As opposed to a ‘one size fits all’ approach, precision medicine uses relevant biological, medical, behavioural and environmental information about a person to further personalize their healthcare. This could mean better prediction of someone’s disease risk and more effective diagnosis and treatment if they have a condition. Big data allows for far more precision and tailoring than was ever before possible by linking together diverse datasets to reveal hitherto-unknown correlations and causal pathways. But it also raises ethical issues (...) relating to the balancing of interests, viability of anonymization, familial and group implications, as well as genetic discrimination. This article analyses these issues in light of the values of public benefit, justice, harm minimization, transparency, engagement and reflexivity and applies the deliberative balancing approach found in theEthical Framework for Big Data in Health and Research to a case study on clinical genomic data sharing. Please refer to that article for an explanation of how this framework is to be used, including a full explanation of the key values involved and the balancing approach used in the case study at the end. Our discussion is meant to be of use to those involved in the practice as well as governance and oversight of precision medicine to address ethical concerns that arise in a coherent and systematic manner. (shrink)

This paper focuses on Precision Public Health (PPH), described in the scientific literature as an effort to broaden the scope of precision medicine by extrap- olating it towards public health. By means of the “All of Us” (AoU) research pro- gram, launched by the National Institutes of Health in the U.S., PPH is being devel- oped based on health data shared through a broad range of digital tools. PPH is an emerging idea to harness (...) the data collected for precision medicine to be able to tai- lor preventive interventions for at-risk groups. The character of these data concern genetic identity, lifestyle and overall health and therefore affect the ‘intimacy’ of personhood. Through the concept of biological citizenship, we elucidate how AoU and its recruitment tactics, by resonating ‘diversity’, at the same time appeal to and constitute identity, defining individuals as ‘data sharing subjects’. Although PPH is called for; the type of bio-citizenship that is enacted here, has a particular definition, where participant recruitment focuses on ‘citizenship’ in terms of empowerment (front), it is the ‘bio’ prefix that has become the main focus in terms of research. i.e. biosubjectivities vs biocapital. This raises the question whether the societal chal- lenges that often underlie public health issues can be sufficiently dealt with based on the way ‘diversity’ is accounted for in the program. We suggest that the AoU still risks of harming underrepresented groups based on the preconditions and the design of the program. (shrink)

In addressing the question of what mental health is we might proceed as if there is a single phenomenon – mental health – denoted by a single overarching concept. The task, then, is to provide an informative analysis of this concept which applies to all and only instances of mental health, and which illuminates what it is to be mentally healthy. In contrast, mental health pluralism is the idea that there are multiple mental health phenomena (...) denoted by multiple concepts of mental health. Analysis and illumination of mental health may still be possible, but there isn’t a single phenomenon or concept to be analysed in addressing the question of what mental health is. The question of pluralism has been overlooked in the philosophy of mental health. The discussion to follow is an attempt to get us to take mental health pluralism seriously. To that end, in this essay I have three primary goals: (1) to give a precise account of what mental health pluralism is, (2) to show that the question of pluralism should not be neglected in debate about what mental health is, and (3) to argue for mental health pluralism. I also draw out some implications of this discussion for philosophy, science, and psychotherapy. (shrink)

One of the lessons of the COVID-19 pandemic is that the lay public relies immensely on the knowledge of public health officials. At every phase of the pandemic, the testimony of public health officials has been crucial for guiding public policy and individual behavior. The reason is simple: public health officials know a lot more than you and I do about public health. As lay people, we rely on experts. This seems straightforward. But the COVID-19 pandemic (...) has shown that public health officials seem undecided as to what, precisely, their role is; are they providing the public information as it presents itself, or are they informing the public in a way that produces a desired or optimal outcome? In this article, I answer the following question: what are public health officials morally obligated to tell the public? As I see it, these are the main options: (1) public health officials should tell the full truth, regardless of outcome; or (2) they should tell partial truths or lies that are aimed to promote a socially optimal outcome. My answer to this question is that public health officials are only allowed to lie under very narrow and rare conditions. -/- . (shrink)

Electronic Health Records (EHRs) are organized around two kinds of statements: those reporting observations made, and those reporting acts performed. In neither case does the record involve any direct reference to what such statements are actually about. They record not: what is happening on the side of the patient, but rather: what is said about what is happening. While the need for a unique patient identifier is generally recognized, we argue that we should now move to an EHR regime (...) in which all clinically salient particulars – from the concrete disorder on the side of the patient and the body parts in which it occurs to the concrete treatments given – should be uniquely identified. This will allow us to achieve interoperability among different systems of records at the level where it really matters: in regard to what is happening in the real world. It will also allow us to keep track of particular disorders and of the effects of particular treatments in a precise and unambiguous way. We discuss the ontological and epistemological aspects of our claim and describe a scenario for implementation within EHR systems. (shrink)

The future of health research will be characterised by three continuing trends: rising demand for health data; increasing impracticability of obtaining specific consent for secondary research; and decreasing capacity to effectively anonymise data. In this context, governments, clinicians and the research community must demonstrate that they can be responsible stewards of health data. IRBs and RECs sit at heart of this process because in many jurisdictions they have the capacity to grant consent waivers when research is judged (...) to be of particular value. However, several different terms are used to refer to this value, indicating a lack of conceptual clarity regarding the appropriate test for access to health data for research without consent. In this paper we do three things. First we describe the current confusion and instability in terminology relating to public interest in the context of consent waivers. Second we argue for harmonisation of terminology on the grounds of clarity, transparency and consistency. Third we argue that the term ‘public interest’ best reflects the normative work required to justify consent waivers because it is the broadest of the competing terms. ‘Public interest’ contains within its scope positive and negative implications of a study, as well as welfare, justice and rights considerations. In making this argument, we explain the normative basis for consent waivers, and provide a starting place for further discussion about the precise conditions in which a given study can be said to advance the public interest. Ipsos MORI study found that: … the public would be broadly happy with administrative data linking for research projects provided Those projects have social value, broadly defined. Data are de-identified. Data are kept secure. Businesses are not able to access the data for profit. (shrink)

Policies that require male-female sex comparisons in all areas of biomedical research conflict with the goal of improving health outcomes through context-sensitive individualization of medical care. Sex, like race, requires a rigorous, contextual approach in precision medicine. A “sex contextualist” approach to gender-inclusive medicine better aligns with this aim.

