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)

Biomarker-based predictive tests for subjectively asymptomatic Alzheimer’s disease (AD) are utilized in research today. Novel applications of artificial intelligence (AI) promise to predict the onset of AD several years in advance without determining biomarker thresholds. Until now, little attention has been paid to the new ethical challenges that AI brings to the early diagnosis in asymptomatic individuals, beyond contributing to research purposes, when we still lack adequate treatment. The aim of this paper is to explore the ethical arguments put (...) forward for AI aided AD prediction in subjectively asymptomatic individuals and their ethical implications. The ethical assessment is based on a systematic literature search. Thematic analysis was conducted inductively of 18 included publications. The ethical framework includes the principles of autonomy, beneficence, non-maleficence, and justice. Reasons for offering predictive tests to asymptomatic individuals are the right to know, a positive balance of the risk-benefit assessment, and the opportunity for future planning. Reasons against are the lack of disease modifying treatment, the accuracy and explicability of AI aided prediction, the right not to know, and threats to social rights. We conclude that there are serious ethical concerns in offering early diagnosis to asymptomatic individuals and the issues raised by the application of AI add to the already known issues. Nevertheless, pre-symptomatic testing should only be offered on request to avoid inflicted harm. We recommend developing training for physicians in communicating AI aided prediction. (shrink)

Heart disease is the deadliest disease and one of leading causes of death worldwide. Machine learning is playing an essential role in the medical side. In this paper, ensemble learning methods are used to enhance the performance of predicting heart disease. Two features of extraction methods: linear discriminant analysis and principal component analysis, are used to select essential features from the dataset. The comparison between machine learning algorithms and ensemble learning methods is applied to selected features. The (...) different methods are used to evaluate models: accuracy, recall, precision, F-measure, and ROC.The results show the bagging ensemble learning method with decision tree has achieved the best performance. (shrink)

Day-to-day lives are affected globally by the epidemic coronavirus 2019. With an increasing number of positive cases, India has now become a highly affected country. Chronic diseases affect individuals with no time identification and impose a huge disease burden on society. In this article, an Efficient Recurrent Neural Network with Ensemble Classifier is built using VGG-16 and Alexnet with weighted model to predict disease and its level. The dataset is partitioned randomly into small subsets by utilizing mean-based splitting (...) method. Various models of classifier create a homogeneous ensemble by utilizing an accuracy-based weighted aging classifier ensemble, which is a weighted model’s modification. Two state of art methods such as Graph Sequence Recurrent Neural Network and Hybrid Rough-Block-Based Neural Network are used for comparison with respect to some parameters such as accuracy, precision, recall, f1-score, and relative absolute error. As a result, it is found that the proposed ERNN-EC method accomplishes accuracy of 95.2%, precision of 91%, recall of 85%, F1-score of 83.4%, and RAE of 41.6%. (shrink)

Though predictive processing approaches to the mind were originally applied to exteroceptive perception, i.e., vision and action, recent work has started to explore the role of interoceptive perception, i.e., emotion and affect. This article builds on this work by extending PP beyond emotion to the construction of emotional dispositions. I employ principles from dynamical systems theory and PP to provide a model of how dispositional anger can develop in response to early experiences of psychosocial stress. The model is then deployed (...) to explain the established link between psychosocial stress in early life and the appearance of certain organic disease phenotypes in later life. This phenomenon can appear mysterious when viewed through the standard biomedical explanatory lens, which has difficulty accounting for the causal influence of subjective perceptions and evaluations of the social and material environment on the development of organic disease processes. The model provided presents such cases as instances of developmental mismatch. They occur when an organism develops an emotional disposition that leads them to make habitually-biased appraisals of what the social environment affords. The model provides a novel explanation of certain organic disease phenotypes with top-down and developmental causes, and demystifies one class of cases involving apparently spooky ‘mind-to-matter’ causation. (shrink)

