Medical classification systems aim to provide a manageable taxonomy for sorting diagnoses into their proper classes. The question, this paper wants to critically examine, is how to correctly systematise diseases within classification systems that are applied in a variety of different settings. ICD and DSM , the two major classification systems in medicine and psychiatry, will be the main subjects of this paper; however, the arguments are not restricted to these classification (...) class='Hi'>systems but point out general methodological and epistemological challenges of classifying diseases for differing purposes. Deciding what qualifies as a disease to be included into a classification system as well as choosing a specific validator for correctly systematising diseases is complicated because the broad applicability of medical classification systems simultaneously appears as aim and challenge. Drawing upon the case study of classifying Alzheimer’s disease, this paper will address three dilemmas in designing ‘good’ medical classification systems. They are due to general epistemological problems of medicine, such as the relationship between individual manifestations of diseases and the necessity of building groups in order to scientifically elucidate causes of diseases. Moreover, they involve pragmatic issues of designing usable classifications that allow for easily discriminating between classes of diseases, restricting, however, the completeness of disease representations. This paper wants to trace how the choice of certain validators is unavoidably value-laden and deeply intertwined with epistemological assumptions of how different uses relate to each other, resulting either in a prioritisation of (constrained) coherence or of (vague) pluralistic connectibility. (shrink)

Medical expert systems (MES) are knowledge-based computer programs that are designed for advising physicians on diagnostical and therapeutical decision-making. They use heuristic methods developed by Artificial Intelligence researchers in order to retrieve from large knowledge-bases information needed in the situation. Constructing the knowledge-base of a MES embraces the problem of explicating and fixing the conceptual, causal and epistemic relations between a lot of medical objects. There is a number of preconditions which any adequate representation of such knowledge (...) must fulfil, among them the conditions of consistency, of completeness, of unequivocality, etc. Existing systems for classification and coding, like ICD and SNOMED, are not designed for the needs of constructing expert systems or, more generally, of knowledge processing and knowledge engineering. Their syntax is not sufficiently rich for expressing the more complex structures of (medical) knowledge. What is needed, is a language that can be used for expressing logical and descriptive relations between medical objects and facts, approximately at the level of the language of second order predicate logic. Simultaneously, it must be possible to process this language at the machine level. This can be achieved by using some dialects of the programming language LISP or, particularly, by using the programming language PROLOG. Thus, in order to achieve a suitable classification system, it is necessary to develop a system of medical data structures and predicates expressed in LISP or PROLOG. (shrink)

B a c k g r o u n d. The article demonstrates the development and influence of the main diagnostic systems in psychiatry, such as the DSM and the ICD, on the concept of psychiatric diseases. The problem of classification of psychiatric disorders is one of the main topics that is the field of study of the philosophy of psychiatry. The correct diagnosis within a particular diagnostic system directly affects the choice of appropriate drug treatment, psychotherapy and (...) social support for people suffering from mental disorders. Misdiagnosis has not only negative medical but also social consequences, leading to stigmatization and avoidance of people suffering from mental disorders. M e t h o d s. The research uses an integrated approach to the analysis of psychiatric diagnostic systems, including historical, comparative and interdisciplinary methods. The application of the historical method contributed to a detailed study of the evolution of psychiatric diagnostic systems and changes in approaches to the classification of mental disorders. A comparative analysis of the main diagnostic systems was carried out, considering their structure, diagnostic criteria and interaction. The interdisciplinary method involves the integration of knowledge from psychiatry, philosophy, medical history, cultural studies, and psychology, providing a deep understanding of the medical, philosophical, social, and cultural aspects of mental disorders. R e s u l t s. The article analyzes the historical development of ideas and methodology for the classification of mental disorders from ancient Greece to the present. From Hippocrates and Plato to Sydenham, Kabanis, Pinel, and Eskierol, the authors trace the evolution of understanding of mental disorders, emphasizing the shift in emphasis from etiological theories to more descriptive and categorical models. The authors describe the current state of the DSM and ICD classification systems, which play a dominant role in psychiatric science and subsequent social support and welfare for people suffering from mental disorders. Changes that have occurred in these systems and their impact on psychiatric practice and research are discussed. Considerable emphasis is placed on the history of the diagnosis of post-traumatic stress disorder (PTSD), which becomes a symbolic indicator of the complexities and challenges associated with the modern classification of mental illness. The authors consider the phenomenological method as a tool for a deeper understanding of PTSD, especially in the context of the constant psychotraumatic factors of war, the consequences of which generate new challenges for Ukrainian psychiatric practice. The authors present a case study using the phenomenological approach to diagnose PTSD, which demonstrates the need for a detailed analysis of patients' internal experience for accurate diagnosis and effective treatment. C o n c l u s i o n s. The conclusions emphasize the importance of integrating philosophical methods into the diagnosis and analysis of mental illness in clinical practice. The authors call for the development of interdisciplinary research involving both philosophers and mental health professionals to develop more detailed and person-centered approaches to the diagnosis and treatment of mental disorders. (shrink)

Work injury compensation and pensions are often determined according to medical disability rating scales attributing a percentage to each impaired body part or function. Incorporated into central medical–administrative networks of committees and examinations, these produce disability as a calculable space. This article examines the specific case of the Israeli National Insurance regulations regarding work injuries of 1956 and analyzes the shifted order they set. Looking at this system in the specific historical context of transition from the British Mandate (...) workmen’s compensation system to the “disability percentages” system, provides insight into the process of structuring and stabilizing durable numerical forms and the production of centralized government. Drawing on conceptualizations regarding the sociotechnical role of classification systems, I contend that through encoding and calculating classifications of disability, heterogeneity is eliminated and an ordering based on physiology is “black boxed,” illustrating the importance of technoscientific formations in structuring the “medical model of disability.”. (shrink)

