Rare diseases pose a particular priority setting problem. The UK gives rare diseases special priority in healthcare priority setting. Effectively, the National Health Service is willing to pay much more to gain a quality-adjusted life-year related to a very rare disease than one related to a more common condition. But should rare diseases receive priority in the allocation of scarce healthcare resources? This article develops and evaluates four arguments in favour of such a priority. These pertain to public values, (...) luck egalitarian distributive justice the epistemic difficulties of obtaining knowledge about rare diseases and the incentives created by a higher willingness to pay. The first is at odds with our knowledge regarding popular opinion. The three other arguments may provide a reason to fund rare diseases generously. However, they are either overinclusive because they would also justify funding for many non-rare diseases or underinclusive in the sense of justifying priority for only some rare diseases. The arguments thus fail to provide a justification that tracks rareness as such. There are no data in this work. (shrink)

Technological developments have resulted in tremendous increases in the volume and diversity of the data and information that must be processed in the course of biomedical and clinical research and practice. Researchers are at the same time under ever greater pressure to share data and to take steps to ensure that data resources are interoperable. The use of ontologies to annotate data has proven successful in supporting these goals and in providing new possibilities for the automated processing of data and (...) information. In this chapter, we describe different types of vocabulary resources and emphasize those features of formal ontologies that make them most useful for computational applications. We describe current uses of ontologies and discuss future goals for ontology-based computing, focusing on its use in the field of infectious diseases. We review the largest and most widely used vocabulary resources relevant to the study of infectious diseases and conclude with a description of the Infectious Disease Ontology (IDO) suite of interoperable ontology modules that together cover the entire infectious disease domain. (shrink)

Debates about the genuine disease status of controversial diseases rely on intuitions about a range of factors. Adopting tools from experimental philosophy, this paper explores some of the factors that influence judgments about whether low sexual desire should be considered a disease and whether it should be medically treated. Drawing in part on some assumptions underpinning a divide in the literature between viewing low sexual desire as a genuine disease and seeing it as improperly medicalized, we investigate (...) whether health and disease judgments are affected by factors such as an individual’s gender, the cause of the low desire, whether the desire is high or low, and both personal and societal valuations of the condition. Our main findings indicate that (a) the cause of a condition influences whether it is judged a disorder, (b) how the individual values the condition influences whether the condition is seen as a proper target of medical intervention, and (c) perceived dysfunction influences judgments regarding health, disorder classification, medicalization, and medical intervention. Our findings help further illuminate the intricate interplay of factors that influence judgments about health and disease in controversial conditions. (shrink)

The Neurological Disease Ontology (ND) is being developed to provide a comprehensive framework for the representation of neurological diseases (Diehl et al., 2013). ND utilizes the model established by the Ontology for General Medical Science (OGMS) for the representation of entities in medicine and disease (Scheuermann et al., 2009). The goal of ND is to include information for each disease concerning its molecular, genetic, and environmental origins, the processes involved in its etiology and realization, as well as (...) its clinical presentation including signs and symptoms. (shrink)

In the contemporary biomedical literature, every disease is considered genetic. This extension of the concept of genetic disease is usually interpreted either in a trivial or genocentrist sense, but it is never taken seriously as the expression of a genetic theory of disease. However, a group of French researchers defend the idea of a genetic theory of infectious diseases. By identifying four common genetic mechanisms (Mendelian predisposition to multiple infections, Mendelian predisposition to one infection, and major gene (...) and polygenic predispositions), they attempt to unify infectious diseases from a genetic point of view. In this article, I analyze this explicit example of a genetic theory, which relies on mechanisms and is applied only to a specific category of diseases, what we call “a regional genetic theory.” I have three aims: to prove that a genetic theory of disease can be devoid of genocentrism, to consider the possibility of a genetic theory applied to every disease, and to introduce two hypotheses about the form that such a genetic theory could take by distinguishing between a genetic theory of diseases and a genetic theory of Disease. Finally, I suggest that network medicine could be an interesting framework for a genetic theory of Disease. (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)

There are many eye diseases but the most two common retinal diseases are Age-Related Macular Degeneration (AMD), which the sharp, central vision and a leading cause of vision loss among people age 50 and older, there are two types of AMD are wet AMD and DRUSEN. Diabetic Macular Edema (DME), which is a complication of diabetes caused by fluid accumulation in the macula that can affect the fovea. If it is left untreated it may cause vision loss. Therefore, early detection (...) of diseases is a critical importance. Our main goal is to help doctors detect these diseases quickly before reaching a late stage of the disease. In ophthalmology, optical coherence tomography (OCT) is critical for evaluating retinal conditions. OCT is an imaging technique used to capture high-resolution cross-sections of the retinas of patient. In this thesis, we review ways and techniques to use deep learning classification of the optical coherence tomography images of diseases from which a Retinal is suffering. The models used to improve patient care are (VGG-16, MobileNet, ResNet-50, Inception V3, and Xception) to reduce costs and allow fast and reliable analysis in large studies. The obtained results are encouraging, since the best model ResNet-50 reaching 96.21% of testing accuracy, which is very useful for doctors, to diagnose retinal diseases. (shrink)