One of the central arguments given to resist testing currently healthy, asymptomatic children for adult-onset diseases is that they may be psychologically harmed by the knowledge gained from such tests. In this discussion I examine two of the most serious arguments: children who are tested may face limited futures, and that testing may result in damage to the child’s self esteem . I claim that these arguments do not stand up to critical evaluation. In conclusion, whilst I do not suggest (...) that all at-risk children should be tested for adult-onset diseases we ought to listen carefully to some parental requests for such testing because the putative psychological harms may not be as significant or likely as initially thought. This is because parents generally have the best interests of their children at heart and if they are properly supported and educated about predictive genetic testing and the possible consequences, then the risk of psychological harms occurring may be ameliorated. (shrink)

Ghosn, Mariam Predictive genetic testing Part 2 will examine the issues and ethical aspects that must be considered when adolescents below the age of majority make a request to undergo predictive genetic testing for Huntington's disease.

Prediction markets, which trade contracts based on the results of predictions, have been remarkably successful in predicting the results of political events. A number of proposals have been made to extend prediction markets to scientific questions, and some small-scale science prediction markets have been implemented. Advocates for science prediction markets argue that they could alleviate problems in science such as bias in peer review and epistemically unjustified consensus. I argue that bias in peer review and epistemically (...) unjustified consensuses are genuine problems in science, and that current attempts to solve them fail in practical circumstances. Prediction markets do show some promise for answering scientific questions while avoiding the pitfalls of consensus and peer review. However, there are strong reasons to believe that science prediction markets will not perform nearly so well as election prediction markets, and implementing prediction markets on a large scale could have significant unintended consequences for the organization of scientific research. The promise of prediction markets to solve current problems in assessing scientific claims is therefore largely illusory. (shrink)

PurposeThe purpose of this study was to introduce an orthogonal experimental design to improve the efficiency of building and optimizing models for freezing of gait prediction.MethodsA random forest model was developed to predict FOG by using acceleration signals and angular velocity signals to recognize possible precursor signs of FOG. An OED was introduced to optimize the feature extraction parameters.ResultsThe main effects and interaction among the feature extraction hyperparameters were analyzed. The false-positive rate, hit rate, and mean prediction time (...) were 27%, 68%, and 2.99 s, respectively.ConclusionThe OED was an effective method for analyzing the main effects and interactions among the feature extraction parameters. It was also beneficial for optimizing the feature extraction parameters of the FOG prediction model. (shrink)

Ghosn, Mariam Huntington's disease (HD) is an inherited disorder. Sufferers usually develop symptoms in midlife between the ages of 30 and 50 years. HD causes neurodegeneration resulting in the progressive development of physical, cognitive and emotional symptoms. The impact on sufferers worsens over time with the final stage of the disease resulting in the need for professional assistance in a long-term care facility. More rarely HD develops in children and young adults, with less than 5% of HD sufferers (...) being affected by Juvenile HD. This article considers the ethical aspects of such testing. (shrink)

BackgroundIn Parkinson's disease, the side of motor symptoms onset may influence disease progression, with a faster motor symptom progression in patients with left side lateralization. Moreover, worse neuropsychological outcomes after subthalamic nucleus deep brain stimulation have been described in patients with predominantly left-sided motor symptoms. The objective of this study was to evaluate if the body side of motor symptoms onset may predict motor outcome of bilateral STN-DBS.MethodsThis retrospective study included all consecutive PD patients treated with bilateral STN-DBS (...) at Grenoble University Hospital from 1993 to 2015. Demographic, clinical and neuroimaging data were collected before and 1 year after surgery. The predictive factors of motor outcome at one-year follow-up, measured by the percentage change in the MDS-UPDRS-III score, were evaluated through univariate and multivariate linear regression analysis.ResultsA total of 233 patients were included with one-year follow-up after surgery [143 males ; 121 right body onset; 112 left body onset; mean age at surgery, 55.31 ± 8.44 years; mean disease duration, 11.61 ± 3.87]. Multivariate linear regression analysis showed that the left side of motor symptoms onset did not predict motor outcome.ConclusionsIn this retrospective study, the body side of motor symptoms onset did not significantly influence the one-year motor outcome in a large cohort of PD patients treated with bilateral STN-DBS. (shrink)

