Ontological realism aims at the development of high quality ontologies that faithfully represent what is general in reality and to use these ontologies to render heterogeneous data collections comparable. To achieve this second goal for clinical research datasets presupposes not merely (1) that the requisite ontologies already exist, but also (2) that the datasets in question are faithful to reality in the dual sense that (a) they denote only particulars and relationships between particulars that do in fact exist (...) and (b) they do this in terms of the types and type-level relationships described in these ontologies. While much attention has been devoted to (1), work on (2), which is the topic of this paper, is comparatively rare. Using Referent Tracking as basis, we describe a technical data wrangling strategy which consists in creating for each dataset a template that, when applied to each particular record in the dataset, leads to the generation of a collection of Referent Tracking Tuples (RTT) built out of unique identifiers for the entities described by means of the data items in the record. The proposed strategy is based on (i) the distinction between data and what data are about, and (ii) the explicit descriptions of portions of reality which RTTs provide and which range not only over the particulars described by data items in a dataset, but also over these data items themselves. This last feature allows us to describe particulars that are only implicitly referred to by the dataset; to provide information about correspondences between data items in a dataset; and to assert which data items are unjustifiably or redundantly present in or absent from the dataset. The approach has been tested on a dataset collected from patients seeking treatment for orofacial pain at two German universities and made available for the NIDCR-funded OPMQoL project. (shrink)

Peer commentary on Char, Abràmoff & Feudtner (2020) target article: "Identifying Ethical Considerations for Machine Learning Healthcare Applications" .

The International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC) COVID-19 dataset is one of the largest international databases of prospectively collected clinical data on people hospitalized with COVID-19. This dataset was compiled during the COVID-19 pandemic by a network of hospitals that collect data using the ISARIC-World Health Organization Clinical Characterization Protocol and data tools. The database includes data from more than 705,000 patients, collected in more than 60 countries and 1,500 centres worldwide. (...) Patient data are available from acute hospital admissions with COVID-19 and outpatient follow-ups. The data include signs and symptoms, pre-existing comorbidities, vital signs, chronic and acute treatments, complications, dates of hospitalization and discharge, mortality, viral strains, vaccination status, and other data. Here, we present the dataset characteristics, explain its architecture and how to gain access, and provide tools to facilitate its use. (shrink)

The new protocol for information distribution amongst scientists within the CERN, once slightly refined and publicly announced as the Worldwide Web in the August of 1991, has led to impressive achievements beyond the starting plan.

Immunology researchers are beginning to explore the possibilities of reproducibility, reuse and secondary analyses of immunology data. Open-access datasets are being applied in the validation of the methods used in the original studies, leveraging studies for meta-analysis, or generating new hypotheses. To promote these goals, the ImmPort data repository was created for the broader research community to explore the wide spectrum of clinical and basic research data and associated findings. The ImmPort ecosystem consists of four components–Private (...) Data, Shared Data, Data Analysis, and Resources—for data archiving, dissemination, analyses, and reuse. To date, more than 300 studies have been made freely available through the ImmPort Shared Data portal , which allows research data to be repurposed to accelerate the translation of new insights into discoveries. (shrink)

Diabetic ketoacidosis is a serious, medical emergency that can be fatal but treatable, we aimed to evaluate the clinical profile of patients admitted with diabetic ketoacidosis. This case series study enrolled 213 non-pregnant adult and adolescent patients admitted with diabetic ketoacidosis at Tripoli Diabetes Hospital from January to September 2023. Demographic data, clinical characteristics, laboratory findings, precipitating factors, and patient outcomes were extracted from medical records and analyzed. Type 1 diabetes mellitus was present in 187 (87.8%) of (...) patients, the age range 11-84 years, (30.26±13.28), with 130 patients ≤30 years old (61.0%), females accounting for 110 of the patients (51.6%), 109 had a diabetes duration of less than 10 years (51.2%). The most common precipitating factor was insulin omission 92 (43.2%) in patients with known diabetes mellitus, whereas, diabetic ketoacidosis as the first presentation of diabetes mellitus in 24 patients (11.7%), systolic blood pressure ranged 50-160 mmHg (112.82±16.19), diastolic blood pressure varied 30-100 mmHg (70.24±11.32). Plasma glucose at presentation ranged 183-1494 mg/dl (462.92±169.85), with Eu-glycemic diabetic ketoacidosis 100-249 mg/dl were in nine patients (4.2%), whereas most of cases (135 patients, 63.4%) present with plasma glucose 250-500 mg/dl, while hyperosmolar status (≥701 mg/dl) were present in 13 patients (6.1%). Venous pH varied from 6.7-7.42 (7.13±0.14), 132 (62.0%) patients presented with pH <7.24, while severe acidosis was pH < 7 in 32 of patients (14.6%), serum bicarbonate with 10.93±4.95, severe (<5 mmol) in 91 patients (42.7%) and moderate (5-10 mmol) were in 79 patients (37.08%). The mean length of hospital stay was 3.33 days, with an average of 2.11 days spent in the high-dependent unit. Serum potassium varied from 2.4-6.7 meq/L with 3.66± 0.63, most common complications of diabetic ketoacidosis treatment were hypokalemia observed in 82 (35.7%) of cases, and hypoglycemia was detected in 41 patients (19.2%). 170 patients were discharged in good condition (79.8%), and 17.8% of patients were transferred to another hospital to receive further management for co-morbid diseases with diabetes after controlling their hyperglycemic crises. The majority of patients presented with the critical status of diabetic ketoacidosis. The findings emphasize the importance of patient education about prevention measures prompt presentation to the hospital, and clinicians' awareness for early and aggressive treatment of hyperglycemic crises. (shrink)

Clinical Ethics Consultations are an important tool for physicians in solving difficult cases. They are extremely common in North America and to a lesser extent also present in Europe. However, there is little data on this practice in Poland. We present results of a survey of 521 physicians practising in Poland concerning their opinion on CECs and related practices. We analysed the data looking at such issues as CECs’ perceived availability, use of CECs, and perceived usefulness of (...) such support. Physicians in our study generally encounter hard ethics cases, even—surprisingly—those who do not work in hospitals. Most physicians have no CEC access, and those that do still do not employ CECs. However, physicians perceive this form of support as useful—even more so among actual users of CECs. We compared these findings with similar studies from other European countries and the North America. We point out peculiarities of our results as compared to those in other countries, with some possible explanations. We hope the results may encourage regulatory debate on the need to formally introduce CECs into the Polish healthcare system. (shrink)