Some ‘naturalist’ accounts of disease employ a biostatistical account of dysfunction, whilst others use a ‘selected effect’ account. Several recent authors have argued that the biostatistical account offers the best hope for a naturalist account of disease. We show that the selected effect account survives the criticisms levelled by these authors relatively unscathed, and has significant advantages over the BST. Moreover, unlike the BST, it has a strong theoretical rationale and can provide substantive reasons to decide difficult cases. (...) This is illustrated by showing how life-history theory clarifies the status of so-called diseases of old age. The selected effect account of function deserves a more prominent place in the philosophy of medicine than it currently occupies. _1_ Introduction _2_ Biostatistical and Selected Effect Accounts of Function _3_ Objections to the Selected Effect Account _3.1_ Boorse _3.2_ Kingma _3.3_ Hausman _3.4_ Murphy and Woolfolk _4_ Problems for the Biostatistical Account _4.1_ Schwartz _5_ Analysis versus Explication _6_ Explicating Dysfunction: Life History Theory and Senescence _7_ Conclusion. (shrink)

In this paper, we identify some key features of what makes something a disease, and consider whether these apply to pregnancy. We argue that there are some compelling grounds for regarding pregnancy as a disease. Like a disease, pregnancy affects the health of the pregnant person, causing a range of symptoms from discomfort to death. Like a disease, pregnancy can be treated medically. Like a disease, pregnancy is caused by a pathogen, an external organism invading (...) the host’s body. Like a disease, the risk of getting pregnant can be reduced by using prophylactic measures. We address the question of whether the ‘normality’ of pregnancy, its current necessity for human survival, or the value often attached to it are reasons to reject the view that pregnancy is a disease. We point out that applying theories of disease to the case of pregnancy, can in many cases illuminate inconsistencies and problems within these theories. Finally, we show that it is difficult to find one theory of disease that captures all paradigm cases of diseases, while convincingly excluding pregnancy. We conclude that there are both normative and pragmatic reasons to consider pregnancy a disease. (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)

Our goal in this paper is to articulate a precise concept of at least a certain kind of disease-mongering, showing how pharmaceutical marketing can commercially exploit certain diseases when their best definition is given through the success of a treatment in a clinical trial. We distinguish two types of disease-mongering according to the way they exploit the definition of the trial population for marketing purposes. We argue that behind these two forms of disease-mongering there are two well-known (...) problems in the statistical methodology of clinical trials (the reference class problem and the distinction between statistical and clinical significance). Overcoming them is far from simple. (shrink)

This paper addresses the use of dispositions in the Infectious Disease Ontology (IDO). IDO is an ontology constructed according to the principles of the Open Biomedical Ontology (OBO) Foundry and uses the Basic Formal Ontology (BFO) as an upper ontology. After providing a brief introduction to disposition types in BFO and IDO, we discuss three general techniques for representing combinations of dispositions under the headings blocking dispositions, complementary dispositions, and collective dispositions. Motivating examples for each combination of dispositions is (...) given along with a specific use case in IDO. Description logic restrictions are used to formalize statements relating to these combinations. (shrink)

Response to commentary. We are grateful to Crockett and Craigie for their interesting remarks on our paper. We accept Crockett’s claim that there is a need for caution in drawing inferences about patient groups from work on healthy volunteers in the laboratory. However, we believe that the evidence we cited established a strong presumption that many of the patients who are routinely taking a medication, including many people properly prescribed the medication for a medical condition, have morally significant aspects of (...) their cognition and behavior modified in a way that is unintended and may sometimes be unwelcome. Crockett notes that in some cases the effects of long-term drug use may differ, sometimes markedly, from the effects of short-term use. However, if acute use of a drug affects a neural system involved in mediating moral cognition or behavior, this nevertheless provides some evidence that chronic use of the drug may affect that same system and thus have morally significant effects. It is also plausible, in some cases, that an acute moral effect would give rise to a chronic moral effect via cognitive mechanisms. ... (shrink)

Parkinson's Disease (PD) is a long-term degenerative disorder of the central nervous system that mainly affects the motor system. The symptoms generally come on slowly over time. Early in the disease, the most obvious are shaking, rigidity, slowness of movement, and difficulty with walking. Doctors do not know what causes it and finds difficulty in early diagnosing the presence of Parkinson’s disease. An artificial neural network system with back propagation algorithm is presented in this paper for helping (...) doctors in identifying PD. Previous research with regards to predict the presence of the PD has shown accuracy rates up to 93% [1]; however, accuracy of prediction for small classes is reduced. The proposed design of the neural network system causes a significant increase of robustness. It is also has shown that networks recognition rates reached 100%. (shrink)