In their article ‘Who is afraid of black box algorithms? On the epistemological and ethical basis of trust in medical AI’, Durán and Jongsma discuss the epistemic and ethical challenges raised by black box algorithms in medical practice. The opacity of black box algorithms is an obstacle to the trustworthiness of their outcomes. Moreover, the use of opaque algorithms is not normatively justified in medical practice. The authors introduce a formalism, called computational reliabilism, which allows generating justified (...) beliefs on the algorithm reliability and trustworthy outcomes of artificial intelligence systems by means of epistemic warrants, called reliability indicators. However, they remark the need for reliability indicators specific to black box algorithms and that justified knowledge is not sufficient to justify normatively the actions of the physicians using medical AI systems. Therefore, Durán and Jongsma advocate for a more transparent design and implementation of black box algorithms, providing a series of recommendations to mitigate the epistemic and ethical challenges behind their use in medical practice. In this response, I argue that a peculiar form of black box algorithm transparency, called design publicity, may efficiently implement these recommendations. Design publicity encodes epistemic, that is, reliability indicators, and ethical recommendations for black box algorithms by means of four subtypes of transparency. These target the values and goals, their translation into design requirements, the performance and consistency of the algorithm altogether. I discuss design publicity applying it to a use case focused on the automated classification of skin lesions from medical images. (shrink)

Blood is a vital body fluid and can be instrumental in identifying various pathological conditions. Nowadays, a lot of people are suffering from COVID-19 and every country has its own limited testing capacity. Consequently, a system is required to help doctors analyze a patient’s blood structure including COVID-19. Therefore, in this paper, we extracted and selected blood features by proposing a new feature extraction and selection method named stepwise linear discriminant analysis. SWLDA emphasizes on picking confined features from blood structure (...) images and discerning its class based on reversion value such as partial F value. SWLDA begins with picking an equivalence comprising the sole finest X variable and then puts in effort to add more Xs individually, providing the situations are adequate. The process of adding and picking is based on F value to determine which variable would be entered. Then, the picked or the default F-to-enter value is compared with the uppermost partial F value. After this step, the forward addition or backward removal begins and whether the partial test values for all the predictor variables already in the line are estimated is known. Then, the comparison is made between the lowermost partial test value and preselected or defaulting consequence levels such as F0. Finally, the system is trained by employing support vector machine to label the blood images. The performance of the proposed approach is assessed by employing 8 different datasets of blood structures. It is assured that the proposed method has achieved significant results under different blood structure images including COVID-19. (shrink)

This article presents an analysis of contractual relations in sport from the standpoint of the Croatian legislative system. Due to the complexity of the subject matter, the author considers only a small fragment of it - the significance and the role of sport in Croatian society and the law of contracts „as a cornerstone on which „sports law“ has been built and which is of primary importance in most areas where there is an interface between sport and the law, irrespective (...) of whether the sport is being played at an elite level or at a more humble one“. Bearing in mind the limited extent of this article, the autor tries to provide some clarifications and some guidance on how to deal with the designation and the determination of the applicable law for contracts in the sporting context. Considering that there are different legal regimes which may come into play with regard to the designation of the law applicable to contracts in the sporting context, the author first presents the determination of the applicable law according to the Rome I Regulation and then according to the Croatian Arbitration Act, Swiss Private International Law Act and the CAS Code of Sports-related Arbitration, as the most common in sports practice. Then, the author deals with contracts concluded with minor athletes and the determination of the law applicable to some preliminary questions. Finally, the author presents her perception of the problems encountered and some suggestions for the improvement of the existing legal framework. (shrink)

Recently, big data analytics have gained significant attention in healthcare industry due to generation of massive quantities of data in various forms such as electronic health records, sensors, medical imaging, and pharmaceutical details. However, the data gathered from various sources are intrinsically uncertain owing to noise, incompleteness, and inconsistency. The analysis of such huge data necessitates advanced analytical techniques using machine learning and computational intelligence for effective decision making. To handle data uncertainty in healthcare sector, this article presents a (...) novel metaheuristic rough set-based feature selection with rule-based medical data classification technique on MapReduce framework. The proposed MRSFS-RMDC technique designs a butterfly optimization algorithm for minimal rough set selection. In addition, Hadoop MapReduce is applied to process massive quantity of data. Moreover, a rule-based classification approach named Repeated Incremental Pruning for Error Reduction is used with the inclusion of a set of conditional rules. The RIPPER will scale in a linear way with the number of training records utilized and is suitable to build models with data uncertainty. The proposed MRSFS-RMDC technique is validated using benchmark dataset and the results are inspected under varying aspects. The experimental results highlighted the supremacy of the MRSFS-RMDC technique over the recent state of art methods in terms of different performance measures. The proposed methodology has achieved a higher F-score of 96.49%. (shrink)