Decision-making by a Data and Safety Monitoring Board (DSMB) regarding clinical trial conduct and termination is intricate and largely limited by cases and rules. Decision-making by legal jury is also intricate and largely constrained by cases and rules. In this paper, I argue by analogy that legal decision-making, which strives for a balance between competing demands of conservatism and innovation, supplies a good basis to the logic behind DSMB decision-making. Using the doctrine of precedents in legal reasoning as (...) my central analog will lead us to a decision-theoretic analogy for much more systematic documentation of decisions in clinical trials. My conclusion is twofold: every DSMB decision should articulate a clear general principle (a ratio decidendi) that gives reason for the decision; and all such decisions should be made public. I use Bartha’s (2010) theory of analogical arguments to both frame and assess my argument by analogy. (shrink)

Biomarkers are widely used not only as prognostic or diagnostic indicators, or as surrogate markers of disease in clinical trials, but also to formulate theories of pathogenesis. We identify two problems in the use of biomarkers in mechanistic studies. The first problem arises in the case of multifactorial diseases, where different combinations of multiple causes result in patient heterogeneity. The second problem arises when a pathogenic mediator is difficult to measure. This is the case of the oxidative stress (OS) (...) theory of disease, where the causal components are reactive oxygen species (ROS) that have very short half-lives. In this case, it is usual to measure the traces left by the reaction of ROS with biological molecules, rather than the ROS themselves. Borrowing from the philosophical theories of signs, we look at the different facets of biomarkers and discuss their different value and meaning in multifactorial diseases and system medicine to inform their use in patient stratification in personalized medicine. (shrink)

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

Machine learning algorithms may radically improve our ability to diagnose and treat disease. For moral, legal, and scientific reasons, it is essential that doctors and patients be able to understand and explain the predictions of these models. Scalable, customisable, and ethical solutions can be achieved by working together with relevant stakeholders, including patients, data scientists, and policy makers.

Medicine is often thought of as a science of the body, but it is also a science of data. In some contexts, it can even be asserted that data drive health. This article focuses on a key piece of data technology central to contemporary practices of medicine: the medical record. By situating the medical record in the perspective of its history, we inquire into how the kinds of data that are kept at sites of clinical (...) encounter often depend on informational requirements that originate well outside of the clinic, in particular in health insurance records systems. Although this dependency of today's electronic medical records on billing requirements is widely lamented by clinical providers, its history remains little studied. Following the archaeology of medicine developed by Michel Foucault in The Birth of the Clinic and expanding his methodology in light of more recent contributions to the field of media archaeology, this article excavates some of the underexplored technological conditions that help constitute today's electronic medical record. If in some contexts, it is true that data drive health, then an archaeology of medical records helps reveal how health insurance records often impact clinical care and, by extension, health and disease. (shrink)

The objective of this research programme is to contribute to the establishment of the emerging science of Formal Ontology in Information Systems via a collaborative project involving researchers from a range of disciplines including philosophy, logic, computer science, linguistics, and the medical sciences. The re­searchers will work together on the construction of a unified formal ontology, which means: a general framework for the construction of ontological theories in specific domains. The framework will be constructed using the axiomatic-deductive method of modern (...) formal ontology. It will be tested via a series of applications relating to on-going work in Leipzig on medical taxonomies and data dictionaries in the context of clinical trials. This will lead to the production of a domain-specific ontology which is designed to serve as a basis for applications in the medical field. (shrink)

The translational research community, in general, and the Clinical and Translational Science Awards (CTSA) community, in particular, share the vision of repurposing EHRs for research that will improve the quality of clinical practice. Many members of these communities are also aware that electronic health records (EHRs) suffer limitations of data becoming poorly structured, biased, and unusable out of original context. This creates obstacles to the continuity of care, utility, quality improvement, and translational research. Analogous limitations to sharing (...) objective data in other areas of the natural sciences have been successfully overcome by developing and using common ontologies. This White Paper presents the authors’ rationale for the use of ontologies with computable semantics for the improvement of clinical data quality and EHR usability formulated for researchers with a stake in clinical and translational science and who are advocates for the use of information technology in medicine but at the same time are concerned by current major shortfalls. This White Paper outlines pitfalls, opportunities, and solutions and recommends increased investment in research and development of ontologies with computable semantics for a new generation of EHRs. (shrink)