The coronavirus disease 2019 pandemic has affected most countries of the world. The detection of Covid-19 positive cases is an important step to fight the pandemic and save human lives. The polymerase chain reaction test is the most used method to detect Covid-19 positive cases. Various molecular methods and serological methods have also been explored to detect Covid-19 positive cases. Machine learning algorithms have been applied to various kinds of datasets to predict Covid-19 positive cases. The machine learning algorithms (...) were applied on a Covid-19 dataset based on commonly taken laboratory tests to predict Covid-19 positive cases. These types of datasets are easy to collect. The paper investigates the application of decision tree ensembles which are accurate and robust to the selection of parameters. As there is an imbalance between the number of positive cases and the number of negative cases, decision tree ensembles developed for imbalanced datasets are applied. F-measure, precision, recall, area under the precision-recall curve, and area under the receiver operating characteristic curve are used to compare different decision tree ensembles. Different performance measures suggest that decision tree ensembles developed for imbalanced datasets perform better. Results also suggest that including age as a variable can improve the performance of various ensembles of decision trees. (shrink)

What ethical justification can be found for informing a person that he or she will later develop a lethal disease for which no therapy is available? This question has been discussed during the past twenty years by specialists concerned with the prevention of Huntington's Disease, an incurable late-onset hereditary disorder. Many of them have played an active role in developing experimental testing programmes for at-risk persons. This paper is based on a corpus of 119 articles; it reviews the (...) development of their reflection and includes an outline of the ethical problems identified and the solutions adopted in pre-clinical protocols. Seen in a broader perspective, the experience of presymptomatic testing for Huntington's Disease has given medical geneticists the opportunity to clarify their ethical position in the as yet little explored field of predictive medicine. (shrink)

Cardiovascular disease is a major complication of type 2 diabetes mellitus. In addition to traditional risk factors, psychological determinants play an important role in CVD risk. This study applied Deep Neural Network to develop a CVD risk prediction model and explored the bio-psycho-social contributors to the CVD risk among patients with T2DM. From 2017 to 2020, 834 patients with T2DM were recruited from the Department of Endocrinology, Affiliated Hospital of Harbin Medical University, China. In this cross-sectional study, the (...) patients' bio-psycho-social information was collected through clinical examinations and questionnaires. The dataset was randomly split into a 75% train set and a 25% test set. DNN was implemented at the best performance on the train set and applied on the test set. The receiver operating characteristic curve analysis was used to evaluate the model performance. Of participants, 272 were diagnosed with CVD. The developed ensemble model for CVD risk achieved an area under curve score of 0.91, accuracy of 87.50%, sensitivity of 88.06%, and specificity of 87.23%. Among patients with T2DM, the top five predictors in the CVD risk model were body mass index, anxiety, depression, total cholesterol, and systolic blood pressure. In summary, machine learning models can provide an automated identification mechanism for patients at CVD risk. Integrated treatment measures should be taken in health management, including clinical care, mental health improvement, and health behavior promotion. (shrink)

Diabetes is one of the most common diseases worldwide where a cure is not found for it yet. Annually it cost a lot of money to care for people with diabetes. Thus the most important issue is the prediction to be very accurate and to use a reliable method for that. One of these methods is using artificial intelligence systems and in particular is the use of Artificial Neural Networks (ANN). So in this paper, we used artificial neural networks (...) to predict whether a person is diabetic or not. The criterion was to minimize the error function in neural network training using a neural network model. After training the ANN model, the average error function of the neural network was equal to 0.01 and the accuracy of the prediction of whether a person is diabetics or not was 87.3%. (shrink)

