accuracy of 98.67% and an average error of 0.012. In addition to model performance, feature importance analysis was conducted to determine the most influential factors in predicting kidney stone presence. The findings underscore the significance of urea concentration, specific gravity, calcium concentration, conductivity, osmolarity, and pH as key indicators. This research contributes to the early diagnosis of kidney stones and demonstrates the potential of neural networks in medical diagnostics. The clinical implications of these findings are discussed, emphasizing the importance of timely intervention in managing kidney stone-related health issues. (shrink)

Background: With the coming of the Industrial Revolution, the levels of pollution grow significantly. This Technological development contributed to the worsening of shortness breath problems in great shape. especially in infants and children. There are many shortness breath diseases that infants and children face in their lives. Shortness of breath is one of a very serious symptom in children and infants and should never be ignored. Objectives: Along these lines, the main goal of this expert system is to help physician (...) in diagnosing and describe some common causes of shortness of breath in infants and children by diagnosing their cases through our expert system. Moreover, this system which we are presenting will give patient the appropriate diagnosis of disease and the treatment required. Methods: In this paper the strategy of the expert system for diagnosing a number of the existed shortness of breath in infants and children diseases such as (Asthma , Bronchiolitis, Viral Pneumonia, cough, Shortness of breath' dyspnea ', Epiglottitis, Croup, ABSCESS in the tonsil 'peritonsillar abscess', Bronchitis, Viral Bronchitis, Wheezing, sudden infant death syndrome 'SIDS' ) is introduced, an overview about the shortness of breath in children and infants diseases are delineated, the cause of diseases are sketched and the treatment of disease whenever conceivable is given . SL5 Object Expert System language was utilized for designing and implementing the proposed expert system. Results: The proposed shortness of breath in children and infants diseases diagnosis expert system was estimated by Medical students and they were satisfied with its result. Conclusions: The Proposed expert system is very useful for Respiratory physician, pediatrician, recently graduated physician, and for children's parents with shortness of breath problem. (shrink)

Lung cancer is a serious and deadly disease that affects the lungs, which are responsible for taking in oxygen and expelling carbon dioxide from the body. The disease can develop in any part of the lungs and is usually caused by smoking or exposure to certain chemicals. The main Objective: of this expert system is to provide an accurate diagnosis of lung cancer and the appropriate treatment options. In this paper, Methods: we present the design and implementation of an expert (...) system that can assist physicians in diagnosing lung cancer and determining the best course of treatment. The system provides an overview of the disease, its causes, symptoms, diagnosis, and treatment, along with relevant medical information. Results: The system was developed using the Clips expert system language and evaluated by medical professionals who found it to be an effective tool for diagnosing and treating lung cancer. Conclusion: The proposed expert system has the potential to improve patient outcomes and reduce healthcare costs associated with the treatment of lung cancer. (shrink)

Except in extraordinary circumstances, patients' clinical care should reflect their preferences. Incapacitated patients cannot report their preferences. This is a problem. Extant solutions to the problem are inadequate: surrogates are unreliable, and advance directives are uncommon. In response, some authors have suggested developing algorithmic "patient preference predictors" (PPPs) to inform care for incapacitated patients. In a recent paper, Earp et al. propose a new twist on PPPs. Earp et al. suggest we personalize PPPs using modern machine learning (ML) techniques. In (...) this paper, I argue that, contrary to Earp et al.'s claim, personalized patient preference predictors are neither technically feasible nor ethically desirable. (shrink)