Odată cu creșterea volumului de date pe Internet, în media socială, cloud computing, dispozitive mobile și date guvernamentale, Big Data devine în același timp o amenințare și o oportunitate în ceea ce privește gestionarea și utilizarea acestor date, menținând în același timp drepturile persoanelor implicate. În fiecare zi, folosim și generăm tone de date, alimentând bazele de date ale agențiilor guvernamentale, companiilor private și chiar cetățenilor privați. Beneficiem în multe feluri de existența și utilizarea Big Data, dar trebuie (...) să ne amintim de asemenea că „nu există nimic gratuit”. Există riscuri în utilizarea Big Data, un fel de parte întunecată. Schimbările de piață pot fi obținute prin utilizarea Big Data: în timp ce acestea pot fi pozitive pentru cineva, ele pot dăuna altcuiva. Riscurile potențiale provin și de la agențiile publice. Este clar că datele mari pot aduce beneficii cetățenilor și companiilor, dar trebuie să fie echilibrate de o conștientizare sporită a „părții întunecate” a Big Data în exploatarea noilor capacități și oportunități pe care le oferă tuturor . -/- CUPRINS -/- 1. Big Data - Definiție - Caracteristici - Clasificarea datelor mari - Arhitectura - Tehnologii - Oportunități - Provocări - Critici - - Criticile paradigmei Big Data - - Criticile modelului "V" - - Criticile de noutate - - Criticile execuției Big Data - 1.1 Viața datelor mari - - Nașterea - - Copilăria - - - Educație - - - Divertisment - - Adolescența - - - Servicii online - - - Comunicații mobile - - - Servicii de streaming - - Adulți tineri - - - Căutarea de locuri de muncă - - - Servicii bancare și asigurări - - - Utilități - - - Dating online și pornografie - - Vârsta medie - - - Servicii de sănătate - - - Aplicații pentru îngrijire și aplicații pentru sănătate și fitness - - - Căutarea de locuri de muncă - - - Dating online - - Bătrânețea - - - Îngrijirea sănătății - - Moartea - 1.2 Aplicații - - Guvern - - Dezvoltare internațională - - Fabrici - - Sănătate - - Educaţie - - Mass-media - - Asigurare - - Internetul obiectelor (IoT) - - Tehnologia informației - 1.3 Studii de caz - - Guvern - - - China - - - India - - - Israel - - - Regatul Unit - - - Statele Unite ale Americii - - Cu amănuntul - - Ştiinţă - - Sport - - Tehnologie - - Activități de cercetare - - - Prelevarea de date mari - 1.4 Agenda europeană privind societatea digitală - - Cadrul juridic - - Peisajul opiniei părților interesate - 1.5 GDPR - - Etapele procesării datelor personale - - Principiile procesării datelor - - Politica de confidențialitate și transparența - - Scopurile procesării datelor - - Confidențialitate prin design și implicită - - - Paradoxul (legal) al Big Data - - Domeniul teritorial al GDPR - - Definirea datelor mari, a datelor personale și a datelor anonime - - Etapele procesării datelor cu caracter personal - - Principii de prelucrare a datelor - - Politica de confidențialitate și Transparența - - Scopuri de prelucrare a datelor - - Confidențialitate prin design și confidențialitate în mod implicit - - Paradoxul (legal) al datelor mari 2. Analiza Big Data - 2.1 Analitica - - Analitica vs. analiză - - Aplicații ale analiticii - - - Optimizarea marketingului - - - Analiza oamenilor - - - Analiza portofoliului - - - Analitica riscurilor - - - Analitica digitală - - - Analitica securității - - - Analitica software-ului - - Provocări - - Riscuri - 2.2 Analitica predictivă - - Definiție - - Procesul de analitica predictivă - - Tipuri - - - Modele predictive - - - Modele descriptive - - - Modele de decizie - - Aplicații - - - Managementul relațiilor cu clienții analitic (CRM) - - - Protecția copilului - - - Sisteme de suport decizional clinic - - - Analiticile colecțiilor - - - Vânzări încrucișate - - - Retenția clienților - - - Marketing direct - - - Detectarea fraudelor - - - Prezicerea rezultatelor deciziilor legale - - - Previziuni privind portofoliul, produsele sau economia - - - Managementul riscului de proiect - - - Acceptanța (Underwriting) - - Tehnologia și influențele big data - - Tehnici analitice - - - Tehnici de regresie - - - - Model de regresie liniară - - - - Modele de alegere discretă - - - - Regresie logistică - - - - Regresie logistică multinomială - - - - Regresie probit - - - - Logit versus probit - - - - Modele pentru serii de timp - - - - Analiza supraviețuirii sau a duratei - - - - Arborele de clasificare și regresie (CART) - - - - Funcții spline adaptive de regresie multivariate - - - Tehnici de învățare automată - - - - Rețele neuronale - - - - Perceptron multistrat (MLP) - - - - Funcții radiale de bază - - - - Mașini cu vectori de suport - - - - Bayesiene naive - - - - Vecinul k-cel mai apropiat - - - - Modelarea predictivă geospațială - - Instrumente - - - PMML - - Critici - 2.3 Analitica prescriptivă - - Istorie - 2.4 Studiul critic al datelor - - Perspective critice - - Abordare: Pragmatismul și etica discursului 3. Etica Big Data - Datele personale ale copiilor - Rolul instituțiilor - - State naționale - - Bănci - Articole relevante de știri despre etica datelor - Legislație relevantă privind etica datelor - 3.1 Principii ale eticii Big Data - - Etica în cercetare - - Conștientizarea - - Consimţământul - - Controlul - - Transparența - - Încrederea - - Proprietatea - - Supravegherea și securitatea - - Identitatea digitală - - Realitatea ajustată - - De-anonimizarea - - Inegalitatea digitală - - Confidențialitatea - - Financiar - - - Cât de mult merită datele? - - Deschidere - 3.2 Probleme etice în cercetare - - Etica cercetării pentru cercetarea socială - - Principalele aspecte etice ale cercetării sociale - - - Intimitate - - - Consimțământ informat - - - De-identificarea - - - Inegalitate – Diviziune digitală - - Integritatea cercetării - - Probleme emergente - - Aviz legal - 3.3 Aspecte filosofice - 3.4 10 reguli de bază în lucrul cu Big Data - - Concluzie Referințe Despre autor - Nicolae Sfetcu - - De același autor - - Contact Editura - MultiMedia Publishing . 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As opposed to a ‘one size fits all’ approach, precision medicine uses relevant biological, medical, behavioural and environmental information about a person to further personalize their healthcare. This could mean better prediction of someone’s disease risk and more effective diagnosis and treatment if they have a condition. Big data allows for far more precision and tailoring than was ever before possible by linking together diverse datasets to reveal hitherto-unknown correlations and causal pathways. But it also raises ethical issues relating (...) to the balancing of interests, viability of anonymization, familial and group implications, as well as genetic discrimination. This article analyses these issues in light of the values of public benefit, justice, harm minimization, transparency, engagement and reflexivity and applies the deliberative balancing approach found in theEthical Framework for Big Data in Health and Research to a case study on clinical genomic data sharing. Please refer to that article for an explanation of how this framework is to be used, including a full explanation of the key values involved and the balancing approach used in the case study at the end. Our discussion is meant to be of use to those involved in the practice as well as governance and oversight of precision medicine to address ethical concerns that arise in a coherent and systematic manner. (shrink)

Background Analysis of aphasic narratives can be a challenge for clinicians. Previous studies have mainly employed measures that categorized speech samples at the word level. They included quantification of the use and misuse of different word classes, presence and absence of narrative contents and errors, paraphasias, and perseverations, as well as morphological structures and errors within a narrative. In other words, a great amount of research has been conducted in the aphasiology literature focusing on micro-linguistic structures of oral narratives. Aspects (...) of macro- linguistic structures, such as the analysis of content information by a speaker, consistency of using cohesive devices to present information within a narrative, and order of presenting information necessary to form a coherent discourse, have not been extensively investigated. The current investigation proposes a clinical analytic system to target three aspects of macro-linguistic structures in narratives among speakers with aphasia. Specifically, (1) the presence of search events (i.e., the mentioning of key events that allow the listener to understand; Capilouto, Wright, &Wagovich, 2006) within a narrative, (2) the sequence of the mentioned events, and (3) the informativeness (i.e., the fulfillment of lexical items that allow the user to understand what the event is detailing) of the event contents, were focused in the proposed framework. Method Ten controls transcripts from were selected from the AphasiaBank (MacWhinney, Fromm, Forbes, & Holland, 2011). Three narrative tasks, including sequential picture description of ‘Refused Umbrella’, procedural narrative of making a ‘Peanut Butter and Jelly Sandwich’, and telling of ‘Cinderella’ story, were used to establish normative data for the basis of analysis. Specifically, the Search Events (e) and Informative Words (i) used by at least 70% of the speakers were listed for each genre. The sequential order of mentioning the Search Events, i.e., common order of events in 90% of the speakers, (s.total) was determined. Twelve speakers with aphasia (nine fluent and three non-fluent) were recruited and administered the Western Aphasia Battery and Object and Action Naming Battery. Their performance in the above three discourse tasks were orthographically transcribed and analyzed using the following measures: e.total, e.matched with norm, e.missed, e.irrlevant, e.extra, s.total, i.pb&j, i.umbr, i.cind, and i.total. Three samples were randomly chosen to be re-analyzed for inter- and intra-rater reliability. Results Results of an independent t-test suggested significant differences between the fluent and non-fluent subjects in sequential order of Search Events for all genres (s.total) and i.total, suggesting sensitivity of this framework to differentiate between the two fluency groups. Results of Pearson product-moment correlations revealed significant relation between Informative Words and WAB spontaneous speech scores, AQ, as well as OAB scores, for both aphasic groups. The mean value of coefficients for intra-rater reliability (0.992), with significant correlations on e.match, e.miss, and e.extra, was higher than that of inter-rater reliability (0.897). Discussion Based on our preliminary results, this objective framework allowed clinical evaluation of impairment in macro-linguistic structures present in aphasic discourse. Further extension should involve more subjects encompassing a wider range of severity with different types of aphasia. (shrink)