Abstract: This research employs a Random Forest classification model to predict and assess obesity and cardiovascular disease (CVD) risk based on a comprehensive dataset collected from individuals in Mexico, Peru, and Colombia. The dataset comprises 17 attributes, including information on eating habits, physical condition, gender, age, height, and weight. The study focuses on classifying individuals into different health risk categories using machine learning algorithms. Our Random Forest model achieved remarkable performance with an accuracy, F1-score, recall, and precision all (...) reaching 97.23%. The model's success is attributed to its ability to effectively leverage attributes related to eating habits, physical condition, and demographic factors. Feature importance analysis reveals key factors contributing to accurate predictions. This paper contributes to the field of health analytics by demonstrating the effectiveness of Random Forest in predicting and categorizing health risks. The insights gained from this research have the potential to inform personalized health interventions and contribute to the advancement of precision health strategies. Furthermore, the study highlights the importance of leveraging machine learning techniques for health risk assessment in diverse populations. (shrink)

Memory modification technologies (MMTs)—interventions within the memory affecting its functions and contents in specific ways—raise great therapeutic hopes but also great fears. Ethicists have expressed concerns that developing and using MMTs may endanger the very fabric of who we are—our personal identity. This threat has been mainly considered in relation to two interrelated concerns: truthfulness and narrative self‐constitution. In this article, we propose that although this perspective brings up important matters concerning the potential aftermaths of MMT utilization, it fails to (...) tell the whole story. We suggest that capturing more tangible potential consequences of MMT use, namely, its psychological ramifications is crucial both in ethical considerations and in making decisions regarding the permissibility of such interventions. To this end, we first examine what current MMTs are capable of and what are the prospects of emerging MMTs. Subsequently, we outline the relationship between memory and personal identity; specifically, we indicate that concepts of self‐defining memories and narrative identity are crucial to considering how MMTs may influence one's psychological functioning. On this basis, we analyze potential consequences of narrative disruption that may be the result of the use of MMTs; more precisely, we consider its potential effects on mental health, well‐being, and personal agency, and outline the ethical dilemmas that decision‐makers face in this context. We conclude by considering the broader cultural context that may have influence on policymaking regarding permissibility of memory modification interventions. (shrink)

Ethical decision-making frameworks assist in identifying the issues at stake in a particular setting and thinking through, in a methodical manner, the ethical issues that require consideration as well as the values that need to be considered and promoted. Decisions made about the use, sharing, and re-use of big data are complex and laden with values. This paper sets out an Ethics Framework for Big Data in Health and Research developed by a working group convened by the Science, (...) class='Hi'>Health and Policy-relevant Ethics in Singapore Initiative. It presents the aim and rationale for this framework supported by the underlying ethical concerns that relate to all health and research contexts. It also describes a set of substantive and procedural values that can be weighed up in addressing these concerns, and a step-by-step process for identifying, considering, and resolving the ethical issues arising from big data uses in health and research. This Framework is subsequently applied in the papers published in this Special Issue. These papers each address one of six domains where big data is currently employed: openness in big data and data repositories, precision medicine and big data, real-world data to generate evidence about healthcare interventions, AI-assisted decision-making in healthcare, public-private partnerships in healthcare and research, and cross-sectoral big data. (shrink)

One of the great challenges for students of any discipline is to be able to put into practice the knowledge learned in theory. Public health does not escape this challenge. Research Methods for Public Health is a book that seeks to help students understand in a simple way how to enter into the practice of public health research. This book stands out for its easy reading, but especially because it emphasizes the existence of quantitative and qualitative methods, (...) as well as the combination of both methods. On the other hand, the authors show between the lines, and sometimes more clearly, the importance of teamwork in research groups, a key element to avoid errors, validate procedures or instruments and increase the reliability of the results, as well as the importance of criteria of validity, reliability, precision, relevance, transparency and credibility, among others. -/- Uno de los grandes retos para los estudiantes de cualquier disciplina es poder poner en práctica los conocimientos aprendidos en la teoría. La salud pública no se escapa de este desafío. Research Methods for Public Health es un libro que busca ayudar a comprender de manera sencilla cómo introducirse en la práctica de la investigación en salud pública. Este libro se destaca por su fácil lectura, pero especialmente porque pone el énfasis en la existencia de los métodos cuantitativos y cualitativos, así como en la combinación de ambos métodos. Por otro lado, los autores dejan ver entrelíneas, y a veces más claramente, la importancia del trabajo en equipo en los grupos de investigación, elemento clave para evitar errores, validar procedimientos o instrumentos y aumentar la confia-bilidad de los resultados, así como la importancia de criterios de validez, confiabilidad, precisión, relevancia, transparencia y credibilidad, entre otros. (shrink)

With the current improvement of development towards pharmaceutical, distinctive ultrasound methodologies are open to find the fetal prosperity. It is analyzed with diverse clinical parameters with 2-D imaging and other test. In any case, prosperity desire of fetal heart still remains an open issue due to unconstrained works out of the hatchling, the minor heart appraise and inadequate of data in fetal echocardiography. The machine learning strategies can find out the classes of fetal heart rate which can beutilized for earlier (...) evaluating. With this background, we have utilized Cardiotocographic Fetal heart rate dataset removed from UCI Machine Learning Store for predicting the fetal heart rate health classes. The Prediction of fetal health rate are achieved in six ways. Firstly, the data set is preprocessed with Feature Scaling and missing values. Secondly, exploratory data investigation is done and the dispersion of target feature is visualized. Thirdly, the raw data set is fitted to all the classifiers and the performance is analysed before and after feature scaling. Fourth, the raw data set is subjected to undersampling methods like ClusterCentroids, RepeatedENN, AllKNN, CondensedNearestNeighbour, EditedNearestNeighbours, InstanceHardnessThreshold and NearMiss. Fifth, the undersampled dataset by above mentioned methods are fitted to all the classifiers and the performance is analyzed before and after feature scaling. Sixth, performance analysis is done using metrics like Precision, Recall, F-score, Accuracy and running time. The execution is done using python language under Spyder platform with Anaconda Navigator. Experimental results shows that the Decision Tree classifier tends to retain 98% before and after feature scaling for the underrsampling with EditedNearestNeighbours, RepeatedENN and AllKNN methods. (shrink)

This research aims to investigate managing information and planning for the participation of health charities. With the Covid-19 outbreak, it was important to use information systems to optimize health management. The correct processing of information is the basis for better use of health charities’ participation. Using the snowball technique, and semi-structured interviews, 47 experts and non-experts in Fars province were asked for their opinions. Next, based on the fuzzy network analysis, important factors were prioritized. Four categories and (...) 8 important criteria were identified. To prioritize the effective aspects of the participation of health charities in Fars province, a network analysis technique was used via MATLAB software. Strategy and programs with a normal weight of 0.34 showed the highest priority. Research and education with a normal weight of 0.26 received the second priority. Laws and regulations with a normal weight of 0.24 were placed as the third priority. Based on the obtained results, creating strategy and planning, training and building cultural in this field, ease of rules and regulations as well as planning of financial resources play an important role in managing the participation of health donors. With precise planning of health programs, donors see the transparency of decisions. They check the part that needs financing and equipment completion. Because the quality of plans and programs, effectiveness and efficiency of strategies, and the quality of results depend on the decisions made by donors. (shrink)