Cardiovascular diseases (CVD) represent a significant cause of morbidity and mortality worldwide, necessitating early detection for effective intervention. This research explores the application of machine learning (ML) algorithms in predicting cardiovascular diseases with enhanced accuracy by integrating optimization techniques. By leveraging data-driven approaches, ML models can analyze vast datasets, identifying patterns and risk factors that traditional methods might overlook. This study focuses on implementing various ML algorithms, such as Decision Trees, Random Forest, Support Vector Machines, and Neural Networks, optimized through (...) techniques like hyperparameter tuning, cross-validation, and feature selection to improve prediction accuracy. (shrink)

Similarly to other accounts of disease, Christopher Boorse’s Biostatistical Theory (BST) is generally presented and considered as conceptual analysis, that is, as making claims about the meaning of currently used concepts. But conceptual analysis has been convincingly critiqued as relying on problematic assumptions about the existence, meaning, and use of concepts. Because of these problems, accounts of disease and health should be evaluated not as claims about current meaning, I argue, but instead as proposals about how to define (...) and use these terms in the future, a methodology suggested by Quine and Carnap. I begin this article by describing problems with conceptual analysis and advantages of “philosophical explication,” my favored approach. I then describe two attacks on the BST that also question the entire project of defining “disease.” Finally, I defend the BST as a philosophical explication by showing how it could define useful terms for medical science and ethics. (shrink)

Abstract: Alzheimer's disease (AD) is one of the most common types of dementia. Symptoms appear gradually and end with severe brain damage. People with Alzheimer's disease lose the abilities of knowledge, memory, language and learning. Recently, the classification and diagnosis of diseases using deep learning has emerged as an active topic covering a wide range of applications. This paper proposes examining abnormalities in brain structures and detecting cases of Alzheimer's disease especially in the early stages, using features (...) derived from medical images. The entire brain image was passed on through the transmission of Xception learning architectures. The Convolutional Neural Network (CNN) was constructed with the help of separable convolution layers that It can automatically learn general features from imaging data for classification. (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)

Claims about whether or not infertility is a disease are sometimes invoked to defend or criticize the provision of state-funded treatment for infertility. In this paper, I suggest that this strategy is problematic. By exploring infertility through key approaches to disease in the philosophy of medicine, I show that there are deep theoretical disagreements regarding what subtypes of infertility qualify as diseases. Given that infertility's disease status remains unclear, one cannot uncontroversially justify or undermine its claim to (...) medical treatment by claiming that it is or is not a disease. Instead of focusing on disease status, a preferable strategy to approach the debate about state-funded treatment is to explicitly address the specific ethical considerations raised by infertility. I show how this alternative strategy can be supported by a recent theoretical framework in the philosophy of medicine, which avoids the problems associated with the concepts of health and disease. (shrink)

On 11 March 2020, the World Health Organization declared a pandemic of the COVID-19 coronavirus disease that was first recognized in China in late 2019. Among the primary effects caused by the pandemic, there was the dissemination of health preventive measures such as physical distancing, travel restrictions, self-isolation, quarantines, and facility closures. This includes the global disruption of socio-economic systems including the postponement or cancellation of various public events (e.g., sporting, cultural, or religious), supply shortages and fears of the (...) same, schools and universities closure, evacuation of foreign citizens, a rise of unemployment, changes in the international aid schemes, misinformation, and incidents of discrimination toward people affected by or suspected of having the COVID-19 disease. The pandemic has brought to the fore unpreparedness in critical areas that require attention, amid prospects and challenges. Moreover, considerable reorganization efforts are required with implications for assets, resources, norms, and value systems. COVID-19 is challenging the concept of globalization and stimulating responses at the levels of local and regional socio-economic systems that lead to the mobilization of assets that have been unrecognized earlier on, such as various forms of economic capital, social capital, cultural capital, human capital, and creative capital. For example, through digital channels, local groups are forming to create schemes of support for physical and mental wellbeing. These emerging exchanges lead to various social and technological innovations by building on skills and assets that are less important in the free-market economy, such as empathy, skills for crafts, making and fixing; locally grown microgreens; and micromanufacturing. Isolation and local living are also making it much harder to ignore the civic responsibilities towards communities, meant as individuals, vulnerable groups, and local businesses. Whilst the pandemic is limiting physical participation, this challenging time is uncovering alternative ways of mutual support, which may create long-term benefits for socio-economic systems, including environmental and biodiversity protection, reduction of the air pollution, and climate action. The pandemic’s threat to public health will hopefully be overcome with implications for disruption for an extended period that we are unable to forecast at this stage. It is key to focus on studies recognizing the activities and interventions leading to the recovery of socio-economic systems after the pandemic. Reflecting and planning on how societies and economies will go back to “business as usual” requires new forms of communication and cooperation, imaginative design thinking, new styles of management, as well as new tools and forms of participation in various public policies. Many questions related to the care of the vulnerable, economic restart, and the risk of future pandemics, to mention but a few, are already occupying the academic, scientific, experts, and activist communities, who have started to imagine the “new normal.”. (shrink)