GraphicalPatients with early- to middle-stage PD were enrolled for C-Gait assessment and traditional walking ability assessments. The correlation of C-Gait assessment and traditional walking tests were studied. Two models were established based on C-Gait assessment and traditional walking tests to explore the value of C-Gait assessment in predicting freezing of gait.ObjectiveEfficient methods for assessing walking adaptability in individuals with Parkinson’s disease are urgently needed. Therefore, this study aimed to assess C-Gait for detecting freezing of gait in patients with early- (...) to middle-stage PD.MethodPeople with PD diagnosis were recruited from April 2019 to November 2019 in Beijing Rehabilitation Hospital. The participants performed six items of walking adaptability on an instrumented treadmill augmented with visual targets and obstacles. The patient’s walking adaptability was evaluated by C-Gait assessment and traditional walking tests, and FOG-related indexes were collected as outcome measures. Two discriminant models were established by stepwise discriminant analysis; area under the receiver operating characteristic curve was used to validate the models.ResultIn total, 53 patients were included in this study. Most C-Gait assessment items had no or low correlations with traditional walking tests. The obstacle avoidance and speed of adaptation items could lead to FOG with high sensitivity. In addition, the C-Gait assessment model had slightly better discrimination of freezers from non-freezers compared with traditional walking test models ; specifically, obstacle avoidance and speed of adaptation have uniquely discriminant potential.ConclusionC-gait assessment could provide additional value to the traditional walking tests for PD. Gait adaptability assessment, as measured by C-Gait, may be able to help identify freezers in a PD population. (shrink)

The aim of this study was to describe the experiences of undergoing a presymptomatic genetic test for the hereditary and fatal Huntington’s disease, using a case study approach. The study was based on 18 interviews with a young woman and her husband from the decision to undergo the test, to receiving the results and trying to adapt to them, which were analysed using a life history approach. The findings show that the process of undergoing a presymptomatic test involves several (...) closely connected ethical and medical questions, such as the reason for the test, the consequences of the test results and how health-care services can be developed to support people in this situation. (shrink)

Predicting the phenotype of an organism from its genotype is a central question in genetics. Most importantly, we would like to find out if the perturbation of a single gene may be the cause of a disease. However, our current ability to predict the phenotypic effects of perturbations of individual genes is limited. Network models of genes are one tool for tackling this problem. In a recent study, (Lee et al.) it has been shown that network models covering the (...) majority of genes of an organism can be used for accurately predicting phenotypic effects of gene perturbations in multicellular organisms. BioEssays 30:707–710, 2008. © 2008 Wiley Periodicals, Inc. (shrink)

Recombinant DNA technology can now be applied to the analysis of complex human diseases such as polygenic disorders, where the inheritance of several unknown genes appears to be involved. We review here the progress in the analysis of genes which may be involved in the development of hyperlipidaemia, and show how the approach may be important in our understanding of the aetiology of coronary artery disease.

The aim of this study was to describe the experiences of undergoing a presymptomatic genetic test for the hereditary and fatal Huntington’s disease, using a case study approach. The study was based on 18 interviews with a young woman and her husband from the decision to undergo the test, to receiving the results and trying to adapt to them, which were analysed using a life history approach. The findings show that the process of undergoing a presymptomatic test involves several (...) closely connected ethical and medical questions, such as the reason for the test, the consequences of the test results and how health-care services can be developed to support people in this situation. (shrink)

Predictive genetic testing for susceptibility to psychiatric conditions is likely to become part of standard practice. Because the onset of most psychiatric diseases is in late adolescence or early adulthood, testing minors could lead to early identification that may prevent or delay the development of these disorders. However, due to their complex aetiology, psychiatric genetic testing does not provide the immediate medical benefits that current guidelines require for testing minors. While several authors have argued non-medical benefits may play a crucial (...) role in favour of predictive testing for other conditions, little research has explored such a role in psychiatric disorders. This paper outlines the potential non-medical benefits and harms of psychiatric genetic testing in minors in order to consider whether the non-medical benefits could ever make such testing appropriate. Five non-medical themes arise in the literature: psychological impacts, autonomy/self-determination, implications of the biomedical approach, use of financial and intellectual resources, and discrimination. Non-medical benefits were prominent in all of them, suggesting that psychiatric genetic testing in minors may be appropriate in some circumstances. Further research needs to empirically assess these potential non-medical benefits, incorporate minors in the debate, and include normative reflection to evaluate the very purposes and motivations of psychiatric genetic testing in minors. (shrink)