Since March of 2020, the world has been brought to its knees by unscientific and unethical mandates. These mandates have destroyed the world economy and the lives of countless innocent individuals. The “cure” that has been offered by medical bureaucrats and politicians has been more deadly than the disease (COVID-19). The imposition of ludicrous lockdowns, mask-wearing, coerced vaccination, and vaccine passports have not only proved to be ineffective, but also much more harmful than SARS-CoV-2 and all its variants. COVID-19 has (...) a recovery rate of close to 99% for most of the world’s population, however, despite this, institutions and power-hungry individuals have trampled upon our civil liberties and ignored our inalienable human rights. It is precisely as Thomas Paine famously stated: “The greatest tyrannies are always perpetrated in the name of the noblest causes.” -/- Many lives have been gratuitously lost because of the administration of deadly medical treatments, and the rejection of effective treatments that follow genuine science. In the process, informed consent has been disparaged and desecrated. For over two years, we were instructed to “trust the science,” but the “science” advocated by medical bureaucrats and greedy politicians was the reason why the world was turned upside-down. In the face of these crimes against humanity, justice will only be brought by real courts and real judges, but this will require the awakening of a critical mass. This book serves as an instrument for this awakening. (shrink)

By leveraging convolutional neural networks (CNNs), the model identifies various plant diseases with high accuracy. The experimental setup includes a dataset consisting of healthy and diseased leaf images of different plant species. The dataset is preprocessed to remove noise and augmented to address the issue of class imbalance. The CNN model is then trained, validated, and tested on this dataset. The results indicate that the deep learning model achieves a classification accuracy of over 95% for most plant diseases. Additionally, the (...) system is designed to provide real-time feedback to farmers, helping them take immediate corrective action. This automated approach eliminates the need for expert human intervention and can be deployed on mobile devices for ease of use in rural areas. The potential future applications of this system include integration with precision agriculture techniques and expansion to a wider range of crops. (shrink)

The mental health crisis and loneliness epidemic have sparked a growing interest in leveraging artificial intelligence (AI) and chatbots as a potential solution. This report examines the benefits and risks of incorporating chatbots in mental health treatment. AI is used for mental health diagnosis and treatment decision-making and to train therapists on virtual patients. Chatbots are employed as always-available intermediaries with therapists, flagging symptoms for human intervention. But chatbots are also sold as stand-alone virtual therapists or as (...) friends and lovers. There is evidence that these uses of AI and chatbots can provide better quality service, improve accessibility, and lower costs. The systems can reduce the stigma and shame of sharing their problems and leverage a mass of biometric and behavioral data to supplement self-reports. As the systems' intelligence rapidly improves, they will need to be rigorously tested for the accuracy and precision of their diagnoses and the quality of their interactions with patients. As chatbots become indistinguishable from humans and leverage their superhuman capacity to detect affect and draw on knowledge of a patient’s life, patients will be drawn to attribute personality to and relationship with the chatbot. Consequently, it will be essential to study what the “therapeutic alliance” with an actual human counselor provides and the risks of patients attributing such a relationship to a one-sided or “parasocial” relationship. (shrink)

Precision Medicine is driven by the idea that the rapidly increasing range of relatively cheap and efficient self-tracking devices make it feasible to collect multiple kinds of phenotypic data. Advocates of N = 1 research emphasize the countless opportunities personal data provide for optimizing individual health. At the same time, using biomarker data for lifestyle interventions has shown to entail complex challenges. In this paper, we argue that researchers in the field of precision medicine need to address (...) the performative dimension of collecting data. We propose the fun-house mirror as a metaphor for the use of personal health data; each health data source yields a particular type of image that can be regarded as a ‘data mirror’ that is by definition specific and skewed. This requires competence on the part of individuals to adequately interpret the images thus provided. (shrink)

Precision medicine and molecular systems medicine (MSM) are highly utilized and successful approaches to improve understanding, diagnosis, and treatment of many diseases from bench-to-bedside. Especially in the COVID-19 pandemic, molecular techniques and biotechnological innovation have proven to be of utmost importance for rapid developments in disease diagnostics and treatment, including DNA and RNA sequencing technology, treatment with drugs and natural products and vaccine development. The COVID-19 crisis, however, has also demonstrated the need for systemic thinking and transdisciplinarity and the (...) limits of MSM: the neglect of the bio-psycho-social systemic nature of humans and their context as the object of individual therapeutic and population-oriented interventions. COVID-19 illustrates how a medical problem requires a transdisciplinary approach in epidemiology, pathology, internal medicine, public health, environmental medicine, and socio-economic modeling. Regarding the need for conceptual integration of these different kinds of knowledge we suggest the application of general system theory (GST). This approach endorses an organism-centered view on health and disease, which according to Ludwig von Bertalanffy who was the founder of GST, we call Organismal Systems Medicine (OSM). We argue that systems science offers wider applications in the field of pathology and can contribute to an integrative systems medicine by (i) integration of evidence across functional and structural differentially scaled subsystems, (ii) conceptualization of complex multilevel systems, and (iii) suggesting mechanisms and non-linear relationships underlying the observed phenomena. We underline these points with a proposal on multi-level systems pathology including neurophysiology, endocrinology, immune system, genetics, and general metabolism. An integration of these areas is necessary to understand excess mortality rates and polypharmacological treatments. In the pandemic era this multi-level systems pathology is most important to assess potential vaccines, their effectiveness, short-, and long-time adverse effects. We further argue that these conceptual frameworks are not only valid in the COVID-19 era but also important to be integrated in a medicinal curriculum. (shrink)

In this chapter, we describe three areas within the broad field of ecogenomics or postgenomics: epigenetics, proteomics, and microbiomics. We argue that these fields challenge traditional bioethics in different ways. Since epigenetic, proteomic, and microbiomic data may contain phenotypical information, they may intensify discussions about consent, privacy, and return of results. But these fields also firmly position organisms, including human beings, as deeply entangled with their environments, as constituted by context, history, and experiences as much as genes. This yields new (...) insights into concepts of health and development. We discuss precision medicine as an example of a systemic approach to health. We argue that acknowledging the entanglement of organism and environment also means recognizing the importance of interdisciplinarity for bioethics and thinking together biomedical ethics and environmental ethics. (shrink)