Aim To examine the contribution of programme year and demographic factors to medical students’ perceptions of evidence-based classification categories of professional misconduct. Methods Students at an Irish medical school were administered a cross-sectional survey comprising 31 vignettes of professional misconduct, which mapped onto a 12-category classification system. Students scored each item using a 5-point Likert scale, where 1 represents the least severe form of misconduct and 5 the most severe. Results Of the 1012 eligible respondents, 561 (...) students completed the survey, providing a response rate of 55%. Items pertaining to disclosure of conflict of interest were ranked as the least severe examples of professional misconduct, and this perception was highest among finalyear students. While ratings of severity declined for items related to ‘inappropriate conduct not in relation to patient’ and ‘inappropriate use of social media’ between years 1 and 3, ratings for both categories increased again among clinical cycle students. Conclusions Increased clinical exposure during years 4 and 5 of the undergraduate programme was associated with better recognition of the importance of selected professional domains. Disclosure of conflict of interest is identified as an area of medical professionalism that requires greater emphasis for students who are at the point of transition from student to doctor. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:Deflating Psychiatric ClassificationClaudio E. M. Banzato (bio)Keywordsnosography, comorbidity, utility, pragmatismSystems of classification bring order into the world. They are a key part of the informational working infrastructure of the world we inhabit (Bowker and Star 1999). Thus, much of the human interaction hinges on these ordering—pattern identifying and creating—systems. Formal or informal, standardized or ad hoc, visible or invisible, enforced or optional, there are a myriad of (...) classifications we necessarily live by.Typically, classifications convey representations of states of affairs to make interventions on them feasible and, to some extent, predictable. In other words, classificatory systems and schemes try to confer intelligibility to the world. By succeeding in such task, they may provide rational grounds to human operation on reality (toward the most diverse ends). This account fully applies to medical classifications, which struggle to sort out all kinds of diseases, ailments, complaints, and sufferings that can possibly be targeted by medical interventions. This means classifications are instrumental to the ends pursued by medicine, quintessential modificatory activity. In sum, classifications are cognitive tools devised to build up knowledge by making relevant distinctions, which may prove helpful to guide human action.By working well, classifications eventually tend to become invisible. They end up looking just natural, as if they were carved by nature itself. But indeed purpose is the touchstone here. Even if a given class of objects fully qualifies as a 'natural kind' and necessary and sufficient criteria for class membership can be definitely established, it does not entail that it constitutes the best way to classify these objects irrespective of the context. Peter Zachar (2002) offers a compelling example from natural sciences, the utmost practical importance of distinguishing between hydrogen and its isotopes (hydrogen, deuterium, and tritium). In this case, the classificatory thrust stretches beyond the shared atomic number. In his words, "A crucial factor in recognizing them as distinct is that they are used in distinct activities and practices. We get a better neutron bounce with heavy water or D2O. Deciding what aspects of internal structure are important therefore cannot be isolated from external factors." (p. 220)In contemporary psychiatry, diagnostic classification has been center stage for several reasons. But before addressing them, however, a brief historical detour is necessary. Back in the nineteenth century, putting forward a new classification of mental diseases was taken somewhat as a token of psychiatric proficiency according to the witty remark by the French physician and publicist Philippe Buchez: "Upon believing that they have [End Page 23] completed their studies, rhetoricians will compose a tragedy and alienists a classification" (Berrios 1999, 152). By the middle of the twentieth century, many psychiatric classifications (based on diverse principles and underlying theoretical orientations) were still used across the world. The British psychiatrist Erwin Stengel (1959), in his influential report on classification of mental disorders, pointed out that the issue of difficulty of communication was then the order of the day for psychiatry, because it was widely perceived as one of the most serious obstacles to the progress in the field.The broad, worldwide survey carried out by Stengel made it clear that classifications of all shapes were used in different contexts, but they were seldom appealing outside the boundaries of the respective centers that generated them. Such diversity of classifications reflected not only different functions or purposes they were meant to serve, but also the role played by the history of a given problem within a particular medical tradition and cultural background. Thus, Stengel's aims for an acceptable statistical international classification were relatively modest: it should simply ground epidemiological work.Stengel's stance on classification was frankly pragmatic and utilitarian. He understood well the difficulties posed by the 'disease entity' assumption in psychiatry and asked the straight question: "Firstly, what do we classify in this field? Are we classifying diseases or people?", to which he offered a compromised answer: "It may be said that the material the psychiatrist has to classify consists neither of diseases nor of people but of a variety of disorders or reactions, a material which does not readily lend itself to classification" (Stengel 1959, 612). For him, the pursued agreement on the... (shrink)

Background Medical assistance in dying (MAiD) sparks debate in several countries, some of which allow or plan to allow MAiD where a mental disorder is the sole underlying medical condition (MAiD-MD). Since MAiD-MD is becoming permissible in a growing number of jurisdictions, there is a need to better understand the moral concerns related to this option. Gaining a better understanding of the moral concerns at stake is a first step towards identifying ways of addressing them so that MAiD-MD (...) can be successfully introduced and implemented, where legislations allow it. Methods Thus, this article aims (1) to better understand the moral concerns regarding MAiD-MD, and (2) to identify potential solutions to promote stakeholders’ well-being. A qualitative thematic review was undertaken, which used systematic keyword-driven search and thematic analysis of content. Seventy-four publications met the inclusion criteria. Results Various moral concerns and proposed solutions were identified and are related to how MAiD-MD is introduced in 5 contexts: (1) Societal context, (2) Healthcare system, (3) Continuum of care, (4) Discussions on the option of MAiD-MD, (5) MAiD-MD practices. We propose this classification of the identified moral concerns because it helps to better understand the various facets of discomfort experienced with MAiD-MD. In so doing, it also directs the various actions to be taken to alleviate these discomforts and promote the well-being of stakeholders. Conclusion The assessment of MAiD-MD applications, which is part of the context of MAiD-MD practices, emerges as the most widespread source of concern. Addressing the moral concerns arising in the five contexts identified could help ease concerns regarding the assessment of MAiD-MD. (shrink)