Summary.— Recent empirical work in social cognition suggests that in building a self-concept people make inferences about themselves based on overt behavior or private thoughts and feelings. This article addresses the question of how, exactly, people make these inferences about themselves and raises the possibility that they do so through self-talk. It is proposed that the more on talks to oneself to construct a selfimage, the more this image will gain coherence and sophistication. A correlational study was conducted to explore (...) the relation between richness of the self-concept (using the W-A-Y) and natural disposition to talk to oneself (using a pilot questionnaire). A moderate but positive correlation of .30 is obtained. The article concludes with clinical implications. (shrink)

Most people are completely oblivious to the danger that their medical data undergoes as soon as it goes out into the burgeoning world of big data. Medical data is financially valuable, and your sensitive data may be shared or sold by doctors, hospitals, clinical laboratories, and pharmacies—without your knowledge or consent. Medical data can also be found in your browsing history, the smartphone applications you use, data from wearables, your shopping list, and more. (...) At best, data about your health might end up in the hands of researchers on whose good will we depend to avoid abuses of power.2 Most likely, it will end up with data brokers who might sell it to a future employer, or an insurance company, or the government. At worst, your medical data may end up in the hands of criminals eager to commit extortion or identity theft. In addition to data harms related to exposure and discrimination, the collection of sensitive data by powerful corporations risks the creation of data monopolies that can dominate and condition access to health care. -/- This chapter aims to explore the challenge that big data brings to medical privacy. Section I offers a brief overview of the role of privacy in medical settings. I define privacy as having one’s personal information and one’s personal sensorial space (what I call autotopos) unaccessed. Section II discusses how the challenge of big data differs from other risks to medical privacy. Section III is about what can be done to minimise those risks. I argue that the most effective way of protecting people from suffering unfair medical consequences is by having a public universal healthcare system in which coverage is not influenced by personal data (e.g., genetic predisposition, exercise habits, eating habits, etc.). (shrink)

KEY POINTS In Canada, commercial data brokers collect deidentified patient data from pharmacies, private drug insurers, the federal government and medical clinics without patient consent. Although pharmaceutical companies are the data brokers’ primary customers, academics and nonprofit and public entities also use commercial data sets, given the absence of a coordinated public approach to collecting these data across Canada. Risks of commercialized patient data include loss of anonymity, surveillance and marketing, discrimination and violation of (...) Indigenous data sovereignty. Coordinated infrastructure for the collection and coordination of health data across Canada and updated privacy legislation would protect individuals and communities and enable appropriate data uses. (shrink)

A background assumption of this paper is that the repertoire of inference schemes available to humanity is not fixed, but subject to change as new schemes are invented or refined and as old ones are obsolesced or abandoned. This is particularly visible in areas like health and environmental sciences, where enormous societal investment has been made in finding ways to reach more dependable conclusions. Computational modeling of argumentation, at least for the discourse in expert fields, will require the possibility of (...) modeling change in a stock of schemes that may be applied to generate conclusions from data. We examine Randomized Clinical Trial, an inference scheme established within medical science in the mid-20th Century, and show that its successful defense by means of practical reasoning allowed for its black-boxing as an inference scheme that generates (and warrants belief in) conclusions about the effects of medical treatments. Modeling the use of a scheme is well-understood; here we focus on modeling how the scheme comes to be established so that it is available for use. (shrink)

Recent work on the quality assurance of the Gene Ontology (GO, Gene Ontology Consortium 2004) from the perspective of both linguistic and ontological organization has made it clear that GO lacks the kind of formalism needed to support logic-based reasoning. At the same time it is no less clear that GO has proven itself to be an excellent terminological resource that can serve to combine together a variety of biomedical database and information systems. Given the strengths of GO, it is (...) worth investigating whether, by overcoming some of its weaknesses from the point of view of formal-ontological principles, we might not be able to enhance a version of GO which can come even closer to serving the needs of the various communities of biomedical researchers and practitioners. It is accepted that clinical and bioinformatics need to find common ground if the results of data-intensive biomedical research are to be harvested to the full. It is also widely accepted that no single method will be sufficient to create the needed common framework. We believe that the principles-based approach to life-science data integration and knowledge representation must be one of the methods applied. Indeed in dealing with the ontological representation of carcinomas, and specifically of colon carcinomas, we have established that, had GO (and related biomedical ontologies) followed some of the basic formal-ontological principles we have identified (Smith et al. 2004, Ceusters et al. 2004), then the effort required to navigate successfully between clinical and bioinformatics systems would have been reduced. We point here to the sources of ontologically-related errors in GO, and also provide arguments as to why and how such errors need to be resolved. (shrink)

Driven by the use cases of PubChemRDF and SCAIView, we have developed a first community-based clinical trial ontology (CTO) by following the OBO Foundry principles. CTO uses the Basic Formal Ontology (BFO) as the top level ontology and reuses many terms from existing ontologies. CTO has also defined many clinical trial-specific terms. The general CTO design pattern is based on the PICO framework together with two applications. First, the PubChemRDF use case demonstrates how a drug Gleevec is linked (...) to multiple clinical trials investigating Gleevec’s related chemical compounds. Second, the SCAIView text mining engine shows how the use of CTO terms in its search algorithm can identify publications referring to COVID-19-related clinical trials. Future opportunities and challenges are discussed. (shrink)

Objective: This main aim of the study is to explore COVID-19 pandemic problems from the perspective of public health-clinical care ethics through online mediareports in Turkey. Method: This research was designed as a descriptive and qualitative study that assesses COVID-19 through online media reports on critics between the periods of March 11, 2020 and April 2 2020 as a quantitative as number of reports and qualitative study, across Turkey. Reports were from Turkish Medical Association websites which included newspaper reports. (...) Study data were presented as statistically and qualitative data case and headlines. No ethical or ofTicial permission was sought as the study was conducted through open access internet news sites. Results: This online reports analysis retrieved about 6723 articles about the COVID-19. According to study data, information about COVID-19 were themed as follows: general deTiciencies in taking action and isolation, lack of isolation, passengers and transport vehicles not quarantined ; insufTicient diagnostic tests, decision to test after healthcare professionals become infected, lack of equipment, lack of evaluation outbreak countries [Table 2]. Conclusion and Suggestions: COVID-19 is a pandemic and is a global public health problem that concerns every individual and needs to be handled carefully. This requires a multi-faceted preparation and education. In this context, healthcare professionals should be well trained in this aspect and have all the necessary equipment throughout the process. Additionally, it should work systematically with the cooperation of all health organizations, the Ministry of Health, local governments and of course the media, in order to inform society, fairly distribute the resources and to implement the safety measures effectively. BrieTly, lack of transparency, insufTicient information, limited resources, lack of public health protection measures such as partial quarantine decision, partial implementation of the scientiTic board's re c ommenda ti on s fo r e c on omi c rea s on s, and contradiction of the explanations are revealed as serious ethical problems. (shrink)