This review was conducted to examine the causes, diagnoses, clinical manifestations, and available treatments for Graves' disease. Keywords like "Graves' disease," "radioactive iodine," "etiology," and "treatment" were used to search for data pertaining to Textbooks on endocrinology and other papers from these sources were also located. The introduction, etiology, risk factors, symptoms, diagnosis, course of treatment, and the contribution of many factors to the beginning of Graves' disease are all covered in this review article.

The Infectious Disease Ontology (IDO) is a suite of interoperable ontology modules that aims to provide coverage of all aspects of the infectious disease domain, including biomedical research, clinical care, and public health. IDO Core is designed to be a disease and pathogen neutral ontology, covering just those types of entities and relations that are relevant to infectious diseases generally. IDO Core is then extended by a collection of ontology modules focusing on specific diseases and pathogens. In (...) this paper we present applications of IDO Core within various areas of infectious disease research, together with an overview of all IDO extension ontologies and the methodology on the basis of which they are built. We also survey recent developments involving IDO, including the creation of IDO Virus; the Coronaviruses Infectious Disease Ontology (CIDO); and an extension of CIDO focused on COVID-19 (IDO-CovID-19).We also discuss how these ontologies might assist in information-driven efforts to deal with the ongoing COVID-19 pandemic, to accelerate data discovery in the early stages of future pandemics, and to promote reproducibility of infectious disease research. (shrink)

Whipple disease is a rare, infectious, disease first described from a single case by Whipple in 1907. As well as characterising the clinical and pathological features of the condition, Whipple made two suggestions regarding its aetiology. These were either than the disease was caused by an infectious agent, or that it was of metabolic origin. As the disease is now thought to be caused by infection with the bacterium Tropheryma whipplei, historical reviews of the history of (...) the disease typically mention only the first of these suggestions. In this paper, we therefore revisit Whipple’s other theory. We argue that a diverse and often successful research programme was developed around this mechanism of disease causation which gave rise to many useful findings on the condition. In the later parts of this article, we then turn to discuss the surprising neglect of this period of Whipple disease research in the current literature, and conclude by offering a brief reconstruction of this early history suitable for use in a technical context. (shrink)

In this article, I will reconstruct the monocausal model and argue that modern 'multifactorial diseases' are not monocausal by definition. 'Multifactorial diseases' are instead defined according to a constitutive disease model. On closer analysis, infectious diseases are also defined using the constitutive model rather than the monocausal model. As a result, our classification models alone cannot explain why infectious diseases have a universal etiology while chronic and noncommunicable diseases lack one. The explanation is instead provided by the nineteenth-century germ (...) theorists. (shrink)

Artificial Intelligence (AI) technologies are now widely used in a variety of fields to aid with knowledge acquisition and decision-making. Health information systems, in particular, can gain the most from AI advantages. Recently, symptoms-based illness prediction research and manufacturing have grown in popularity in the healthcare business. Several scholars and organisations have expressed an interest in applying contemporary computational tools to analyse and create novel approaches for rapidly and accurately predicting illnesses. In this study, we present a paradigm for assessing (...) the efficacy of combining Machine Learning (ML) and Natural Language Processing (NLP) technologies in a disease prediction system. We scraped a disease-symptom dataset with NLP characteristics from one of the UK's most trusted National Health Service (NHS) websites as an example. In addition, we will thoroughly examine our data using symptom frequency, similarity, and clustering analysis. As a consequence, we can observe that the forecast has a high efficiency rate, but there are still some challenges to work out. (shrink)

We recently suggested that there are both pragmatic and normative reasons to classify pregnancy as a disease. Several scholars argued against our claims. In this response, we defend the disease view of pregnancy against their criticism. We claim that the dysfunctional account of disease that some of our critics rely on has some counterintuitive results. Furthermore, we claim that our critics assume what needs to be argued that the primary function of our sexual organs is to reproduce. (...) Since only a small percentage of sexual intercourse leads to pregnancy, it is far from obvious that reproduction is the primary biological function of our sexual organs. We also claim that while taking pregnancy itself as a reference class could avoid the conclusion that pregnancy is a disease, the strategy is problematic since it renders the Boorsean approach to disease and health circular and effectively deprives it of any utility in determining whether a particular phenomenon is a disease or not. (shrink)