According to the Differences of Sex Development Regulations of the International Association of Athletics Federations, Caster Semenya and other athletes with heightened testosterone levels are considered non-eligible for middle distance running races in the women’s class. Based on an analysis of fair equality of opportunity in sport, I take a critical look at the Semenya case and at IAAF’s DSD Regulations. I distinguish between what I call stable and dynamic inequalities between athletes. Stable inequalities are those that athletes cannot impact (...) or control in any significant way such as inequalities in biological sex, body size and chronological age. Dynamic inequalities, such as inequalities in strength, speed and endurance, or in technical and tactical skills, can be impacted and to a certain extent controlled by athletes. If stable inequalities exert significant and systematic impact on performance, they provide a rationale for classification. If high testosterone level is an inborn, strong and systemic driver of performance development, inequalities in such levels can provide a rationale for classification. As is emphasised by the Court of Arbitration for Sport, this leads to a dilemma of rights: the right of Semenya to compete in sport according to her legal sex and gender identity, and the right of other athletes within the average female testosterone range to compete under fair conditions. I conclude with providing conditional support of the CAS decision in the Semenya case and of IAAF’s DSD Regulations. (shrink)

Medical terminology collects and organizes the many different kinds of terms employed in the biomedical domain both by practitioners and also in the course of biomedical research. In addition to serving as labels for biomedical classes, these names reflect the organizational principles of biomedical vocabularies and ontologies. Some names represent invariant features (classes, universals) of biomedical reality (i.e., they are a matter for ontology). Other names, however, convey also how this reality is perceived, measured, and understood by health professionals (...) (i.e., they belong to the domain of epistemology). We analyze terms from several biomedical vocabularies in order to throw light on the interactions between ontological and epistemological components of these terminologies. We identify four cases: 1) terms containing classification criteria, 2) terms reflecting detectability, modality, uncertainty, and vagueness, 3) terms created in order to obtain a complete partition of a given domain, and 4) terms reflecting mere fiat boundaries. We show that epistemology-loaded terms are pervasive in biomedical vocabularies, that the “classes” they name often do not comply with sound classification principles, and that they are therefore likely to cause problems in the evolution and alignment of terminologies and associated ontologies. (shrink)

This article analyzes India’s Traditional Knowledge Digital Library (TKDL) as a potential intervention in the administration of patent law. The TKDL is a database including a vast body of traditional medical knowledge from India, aiming to prevent the patenting and misappropriation of that knowledge. This article contextualizes the TKDL in relation to documentation theory as well as to existing research on the uses of databases to protect traditional knowledge. It explores the TKDL’s potential consequences for India’s traditional medical (...) knowledge and the wider implications that traditional knowledge databases can have for the safeguarding of traditional knowledge in general. The article concludes that on the one hand the TKDL bridges the gap between the main branches of Indian traditional medicine and the formal knowledge system of International Patent Classifications. Furthermore, it has also inspired revisions of the International Patent Classification system, which makes it better adapted to incorporate traditional medical knowledge. On the other hand, critical research on traditional knowledge documentation argues that traditional knowledge databases, like the TKDL, can decontextualize the knowledge they catalogue and dispossess its original owners. The TKDL, however, also fits into a national, Indian agenda of documenting and modernizing traditional medicine that predates the formation of the TKDL by several decades and challenges the dichotomy between traditional and scientific knowledge systems that originally motivated the formation of the TKDL. (shrink)

Multiple studies show that periodic reanalysis of genomic test results held by clinical laboratories delivers significant increases in overall diagnostic yield. However, while there is a widespread consensus that implementing routine reanalysis procedures is highly desirable, there is an equally widespread understanding that routine reanalysis of individual patient results is not presently feasible to perform for all patients. Instead, researchers, geneticists and ethicists are beginning to turn their attention to one part of reanalysis—reinterpretation of previously classified variants—as a means of (...) achieving similar ends to large-scale individual reanalysis but in a more sustainable manner. This has led some to ask whether the responsible implementation of genomics in healthcare requires that diagnostic laboratories routinely reinterpret their genomic variant classifications and reissue patient reports in the case of materially relevant changes. In this paper, we set out the nature and scope of any such obligation, and analyse some of the main ethical considerations pertaining to a putative duty to reinterpret. We discern and assess three potential outcomes of reinterpretation—upgrades, downgrades and regrades—in light of ongoing duties of care, systemic error risks and diagnostic equity. We argue against the existence of any general duty to reinterpret genomic variant classifications, yet we contend that a suitably restricted duty to reinterpret ought to be recognised, and that the responsible implementation of genomics into healthcare must take this into account. (shrink)

Diabetes is one of the most common metabolic diseases that cause high blood sugar. Early diagnosis of such a condition is challenging due to its complex interdependence on various factors. There is a need to develop critical decision support systems to assist medical practitioners in the diagnosis process. This research proposes developing a predictive model that can achieve a high classification accuracy of type 2 diabetes. The study consisted of two fundamental parts. Firstly, the study investigated handling (...) missing data adopting data imputation, namely, median value imputation, K-nearest neighbor imputation, and iterative imputation. Consequently, the study validated the implications of these imputations using various classification algorithms, i.e., linear, tree-based, and ensemble algorithms, to see how each method affected classification accuracy. Secondly, Artificial Neural Network was employed to model the best performing imputed data, balanced with SMOTETomek ensuring each class is represented fairly. This approach provided the best accuracy of 98% on the test data, outperforming accuracies achieved in prior studies using the same dataset. The dataset used in this study is concerned with gender and population. As a prospect, the study recommends adopting a larger population sample without geographic boundaries. Additionally, as the developed Artificial Neural Network model did not undergo any specific hyperparameter tuning, it would be interesting to explore tuning on top of normalized data to optimize accuracy further. (shrink)

The skin, uniquely positioned at the interface between the human body and the external world, plays a multifaceted immunologic role in human life. In medical practice, early accurate detection of all types of skin tumours is essential to guide appropriate management and improve patients’ survival. The most important issue is to differentiate between malignant skin tumours and benign lesions. The aim of this research is the classification of skin tumours by analysing medical skin tumour dermoscopy images. This (...) paper is focused on a new strategy based on deep convolutional neural networks which have recently shown a state-of-the-art performance to define strategy to automatic classification for skin tumour images. The proposed system is tested on well-known HAM10000 data set. For experimental results, verification is performed and the results are compared with similar researches. (shrink)