Objective: The use of neoadjuvant chemotherapy (NACT) in breast cancer is increasing. The management of locally advanced breast cancer differs due to the approach of the center to which the patient applied and the approach of the following physician. From this point of view, we aimed to evaluate the real life data of our region. -/- Methods: The study included 106 patients treated with neoadjuvant chemotherapy in the medical oncology clinic of two different university hospitals. Association between clinicopathological features (...) and pathological complete response (pCR) were analyzed. -/- Results: The pCR rate was higher in patients with negative hormone receptors and this difference was statistically significant (p:0.000). The rate of obtaining pCR increased as the NACT duration increased, and this increase was statistically significant. The mean NACT duration applied to the patients with pCR was 5.48 ± 0.22 months, and the mean NACT duration for those who could not obtain pCR was 5.01 ± 0.1 months (p:0.041). The recurrence rate of patients with pCR was 11.1%, while the recurrence rate of patients who could not obtain pCR was 31.6% (p:0.04). -/- Conclusions: Pathological response to chemotherapy is an important factor in determining prognosis. There appears to be a need for new biomarkers that allow the prediction of pCR and long-term outcomes. (shrink)

Ontologies are being ever more commonly used in biomedical informatics and we provide a survey of some of these uses, and of the relations between ontologies and other terminology resources. In order for ontologies to become truly useful, two objectives must be met. First, ways must be found for the transparent evaluation of ontologies. Second, existing ontologies need to be harmonised. We argue that one key foundation for both ontology evaluation and harmonisation is the adoption of a realist paradigm in (...) ontology development. For science-based ontologies of the sort which concern us in the eHealth arena, it is reality that provides the common benchmark against which ontologies can be evaluated and aligned within larger frameworks. Given the current multitude of ontologies in the biomedical domain the need for harmonisation is becoming ever more urgent. We describe one example of such harmonisation within the ACGT project, which draws on ontology-based computing as a basis for sharing clinical and laboratory data on cancer research. (shrink)

Health data processing fields face ethical and legal problems regarding fundamental rights. As we know, patients can benefit in the Digital Era from having health or medical information available, and medical decisions can be more effective with a better understanding of clinical histories, medical and health data thanks to the development of Artificial Intelligence, Internet of Things and other Digital technologies. However, at the same time, we need to guarantee fundamental rights, including privacy ones. The complaint about (...) ethical and legal requirements – including constitutional ones – is particularly relevant in the processing of health data. This paper is focused on the problem of the consent required to the processing of health data and the exceptions established in the new European Union General Data Regulation, which cover the processing of this special data -within other aims- for scientific, historical and statistical research as legitimate purposes, which include biomedical research. The conclusion is that these open concepts are problematic both for the protection of privacy rights and for the legal security/certainty of research. On one hand there are several interpretation problems, regarding the processing of health data and the protection of information privacy. On the other hand, professionals must follow the Multilevel legal framework and to guarantee fundamental rights in the processing of health data, so there are also problems of interpretation for researchers. Therefore, we need a clearer legal framework for biomedical research. (shrink)

Interventions aiming to slow, stop, or reverse the aging process are starting to enter clinical trials. Though this line of research is nascent, it has the potential to not only prevent prolonged human suffering, but also to extend human well-being. As this line of research develops, it is important to understand the ethical constraints of conducting such research. This paper discusses some of these constraints. In particular, it discusses the ethical difficulties of conducting this research in a way that (...) would produce reliable data regarding the effectiveness of an anti-aging intervention. Clinical trials of such interventions, I argue, will be faced with a dilemma between two confounding variables. Eliminating the variables requires introducing ethically problematic research practices. Thus, researchers must either perform research in ethically problematic ways, or forego the conduct of high-impact clinical research on anti-aging interventions. (shrink)

Aims: The Research Diagnostic Criteria for Temporomandi¬bular Disorders (RDC/TMD) Axis I diagnostic algorithms were demonstrated to be reliable but below target sensitivity and specificity. Empirical data supported Axis I algorithm revisions that were valid. Axis II instruments were shown to be both reliable and valid. An international consensus workshop was convened to obtain recommendations and finalization of new Axis I diagnostic algorithms and new Axis II instruments. Methods: A comprehensive search of published TMD diagnostic literature was followed by review (...) and consensus via a formal structured process by a panel of experts for revision of the RDC/TMD. Results: The recommended Diagnostic Criteria for TMD (DC/TMD) Axis I protocol includes both a valid screener for pain diagnoses and valid criteria for the most common pain-related TMDs and for one intra-articular disorder. The Axis II protocol retains selected RDC/TMD screening instruments augmented with new instruments to better assess the interactions between pain and psychosocial functioning. A comprehensive classification system is also presented. Conclusion: The recommended evidence-based DC/TMD protocol is appropriate for use in both the clinical and research settings. Simple Axis I and II screening tests augmented by validated Axis I and Axis II instruments allow for identification of simple to complex TMD patients. (shrink)

Statistics play a critical role in biological and clinical research. However, most reports of scientific results in the published literature make it difficult for the reader to reproduce the statistical analyses performed in achieving those results because they provide inadequate documentation of the statistical tests and algorithms applied. The Ontology of Biological and Clinical Statistics (OBCS) is put forward here as a step towards solving this problem. Terms in OBCS, including ‘data collection’, ‘data transformation in statistics’, (...) ‘data visualization’, ‘statistical data analysis’, and ‘drawing a conclusion based on data’, cover the major types of statistical processes used in basic biological research and clinical outcome studies. OBCS is aligned with the Basic Formal Ontology (BFO) and extends the Ontology of Biomedical Investigations (OBI), an OBO (Open Biological and Biomedical Ontologies) Foundry ontology supported by over 20 research communities. We discuss two examples illustrating how the ontology is being applied. In the first (biological) use case, we describe how OBCS was applied to represent the high throughput microarray data analysis of immunological transcriptional profiles in human subjects vaccinated with an influenza vaccine. In the second (clinical outcomes) use case, we applied OBCS to represent the processing of electronic health care data to determine the associations between hospital staffing levels and patient mortality. Our case studies were designed to show how OBCS can be used for the consistent representation of statistical analysis pipelines under two different research paradigms. By representing statistics-related terms and their relations in a rigorous fashion, OBCS facilitates standard data analysis and integration, and supports reproducible biological and clinical research. (shrink)