The highly contagious 2019 novel coronavirus disease (COVID-19) outbreak has not only impacted health systems, economies, and governments, it has also rapidly grown into a global health crisis, which is now threatening the lives of millions of people globally. While, on one hand, medical institutions are critically attempting to find a cure, on the other hand, governments have introduced striking measures and policies to curtail the rapid spread of the disease. Although COVID-19 has achieved pandemic status and is (...) predominantly viewed as a biomedical issue, it is argued that it should also be treated as a psychological crisis. This paper also reviews the literature to examine and comment on the detrimental effects of isolation, which has been enforced as one of the primary preventative measures to manage the spread of COVID-19. This paper further outlines key recommendations that should be addressed across different levels to buffer against the known adverse effects of isolation, which is especially relevant for the current COVID-19 situation, where a large proportion of the global population is isolated, confined, and/or quarantined. (shrink)

There is no doubt that custard apple diseases are among the important reasons that destroy the Custard Apple plant and its agricultural crops. This leads to obvious damage to these plants and they become inedible. Discovering these diseases is a good step to provide the appropriate and correct treatment. Determining the treatment with high accuracy depends on the method used to correctly diagnose the disease, expert systems can greatly help in avoiding damage to these plants. The expert system correctly (...) diagnoses Custard Apple disease to make it easier for farmers to find the right treatment based on the appropriate diagnosis. Objectives: A specialized syllable language system was established for the diagnosis of Custard Apple plant disease. (shrink)

Deutsche Krankheit: eine Diagnosestellung mit Rückblick und Ausblick auf Krankheitsverlauf.

Background: The castor bean is a large grassy or semi-wooden shrub or small tree. Any part of the castor plant parts can suffering from a disease that weakens the ability to grow and eliminates its production. Therefore, in this paper will identify the pests and diseases present in castor culture and detect the symptoms in each disease. Also images is showing the symptom form in this disease. Objectives: The main objective of this expert system is to obtain (...) appropriate diagnosis of the disease. Methods: In this paper, the expert system is designed for the ability of agricultural engineers to detect and diagnose disease of castor like as: seeding blight, alternaria blight, cercospora leaf spot, powdery mildew and wilt. This system presents the disease symptoms, survival and spread, favorable conditions and image for each disease. Clips and Delphi expert system languages are used for designing and implementing the proposed expert system. Results: The expert system in the diagnosis of castor diseases was assessed by farmers and agricultural engineers and they were satisfied and accepted with its quality of performance. Conclusions: The expert system is easy for farmers and people have experience in the plant of castor to detect and diagnosis the symptoms that may face this plant from several disease. (shrink)

Abstract: In recent years, the outbreak of various poultry diseases has posed a significant threat to the global poultry industry. Therefore, the accurate and timely detection of chicken diseases is critical to reduce economic losses and prevent the spread of diseases. In this study, we propose a method for classifying chicken diseases using a convolutional neural network (CNN). The proposed method involves preprocessing the chicken images, building and training a CNN model, and evaluating the performance of the model. The dataset (...) used in this study comprises chicken images of three different diseases: Newcastle disease, avian influenza, and infectious bursal disease. The proposed method achieved an overall F1-score of 99.04% in classifying the chicken diseases. The results demonstrate the effectiveness of using CNN in classifying chicken diseases and provide a promising approach for early detection and diagnosis of poultry diseases. (shrink)

Philosophers of medicine have formulated different accounts of the concept of disease. Which concept of disease one assumes has implications for what conditions count as diseases and, by extension, who may be regarded as having a disease and for who may be accorded the social privileges and personal responsibilities associated with being sick. In this article, we consider an ideal diagnostic test for coronavirus disease 2019 infection with respect to four groups of people—positive and asymptomatic; positive (...) and symptomatic; negative; and untested—and show how different concepts of disease impact on the disease and sickness judgements for these groups. The suggestion is that sickness judgements and social measures akin to those experienced during the current COVID-19 outbreak presuppose a concept of disease containing social harm as a component. We indicate the problems that arise when adopting this kind of disease concept beyond a state of emergency. (shrink)

The early Stoics diagnose vicious agents with various psychological diseases, e.g. love of money and love of wine. Such diseases are characterized as false evaluative opinions that lead the agent to form emotional impulses for certain objects, e.g. money and wine. Scholars have therefore analyzed psychological diseases simply as dispositions for assent. This interpretation is incomplete, I argue, and should be augmented with the claim that psychological disease also affects what kind of action-guiding impressions are created prior to giving (...) assent. This proposal respects the Stoic insistence that impression-formation, no less than assent, is an activity of reason. In so far as the wine-lover’s reason is corrupted in a different way from the money-lover’s, the two vicious agents will form different action-guiding impressions when faced with similar stimuli. Here I juxtapose the Stoic account of expertise, according to which experts form more precise action-guiding impressions compared to the amateur, in virtue of possessing a system of grasps (katalēpseis). So expertise enhances, whereas psychological disease degrades, the representational fidelity of the impressions that prefigure action. With these commitments, the Stoics can be seen to offer a nuanced and principled theory of cognitive penetration and to anticipate some recent proposals in epistemology and cognitive science. (shrink)