Initially the problems of data integration, for example in the field of medicine, were resolved in case by case fashion. Pairs of databases were cross-calibrated by hand, rather as if one were translating from French into Hebrew. As the numbers and complexity of database systems increased, the idea arose of streamlining these efforts by constructing one single benchmark taxonomy, as it were a central switchboard, into which all of the various classification systems would need to be translated (...) only once. By serving as a lingua franca for database integration this benchmark taxonomy would ensure that all databases calibrated in its terms would be automatically compatible with each other. We describe one strategy for creating such a lingua franca, in which philosophical ontology plays a central role. (shrink)

This paper explains the importance of classifying argumentation schemes, and outlines how schemes are being used in current research in artificial intelligence and computational linguistics on argument mining. It provides a survey of the literature on scheme classification. What are so far generally taken to represent a set of the most widely useful defeasible argumentation schemes are surveyed and explained systematically, including some that are difficult to classify. A new classification system covering these centrally important schemes is built.

This paper explains the importance of classifying argumentation schemes, and outlines how schemes are being used in current research in artificial intelligence and computational linguistics on argument mining. It provides a survey of the literature on scheme classification. What are so far generally taken to represent a set of the most widely useful defeasible argumentation schemes are surveyed and explained systematically, including some that are difficult to classify. A new classification system covering these centrally important schemes is built.

Medical decisions, including diagnosis, prognosis, and disease classification, must often be made on the basis of incomplete or unsatisfactory information. Data which are essential to the care of one patient may be unobtainable for technical or ethical reasons in another patient. For this reason the principles of controlled experimentation may be impossible to satisfy in human studies. In this paper, some formal aspects of medical decision making are discussed. Special operators for the intuitive concepts of certainty, demand, (...) and effort, akin to the operators of modal logic, are used to accommodate the technical and ethical limitations on human studies. Theorems are stated and proved which show how this system handles incomplete information. The embryogenesis of the human heart is presented as a sample problem in classification. (shrink)

This paper explains the importance of classifying argumentation schemes, and outlines how schemes are being used in current research in artificial intelligence and computational linguistics on argument mining. It provides a survey of the literature on scheme classification. What are so far generally taken to represent a set of the most widely useful defeasible argumentation schemes are surveyed and explained systematically, including some that are difficult to classify. A new classification system covering these centrally important schemes is built.

The exponential digitization of medical data has led to a transformation of the practice of medicine. This change notably raises a new complexity of issues surrounding health IT. The proper use of these communication tools, such as telemedicine, e-health, m-health the big medical data, should improve the quality of monitoring and care of patients for an information system to "human face". Faced with these challenges, the author analyses in an ethical angle the patient-physician relationship, sharing, transmission and storage (...) of medical information, setting pins to an ethic for the digitization of medical information. Drawing on good practice recommendations closely associated with values, this model is developing tools for reflection and present the keys to understanding the decision-making issues that reflect both the technological constraints and the complex nature of human reality in medicine. (shrink)

I appreciate all of the commentaries as they raise a series of critical and constructive points. Due to limited space, I can only address some of the issues in brief ways. Knox and Anderson indicate that classification according to testosterone levels should be applied only at the elite level and with a more inclusive approach in amateur and recreational sport. Indeed, I agree, and this is also the case in the International Association of Athletics Federation's Differences of Sexual Development (...) regulations that hold only for international events. A second comment questions whether my use of the adjective ‘systemic’ adds meaning beyond what is already included in ‘stable inequality’. I find ‘systemic’ useful as it emphasises inequalities with effects more or less on the whole organism, but I am definitely open for improving the terminology. A third comment relates to whether inequalities must be inborn to justify classification. This is a valid point, and I need to be more precise. Inequalities need not be inborn. For instance, as Knox and Anderson note, stable testosterone levels can be outcomes of hormone therapy as in the case of elite transwomen athletes. Chambers challenges my view that sport is structured on a luck egalitarian principle. She points to significant impact of inequalities in …. (shrink)

Attempts to formalize the diagnostic process are by no means a recent undertaking; what is new is the availability of an engine to process these formalizations. The digital computer has therefore been increasingly turned to in the expectation of developing systems which will assist or replace the physician in diagnosis. Such efforts involve a number of assumptions regarding the nature of the diagnostic process: e.g. where it begins, and where it ends. ‘Diagnosis’ appears to include a number of quite (...) different cognitive processes, some of which seem more subject to formalization than others. Underlying the difficulties inherent in these efforts at formalization, is the hierarchical structure of medical knowledge including that of diseases. (By ‘hierarchical structure’, I mean no more than that our descriptions of clinical objects necessarily refer to different levels of organization; atomic, molecular, cellular, physiological system, entire patient.) Since diagnosis is in part a form of classification, measures of similarity between the clinical findings of a particular patient and a potential disease, are needed. This is complicated by the fact that some diseases are described using the vocabularies of physics and chemistry, others with the vocabulary of psychology, and most, with terms taken from a number of different hierarchical levels. The differences between the descriptions made using these vocabularies will be seen to exert systematic (if not uniform) influences upon the diagnostic process. CiteULike Connotea Del.icio.us What's this? (shrink)