Surveillance is essential for communicable disease prevention and control. Traditional notification of demographic and clinical information, about individuals with selected infectious diseases, allows appropriate public health action and is protected by public health and privacy legislation, but is slow and insensitive. Big data–based electronic surveillance, by commercial bodies and government agencies, which draws on a plethora of internet- and mobile device–based sources, has been widely accepted, if not universally welcomed. Similar anonymous digital sources also contain syndromic information, which can (...) be analysed, using customised algorithms, to rapidly predict infectious disease outbreaks, but the data are nonspecific and predictions sometimes misleading. However, public health authorities could use these online sources, in combination with de-identified personal health data, to provide more accurate and earlier warning of infectious disease events—including exotic or emerging infections—even before the cause is confirmed, and allow more timely public health intervention. Achieving optimal benefits would require access to selected data from personal electronic health and laboratory records and the potential to re-identify individuals found to be involved in outbreaks, to ensure appropriate care and infection control. Despite existing widespread digital surveillance and major potential community benefits of extending its use to communicable disease control, there is considerable public disquiet about allowing public health authorities access to personal health data. Informed public discussion, greater transparency and an ethical framework will be essential to build public trust in the use of new technology for communicable disease control. (shrink)

Invasion biology is a relatively young discipline which is important, interesting and currently in turmoil. Biological invaders can threaten native ecosystems and global biodiversity; they can incur massive economic costs and even introduce diseases. Invasion biologists generally agree that being able to predict when and where an invasion will occur is essential for progress in their field. However, successful predictions of this type remain elusive. This has caused a rift, as some researchers are pessimistic and believe that invasion biology has (...) no future, whereas others are more optimistic and believe that the key to successful prediction is the creation of a general, unified theoretical framework which encompasses all invasion events. Although I agree that there is a future for invasion biology, extensive synthesis is not the way to better predictions. I argue that the causes of invasion phenomena are exceedingly complex and heterogeneous, hence it is impossible to make generalizations over particular events without sacrificing causal detail. However, this causal detail is just what is needed for the specific predictions which the scientists wish to produce. Instead, I show that a limited type of synthesis is a more useful tool for generating successful predictions. An important implication of my view is that it points to a more pluralistic approach to invasion biology, where generalization and prediction are treated as important yet distinct research goals. (shrink)

In the last two decades, philosophy of neuroscience has predominantly focused on explanation. Indeed, it has been argued that mechanistic models are the standards of explanatory success in neuroscience over, among other things, topological models. However, explanatory power is only one virtue of a scientific model. Another is its predictive power. Unfortunately, the notion of prediction has received comparatively little attention in the philosophy of neuroscience, in part because predictions seem disconnected from interventions. In contrast, we argue that topological (...) predictions can and do guide interventions in science, both inside and outside of neuroscience. Topological models allow researchers to predict many phenomena, including diseases, treatment outcomes, aging, and cognition, among others. Moreover, we argue that these predictions also offer strategies for useful interventions. Topology-based predictions play this role regardless of whether they do or can receive a mechanistic interpretation. We conclude by making a case for philosophers to focus on prediction in neuroscience in addition to explanation alone. (shrink)

What is medicine? One obvious answer in the context of the contemporary clinical tradition is that medicine is the process of curing sick people. However, this “curative thesis” is not satisfactory, even when “cure” is defined generously and even when exceptions such as cosmetic surgery are set aside. Historian of medicine Roy Porter argues that the position of medicine in society has had, and still has, little to do with its ability to make people better. Moreover, the efficacy of medicine (...) for improving population health has been famously doubted by historians and epidemiologists. The curative thesis demands that we have mostly been stupid, duped, or staggeringly hopeful, given that medicine has not until recently offered more than a handful of effective cures. I suggest, in this article, that the core medical competence is neither to cure, nor to prevent, disease, but to understand and to predict it. I argue that this approach does a better job than the curative thesis at explaining why not all medicine is concerned with curative efforts and that it enjoys historical support from the ancient entanglement of prophecy and medicine and from the fact that medicine thrived for centuries with almost no effective cures and continues to thrive today in various forms that are mostly without curative efficacy. I suggest that this approach grounds a fairer approach to alternative, traditional, and other medical practices, as well as some fresh lessons for the development of mainstream medicine. (shrink)