In this article I seek to say what it is for something to count as a neglected disease. I argue that neglect should be defined in terms of efforts at prevention, mitigation and cure, and not solely in terms of research dollars per disability-adjusted life-year. I further argue that the trend towards multifactorialism and risk factor thinking in modern epidemiology has lent credibility to the erroneous view that the primary problem with neglected diseases is a lack of research. A (...) more restrictive contrastive model of disease is endorsed as better suited to the definition of neglected disease. (shrink)

Cardiovascular diseases (CVD) represent a significant cause of morbidity and mortality worldwide, necessitating early detection for effective intervention. This research explores the application of machine learning (ML) algorithms in predicting cardiovascular diseases with enhanced accuracy by integrating optimization techniques. By leveraging data-driven approaches, ML models can analyze vast datasets, identifying patterns and risk factors that traditional methods might overlook. This study focuses on implementing various ML algorithms, such as Decision Trees, Random Forest, Support Vector Machines, and Neural Networks, optimized through (...) techniques like hyperparameter tuning, cross-validation, and feature selection to improve prediction accuracy. (shrink)

It often seems incorrect to say that psychiatric conditions are diseases, and equally incorrect to say that they are not. This results in what would seem to be an unsatisfactory stalemate. The present essay examines the considerations that have brought us to such a stalemate in our discussions of autism. It argues that the stalemate in this particular case is a reflection of the fact that we need to find the logical space for a position that rejects both positive and (...) negative answers. It then suggests one way in which we might find such space, by applying Michael Dummett’s notion of semantic disharmony. (shrink)

Many existing biomedical vocabulary standards rest on incomplete, inconsistent or confused accounts of basic terms pertaining to diseases, diagnoses, and clinical phenotypes. Here we outline what we believe to be a logically and biologically coherent framework for the representation of such entities and of the relations between them. We defend a view of disease as involving in every case some physical basis within the organism that bears a disposition toward the execution of pathological processes. We present our view in (...) the form of a list of terms and definitions designed to provide a consistent starting point for the representation of both disease and diagnosis in information systems in the future. (shrink)

The declaration of the COVID-19 pandemic by the World Health Organization on March 11, 2020, led to unprecedented events. All regions of the world participated in implementing preventive health measures such as physical distancing, travel restrictions, self-isolation, quarantines, and facility closures. The pandemic started global disruption of socio-economic systems, covering the postponement or cancellation of public events, supply shortages, schools and universities’ closure, evacuation of foreign citizens, a rise in unemployment and inflation, misinformation, the anti-vaccine movement, and incidents of discrimination (...) toward people affected by or suspected of having coronavirus disease. Attempts have been made to protect the oldest age group at risk, but in many cases, this has led to over-restriction and age discrimination. The rationale for working on the Research Topic "Socio-economic systems in the post-pandemic world: Design thinking, strategic planning, management, and public policy" was the need to start reflecting on resilience and lessons learned from this public health event that revealed the global unpreparedness in critical areas. Also, the pandemic triggered both top-down and bottom-up responses that needed more in-depth analyzes. This Research Topic covers interdisciplinary contributions addressing new thinking, challenges, and transformations required for post-pandemic global, national, regional, and local realities. The presented Research Topic combines studies focused on recognizing the actions and interventions leading to the recovery of socio-economic systems during the tail end and after the pandemic. The studies delivered recommendations regarding, among others, the care of vulnerable, planning socio-economic restart, and imagining the "new normal.". (shrink)

Identifying the necessary and sufficient conditions for individuating and classifying diseases is a matter of great importance in the fields of law, ethics, epidemiology, and of course, medicine. In this paper, I first propose a means of achieving this goal, ensuring that no two distinct disease-types could correctly be ascribed to the same disease-token. I then posit a metaphysical ontology of diseases—that is, I give an account of what a disease is. This is essential to providing the (...) most effective means of interfering with disease processes. Following existing work in the philosophy of medicine and epidemiology (primarily Christopher Boorse; Caroline Whitbeck; Alexander Broadbent), philosophy of biology (Joseph LaPorte; D.L. Hull), conditional analyses of causation (J.L. Mackie; David Lewis), and recent literature on dispositional essentialism (Stephen Mumford and Rani Anjum; Alexander Bird), I endorse a dispositional conception of disease. Following discussion of various conceptions of disease-identity, their relations to the clinical and pathological effects of the diseases in question, and how diseases are treated, I conclude (i) that diseases should be individuated by their causes, and (ii) that diseases are causal processes best seen as simultaneously acting sequences of mutually manifesting dispositions. (shrink)