The data mining field in computer science specializes in extracting implicit information that is distributed across the stored data records and/or exists as associations among groups of records. Criminal databases contain information on the crimes themselves, the offenders, the victims as well as the vehicles that were involved in the crime. Among these records lie groups of crimes that can be attributed to serial criminals who are responsible for multiple criminal offenses and usually exhibit patterns in their operations, by specializing (...) in a particular crime category (i.e., rape, murder, robbery, etc.), and applying a specific method for implementing their crimes. Discovering serial criminal patterns in crime databases is, in general, a clustering activity in the area of data mining that is concerned with detecting trends in the data by classifying and grouping similar records. In this paper, we report on the different statistical and neural network approaches to the clustering problem in data mining in general, and as it applies to our crime domain in particular. We discuss our approach of using a cascaded network of Kohonen neural networks followed by heuristic processing of the networks outputs that best simulated the experts in the field. We address the issues in this project and the reasoning behind this approach, including: the choice of neural networks, in general, over statistical algorithms as the main tool, and the use of Kohonen networks in particular, the choice for the cascaded approach instead of the direct approach, and the choice of a heuristics subsystem as a back-end subsystem to the neural networks. We also report on the advantages of this approach over both the traditional approach of using a single neural network to accommodate all the attributes, and that of applying a single clustering algorithm on all the data attributes. (shrink)

At present, most of the research in the field of medical-assisted diagnosis is carried out based on image or electronic medical records. Although there is some research foundation, they lack the comprehensive consideration of comprehensive image and text modes. Based on this situation, this article proposes a fusion classification auxiliary diagnosis model based on GoogleNet model and Bi-LSTM model, uses GoogleNet to process brain computed tomographic images of ischemic stroke patients and extract CT image features, uses Bi-LSTM (...) model to extract the electronic medical record text, integrates the two features using the full connection layer network and Softmax classifier, and obtains a method that can assist the diagnosis from two modes. Experiments show that the proposed scheme on average improves 3.05% in accuracy compared to individual image or text modes, and the best performing GoogleNet + Bi-LSTM model achieves 96.61% accuracy; although slightly less in recall, it performs better on F1 values, and has provided feasible new ideas and new methods for research in the field of multi-model medical-assisted diagnosis. (shrink)

Human gait is a complex activity that requires high coordination between the central nervous system, the limb, and the musculoskeletal system. More research is needed to understand the latter coordination's complexity in designing better and more effective rehabilitation strategies for gait disorders. Electroencephalogram and functional near-infrared spectroscopy are among the most used technologies for monitoring brain activities due to portability, non-invasiveness, and relatively low cost compared to others. Fusing EEG and fNIRS is a well-known and established methodology proven to enhance (...) brain–computer interface performance in terms of classification accuracy, number of control commands, and response time. Although there has been significant research exploring hybrid BCI involving both EEG and fNIRS for different types of tasks and human activities, human gait remains still underinvestigated. In this article, we aim to shed light on the recent development in the analysis of human gait using a hybrid EEG-fNIRS-based BCI system. The current review has followed guidelines of preferred reporting items for systematic reviews and meta-Analyses during the data collection and selection phase. In this review, we put a particular focus on the commonly used signal processing and machine learning algorithms, as well as survey the potential applications of gait analysis. We distill some of the critical findings of this survey as follows. First, hardware specifications and experimental paradigms should be carefully considered because of their direct impact on the quality of gait assessment. Second, since both modalities, EEG and fNIRS, are sensitive to motion artifacts, instrumental, and physiological noises, there is a quest for more robust and sophisticated signal processing algorithms. Third, hybrid temporal and spatial features, obtained by virtue of fusing EEG and fNIRS and associated with cortical activation, can help better identify the correlation between brain activation and gait. In conclusion, hBCI system is not yet much explored for the lower limb due to its complexity compared to the higher limb. Existing BCI systems for gait monitoring tend to only focus on one modality. We foresee a vast potential in adopting hBCI in gait analysis. Imminent technical breakthroughs are expected using hybrid EEG-fNIRS-based BCI for gait to control assistive devices and Monitor neuro-plasticity in neuro-rehabilitation. However, although those hybrid systems perform well in a controlled experimental environment when it comes to adopting them as a certified medical device in real-life clinical applications, there is still a long way to go. (shrink)

Nowadays, lung cancer is one of the most dangerous diseases that require early diagnosis. Artificial intelligence has played an essential role in the medical field in general and in analyzing medical images and diagnosing diseases in particular, as it can reduce human errors that can occur with the medical expert when analyzing medical image. In this research study, we have done a systematic survey of the research published during the last 5 years in the diagnosis of (...) lung cancer classification of lung nodules in 4 reliable databases, and we selected 50 research paper using systematic literature review. The goal of this review work is to provide a concise overview of recent advancements in lung cancer diagnosis issues by machine learning and deep learning algorithms. This article summarizes the present state of knowledge on the subject. Addressing the findings offered in recent research publications gives the researchers a better grasp of the topic. We checked all the characteristics, such as challenges, recommendations for future work were analyzed in detail, and the published datasets and their source were presented to facilitate the researchers’ access to them and use it to develop the results achieved previously. (shrink)

Deep learning techniques, which use a massive technology known as convolutional neural networks, have shown excellent results in a variety of areas, including image processing and interpretation. However, as the depth of these networks grows, so does the demand for a large amount of labeled data required to train these networks. In particular, the medical field suffers from a lack of images because the procedure for obtaining labeled medical images in the healthcare field is difficult, expensive, and requires (...) specialized expertise to add labels to images. Moreover, the process may be prone to errors and time-consuming. Current research has revealed transfer learning as a viable solution to this problem. Transfer learning allows us to transfer knowledge gained from a previous process to improve and tackle a new problem. This study aims to conduct a comprehensive survey of recent studies that dealt with solving this problem and the most important metrics used to evaluate these methods. In addition, this study identifies problems in transfer learning techniques and highlights the problems of the medical dataset and potential problems that can be addressed in future research. According to our review, many researchers use pre-trained models on the Imagenet dataset in many applications such as skin cancer, breast cancer, and diabetic retinopathy classification tasks. These techniques require further investigation of these models, due to training them on natural, non-medical images. In addition, many researchers use data augmentation techniques to expand their dataset and avoid overfitting. However, not enough studies have shown the effect of performance with or without data augmentation. Accuracy, recall, precision, F1 score, receiver operator characteristic curve, and area under the curve were the most widely used measures in these studies. Furthermore, we identified problems in the datasets for melanoma and breast cancer and suggested corresponding solutions. (shrink)