Predictive genetic testing is now routinely offered to asymptomatic adults at risk for genetic disease. However, testing of minors at risk for adult-onset conditions, where no treatment or preventive intervention exists, has evoked greater controversy and inspired a debate spanning two decades. This review aims to provide a detailed longitudinal analysis and concludes by examining the debate's current status and prospects for the future. Fifty-three relevant theoretical papers published between 1990 and December 2010 were identified, and interpretative content analysis (...) was employed to catalogue discrete arguments within these papers. Novel conclusions were drawn from this review. While the debate's first voices were raised in opposition of testing and their arguments have retained currency over many years, arguments in favour of testing, which appeared sporadically at first, have gained momentum more recently. Most arguments on both sides are testable empirical claims, so far untested, rather than abstract ethical or philosophical positions. The dispute, therein, lies not so much in whether minors should be permitted to access predictive genetic testing but whether these empirical claims on the relative benefits or harms of testing should be assessed. (shrink)

This paper starts by establishing a prima facie case that disadvantaged groups or individuals are more likely to get a chronic disease and are in a disadvantaged position to adhere to chronic treatment despite access through Universal Health Coverage. However, the main aim of this paper is to explore the normative implications of this claim by examining two different but intertwined argumentative lines that might contribute to a better understanding of the ethical challenges faced by chronic disease health (...) policy. The paper develops the argument that certain disadvantages which may predispose to illness might overlap with disadvantages that may hinder self-management, potentially becoming disadvantageous in handling chronic disease. If so, chronic diseases may be seen as disadvantages in themselves, describing a reproduction of disadvantage among the chronically ill and a vicious circle of disadvantage that could both predict and shed light on the catastrophic health outcomes among disadvantaged groups—or individuals—dealing with chronic disease. (shrink)

The ability to predict events within its field indicates that a science has reached a high level of development, that its essential facts stand in systematic relationship to each other. It is important to note that prediction does not always culminate in control, but effective control is impossible without it. Thus, medicine can predict the course of certain fatal diseases with which it is unable to cope, and the astronomer can forsee eclipses and other cosmic events, yet remain powerless (...) to intervene. Since events occurring at the human level are essentially the products of human effort and experience, it is reasonable to suppose that greater predictability in the social sciences will enhance man's control over his destiny as a social being. (shrink)

The development of predictive brain implant (PBI) technology that is able to forecast specific neuronal events and advise and/or automatically administer appropriate therapy for diseases of the brain raises a number of ethical issues. Provided that this technology satisfies basic safety and functionality conditions, one of the most pressing questions to address is its relation to the autonomy of patients. As Frederic Gilbert in his article asks, if autonomy implies a certain idea of freedom, or self-government, how can an individual (...) be considered to decide freely if the implanted device stands at the inception of the causal chain producing his decisions? He claims that PBIs threaten persons’ autonomy by diminishing their post-operative experience of self-control. In this commentary, I wish to discuss this claim. Contrary to Gilbert, I will suggest that PBIs do not pose a significant threat to patient’s autonomy, as self-control, but rather to his/her sense of authenticity. My claim is that the language of authenticity, already introduced in the recent bioethical literature, may offer a better way to voice some of the concerns with PBIs that Gilbert recognized. (shrink)