Abstract: The study aimed to determine the quality of services and its role in enhancing strategic flexibility, from the point of view of the internal beneficiary in non-governmental hospitals in Gaza Strip. The study relied on the descriptive and analytical approach, and the questionnaire was designed as a tool to collect data and consisted of (39) items, and the researchers used the comprehensive survey method, and the number of the study population was (536) individuals, where (434) questionnaires were retrieved, and (...) the recovery rate was (80.97%). The study revealed many results, the most important of which were: the existence of a high degree of approval by the study sample regarding the quality of services, as it was evident through the field of service quality obtaining a relative weight (79.90%), and the results of the study indicated that there is a moderate degree of approval by the members of the study sample. On the strategic flexibility, as it was evident by obtaining the area of strategic flexibility as a whole on a relative weight (60.44%). The results of the study revealed a statistically significant relationship between the quality of services and the strategic flexibility in non-governmental hospitals in Gaza Strip, with a correlation coefficient of 0.490). The results of the study also revealed a statistically significant effect of the quality of services on the strategic flexibility in non-governmental hospitals in Gaza Strip. The study reached many recommendations, the most important of which were: Establishing an effective system to receive patients' complaints in a manner that ensures rapid response and treatment, to achieve continuous communication between patients and the hospital administration, to notify patients of dealing with the complaints they submit, and to work to provide all medical and health specialties in hospitals in place. The study, by making use of medical delegations visiting Gaza Strip, involving them in therapeutic operations, bringing in doctors and specialists from abroad, updating the criteria for measuring services provided to patients on an ongoing basis, based on patients' suggestions and complaints, developing facilities in hospitals, as well as updating medical devices and equipment Used in hospitals periodically and the need to work on appointing young people and people of energy, for vacancies in the hospitals under study, and the need to seek the help of an administrative staff that is distinguished by scientific and practical qualifications, and to work on updating information systems, archiving and networks through which data and information are transferred between departments, and finding mechanisms to be done from During which the information stored can be used to enhance the decision-making process. (shrink)

Warts and verrucas are common skin conditions caused by the human papillomavirus (HPV) infection. They present as raised, rough, or bumpy growths on the hands, feet, or other areas subjected to friction or pressure. Plantar warts exhibit a rough surface with small black dots, while genital warts have a cauliflower-like appearance. Pain or itchiness may accompany these lesions. Factors such as close contact with infected individuals and immune compromise can impact the severity and spread of warts. Diagnosis is primarily based (...) on clinical examination, and referral to a dermatologist may be necessary for uncertain cases. Treatment options range from over-the-counter remedies to professional interventions. Persistent or symptomatic lesions should be evaluated by a healthcare professional. Improved understanding and effective management of warts and verrucas are essential for optimal patient outcomes. (shrink)

Context — Children and adults with psychopathic traits and conduct or oppositional defiant disorder demonstrate poor decision making and are impaired in reversal learning. However, the neural basis of this impairment has not previously been investigated. Furthermore, despite high comorbidity of psychopathic traits and attention deficit/hyperactivity disorder, to our knowledge, no research has attempted to distinguish neural correlates of childhood psychopathic traits and attention-deficit/hyperactivity disorder. Objective—To determine the neural regions that underlie the reversal learning impairments in children with psychopathic traits (...) plus conduct or oppositional defiant disorder. Design — Case-control study. Setting — Government clinical research institute. Participants — Forty-two adolescents aged 10 to 17 years: 14 with psychopathic traits and oppositional defiant disorder or conduct disorder, 14 with attention-deficit/hyperactivity disorder only, and 14 healthy controls. Main Outcome Measure — Blood oxygenation level–dependent signal as measured via functional magnetic resonance imaging during a probabilistic reversal task. Results — Children with psychopathic traits showed abnormal responses within the ventromedial prefrontal cortex (Brodmann area 10) during punished reversal errors compared with children wit hattention deficit/hyperactivity disorder and healthy children (P < .05 corrected for multiple comparisons). Conclusions — To our knowledge, this study provides the first evidence of abnormal ventromedial prefrontal cortex responsiveness in children with psychopathic traits and demonstrates this dysfunction was not attributable to comorbid attention-deficit/hyperactivity disorder. These findings suggest that reversal learning impairments in patients with developmental psychopathic traits relate to abnormal processing of reinforcement information. (shrink)