Music can express people’s thoughts and emotions. Music therapy is to stimulate and hypnotize the human brain by using various forms of music activities, such as listening, singing, playing and rhythm. With the empowerment of artificial intelligence, music therapy technology has made innovative development in the whole process of “diagnosis, treatment and evaluation.” It is necessary to make use of the advantages of artificial intelligence technology to innovate music therapy methods, ensure the accuracy of treatment schemes, and provide more paths (...) for the development of the medical field. This paper proposes an long short-term memory -based generation and classification algorithm for multi-voice music data. A Multi-Voice Music Generation system called MVMG based on the algorithm is developed. MVMG contains two main steps. At first, the music data are modeled to the MDPI and text sequence data by using an autoencoder model, including music features extraction and music clip representation. And then an LSTM-based music generation and classification model is developed for generating and analyzing music in specific treatment scenario. MVMG is evaluated based on the datasets collected by us: the single-melody MIDI files and the Chinese classical music dataset. The experiment shows that the highest accuracy of the autoencoder-based feature extractor can achieve 95.3%. And the average F1-score of LSTM is 95.68%, which is much higher than the DNN-based classification model. (shrink)

The Unified Medical Language System and the Gene Ontology are among the most widely used terminology resources in the biomedical domain. However, when we evaluate them in the light of simple principles for wellconstructed ontologies we find a number of characteristic inadequacies. Employing the theory of granular partitions, a new approach to the understanding of ontologies and of the relationships ontologies bear to instances in reality, we provide an application of this theory in relation to an example drawn from (...) the context of the pathophysiology of hypertension. This exercise is designed to demonstrate how, by taking ontological principles into account we can create more realistic biomedical ontologies which will also bring advantages in terms of efficiency and robustness of associated software applications. (shrink)

Speech is a primary means of human communication and one of the most basic features of human conduct. Voice is an important part of its subsystems. A speech disorder is a condition that affects the ability of a person to speak normally, which occasionally results in voice impairment with psychological and emotional consequences. Early detection of voice problems is a crucial factor. Computer-based procedures are less costly and easier to administer for such purposes than traditional methods. This study highlights the (...) following issues: recent studies, methods of voice pathology detection, machine learning and deep learning (DL) methods used in data classification, main datasets utilized, and the role of Internet of things (IoT) systems employed in voice pathology diagnosis. Moreover, this study presents different applications, open challenges, and recommendations for future directions of IoT systems and artificial intelligence (AI) approaches in the voice pathology diagnosis. Finally, this study highlights some limitations of voice pathology datasets in comparison with the role of IoT in the healthcare sector, which shows the urgent need to provide efficient approaches and easy and ideal medical diagnostic procedures and treatments of disease identification for doctors and patients. This review covered voice pathology taxonomy, detection techniques, open challenges, limitations, and recommendations for future directions to provide a clear background for doctors and patients. Standard databases, including the Massachusetts Eye and Ear Infirmary, Saarbruecken Voice Database, and the Arabic Voice Pathology Database, were used in most articles reviewed in this article. The classes, features, and main purpose for voice pathology identification are also highlighted. This study focuses on the extraction of voice pathology features, especially speech analysis, extends feature vectors comprising static and dynamic features, and converts these extended feature vectors into solid vectors before passing them to the recognizer. (shrink)

There is much to learn from Durán and Jongsma’s paper.1 One particularly important insight concerns the relationship between epistemology and ethics in medical artificial intelligence. In clinical environments, the task of AI systems is to provide risk estimates or diagnostic decisions, which then need to be weighed by physicians. Hence, while the implementation of AI systems might give rise to ethical issues—for example, overtreatment, defensive medicine or paternalism2—the issue that lies at the heart is an epistemic problem: (...) how can physicians know whether to trust decisions made by AI systems? In this manner, various studies examining the interaction of AI systems and physicians have shown that without being able to evaluate their trustworthiness, especially novice physicians become over-reliant on algorithmic support—and ultimately are led astray by incorrect decisions.3–5 This leads to a second insight from the paper, namely that even if some AI system happens to be opaque, it is still not built on the moon. To assess its trustworthiness, AI developers or physicians have different sorts of higher order evidence at hand. Most importantly, …. (shrink)

Systems with human-centered artificial intelligence are always as good as their ability to consider their users’ context when making decisions. Research on identifying people’s everyday activities has evolved rapidly, but little attention has been paid to recognizing both the activities themselves and the motions they make during those tasks. Automated monitoring, human-to-computer interaction, and sports analysis all benefit from Web 4.0. Every sport has gotten its move, and every move is not known to everyone. In ice hockey, every move (...) cannot be monitored by the referee. Here, Convolution Neural Network-based Real-Time Image Processing Framework (CNN-RTIPF) is introduced to classify every move in Ice Hockey. CNN-RTIPF can reduce the challenges in monitoring the player’s move individually. The image of every move is captured and compared with the trained data in CNN. These real-time captured images are processed using a human-centered artificial intelligence system. They compared images predicted by probability calculation of the trained set of images for effective classification. Simulation analysis shows that the proposed CNN-RTIPF can classify real-time images with improved classification ratio, sensitivity, and error rate. The proposed CNN-RTIPF has been validated based on the optimization parameter for reliability. To improve the algorithm for movement identification and train the system for many other everyday activities, human-centered artificial intelligence-based Web 4.0 will continue to develop. (shrink)