This paper aims is to present the design and development of an intelligent tutoring system for teaching students about stomach diseases to help and give them a clear idea about stomach ulcer diseases. Furthermore, the researchers designed an intelligent tutoring system with a clear interface including 3D animation with Delphi that show how the ulcer appears in the stomach with more details about the disease, after that there are some questions researchers going to adopt from clinical books about the (...) diseases that show the students how to diagnose the diseases. This system integrated Delphi programming with multimedia elements such as (voice, motion, text, photo, and interactive) to carry out the project. The idea of this paper is considering the intelligent tutoring system for students who are studying medicine to enhance their knowledge and deep understanding about the diseases. (shrink)

Plants from a handful of species provide the primary source of food for all people, yet this source is vulnerable to multiple stressors, such as disease, drought, and nutrient deficiency. With rapid population growth and climate uncertainty, the need to produce crops that can tolerate or resist plant stressors is more crucial than ever. Traditional plant breeding methods may not be sufficient to overcome this challenge, and methods such as highOthroughput sequencing and automated scoring of phenotypes can provide significant (...) new insights. Ontologies are essential tools for accessing and analysing the large quantities of data that come with these newer methods. As part of a larger project to develop ontologies that describe plant phenotypes and stresses, we are developing a plant disease extension of the Infectious Disease Ontology (IDOPlant). The IDOPlant is envisioned as a reference ontology designed to cover any plant infectious disease. In addition to novel terms for infectious diseases, IDOPlant includes terms imported from other ontologies that describe plants, pathogens, and vectors, the geographic location and ecology of diseases and hosts, and molecular functions and interactions of hosts and pathogens. To encompass this range of data, we are suggesting inOhouse ontology development complemented with reuse of terms from orthogonal ontologies developed as part of the Open Biomedical Ontologies (OBO) Foundry. The study of plant diseases provides an example of how an ontological framework can be used to model complex biological phenomena such as plant disease, and how plant infectious diseases differ from, and are similar to, infectious diseases in other organism. (shrink)

Background: Despite the at least decades long record of philosophical recognition and interest, the intricacy of the deceptively familiar appearing concepts of ‘disease’, ‘disorder’, ‘disability’, etc., has only recently begun showing itself with clarity in the popular discourse wherein its newly emerging prominence stems from the liberties and restrictions contingent upon it. Whether a person is deemed to be afflicted by a disease or a disorder governs their ability to access health care, be it free at the point (...) of use or provided by an insurer; it also influences the treatment of individuals by the judicial system and employers; it even affects one’s own perception of self. -/- Aims: All existing philosophical definitions of disease struggle with coherency, causing much confusion and strife, and leading to inconsistencies in real-world practice. Hence, there is a real need for an alternative. -/- Materials & Methods: In the present article I analyse the variety of contemporary views of disease, showing them all to be inadequate and lacking in firm philosophical foundations, and failing to meet the desideratum of patient driven care. -/- Results: Illuminated by the insights emanating from the said analysis, I introduce a novel approach with firm ethical foundations, which foundations are rooted in sentience, that is the subjective experience of sentient beings. -/- Discussion: I argue that the notion of disease is at best superfluous, and likely even harmful in the provision of compassionate and patient-centred care. -/- Conclusion: Using a series of presently contentious cases illustrate the power of the proposed framework which is capable of providing actionable and humane solutions to problems that leave the current theories confounded. (shrink)

This essay explores the theoretical treatment of leprosy in 13th-century theological discussions on the transmission of the original sin. According to scholastic theologians, both the existence of the original sin and its transmission from parents to progeny were factual truths, whose dynamics could be explained by analogy with inherited diseases, such as leprosy. Different uses of natural philosophy and medicine in discussing the transmission of leprosy will be shown in theological and biblical-exegetical works of William of Auvergne, Roland of Cremona, (...) Bonaventure, Thomas Aquinas, and Henry of Ghent. -/- . (shrink)

If one had to identify the biggest change within the philosophical tradition in the twenty-first century, it would certainly be the rapid rise of experimental philosophy to address differences in intuitions about concepts. It is, therefore, surprising that the philosophy of medicine has so far not drawn on the tools of experimental philosophy in the context of a particular conceptual debate that has overshadowed all others in the field: the long-standing dispute between so-called naturalists and normativists about the concepts of (...) health and disease. In this paper, I defend and advocate the use of empirical methods to inform and advance this and other debates within the philosophy of medicine. (shrink)