Wearable devices are increasingly present in the health context, as tools for biomedical research and clinical care. In this context, wearables are considered key tools for a more digital, personalised, preventive medicine. At the same time, wearables have also been associated with issues and risks, such as those connected to privacy and data sharing. Yet, discussions in the literature have mostly focused on either technical or ethical considerations, framing these as largely separate areas of discussion, and the contribution (...) of wearables to the collection, development, application of biomedical knowledge has only partially been discussed. To fill in these gaps, in this article we provide an epistemic (knowledgerelated) overview of the main functions of wearable technology for health: monitoring, screening, detection, and prediction. On this basis, we identify 4 areas of concern in the application of wearables for these functions: data quality, balanced estimations, health equity, and fairness. To move the field forward in an effective and beneficial direction, we present recommendations for the 4 areas: local standards of quality, interoperability, access, and representativity. (shrink)

Here we present what we believe is a novel account of what languages are, along with an axiomatically rich representation of languages and language-related data that is based on this account. We propose an account of languages as aggregates of dispositions distributed across aggregates of persons, and in doing so we address linguistic competences and the processes that realize them. This paves the way for representing additional types of language-related entities. Like demographic data of other sorts, data (...) about languages may be of use to researchers in a number of areas, including biomedical research. Data on the languages used in clinical encounters are typically included in medical records, and capture an important factor in patient-provider interactions. Like many types of patient and demographic data, data on a person’s preferred and primary languages are organized in different ways by different systems. This can be a barrier to data integration. We believe that a robust framework for representing language in general and preferred and primary language in particular – which has been lacking in ontologies thus far – can promote more successful integration of language-related data from disparate data sources. (shrink)

(This version was submitted to Behavioral and Brain Science. A revised version was published by Biological Theory) Estimates of autism’s incidence increased 5-10 fold in ten years, an increase which cannot be genetic. Though many mutations are associated with autism, no mutation seems directly to cause autism. We need to find the direct cause. Complexity science provides a new paradigm - confirmed in biology by extensive hard data. Both the body and the personality are complex dynamic systems which spontaneously (...) self-organize from simple dynamic systems. Autism may therefore be caused by the failure of a simple dynamic system. We know that infants who cannot track their mother’s face often become autistic, that eye-contact initiates intersubjectivity which is blocked in autism, and that the infant-mother pair seems designed to promote eye-contact, as does the eye’s appearance. This author earlier proposed that failure of eye-contact might directly cause autism and that early non-maternal childcare, including television/video, would therefore be statistically linked to autism. Waldman et al. (2008; 2006) recently proved that autism is strongly linked to precipitation (indoor activity) and to the introduction of cable. The most plausible explanation? Early exposure to television/video is linked to autism. Furthermore a normal developmental cascade (blocked in autism) has been deciphered: (a) Infant-mother eye-contact triggers increased maternal attention. (b) Early maternal attention permanently increases not only baseline vasopressin but also that oxytocin release which is triggered by subsequent maternal attention. (c) Vasopressin and oxytocin promote face recognition, gazing-at-the-eyes, emotion recognition, and social bonding. The eye-contact hypothesis suggests a clinical study addressing prevention: recruit prospective parents who agree to curtail television/video/computer/wi-fi in their families; measure autism’s incidence in their children. (shrink)

Objective: To evaluate the potential of arterial spin labeling (ASL) as an alternative to FDG-PET in the diagnosis of disorders of consciousness (DOC), we conducted a comparative study of the two modalities. Methods: A total of 36 DOC patients (11 female; mean age = 49.67 ± 14.54 years) and 17 healthy control (HC) participants (9 female; mean age = 31.9 ± 9.6 years) underwent both FDG-PET scans that measure metabolism via glucose uptake and ASL scans that measure cerebral blood flow (...) (CBF). CBF and metabolism in DOC and HC were compared, globally and for seven functional networks. Comparability of the two modalities was estimated using Spearman partial correlation for both global and network levels. Furthermore, a support vector machine (SVM) algorithm was used to train two classifiers to distinguish DOC patients from HC with normalized CBF or metabolism values from the seven networks. Performance of these classifiers was first evaluated through leave-one-subject-out (LOSO) cross-validation within their respective modalities. Subsequently, cross-modal validation was conducted: testing the ASL-trained classifier with PET data (ASL-to-PET validation) and vice versa (PET-to-ASL validation). Performance of each classifier was assessed using receiver operating characteristic (ROC) analysis, with area under the curve (AUC) as the metric. Results: Both modalities showed agreement in decreased CBF and metabolism in DOC patients compared to HC, at both global and network levels. The global brain and most networks showed significant positive partial correlation between CBF and metabolism. SVM classifiers, utilizing activity from seven networks as features, performed well in the both LOSO cross-validation (ASL-trained classifier: accuracy = 83.02%, AUC = 0.95; PET-trained classifier: accuracy = 98.12%, AUC = 0.98) and cross-modal validation (ASL-to-PET validation: accuracy = 84.90%, AUC =0.92; PET-to-ASL validation: accuracy = 73.58%, AUC = 0.93). Conclusion: Our results demonstrate that ASL provides information comparable to FDG-PET for DOC patients; moreover, classifiers trained on ASL data perform comparably well to those trained on FDG-PET data. Therefore, ASL could be a valuable alternative to FDG-PET in the clinical diagnosis of DOC, especially in light of its advantages: ease of acquisition, avoidance of radiation exposure, brevity of scanning time, and lower-cost. (shrink)

Vast archives of fragmentary structural brain scans that are routinely acquired in medical clinics for diagnostic purposes have so far been considered to be unusable for neuroscientific research. Yet, recent studies have proposed that by deploying machine learning algorithms to fill in the missing anatomy, clinical scans could, in future, be used by researchers to gain new insights into various brain disorders. This chapter focuses on a study published in2019, whose authors developed a novel unsupervised machine learning algorithm for (...) synthesising missing anatomy in extremely sparse clinical MRI scans of thousands of stroke patients. By approaching the study from a media-theoretical perspective, I analyse how its authors dis-cursively negotiated the anatomical and operative plausibility of the unobserved anatomy that their black-boxed algorithm reconstructed from the existing sparse data. My analysis fore-grounds the processual, relational, context-dependent, and essentially unstable character of the thus established plausibility of the algorithmically synthesised neuroanatomy. (shrink)