Deep learning may transform health care, but model development has largely been dependent on availability of advanced technical expertise. The aim of this study is to develop a deep learning model to predict the gender from retinal fundus images. The proposed model was based on the Xception pre-trained model. The proposed model was trained on 20,000 retinal fundus images from Kaggle depository. The dataset was preprocessed them split into three datasets (training, validation, Testing). After training and cross-validating the proposed (...) model, it was evaluated using the testing dataset. The result of testing, the area under receiver operating characteristic curve (AUROC) of the model was 0.99, precision, recall, f1-score and accuracy were 99%, precision, recall, f1-score and accuracy were 96.83%, 96.83%, 96.82% and 96.83% respectively.. Clinicians are presently unaware of dissimilar retinal feature variants between females and males, stressing the importance of model explain ability for the prediction of gender from retinal fundus images. The proposed deep learning may enable clinician-driven automated discovery of novel visions and disease biomarkers. (shrink)

In Bulgaria, integrating technology and innovation is crucial for advancing sustainable healthcare disaster management, enhancing disaster response and recovery, and minimizing long-term environmental and social impacts. The purpose of the study is to assess the impact of modern technological innovations on the effectiveness of disaster management in health care in Bulgaria with a focus on Health Information Systems (HIS), Telemedicine, Telehealth, e-Health, Electronic Health Records, Artificial Intelligence (AI), Public Communication Platforms, and Data Security and Privacy. These (...) innovations, when integrated effectively, can significantly strengthen Bulgaria's preparedness and response capabilities, improving efficiency and reducing the environmental footprint of disaster interventions. Collaboration with technology experts, research institutions, and international organizations is essential for successful implementation. The research aims to assess how these technologies can enhance disaster management efficacy, adopting a methodical approach using Systematic Literature Reviews (SLRs). Systematic Literature Reviews (SLRs) utilize precise criteria for research selection and evaluation to reduce bias, systematically compiling and analyzing data from various studies to offer evidence-based insights. They are crucial for uncovering research gaps, indicating areas needing further exploration. Findings highlight the transformative potential of technology integration in streamlining communication, data management, and operational efficiency. The study underscores the urgent need for an integrated approach and a culture of innovation within the healthcare sector, emphasizing ongoing assessment and adaptation of technology-based strategies. While acknowledging limitations and calling for further research, the study provides valuable insights into Bulgaria's healthcare disaster management strategies and sets the stage for future investigations to refine and expand upon these strategies. It advocates for a strategic framework guiding the efficient integration of technology into disaster management, aiming for resilient and patient-centric healthcare systems. (shrink)

Background: The concept of digital twins has great potential for transforming the existing health care system by making it more personalized. As a convergence of health care, artificial intelligence, and information and communication technologies, personalized health care services that are developed under the concept of digital twins raise a myriad of ethical issues. Although some of the ethical issues are known to researchers working on digital health and personalized medicine, currently, there is no comprehensive review that (...) maps the major ethical risks of digital twins for personalized health care services. Objective: This study aims to fill the research gap by identifying the major ethical risks of digital twins for personalized health care services. We first propose a working definition for digital twins for personalized health care services to facilitate future discussions on the ethical issues related to these emerging digital health services. We then develop a process-oriented ethical map to identify the major ethical risks in each of the different data processing phases. Methods: We resorted to the literature on eHealth, personalized medicine, precision medicine, and information engineering to identify potential issues and developed a process-oriented ethical map to structure the inquiry in a more systematic way. The ethical map allows us to see how each of the major ethical concerns emerges during the process of transforming raw data into valuable information. Developers of a digital twin for personalized health care service may use this map to identify ethical risks during the development stage in a more systematic way and can proactively address them. Results: This paper provides a working definition of digital twins for personalized health care services by identifying 3 features that distinguish the new application from other eHealth services. On the basis of the working definition, this paper further layouts 10 major operational problems and the corresponding ethical risks. Conclusions: It is challenging to address all the major ethical risks that a digital twin for a personalized health care service might encounter proactively without a conceptual map at hand. The process-oriented ethical map we propose here can assist the developers of digital twins for personalized health care services in analyzing ethical risks in a more systematic manner. (shrink)

There is disagreement among physicians and medical ethicists on the precise goals of Hospice and Palliative Medicine (HPM). Some think that HPM's goals should differ from those of other branches of medicine and aim primarily at lessening pain, discomfort, and confusion; while others think that HPM's practices should, like all other branches of medicine, aim at promoting health. I take the latter position: using the ars moriendi to set a standard for what it means to die well, I argue (...) that if HPM's practices were to aim at mitigating suffering with little regard to promoting health, some patients would die worse deaths than if HPM's practices were to aim at health. According to the ars moriendi, flourishing at the end of one's life requires that one exercise her agency and pursue the goods most important to her. On this view, HPM's practices should promote patients’ health to enable them to pursue these goods. (shrink)

In many countries, the COVID‐19 pandemic varied starkly between different racial and ethnic groups. Before vaccines were approved, some considered assigning priority access to worse‐hit racial groups. That debate can inform rationing in future pandemics and in some of the many areas outside COVID‐19 that admit of racial health disparities. However, concerns were raised that “race‐responsive” prioritizations would be ruled unlawful for allegedly constituting wrongful discrimination. This legal argument relies on an understanding of discrimination law as demanding color‐blindness. We (...) argue that a, color‐blind understanding of discrimination would be hostile only to one of two rationales for prioritizing the relevant racial minorities in settings of racial health disparities. We also propose a method for incorporating appropriate race‐responsive concerns that is in many ways ethically and legally superior to ones suggested thus far. That method turns artificial intelligence, thanks precisely to its artificial and “black box” nature (features that underlie recent concerns about artificial intelligence's discriminatory potential), into an instrument of social justice. (shrink)

The COVID-19 crisis has forced a difficult trade-off between limiting the health impacts of the virus and maintaining economic activity. Welfare economics offers tools to conceptualize this trade-off so that policy-makers and the public can see clearly what is at stake. We review four such tools: the Value of Statistical Life (VSL); the Value of Statistical Life Years (VSLYs); Quality-Adjusted Life-Years (QALYs); and social welfare analysis, and argue that the latter are superior. We also discuss how to choose policies (...) that differentially affect people’s wellbeing. We argue in favor of evaluating policies using a Social Welfare Function (SWF), which evaluates the possible distributions of wellbeing across individuals that may result from a policy. Such a function, we argue, should regard increases in the wellbeing of the less well-off as especially valuable. We then use a model to illustrate how such a framework can help evaluate two broad policy types in response to the pandemic: eradication of the virus, and more lenient control of the spread. Our model reveals how such evaluations depend on many empirical facts but also on key value judgments about the relative importance of health and on the extent of special concern for the worse off. The purpose of this brief is not to make precise recommendations, as conditions vary widely across countries and over time, but to provide a methodology. (shrink)