Last year saw the 20th anniversary edition of JECP, and in the introduction to the philosophy section of that landmark edition, we posed the question: apart from ethics, what is the role of philosophy ‘at the bedside’? The purpose of this question was not to downplay the significance of ethics to clinical practice. Rather, we raised it as part of a broader argument to the effect that ethical questions – about what we should do in any given situation – are (...) embedded within whole understandings of the situation, inseparable from our beliefs about what is the case, what it is that we feel we can claim to know, as well as the meaning we ascribe to different aspects of the situation or to our perception of it. Philosophy concerns fundamental questions: it is a discipline requiring us to examine the underlying assumptions we bring with us to our thinking about practical problems. Traditional academic philosophers divide their discipline into distinct areas that typically include logic: questions about meaning, truth and validity; ontology: questions about the nature of reality, what exists; epistemology: concerning knowledge; and ethics: how we should live and practice, the nature of value. Any credible attempt to analyse clinical reasoning will require us to think carefully about these types of question and the relationships between them, as they influence our thinking about specific situations and problems. So, the answers to the question we posed, about the role of philosophy at the bedside, are numerous and diverse, and that diversity is illustrated in the contributions to this thematic edition. (shrink)

Vaccination complacency occurs when perceived risks of vaccine-preventable diseases are sufficiently low so that vaccination is no longer perceived as a necessary precaution. Disease outbreaks can once again increase perceptions of risk, thereby decrease vaccine complacency, and in turn decrease vaccine hesitancy. It is not well understood, however, how change in perceived risk translates into change in vaccine hesitancy. -/- We advance the concept of vaccine propensity, which relates a change in willingness to vaccinate with a change in perceived (...) risk of infection—holding fixed other considerations such as vaccine confidence and convenience. (shrink)

In this paper we focus on some new normativist positions and compare them with traditional ones. In so doing, we claim that if normative judgments are involved in determining whether a condition is a disease only in the sense identified by new normativisms, then disease is normative only in a weak sense, which must be distinguished from the strong sense advocated by traditional normativisms. Specifically, we argue that weak and strong normativity are different to the point that one (...) ‘normativist’ label ceases to be appropriate for the whole range of positions. If values and norms are not explicit components of the concept of disease, but only intervene in other explanatory roles, then the concept of disease is no more value-laden than many other scientific concepts, or even any other scientific concept. We call the newly identified position “value-conscious naturalism” about disease, and point to some of its theoretical and practical advantages. (shrink)

This paper advances a theory of disease as domino dysfunction. It is often argued that diseases are biological dysfunctions. However, a theory of disease as biological dysfunction is complicated by some plausible cases of dysfunction, which seem clearly non-pathological. I argue that pathological conditions are not just dysfunctions but domino dysfunctions, and that domino dysfunctions can be distinguished on principled biological grounds from non-pathological dysfunctions. I then show how this theory can make sense of the problem cases; they (...) are not diseases because they are not domino dysfunctions. (shrink)

Background: Broccoli is an edible green plant in the cabbage family (family Brassicaceae, genus Brassica) whose large flowering head, stalk and small associated leaves are eaten as a vegetable. A leaf of Broccoli might be affected of Several Diseases descriped in this paper . When symptoms is encountered, it requires some kind of medical care. If appropriate Survival of Broccoli Diseases is not taken quickly, it can lead to Broccoli to die . Objectives: The main goal of this expert system (...) is to get the appropriate diagnosis of disease and the correct treatment. Methods: In this paper the design of the proposed Expert System which was produced to help Farmers in diagnosing many of the broccoli diseases such as : Damping Off,Club root of crucifers or Finger and toe disease,Alternaria leaf spot,Black rot,Downy mildew, and White rust. (shrink)

Background: There is no doubt that strawberry diseases are one of the most important reasons that led to the destruction of strawberry plants and their crops. This leads to obvious damage to these plants and they become inedible. Discovering these diseases after a good step for proper and correct treatment. Determining the treatment with high accuracy depends on the method used in the diagnosis. Correctly, expert systems can greatly help in avoiding damage to these plants. The expert system correctly diagnoses (...) strawberry disease to make it easier for farmers to find the right treatment based on the appropriate diagnosis. Objectives: The main goal of this expert system is to get the appropriate diagnosis of disease and the correct treatment. Methods: In this paper the design of the proposed Expert System which was produced to help Farmers and students interested in agriculture strawberry in diagnosing many of the strawberry diseases such as: Leaf Spots, Grey Mold, Red Stele/Red Core, Wilt, Powdery Mildew, Alternaria Spot, Black Root Rot, Anthracnose (black spot), and Angular Leaf Spot. The proposed expert system presents an overview about strawberry diseases are given, the cause of diseases are outlined and the treatment of disease whenever possible is given out. CLIPS language was used for designing and implementing the proposed expert system. Results: The proposed strawberry diseases diagnosis expert system was evaluated by Farmers and they were satisfied with its performance. Conclusions: The Proposed expert system is very useful for Farmers with strawberry problem and students interested in agriculture strawberry. (shrink)