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

Integrating medical imaging with computing technologies, such as Artificial Intelligence (AI) and its subsets: Machine learning (ML) and Deep Learning (DL) has advanced into an essential facet of present-day medicine, signaling a pivotal role in diagnostic decision-making and treatment plans (Huang et al., 2023). The significance of medical imaging is escalated by its sustained growth within the realm of modern healthcare (Varoquaux and Cheplygina, 2022). Nevertheless, the ever-increasing volume of medical images compared to the availability of imaging experts. Biomedical experts (...) and radiologists have resulted in a widening disparity, causing an excess and overwhelming workload on these healthcare professionals (Chen et al., 2021). Several studies indicate that the present-day biomedical radiologist is now saddled with the daunting task of interpreting an image almost every 10 seconds to keep pace with the burgeoning clinical demands (McDonald et al., 2015; Hosny et al., 2018; Lantsman et al., 2022). This cognitive drain has invariably led to inevitable consequences such as delays in diagnosis and an amplified risk of diagnostic errors – thus, the biomedical imaging aspect is in dire need of methods that would aid accurate diagnostics and analytics for improved decision making. In this review, the importance of AI-related technologies such as ML and/or DL methods are reviewed in relation to the processing of medical or biomedical images along with their potentials, challenges, and possible suggestions for future studies in the health landscape. The focus will be on machine learning methods associated with the medical field of image classification. (shrink)

Verbal fluency tasks are often used in neuropsychological research and may have predictive and diagnostic utility in psychiatry and neurology. However, researchers using verbal fluency have uncritically assumed that there are no category-or phoneme-specific effects on verbal fluency performance. We recruited 16 healthy young adult subjects and administered two semantic (animals, trees) and phonemic (K, M) fluency tasks. Because of the small sample size, results should be regarded as preliminary and exploratory. On the animal compared to the tree task, subjects (...) produced significantly more legal words, had a significantly lower intrusion rate, significantly shorter first-response latencies and final silence periods, as well as significantly shorter between-cluster response latencies. These differences may be explained by differences in the category sizes, integrity of the categories’ borders, and efficiency of the functional connectivity between subcategories. On the K compared to the M task, subjects produced significantly more legal words and had significantly shorter between-cluster response times. Counterintuitively, a corpus analysis revealed there are more words starting with ⟨m⟩ compared to ⟨k⟩ in the experimental language. Our results potentially have important implications for research utilizing verbal fluency, including decreased reproducibility, questionable reliability of diagnostic and predictive tools based on verbal fluency, decreased knowledge accumulation, and increased number of publications with potentially misleading clinical interpretations. (shrink)

Ectopic pregnancy is defined as the fertilized ovum implants in a location outside the endometrial cavity, remains to be an important cause of maternal morbidity and mortality worldwide and is a health problem with incidence ranges between 0.25% and 2% of all pregnancies. In our study, in Izmir Katip Celebi University Ataturk Training and Research Hospital, Department of Obstetrics and Gynecology from 2011 to 2013, 96 cases with diagnosis of ectopic pregnancy managed by the adapted RCOG’s Guide were analyzed retrospectively. (...) The data were analyzed as follows: age, the history of operation, smoking, the presence of intrauterine device, blood groups, hemoglobin, platelets, values of B-hCG, the diagnostic interval, intra-abdominal free fluid and/or acute abdomen, the method of treatment and the success of treatment. (shrink)

Introduction In 2013, Dr. J. Muizelaar and Dr. R. Schrot, two neurosurgeons at the University of California Davis Medical Center (UCDMC), were found guilty of research misconduct due to failure to comply with institutional policies as well as Food and Drug Administration (FDA) regulations governing human subjects research. At issue here, however, is the difference between research and innovative therapy in the clinical setting of patient care where clinical judgment is reasonably to be privileged. Methods The UCDMC investigative (...) document is reviewed along with standard literature on clinical ethics and clinical data about glioblastoma multiforme (GBM) cancer. Results In this paper I argue that, by tendentiously focusing on policies, regulations, and procedures governing human subjects research, the UCDMC investigation failed to account for the centrality of clinical judgment and clinical ethics pertinent to judicious review of this matter, especially given the unique clinical context of terminally ill patients having exhausted standard care treatment options for glioblastoma multiforme (GBM) cancer. Conclusions The UCDMC investigation raises serious problems for clinicians who are engaged in innovative therapy in the clinical setting, requiring a regulatory framework separate from the normal Institutional Review Board (IRB) process. (shrink)

We introduce the Vital Sign Ontology (VSO), an extension of the Ontology for General Medical Science (OGMS) that covers the consensus human vital signs: blood pressure, body temperature, respiratory rate, and pulse rate. VSO provides a controlled structured vocabulary for describing vital sign measurement data, the processes of measuring vital signs, and the anatomical entities participating in such measurements. VSO is implemented in OWL-DL and follows OBO Foundry guidelines and best practices. If properly developed and extended, we believe the (...) VSO will find applications for the EMR, clinical informatics, and medical device communities. (shrink)

Identification of non-coding RNAs (ncRNAs) has been significantly improved over the past decade. On the other hand, semantic annotation of ncRNA data is facing critical challenges due to the lack of a comprehensive ontology to serve as common data elements and data exchange standards in the field. We developed the Non-Coding RNA Ontology (NCRO) to handle this situation. By providing a formally defined ncRNA controlled vocabulary, the NCRO aims to fill a specific and highly needed niche in (...) semantic annotation of large amounts of ncRNA biological and clinical data. (shrink)

The current COVID-19 pandemic and the previous SARS/MERS outbreaks of 2003 and 2012 have resulted in a series of major global public health crises. We argue that in the interest of developing effective and safe vaccines and drugs and to better understand coronaviruses and associated disease mechenisms it is necessary to integrate the large and exponentially growing body of heterogeneous coronavirus data. Ontologies play an important role in standard-based knowledge and data representation, integration, sharing, and analysis. Accordingly, we (...) initiated the development of the community-based Coronavirus Infectious Disease Ontology in early 2020. -/- As an Open Biomedical Ontology (OBO) library ontology, CIDO is open source and interoperable with other existing OBO ontologies. CIDO is aligned with the Basic Formal Ontology and Viral Infectious Disease Ontology. CIDO has imported terms from over 30 OBO ontologies. For example, CIDO imports all SARS-CoV-2 protein terms from the Protein Ontology, COVID-19-related phenotype terms from the Human Phenotype Ontology, and over 100 COVID-19 terms for vaccines (both authorized and in clinical trial) from the Vaccine Ontology. CIDO systematically represents variants of SARS-CoV-2 viruses and over 300 amino acid substitutions therein, along with over 300 diagnostic kits and methods. CIDO also describes hundreds of host-coronavirus protein-protein interactions (PPIs) and the drugs that target proteins in these PPIs. CIDO has been used to model COVID-19 related phenomena in areas such as epidemiology. The scope of CIDO was evaluated by visual analysis supported by a summarization network method. CIDO has been used in various applications such as term standardization, inference, natural language processing (NLP) and clinical data integration. We have applied the amino acid variant knowledge present in CIDO to analyze differences between SARS-CoV-2 Delta and Omicron variants. CIDO's integrative host-coronavirus PPIs and drug-target knowledge has also been used to support drug repurposing for COVID-19 treatment. -/- CIDO represents entities and relations in the domain of coronavirus diseases with a special focus on COVID-19. It supports shared knowledge representation, data and metadata standardization and integration, and has been used in a range of applications. (shrink)