John and Curran have convincingly shown that Scanlonian contractualism is a valuable resource for evaluating pandemic response policies, and that we should reject cost–benefit analysis in favour of a contractualist framework. However, they fail to consider the part of contractualism that Scanlon constructed precisely to deal with the question of when the state can restrict individuals from making choices that are harmful to themselves and others: the value of choice view. In doing so, they leave it open for opponents of (...) lockdowns to misuse contractualism to justify mistaken policies. This is because the most powerful contractualist objections to locking down are likely to be based on the VoC. When we apply the value of choice view, we see that a lockdown policy’s justifiability depends on the extent to which particular values of choice are found to be threatened by the policy in question, and what safeguards policy-makers have put in place to increase the value of choice and protect people from the harmful consequences of lockdown. Without the VoC, it is harder to explain why lockdowns, to be non-rejectable, must have certain features. With the VoC, the case for contractualism over cost benefit analysis can be made even stronger. (shrink)

The potential capacity for robots to deceive has received considerable attention recently. Many papers explore the technical possibility for a robot to engage in deception for beneficial purposes (e.g., in education or health). In this short experimental paper, I focus on a more paradigmatic case: robot lying (lying being the textbook example of deception) for nonbeneficial purposes as judged from the human point of view. More precisely, I present an empirical experiment that investigates the following three questions: (a) Are (...) ordinary people willing to ascribe deceptive intentions to artificial agents? (b) Are they as willing to judge a robot lie as a lie as they would be when human agents engage in verbal deception? (c) Do people blame a lying artificial agent to the same extent as a lying human agent? The response to all three questions is a resounding yes. This, I argue, implies that robot deception and its normative consequences deserve considerably more attention than they presently receive. (shrink)

The potential capacity for robots to deceive has received considerable attention recently. Many papers focus on the technical possibility for a robot to engage in deception for beneficial purposes (e.g. in education or health). In this short experimental paper, I focus on a more paradigmatic case: Robot lying (lying being the textbook example of deception) for nonbeneficial purposes as judged from the human point of view. More precisely, I present an empirical experiment with 399 participants which explores the following (...) three questions: (i) Are ordinary people willing to ascribe intentions to deceive to artificial agents? (ii) Are they as willing to judge a robot lie as a lie as they would be when human agents engage in verbal deception? (iii) Do they blame a lying artificial agent to the same extent as a lying human agent? The response to all three questions is a resounding yes. This, I argue, implies that robot deception and its normative consequences deserve considerably more attention than it presently attracts. (shrink)

The application of machine-learning technologies to medical practice promises to enhance the capabilities of healthcare professionals in the assessment, diagnosis, and treatment, of medical conditions. However, there is growing concern that algorithmic bias may perpetuate or exacerbate existing health inequalities. Hence, it matters that we make precise the different respects in which algorithmic bias can arise in medicine, and also make clear the normative relevance of these different kinds of algorithmic bias for broader questions about justice and fairness in (...) healthcare. In this paper, we provide the building blocks for an account of algorithmic bias and its normative relevance in medicine. (shrink)

Development plans at different levels – from local to global – aspire to eliminate poverty, famine, to make health care accessible, to create better access to education, to improve transportation, employment, and the quality of life, all within next decades. Yet, these plans collide with the reality of climate change, more precisely the Anthropocene, which already creates high-dimensional conflicts. These will only intensify within decades because climate change and other consequences of the environment global devastation lead to a real (...) decrease of resources. These resources enable not only development but also an elementary reproduction of the essential preconditions of life. The current model of development has substantially contributed to the intensification of unequal accessibility to resources and existing conflicts have thus heightened and new ones have emerged. Water as an essential resource will cause new conflicts. Growing population, expansion of deserts and a sea level rise then increase conflicts for the land. Heat waves occur more often which strengthens conflicts for land suitable for human life, meaning that inhabitable areas are spreading. Therefore there is a need to re-evaluate the character and hierarchy of resources and their accessibility, and also concepts of development. The existing development of humankind has been done at the expense of others in many cases and mainly at the expense of the environment. The current situation raises a question of climate justice from both historical and systematic viewpoints. It opens a need to reformulate the concept of human rights from the perspective of their essential environmental preconditions and to define the status of environmental migrants. (shrink)

Over the past 25 years, several new “medicines” have come screeching onto health care’s various platforms, including narrative medicine, personalized medicine, precision medicine and person-centred medicine. Philosopher Miriam Solomon calls the first three of these movements different “ways of knowing” or “methods,” and argues that they are each a response to shortcomings of methods that came before them. They should also be understood as reactions to the current dominant model of medicine. In this article, I will describe our (...) dominant model, which I call “the new medical model.” I will argue that several towering problems in modern medicine can be traced to its philosophical foundations, which calls for philosophical analysis. (shrink)

Background: Screen time among adults represents a continuing and growing problem in relation to health behaviors and health outcomes. However, no instrument currently exists in the literature that quantifies the use of modern screen-based devices. The primary purpose of this study was to develop and assess the reliability of a new screen time questionnaire, an instrument designed to quantify use of multiple popular screen-based devices among the US population. -/- Methods: An 18-item screen-time questionnaire was created to quantify (...) use of commonly used screen devices (e.g. television, smartphone, tablet) across different time points during the week (e.g. weekday, weeknight, weekend). Test-retest reliability was assessed through intra-class correlation coefficients (ICCs) and standard error of measurement (SEM). The questionnaire was delivered online using Qualtrics and administered through Amazon Mechanical Turk (MTurk). -/- Results: Eighty MTurk workers completed full study participation and were included in the final analyses. All items in the screen time questionnaire showed fair to excellent relative reliability (ICCs = 0.50–0.90; all < 0.000), except for the item inquiring about the use of smartphone during an average weekend day (ICC = 0.16, p = 0.069). The SEM values were large for all screen types across the different periods under study. -/- Conclusions: Results from this study suggest this self-administered questionnaire may be used to successfully classify individuals into different categories of screen time use (e.g. high vs. low); however, it is likely that objective measures are needed to increase precision of screen time assessment. (shrink)

Plato’s attitude towards drunkenness (μέθη) is surprisingly positive in the Laws, especially as compared to his negative treatment of intoxication in the Republic. In the Republic, Plato maintains that intoxication causes cowardice and intemperance (3.398e-399e, 3.403e, and 9.571c-573b), while in the Laws, Plato holds that it can produce courage and temperance (1.635b, 1.645d-650a, and 2.665c-672d). This raises the question: Did Plato change his mind, and if he did, why? Ultimately, this paper answers affirmatively and argues that this marks a substantive (...) shift in Plato’s attitude towards anti-rational desires. More precisely, this paper argues that in the Republic, Plato holds that anti-rational desires are always detrimental to health and virtue, while in the Laws, Plato maintains that anti-rational desires can be instrumental to health and virtue. (shrink)