First-in-human clinical trials represent a critical juncture in the translation of laboratory discoveries. However, because they involve the greatest degree of uncertainty at any point in the drug development process, their initiation is beset by a series of nettlesome ethical questions [1]: has clinical promise been sufficiently demonstrated in animals? Should trial access be restricted to patients with refractory disease? Should trials be viewed as therapeutic? Have researchers adequately minimized risks? The resolution of such ethical questions inevitably turns on (...) claims about future events like harms, therapeutic response, and clinical translation. Recurrent failures in clinical translation, like Eli Lilly's Alzheimer candidate semagacestat, highlight the severe limitations of current methods of prediction. In this case, patients in the active arm of the placebo-controlled trial had earlier onset of dementia and elevated rates of skin cancer [2]. Various authoritative accounts of human research ethics state that decision-making about risk and benefit should be careful, systematic, and non-arbitrary [3]–[5]. Yet, these sources provide little guidance about what kinds of evidence stakeholders should use to ensure their estimates of such events ground responsible ethical decisions. In this article, we suggest that investigators, oversight bodies, and sponsors often base their predictions on a flawed and inappropriately narrow preclinical evidence base. (shrink)

Rid and Wendler propose the development of a Patient Preference Predictor (PPP), an actuarial model for predicting incapacitated patient’s life-sustaining treatment preferences across a wide range of end-of-life scenarios. An actuarial approach to end-of-life decision making has enormous potential, but transferring the logic of actuarial prediction to end-of-life decision making raises several conceptual complexities and logistical problems that need further consideration. Actuarial models have proven effective in targeted prediction tasks, but no evidence supports their effectiveness in the kind (...) of broad spectrum prediction task that is the proposed goal of the PPP. We argue that a more focused approach, targeting specific medical conditions and generating treatment predictions based on the preferences of individuals with actual disease experience, is both more firmly grounded in past research and is a more prudent initial strategy for exploring the efficacy of actuarial prediction in end-of-life decision making. (shrink)

Data analytics, machine intelligence, and other cognitive algorithms have been employed in predicting various types of diseases in health care. The revolution of artificial neural networks in the medical discipline emerged for data-driven applications, particularly in the healthcare domain. It ranges from diagnosis of various diseases, medical image processing, decision support system, and disease prediction. The intention of conducting the research is to ascertain the impact of parameters on diabetes data to predict whether a particular patient has a (...) disease or not. This paper develops an improved ANN model trained using an artificial backpropagation scaled conjugate gradient neural network algorithm to predict diabetes effectively. For validating the performance of the proposed model, we conduct a large set of experiments on a Pima Indian Diabetes dataset using accuracy and mean squared error as evaluation metrics. We use different number of neurons in the hidden layer, ranging from 5 to 50, to train the ANN models. The experimental results show that the ABP-SCGNN model, containing 20 neurons, attains 93% accuracy on the validation set, which is higher than using the other ANNs models. This result confirms the model’s effectiveness and efficiency in predicting diabetes disease from the required data attributes. (shrink)

In 1949, Linus Pauling and collaborators published in Science a paper provocatively titled: 'Sickle cell anemia, a molecular disease'. What was actually meant by 'molecular disease'? We interpret the concept of molecular disease in the frame of the traditional positions about the nature of diseases: the ontological and the physiological positions. We conclude that the physiological does not give an adequate account of what molecular diseases are. The ontological position, when correctly reinterpreted, leads to an understanding of (...) molecular diseases where the macromolecule is seen as a symptom or as a part of a mechanism leading to the symptoms of the disease. We then show that the concept of molecular disease leads to a particular view of therapy, emphasizing eugenics as a way of eliminating disease. On the individual level, this concept leads to an increased power of diagnosis, and especially predictive diagnosis, but has little therapeutic consequence. Lastly, we examine how this concept of disease unifies two contemporary classifications of diseases, one based on the location of the diseases, the other on the cause of the diseases. (shrink)

The idea that disability insurers would benefit if the use of predictive genetic testing expands may seem little short of obvious. If individuals with higher than species-typical genetic propensities for illness or disease are identified, and barred or discouraged from participating in disability insurance programs, is it not obvious that the amount that disability insurers pay out will decrease? Is there any reason to doubt that insurers thus would gain advantage by promoting genetic testing? Writers on this subject typically (...) have taken on faith that advantage goes to whoever knows most about the genetic characteristics of the individual seeking insurance. They therefore have assumed, without proving, that insurers’ interests lie with proliferating genetic information about insurance seekers.Consequently, from a perspective that gives priority to commercial interests, denying insurers the freedom to obtain genetic information about insurance seekers or holders appears obviously damaging and even unfair. On the other hand, from a perspective that gives priority to the interests of citizens who may use insurance, the greater use of and access to predictive genetic testing sets off ethical alarms. (shrink)