The absence of a national soilclassification system for Nigeria hinderssuccessful agrotechnology transfer inparticular, and agricultural development ingeneral. A discussion of the role of indigenousknowledge in agricultural development showsthat indigenous knowledge of the soil can beintegrated with modern soil science to developa soil classification system for the country.Much as local knowledge is invaluable foradvancing scientific knowledge and vice versa,caution is given against overestimating therole of indigenous knowledge in developmentalactivities. It is important to encourage theproper integration of all knowledge systems (...) increating new paradigms for sustainabledevelopment in Nigeria. (shrink)

Review of the books: Jerry A. Fodor. Concepts: Where Cognitive Science went wrong. Oxford, UK: Oxford University Press, 1998, 174 pp., ISBN 0-19-823636-0. Geoffrey C. Bowker and Susan Leigh Star. Sorting things out: Classification and its consequences. Cambridge, MA: The MIT Press, 1999, 377 pp., ISBN 0-262-02461-6.

Classifying different meditation techniques is essential for the progress of meditation research, as this will enable discerning which effects are associated with which techniques, in addition to supporting the development of increasingly effective and efficient meditation-based training programs and clinical interventions. However, both the task of defining meditation itself, as well as defining specific techniques, faces many fundamental challenges. Here we describe problems involved in this endeavor and suggest an integrated model for defining meditation. For classifying different meditation techniques, we (...) draw on classical, contemporary, and holistic systems of classification. We analyze different techniques and propose that all meditation techniques are based on a specific set of activities, that is: focusing, releasing, imagining, and moving in relation to an object of meditation, including fields of experience. Meditative activities can be combined and unified in the activities of observing, producing, and being aware. All meditative activities are unified in awareness of awareness. Defining specific meditation techniques may be done by specifying which activities and objects are involved. The advantage of our approach is that it can potentially account for the inner workings of all current systems of classification and hence it lays the foundation for formulating an overarching system of meditation that can guide future research and practice. (shrink)

The Direction of Medical Ethics The direction bioethics, and specifically medical ethics, will take in the next few years will be crucial. It is an emerging specialty that has attempted a great deal, that has many differing agendas, and that has its own identity crisis. Is it a subspecialty of clinical medicine? Is it a medical reform movement? Is it a consumer pro tection movement? Is it a branch of professional ethics? Is it a ra tionale for (...) legal decisions and agency regulations? Is it something physicians and ethical theorists do constructively together? Or is it a morally concentrated attack on high technology, with the prac titioners of scientific medicine and the medical ethicists in an adversarial role? Is it a conservative endeavor, exhibiting a Frankenstein syn drome in Medical Genetics ("this time, they have gone too far"), or a Clockwork Orange syndrome in Psychotherapy ("we have met hods to make you talk-walk-cry-kill")? Or does it suffer the afflic tion of overdependency on the informal fallacy of the Slippery Slope ("one step down this hill and we will never be able to stop") that remains an informal fallacy no matter how frequently it's used? Is it a restricted endeavor of analytic philosophy: what is the meaning of "disease," how is "justice" used in the allocation of medical resources, what constitutes "informed" or "consent?" Is it applied ethics, leading in clinical practice to some recommenda tion for therapeutic or preventive action? This incomplete list of questions indicates just how complex. (shrink)

The Journal of Economic Literature codes classification system (JEL) published by the American Economic Association (AEA) is the de facto standard classification system for research literature in economics. The JEL classification system is used to classify articles, dissertations, books, book reviews, and working papers in EconLit, a database maintained by the AEA. Over time, it has evolved and extended to a system with over 850 subclasses. This paper reviews the history and development of the JEL classification (...) system, describes the current version, and provides a selective overview of its uses and applications in research. The JEL codes classification system has been adopted by several publishers, and their instructions are reviewed. There are interesting avenues for future research as the JEL classification system has been surprisingly little used in existing bibliometric and scientometric research as well as in library classification systems. (shrink)

In the last two decades we have witnessed considerable efforts directed towards making electronic healthcare records comparable and interoperable through advances in record architectures and (bio)medical terminologies and coding systems. Deep semantic issues in general, and ontology in particular, have received some interest from the research communities. However, with the exception of work on so-called ‘controlled vocabularies’, ontology has thus far played little role in work on standardization. The prime focus has been rather the rapid population of terminologies (...) at the level of fine detail. In this paper, we argue that more efforts are needed on the side of both research and standardization to ensure that the coding systems used in electronic healthcare records enjoy a semantics that is coherent with the semantics of the record. We propose realist ontology as a method to bring about this coherence by means of a robust system of top-level ontological categories. (shrink)

Political campaigns increasingly rely on Facebook for reaching their constituents, particularly through ad targeting. Facebook’s business model is premised on a promise to connect advertisers with the “right” users: those likely to click, download, engage, purchase. The company pursues this promise by algorithmically inferring users’ interests from their data and providing advertisers with a means of targeting users by their inferred interests. In this study, we explore for whom this interest classification system works in order to build on conversations (...) in critical data studies about the ways such systems produce knowledge about the world rooted in power structures. We critically analyze the classification system from a variety of empirical vantage points—via user data; Facebook documentation, training, and patents; and Facebook’s tools for advertisers—and through theoretical concepts from a variety of domains. In this, we focus on the ways the classification system shapes possibilities for political representation and voice, particularly for people of color, women, and LGBTQ+ people. We argue that this “big data-driven” classification system should be read as political: it articulates a stance not only on what issues are or are not important in the U.S. public sphere, but also on who is considered a significant enough public to be adequately accounted for. (shrink)