Abstract: The COVID-19 pandemic has posed unprecedented challenges to global healthcare systems, necessitating accurate and timely forecasting of cases for effective mitigation strategies. In this research paper, we present a novel approach to predict COVID-19 cases using Artificial Neural Networks (ANNs), harnessing the power of machine learning for epidemiological forecasting. Our ANNs-based forecasting model has demonstrated remarkable efficacy, achieving an impressive accuracy rate of 97.87%. This achievement underscores the potential of ANNs in providing precise and data-driven insights into the dynamics (...) of the pandemic. However, this paper underscores the critical importance of a comprehensive evaluation beyond accuracy, including metrics such as sensitivity, specificity, and the area under the ROC curve (AUC-ROC), to assess the model's performance robustness. The research paper offers detailed insights into the architecture of the ANN model, encompassing critical hyperparameters, data preprocessing techniques, and regularization strategies employed to optimize model accuracy. Ethical considerations surrounding data privacy and potential biases within the COVID-19 dataset are also addressed. While the achieved accuracy is a significant milestone, this study underscores the dynamic and evolving nature of the pandemic, necessitating continuous model refinement and validation. Furthermore, it emphasizes the importance of considering false positives and false negatives in the context of public health decision-making. In conclusion, this research contributes to the arsenal of tools available for pandemic management by showcasing the potential of ANNs in COVID-19 case forecasting. It encourages ongoing exploration and adaptation of predictive models to enhance their applicability in real-world public health scenarios, ultimately contributing to more effective pandemic control and response efforts. (shrink)

Abstract: This study has effectively tackled the critical challenge of accurate calorie prediction in dishes by employing a robust neural network-based model. With an outstanding accuracy rate of 99.32% and a remarkably low average error of 0.009, our model has showcased its proficiency in delivering precise calorie estimations. This achievement equips individuals, healthcare practitioners, and the food industry with a powerful tool to promote healthier dietary choices and elevate awareness of nutrition. Furthermore, our in-depth feature importance analysis has shed light (...) on the indispensable role played by specific nutritional attributes in calorie estimation. This unveiling of crucial factors provides valuable insights for further research endeavors and practical applications. Notably, this research extends its impact beyond the immediate context by making substantial contributions to the domains of nutrition science and dietary planning. It underscores the transformative potential of artificial intelligence, demonstrating how it can revolutionize our approach to food, nutrition, and health. As the world grapples with the challenges of diet-related health issues and environmental concerns, the accuracy and precision achieved in calorie prediction through neural networks represent a significant stride towards more informed and conscientious dietary practices. In this era of data-driven decision-making, our research paves the way for healthier lifestyles, heightened nutritional awareness, and a more health-conscious society. (shrink)

Abstract: Fish are important for both nutritional and economic reasons. They are a good source of protein, vitamins, and minerals and play a significant role in human diets, especially in coastal and island communities. In addition, fishing and fish farming are major industries that provide employment and income for millions of people worldwide. Moreover, fish play a critical role in marine ecosystems, serving as prey for larger predators and helping to maintain the balance of aquatic food chains. Overall, fish play (...) a vital role in supporting human well-being and the health of our planet. . Fishes have a lots of types such that: Red Mullet, Sea Bass, Striped Red Mullet and Shrimp. Each of them has its own shape and characteristics that differ from other types. We proposed a system that recognize nine types of fishes using deep learning. We trained the model with a dataset that contain 9000 images that were slit into 6300 images for training, 1350 for validation and 1350 for testing. The proposed model achieved accuracy (99.68%), precision (99.69%), recall (99.68%), and f1-score (99.68%). This indicates that our proposed model can effectively predicate and classify different types of fish with very high accuracy. (shrink)

Mortality seems important in explaining the value of virtues. It seems that courage is valuable because we face dangers of death and pain, just as it seems temperance is valuable because we can harm our health by overindulgence. This poses a serious problem for Christian ethics. If the virtues are valuable because they ameliorate harms stemming from limitations like mortality, then the virtues will lose much of their value in the new creation, where those limitations are no more. This (...) chapter argues that the threat is serious, and requires the rethinking of the fundamental value of the virtues. The virtues are not, first and foremost, the dispositions that allow us to improve the broken world we live in, but precisely those dispositions necessary for enjoying ideal or perfect conditions. (shrink)

Abstract: Forests are areas with a high density of trees, and they play a vital role in the health of the planet. They provide a habitat for a wide variety of plant and animal species, and they help to regulate the climate by absorbing carbon dioxide from the atmosphere. While in 2010, the world had 3.92Gha of forest cover, covering 30% of its land area, in 2019, there was a loss of forest cover of 24.2Mha according to the Global (...) Forest Watch institute. Discovery and classification depend on human experience and effort, so the error in the results of this process can lead to forest fires and disasters. Therefore, deep learning algorithms from artificial intelligence and machine learning sciences have been applied to help specialists avoid false or inaccurate diagnoses when detecting Forest fires in images using a pre-trained convolutional neural network called VGG16. The model was customized to fit the Forest fires classification and then applied to a dataset consisting of (14,000) of the Forests collected from the Kaggle depository. We trained, validated, and tested the modified VGG16 model. The proposed VGG16 model obtained Precision (99.96%), Recall (99.96%), and F1-Score (99.96%). (shrink)

This thesis examines philosophical controversies surrounding the evaluation of medical treatments, with a focus on the evidential roles of randomised trials and mechanisms in Evidence-Based Medicine. Current 'best practice' usually involves excluding non-randomised trial evidence from systematic reviews in cases where randomised trials are available for inclusion in the reviews. The first paper challenges this practice and evaluates whether adding of evidence from non-randomised trials might improve the quality and precision of some systematic reviews. The second paper compares the (...) alleged methodological benefits of randomised trials over observational studies for investigating treatment benefits. It suggests that claims about the superiority of well-conducted randomised controlled trials over well-conducted observational studies are justified, especially when results from the two methods are contradictory. The third paper argues that postulating the unpredictability paradox in systematic reviews when no detectable empirical differences can be found requires further justification. The fourth paper examines the problem of absence causation in the context of explaining causal mechanisms and argues that a recent solution is incomplete and requires further justification. Solving the problem by describing absences as causes of 'mechanism failure' fails to take into account the effects of absences that lead to vacillating levels of mechanism functionality. The fifth paper criticises literature that has emphasised functioning versus 'broken' or 'non-functioning' mechanisms emphasising that many diseases result from increased or decreased mechanism function, rather than complete loss of function. Mechanistic explanations must account for differences in the effectiveness of performed functions, yet current philosophical mechanistic explanations do not achieve this. The last paper argues that the standard of evidence embodied in the ICE theory of technological function is too permissive for evaluating whether the proposed functions of medical technologies have been adequately assessed and correctly ascribed. It argues that high-quality evidence from clinical studies is necessary to justify functional ascriptions to health care technologies. (shrink)