Integration information theories posit that the integration of information is necessary and/or sufficient for consciousness. In this paper, we focus on three of the most prominent information integration theories: Information Integration Theory, Global Workspace Theory, and Attended Intermediate-Level Theory. We begin by explicating each theory and key concepts they utilize. We then argue that the current evidence indicates that the integration of information is neither necessary nor sufficient for consciousness. Unlike GWT and AIR, IIT maintains (...) that conscious experience is both necessary and sufficient for consciousness. We present empirical evidence indicating that simple features are experienced in the absence of feature integration and argue that it challenges IIT’s necessity claim. In addition, we challenge IIT’s sufficiency claim by presenting evidence from hemineglect cases and amodal completion indicating that contents may be integrated and yet fail to give rise to subjective experience. Moreover, we present empirical evidence from subjects with frontal lesions who are unable to carry out simple instructions and argue that they are irreconcilable with GWT. Lastly, we argue that empirical evidence indicating that patients with visual agnosia fail to identify objects they report being conscious of present a challenge to AIR’s necessity claim. (shrink)

The increased complexity of health information management sows the seeds of inequalities between health care stakeholders involved in the production and use of health information. Patients may thus be more vulnerable to use of their data without their consent and breaches in confidentiality. Health care providers can also be the victims of a health information system that they do not fully master. Yet, despite its possible drawbacks, the management of health information is indispensable for advancing science, (...) medical care and public health. Therefore, the central question addressed by this paper is how to manage health information ethically? This article argues that Paul Ricœur’s ‘‘little ethics’’, based on his work on hermeneutics and narrative identity, provides a suitable ethical framework to this end. This ethical theory has the merit of helping to harmonise self-esteem and solicitude amongst patients and healthcare providers, and at the same time provides an ethics of justice in public health. A matrix, derived from Ricœur’s ethics, has been developed as a solution to overcoming possible conflicts between privacy interests and the common good in the management of health information. (shrink)

In ‘Psychotherapy, Placebos and Informed Consent’, I argued that the minimal standard for informed consent in psychotherapy requires that ‘patients understand that there is currently no consensus about the mechanisms of change in psychotherapy, and that the therapy on offer…is based on disputed theoretical foundations’, and that the dissemination of this information is compatible with the delivery of many theory-specific forms of psychotherapy (including cognitive behavioural therapy [CBT]). I also argued that the minimal requirements for informed consent do not (...) include information about the role of therapeutic common factors in healing (e.g., expectancy effects and therapist effects); practitioners may discuss the common factors with patients, but they are not part of the ‘core set’ of information necessary to obtain informed consent. In a recent reply, Charlotte Blease criticises these two arguments by claiming they are not supported by empirical findings about the therapeutic common factors. Blease’s response is based on serious misunderstandings of both CBT and what the common factor findings actually find. Nevertheless, addressing the reasons for these misunderstandings is instructive and gives us an opportunity to clarify what, exactly, needs to be explained to patients in order to obtain informed consent for psychotherapy. (shrink)

Adaptation and stress are two main concepts useful for better understanding the phases of illness and health-related human behavior. The two faces of adaptation, adaptation as a process and adaptation as a product, have raised the question of how long the adaptation process will take in cancer trajectories. The care setting transition from clinical-based into home-based cancer care has stressed the role of family caregivers (FCG) in cancer management. This study examines how types of demanded healthcare information affect the (...) FCG’s role in catalyzing the adaptation of female cancer patients. The mindsponge theory was used in conceptual development and results interpretation. Bayesian Mindsponge Framework (BMF) analytics was used for statistical analysis on a dataset of 48 spouses (husbands) and 12 other family members in five congested communities of Surabaya, Indonesia. Results showed that among the six types of demanded healthcare information, FCGs with a higher tendency to demand cancer-specific information are more likely to need support in catalyzing the adaptation of female cancer patients. Meanwhile, FCGs with a higher demand for information on alternative therapies are less likely to need support in catalyzing cancer adaptation. Other types of healthcare information have ambiguous effects on the need for support in cancer adaptation assistance. This study reveals that the demanded cancer-specific information, e.g., cancer’s prognosis or likely outcome, must be prioritized to assist FCG’s role in catalyzing adaptation among female cancer patients. Cancer prognosis and outcomes reflect the future of cancer care results. (shrink)