A repository of clinically associated Staphylococcus aureus (Sa) isolates is used to semi‐automatically generate a set of application ontologies for specific subfamilies of Sa‐related disease. Each such application ontology is compatible with the Infectious Disease Ontology (IDO) and uses resources from the Open Biomedical Ontology (OBO) Foundry. The set of application ontologies forms a lattice structure beneath the IDO‐Core and IDO‐extension reference ontologies. We show how this lattice can be used to define a strategy for the construction of a new (...) taxonomy of infectious disease incorporating genetic, molecular, and clinical data. We also outline how faceted browsing and query of annotated data is supported using a lattice application ontology. (shrink)

The automatic integration of rapidly expanding information resources in the life sciences is one of the most challenging goals facing biomedical research today. Controlled vocabularies, terminologies, and coding systems play an important role in realizing this goal, by making it possible to draw together information from heterogeneous sources – for example pertaining to genes and proteins, drugs and diseases – secure in the knowledge that the same terms will also represent the same entities on all occasions of use. In the (...) naming of genes, proteins, and other molecular structures, considerable efforts are under way to reduce the effects of the different naming conventions which have been spawned by different groups of researchers. Electronic patient records, too, increasingly involve the use of standardized terminologies, and tremendous efforts are currently being devoted to the creation of terminology resources that can meet the needs of a future era of personalized medicine, in which genomic and clinical data can be aligned in such a way that the corresponding information systems become interoperable. (shrink)

The “Human Brain Project” (HBP) is a large-scale European neuroscience and information communication technology (ICT) project that has been a matter of heated controversy since its inception. With its aim to simulate the entire human brain with the help of supercomputing technologies, the HBP plans to fundamentally change neuroscientific research practice, medical diagnosis, and eventually the use of computers itself. Its controversial nature and its potential impacts render the HBP a subject of crucial importance for critical studies of science and (...) society. In this paper, we provide a critical exploratory analysis of the potential mid- to long-term impacts the HBP and its ICT infrastructure could be expected to have, provided its agenda will indeed be implemented and executed to a substantive degree. We analyse how the HBP aspires to change current neuroscientific practice, what impact its novel infrastructures could have on research culture, medical practice and the use of ICT, and how, given a certain degree of successful execution of the project’s aims, potential clinical and methodological applications could even transform society beyond scientific practice. Furthermore, we sketch the possibility that research such as that projected by the HBP may eventually transform our everyday world, even beyond the scope of the HBP’s explicit agenda, and beyond the isolated ‘application’ of some novel technological device. Finally, we point towards trajectories for further philosophical, historical and sociological research on the HBP that our exploratory analysis might help to inspire. Our analysis will yield important insights regardless of the actual success of the HBP. What we drive at, for the most part, is the broader dynamics of scientific and technological development of which the HBP agenda is merely one particularly striking exemplification. (shrink)

In this chapter, data concerning the development of principal aspects of vision is reviewed. First, the development of colour vision and luminance perception is discussed. Relevant data accumulated so far indicates that perception of colour and luminance is present by 6-9 months of age. The presence of typical color illusions at this age suggests that the phenomenal character of color experience is comparable to that of adults well before the first birthday. Thus it seems plausible that color perception (...) develops on the grounds of genetically preprogrammed maturation, in which perceptual learning and plasticity play a limited role. This claim also seems to be supported by case studies of newly sighted patients. On the other hand, perceptual integration of edges and forms in the visual environment as well as the development of binocular vision, and the perception of ambiguous figures seem to depend crucially on early perceptual learning. Both developmental and clinical data indicate a more significant involvement of perceptual learning in such mid-level and higher level perceptual phenomena. (shrink)

Abstract: This research explores the application of artificial neural networks (ANNs) in predicting cancer using a synthetically generated dataset designed for research purposes. The dataset comprises 10,000 pseudo-patient records, each characterized by gender, age, smoking history, fatigue, and allergy status, along with a binary indicator for the presence or absence of cancer. The 'Gender,' 'Smoking,' 'Fatigue,' and 'Allergy' attributes are binary, while 'Age' spans a range from 18 to 100 years. The study employs a three-layer ANN architecture to develop a (...) predictive model. The achieved accuracy of 97.24% and a low loss value of 0.02 indicate promising performance. Further analysis involves a comprehensive examination of the confusion matrix, precision, recall, and F1 score metrics, along with the ROC curve and AUC score, to provide a detailed understanding of the model's strengths and weaknesses. Additionally, considerations for cross-validation, hyperparameter tuning, and interpretability are discussed. It is emphasized that while the synthetic nature of the dataset allows for controlled experimentation, results should be cautiously interpreted and validated on authentic clinical data before extrapolation to real-world medical scenarios. This research serves as a valuable contribution to the exploration of predictive models for cancer detection, encouraging continued investigation and development in the field. (shrink)

Coronary Heart Disease (CHD) is one of the leading causes of death nowadays. Prediction of the disease at an early stage is crucial for many health care providers to protect their patients and save lives and costly hospitalization resources. The use of machine learning in the prediction of serious disease events using routine medical records has been successful in recent years. In this paper, a comparative analysis of different machine learning techniques that can accurately predict the occurrence of CHD events (...) from clinical data was performed. Four machine learning classifiers, namely Logistic Regression, Support Vector Machine (SVM), K- Nearest Neighbor (KNN), and Multi-Layer Perceptron (MLP) Neural Networks were identified and applied to a dataset of 462 medical instances and 9 features as well as the class feature from the South African Heart Disease data retrieved from the KEEL repository. The dataset consists of 302 records of healthy patients and 160 records of patients who suffer from CHD. In order to handle the imbalanced classification problem, the K-means algorithm along with Synthetic Minority Oversampling TEchnique (SMOTE) was used in this study. The empirical results of applying the four machine learning classifiers on the oversampled dataset have been very promising. The results reported using different evaluation metrics showed that SVM has achieved the highest overall prediction performance. (shrink)

As health systems around the world turn towards highly distributed, specialized and cooperative structures to increase quality and safety of care as well as efficiency and efficacy of delivery processes, there is a growing need for supporting communication and collaboration of all parties involved with advanced ICT solutions. The Electronic Health Record (EHR) provides the information platform which is maturing towards the eHealth core application. To meet the requirements for sustainable, semantically interoperable, and trustworthy EHR solutions, different standards and different (...) national strategies have been established. The workshop summarizes the requirements for such advanced EHR systems and their underlying architecture, presents different strategies and solutions advocated by corresponding protagonists, discusses pros and cons as well as harmonization and migration strategies for those approaches. It particularly highlights a turn towards ontology-driven architectures. (shrink)