The article by MacKay and Saylor (2020) claims that the principle of fair subject selection yields conflicting imperatives (e.g. in the case of pregnant women) and should be understood as “a bundle of four distinct sub-principles” (i.e. fair inclusion, burden sharing, opportunity, distribution of third-party risks), each having conflicting normative recommendations (MacKay and Saylor 2020). The authors also offer guidance as to how we should navigate between subprinciples that may conflict with each other. The problem is a crucial one since (...) fair subject selection is one of the principles regulating clinical research that produces generalizable knowledge with the potential of improving people’s health. Therefore, there may be cases where the way in which participants are selected directly influences the generalizability (or its lack) of the clinically relevant knowledge and its value (if any) to different groups. In my commentary article, written from the philosophical perspective, I notice a number of interrelated problems which I believe have not been discussed thoroughly in the target article: (1) the precise way in which health care priority setting should influence the content of health research priority setting and fair inclusion principles; (2) the distinction between group and individual benefits and burdens from clinical research; (3) the reference class problem in medical research. (shrink)

Imagine you fly home from vacation with your one-and-a-half-year-old son who is traveling for free as a “lap child.” In the airport parking lot, you put him into his forward-facing car seat, where he sits much more contentedly than he did in the rear-facing one that was mandatory until his first birthday. After he falls asleep on the way home, you transfer him to his crib without waking him, lowering the side rail so you can lift him in more easily. (...) -/- Many parts of this idyllic parenting picture are deemed unacceptably risky according to recent child safety proposals. While these proposals all aim to improve child safety, their possible impact is unclear because there has been little discussion of the absolute risk and risk reduction involved in each. And while precise figures are lacking, rough estimates indicate that the magnitudes are quite small. I will argue that this risk and benefit data raises important questions about the proposals, including whether parents might reasonably believe that the small absolute risk reduction offered by the proposed changes does not justify the attendant burdens. This possibility—termed the “prevention paradox” in other contexts—highlights ethical and theoretical challenges in this area of public health. (shrink)

Nietzsche’s late writings present a value opposition of health and decadence based in his conception of organic life. While this appears to be a moral ideal that risks the naturalistic fallacy of directly deriving norms from facts, it instead describes a meta-ethical ideal: the necessary conditions for any kind of moral agency. Nietzsche’s ideal of health not only evades but also dissolves the naturalistic fallacy by suggesting that the specific content of morality is irrelevant. If health is (...) measured by power over one’s drives rather than the environment, then the source of moral conflict—organic life’s essential tendency toward domination—is surmounted by successful integration of any moral norms, regardless of content. Nietzsche’s concept of health draws on his characterization of organic life as essentially ‘exploitation’: growth through domination of the environment (BGE 259, GM II: 12). In animals, power coincides with happiness, while health and decadence track the correspondence between instinct and happiness (TI II: 11). This simple picture is complicated by the Genealogy, where Nietzsche depicts human nature as an animal illness caused by civilization’s disruption of the natural correspondence of instinct and happiness. Civilization shapes this ‘sick animal’ into the distinctly human ‘sovereign individual’ (GM III: 13, II: 2), restoring the harmony of instinct and happiness in an internal hierarchy of drives. So, sovereign individuality is a distinctly human model of health. Although life is exploitation, human health does not coincide with greater environmental power, but rather an internal qualitative expansion of power: greater variety and tension among drives, greater ‘wholeness in diversity’ in the ‘social structure’ of the self (BGE 212, 12). Nonetheless, health is not a moral criterion, but instead the precondition of moral agency. The subordination of drives to a commanding hierarchy grounds the ability to uphold any moral principles at all, to make and keep promises to oneself and others. Moral agency coincides, in turn, with overcoming the source of moral conflict in organic life’s drive for growth. The unification of opposing drives provides pleasure in power over oneself rather than one’s environment, the ‘happiness of high tension’ among drives (BGE 260), and in environmental resistance, a need for ‘equal opponents’ (TI IX: 38, EH 1: 7). Consequently, Nietzsche’s meta-ethical ideal supports a moral pluralism that supports any moral content. Moral conflicts arise precisely to the degree individuals are incapable of achieving moral agency, but they dissolve as moral agency of any kind is more widely achieved. (shrink)

Many economic measures are structured to reflect ethical values. I describe three attitudes towards this: maximalism, according to which we should aim to build all relevant values into measures; minimalism, according to which we should aim to keep values out of measures; and an intermediate view. I argue the intermediate view is likely correct, but existing versions are inadequate. In particular, economists have strong reason to structure measures to reflect fixed, as opposed to user-assessable, values. This implies that, despite disagreement (...) about precisely how to do so, economists should standardly adjust QALYs and DALYs to reflect egalitarian values. (shrink)

Abstract: Vegetables are an essential component of a healthy diet and play a critical role in promoting overall health and well- being. Vegetables are rich in important vitamins and minerals, including vitamin C, folate, potassium, and iron. They also provide fiber, which helps maintain digestive health and prevent chronic diseases. We are proposing a deep learning model for the classification of vegetables. A dataset was collected from Kaggle depository for Vegetable with 15000 images for 15 different classes. The (...) data was preprocessed, normalized and split into three sets (train, valid, test). The proposed model was trained and validated using the train and valid sets and accuracy of both training and validation was very high. For the evaluation of the proposed model we utilized these metrics: accuracy, F1-score, precision, Recall and time required for testing. We then tested the proposed model using the test set. The result of the testing was accuracy (99.95%), F1-score (99.95%), precision (99.95%), Recall (99.95%) and time required for testing the test set was 1.38 seconds. (shrink)

Deep brain stimulation (DBS), a surgical procedure involving the implantation of electrodes in the brain, has rekindled the medical community’s interest in psychosurgery. Whereas many researchers argue DBS is substantially different from psychosurgery, we argue psychiatric DBS—though a much more precise and refined treatment than its predecessors—is nevertheless a form of psychosurgery, which raises both old and new ethical and legal concerns that have not been given proper attention. Learning from the ethical and regulatory failures of older forms of psychosurgery (...) can help shed light on how to address the regulatory gaps that exist currently in DBS research. To show why it is important to address the current regulatory gaps within psychiatric DBS, we draw on the motivations underlying the regulation of earlier forms of psychosurgery in the US. We begin by providing a brief history of psychosurgery and electrical brain stimulation in the US. Against this backdrop, we introduce psychiatric DBS, exploring current research and ongoing clinical trials. We then draw out the ethical and regulatory similarities between earlier forms of psychosurgery and psychiatric DBS. As we will show, the factors that motivated strict regulation of earlier psychosurgical procedures mirror concerns with psychiatric DBS today. We offer three recommendations for psychiatric DBS regulation, which echo earlier motivations for regulating psychosurgery, along with new considerations that reflect the novel technologies used in DBS. (shrink)