Healthcare professionals decide wisely about personalized medicine, treatment plans, and resource allocation by utilizing big data analytics and machine learning. To guarantee that algorithmic recommendations are impartial and fair, however, ethical issues relating to prejudice and data privacy must be taken into account. Big data analytics and machine learning have a great potential to disrupt healthcare, and as these technologies continue to evolve, new opportunities to reform healthcare and enhance patient outcomes may arise. In order to investigate the patient’s (...) outcomes with empirical evidence, this research was conducted using an online survey to incorporate healthcare professionals, patient’s reviews, and clinical staff. The data were analyzed using SmartPLS 4.0 to predict the structural model. The findings revealed a direct impact as positive influence of using machine learning on healthcare performance and patient outcomes through big data analytics. Moreover, it is evident that this can lead to personalized treatment plans, early interventions, and improved patient outcomes. Additionally, big data analytics can help healthcare providers optimize resource allocation, improve operational efficiency, and reduce costs. The impact of big data analytics on patient outcome and healthcare performance is expected to continue to grow, making it an important area for investment and research. (shrink)

Background: Use of patients’ medical data for secondary purposes such as health research, audit, and service planning is well established in the UK. However, the governance environment, as well as public understanding about this work, have lagged behind. We aimed to systematically review the literature on UK and Irish public views of patient data used in research, critically analysing such views though an established biomedical ethics framework, to draw out potential strategies for future good practice guidance and inform ethical and (...) privacy debates. Methods: We searched three databases using terms such as patient, public, opinion, and electronic health records. Empirical studies were eligible for inclusion if they surveyed healthcare users, patients or the public in UK and Ireland and examined attitudes, opinions or beliefs about the use of patient data for medical research. Results were synthesised into broad themes using a framework analysis. Results: Out of 13,492 papers and reports screened, 20 papers or reports were eligible. While there was a widespread willingness to share patient data for research for the common good, this very rarely led to unqualified support. The public expressed two generalised concerns about the potential risks to their privacy. The first of these concerns related to a party’s competence in keeping data secure, while the second was associated with the motivation a party might have to use the data. Conclusions: The public evaluates trustworthiness of research organisations by assessing their competence in data-handling and motivation for accessing the data. Public attitudes around data-sharing exemplified several principles which are also widely accepted in biomedical ethics. This provides a framework for understanding public attitudes, which should be considered in the development in any guidance for regulators and data custodians. We propose four salient questions which decision makers should address when evaluating proposals for the secondary use of data. (shrink)

Deep Brain Stimulation (DBS) is a relatively new, experimental treatment for patients suffering from treatment-refractory Obsessive Compulsive Disorder (OCD). The effects of treatment are typically assessed with psychopathological scales that measure the amount of symptoms. However, clinical experience indicates that the effects of DBS are not limited to symptoms only: patients for instance report changes in perception, feeling stronger and more confident, and doing things unreflectively. Our aim is to get a better overview of the whole variety of changes that (...) OCD patients experience during DBS treatment. For that purpose we conducted in-depth, semi-structured interviews with 18 OCD patients. In this paper, we present the results from this qualitative study.We list the changes grouped in four domains: with regard to (a) person, (b) (social) world, (c)characteristics of person-world interactions, and (d) existential stance. We subsequently provide an interpretation of these results. In particular, we suggest that many of these changes can be seen as different expressions of the same process; namely that the experience of anxiety and tension gives way to an increased basic trust and increased reliance on one’s abilities. We then discuss the clinical implications of our findings, especially with regard to properly informing patients of what they can expect from treatment, the usefulness of including CBT in treatment, and the limitations of current measures of treatment success. We end by making several concrete suggestions for further research. (shrink)