Most contemporary models of disease development consider the interaction between genotype and environment as static. The authors argue that because time is a key factor in genotype–environment interaction, this approach oversimplifies the pathology analysis and may lead to wrong conclusions. In reviewing the field, the authors suggest that the history of genotype–environment interactions plays an important role in the development of diseases and that this history may be analyzed using the phenotype as a proxy. Furthermore, a theoretical and experimental (...) framework is proposed based on the assumption that phenotypes do not change from one to another randomly but are interconnected and follow certain phenotype trajectories. It then follows that analysis of such phenotype trajectories might be useful to predict the future phenotypes including the onset of disease. In addition, an analysis of phenotype trajectories can be subsequently used to choose better control subjects in comparative studies reducing noise and bias in studies investigating disease mechanisms. (shrink)

Proponents of the emerging field of P4 medicine argue that computational integration and analysis of patient-specific “big data” will revolutionize our health care systems, in particular primary care-based disease prevention. While many ambitions remain visionary, steps to personalize medicine are already taken via personalized genomics, mobile health technologies and pilot projects. An important aim of P4 medicine is to enable disease prevention among healthy persons through detection of risk factors. In this paper, we examine the current status of (...) P4 medicine in light of historical and current challenges to predictive and preventive medicine, including overdiagnosis and overtreatment. Moreover, we ask whether it is likely that in silico integration of patient-specific data will be able to better deal such challenges and to turn risk predictions into disease-preventive actions in a wider social context. Given the lack of evidence that P4 medicine can tip the balance between benefits and harms in preventive medicine, we raise concerns about the current promotion of P4 medicine as a solution to the current challenges in public health. (shrink)

: There is a general consensus in the medical and medical ethics communities against predictive genetic testing of children for late onset conditions, but minimal consideration is given to predictive testing of asymptomatic children for disorders that present later in childhood when presymptomatic treatment cannot influence the course of the disease. In this paper, I examine the question of whether it is ethical to perform predictive testing and screening of newborns and young children for conditions that present later in (...) childhood. I consider the risks and benefits of (1) predictive testing of children from high-risk families; (2) predictive population screening for conditions that are untreatable; and (3) predictive population screening for conditions in which the efficacy of presymptomatic treatment is equivocal. I conclude in favor of parental discretion for predictive genetic testing, but against state-sponsored predictive screening for conditions that do not fulfill public health screening criteria. (shrink)

In the hope of future treatments to prevent or slow down the disease, there is a strong movement towards an ever-earlier detection of Alzheimer's disease. In conjunction with scientific developments, this has prompted a reconceptualization of AD, as a slowly progressive pathological process with a long asymptomatic phase. New concepts such as "preclinical" and "prodromal" AD have been introduced, raising a number of conceptual and ethical questions. We evaluate whether these new concepts are theoretically defensible, in light of (...) theories of health and disease, and whether they should be understood as disease or as an at-risk state. We introduce a pragmatic view on disease concepts and argue that an evaluation of the reconceptualization of AD should also take its aims and effects into account, and assess their ethical acceptability. The reconceptualization of AD is useful to coordinate research into preventive strategies, and may potentially benefit future patients. However, in the short term, early detection and labelling of "preclinical AD" can potentially harm people. Since there is no treatment available and the predictive value is unclear, it may only create a group of "patients in waiting" who may suffer from anxiety, uncertainty and stigmatization, but will never actually develop dementia. We conclude that only if the promise of preventive medication materializes, will the reconceptualization of AD turn out unequivocally to be for the better. Otherwise, the reconceptualization may do more harm than good. (shrink)