Abstract: This essay consists of some clarifying remarks on the doctrine of double effect (DDE). After providing a contemporary formulation of the doctrine we put special emphasis on the distinction between those aspects of an action plan that are intended and those that are merely foreseen (the I/F distinction). Making use of this distinction is often made difficult in practice because salient aspects of the action plan exhibit a felt “closeness” to one another that is difficult if not impossible to (...) articulate with the precision we might like. The essay goes on to examine an especially adroit criticism of DDE best articulated by J.J. Thomson. We conclude with a brand new double effect case (new to the philosophical literature anyway) taken from medicine and Roman Catholic pastoral ministry. (shrink)

In developing nations, pneumonia is the leading cause of death for young children; however, mortality can be effectively decreased with early diagnosis and care. The objectives of the review are to evaluate the significance of clinical signs and symptoms in diagnosing pneumonia, and treatment in children under the age of five, as well as to examine the precision of WHO criteria in diagnosing clinical pneumonia in general people. According to the World Health Organization's definition and the Integrated Management (...) of Childhood Illness (IMCI) initiative, pneumonia is clinically characterized by an acute cough, with or without fever, accompanied by dyspnea or tachypnea. Acute infections of the gut and gastroenteritis can be effectively controlled and treated. Acute respiratory infections, particularly pneumonia, are increasingly regarded as the leading infectious cause of mortality among children in developing nations. The diagnosis of pneumonia is primarily clinical and categorized into four domains: clinical assessment, epidemiological factors, radiographic imaging, and standard laboratory results. Pneumonia can be classified into three categories: bacterial, viral, and acute pneumonia. In every instance, a potential diagnosis should be made and the appropriate course of therapy should be administered based on the features of that pneumonia. Preventing and lowering the death and morbidity of this significant disease in children can be greatly aided by physicians having precise and accurate information on how to identify and treat it without incurring additional expenses. (shrink)

Personalized medicine recommendation systems are increasing in popularity to predict diseases and provide customized health advice on diet, workout plans and medication. The medical suggestion system can be valuable when pandemics, floods, or cyclones hit. In the age of Machine Learning (ML), recommender systems give more accurate, precise, and reliable clinical predictions while using less resources. Through the use of machine learning algorithms like Decision Tree, Random Forest, K-Means Clustering, and Hierarchical Clustering, these systems analyze patient inputs such as (...) lifestyle data, symptoms, and health metrics for accurate predictions of diseases and holistic recommendations on health. This inclusive process ensures each person receives tailored support to enhance the entire management of their health. The system's ability to suggest accurate diets, proper workouts, and appropriate drugs depending on the condition of the user significantly enhances its contribution toward more healthy lifestyles. The variety of algorithms increases accuracy and reliability as well because each model contributes uniquely in analyzing various aspects of patient data. This study presents a framework that demonstrates the system's efficacy in providing personalized disease predictions and health recommendations, which can benefit the development of preventive care and improve treatment outcomes. (shrink)

This report aims to offer insights into cutting-edge research on digital well-being. Many of these insights come from a 2-day academic-impact event, The Future of Digital Well-Being, hosted by a team of researchers working with the Royal Netherlands Academy of Arts and Sciences (KNAW) in February 2024. Today, achieving and maintaining well-being in the face of online technologies is a multifaceted challenge that we believe requires using theoretical resources of different research disciplines. This report explores diverse perspectives on how digital (...) well-being can be actively cultivated, while also emphasising the importance of considering individual differences, societal contexts, and nuanced cultural factors. We aim to offer a holistic view of the future of digital well-being, one that will inspire the next generation of designers of online tools, as well as policymakers who will regulate these tools. We start by asking what digital well-being is – how we can best define a concept that is used by diverse stakeholders and researchers from many disciplines in various ways. To do this, we explore the classic ethical theories of well-being, showing how they can give us insights into how the term digital well-being is currently deployed. We then move to look at the existing strategies that have been proposed to actively cultivate digital well-being, exploring the business models that threaten digital well-being and the relative advantages of the digital and non-digital solutions that are currently proposed. On the one hand, digital tools – such as Apple’s Screen Time and app blockers such as Opal and Forest – integrate seamlessly with the digital lifestyles of users. They also create precise metrics for digital well-being, which facilitates their solutions to reduce screen time. On the other hand, non-digital solutions, including mindfulness practices, digital detoxes, and digital well-being coaching, offer a new set of tools to reconnect individuals to their natural rhythms and help to actively promote offline activities. We then move to discuss diversity and how various groups of users have strikingly different digital well-being needs. Embracing neurodiversity in digital well-being is crucial as it strongly impacts the users’ experience of online technologies. When designing for diversity, organisations and designers alike need to prioritise customization for people with physical disabilities, mitigate harmful content for users with mental well-being conditions, address gender stereotypes and online harassment, and be designed in ways that recognize the very real risks of online technologies. This report closes by examining cultural differences. We believe that non-Western conceptions of well-being offer rich sources for enhancing digital well-being insofar as these traditions can inform and inspire the designers of future online technologies. We focus on East-Asian and South-Asian traditions, although in further work we recognise it would be useful to investigate conceptions of well-being that are influential in the Gulf region, Africa, and South America. Each of these areas have ethical frameworks that discuss well-being in depth as well as a rich cultural heritage. In conclusion, this report’s insights underscore the imperative of recognizing diversity in digital well-being, both in terms of cultural contexts and disciplinary perspectives. It emphasises the need for culturally responsive design methodologies and the integration of non-Western philosophical perspectives into current digital well-being research. Embracing this diversity, we believe, offers the best chance to create digital environments that prioritise well-being for users and the societies they live in across the world. Ultimately, we believe that it is not only about designing better online products; it’s about shaping a digital landscape that promotes well-being and flourishing for everyone. (shrink)

Chronic kidney disease (CKD) is a global health problem with high morbidity and mortality rate, and it induces other diseases. There are no obvious incidental effects during the starting periods of CKD, patients routinely disregard to see the sickness. Early disclosure of CKD enables patients to seek helpful treatment to improve the development of this disease. AI models can effectively assist clinical with achieving this objective on account of their fast and exact affirmation execution. In this appraisal, proposed a (...) Logistic relapse framework for diagnosing CKD. Proposed calculation like NAIVE BAYES , DECISION TREE , KSTAR , LOGISITIC , AND SVM and look at these calculation and get the most noteworthy precision .AI store, which has an enormous number of missing qualities. Missing characteristics are for the most part found, taking everything into account, clinical conditions since patients might miss a couple of assessments for various reasons. By separating the misjudgements delivered by the set up models and proposed a fused model that unites determined backslide and sporadic woods by using perceptron. (shrink)