Cancer care has transitioned from clinical-based to home-based care to support longterm care in a more familiar and comfortable environment. This care transition has put family caregivers (FCGs) in a strategic position as care providers. Cancer care at home involves psychological and emotional treatment at some point, making FCGs deal with the stress of cancer patients frequently. Due to their limited care competencies, they need supportive care from healthcare professionals in cancer stress management. This study aims to examine how types (...) of demanded healthcare information affect the FCG’s role in reducing the stress of female cancer patients. The mindsponge theory was used in conceptual development and interpretation. Bayesian Mindsponge Framework (BMF) analytics were used for statistical analysis on a dataset of 48 spouses (husbands) and 12 other family members in five congested communities of Surabaya, Indonesia. Results showed that among the six types of healthcare information, FCGs with higher demand for cancer-specific information were more likely to need support in reducing the stress of female cancer patients. Meanwhile, FCGs with a higher demand for information about support services were less likely to need support to reduce cancer patients’ stress. Other types of healthcare information have ambiguous effects on the need for support in reducing cancer-induced stress. This study reveals that the demanded cancer-specific information, e.g., cancer prognosis or likely outcome, must be prioritized to assist FCG’s role in managing cancer stress. (shrink)

Fear of cancer is mostly related to cancer recurrence, metastasis, additional cancer, and diagnostic tests. Its legacy as a lethal disease has raised fear of approaching death. Currently, cancer’s total suffering and the worsening phenomena have raised fear, especially among female patients. Family caregivers (FCGs) who are responsible for the day-to-day cancer care at home need to help the patients deal with this fear frequently. Due to the limited care competencies, they need supportive care from healthcare professionals in cancer fear (...) management. This study aims to assess how types of demanded healthcare information affect the FCG’s role in reducing the fear of female cancer patients. The mindsponge theory was used in conceptual development and interpretation. Bayesian Mindsponge Framework (BMF) analytics were used for statistical analysis on a dataset of 48 spouses (husbands) and 12 other family members in five congested communities of Surabaya, Indonesia. Results showed that among the six types of healthcare information, FCGs with higher demand on cancer-specific information and information on cancer physical needs were more likely to need support in reducing the fear of female cancer patients. Meanwhile, FCGs with a higher demand for information on support services were less likely to need support to reduce cancer patients’ fear. Other types of healthcare information have ambiguous effects on the need for support in reducing cancer-induced fear. This study reveals that the demanded cancer-specific information, e.g., cancer’s prognosis or likely outcome and information on cancer physical needs, need to be met in priority to assist FCG’s role in cancer fear management. (shrink)

This paper explores how transformative experience generates decision-making problems of particular seriousness in medical settings. Potentially transformative experiences are especially likely to be encountered in medicine, and the associated decisions are confronted jointly by patients and clinicians in the context of an imbalance of power and expertise. However in such scenarios the principle of informed consent, which plays a central role in guiding clinicians, is unequal to the task. We detail how the principle’s assumptions about autonomy, rationality and information (...) handle transformative experiences poorly, appealing to several difficult cases for medical decision-making to illustrate the resulting problem, and we consider how the existing literature on complications with consent fails to offer a resolution. We argue that recognition of the problem has a role to play in achieving a more effective response to transformative decisions. In Sect. 1 we introduce several representative cases of challenging patient decision-making that clinicians might face. In Sect. 2 we detail how transformative experience has been analysed in the recent literature, before outlining in Sect. 3 the theoretical basis of the principle of informed consent, which plays a central role in how clinicians are expected to support decision-making. In Sect. 4, having laid the groundwork for a clear description, we return to the cases given in Sect. 1 to confirm how their transformative nature presents a problem: either clinicians treat the decisions faced by these patients as ‘normal’, encouraging them to focus on information provision that patients may be unable to act on, or they treat them as transformative, in which case they lack the resources to recognise whether they are helping patients make (subjectively) good decisions. In Sect. 5 we argue that the existing literature does not offer any escape from this problem. We close in Sect. 6 by noting the significant impact that appreciating the problem of transformative experience could have on supporting transformative decisions in medicine and briefly suggesting how we might aim to develop new approaches to dealing with these. (shrink)

The current study aims to examine how types of demanded healthcare information affect the FCG’s role in catalyzing the adaptation of female cancer patients (i.e., cancer-specific information, caregiver-specific information, therapy-specific information, information on cancer physical needs, information on alternative therapies, and information on support services).