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PubMed2005

Measuring paternal discrepancy and its public health consequences.

Paternal discrepancy (PD) occurs when a child is identified as being biologically fathered by someone other than the man who believes he is the father. This paper examines published evidence on levels of PD and its public health consequences. Rates vary between studies from 0.8% to 30% (median 3.7%, n = 17). Using information from genetic and behavioural studies, the article identifies those who conceive younger, live in deprivation, are in long term relationships (rather than marriages), or in certain cultural groups are at higher risk. Public health consequences of PD being exposed include family break up and violence. However, leaving PD undiagnosed means cases having incorrect information on their genetics and fathers continuing to suspect that children may not be theirs. Increasing paternity testing and use of DNA techniques in clinical and judicial procedures means more cases of PD will be identified. Given developing roles for individual's genetics in decisions made by health services, private services (for example, insurance), and even in personal lifestyle decisions, the dearth of intelligence on how and when PD should be exposed urgently needs addressing.

Child↗
PubMed1996

Concept hierarchy memory model: a neural architecture for conceptual knowledge representation, learning, and commonsense reasoning.

This article introduces a neural network based cognitive architecture termed Concept Hierarchy Memory Model (CHMM) for conceptual knowledge representation and commonsense reasoning. CHMM is composed of two subnetworks: a Concept Formation Network (CFN), that acquires concepts based on their sensory representations; and a Concept Hierarchy Network (CHN), that encodes hierarchical relationships between concepts. Based on Adaptive Resonance Associative Map (ARAM), a supervised Adaptive Resonance Theory (ART) model, CHMM provides a systematic treatment for concept formation and organization of a concept hierarchy. Specifically, a concept can be learned by sampling activities across multiple sensory fields. By chunking relations between concepts as cognitive codes, a concept hierarchy can be learned/modified through experience. Also, fuzzy relations between concepts can now be represented in terms of the weights on the links connecting them. Using a unified inferencing mechanism based on code firing, CHMM performs an important class of commonsense reasoning, including concept recognition and property inheritance.

Algorithms↗
PubMed2026

The role of Epstein-Barr virus in NK/T cell lymphoproliferative disorders: molecular mechanisms and potential therapeutic strategies.

Epstein-Barr virus (EBV) is a widely prevalent lymphotropic γ-herpesvirus, with approximately 95% of the population showing evidence of infection at some point during their lifetime. While most infections are asymptomatic or follow a self-limiting clinical course, in certain populations, EBV can lead to a range of lymphoproliferative disorders (LPDs), particularly subtypes originating from T cells and natural killer (NK) cells, which are often characterized by highly aggressive disease progression. This review aims to systematically discuss the molecular basis of EBV infection, covering its viral biological properties, regulation of the latent and lytic cycles, key viral protein functions (e.g., LMP1, LMP2A, EBNA1), miRNA regulatory mechanisms, and the activation of various host signaling pathways (such as NF-κB, PI3K-AKT, JAK-STAT) that contribute to the maintenance of latent infection, cell transformation, and immune evasion. Additionally, the review focuses on the pathogenic contributions of these mechanisms in EBV-related T/NK cell lymphoproliferative diseases. Research highlights include the in-depth analysis of virus-host genome interaction mechanisms, the identification of novel molecular biomarkers, and the development of targeted therapeutic strategies (e.g., PD-1/PD-L1 immune checkpoint inhibitors, EBV-specific T cell therapy). Through this comprehensive review, it is hoped that personalized medicine and artificial intelligence-assisted multimodal decision-making will be applied to the precise prevention and treatment of EBV-related diseases.

Humans↗
PubMed1989

Diagnostic reasoning.

Research in cognitive science, decision sciences, and artificial intelligence has yielded substantial insights into the nature of diagnostic reasoning. Many elements of the diagnostic process have been identified, and many principles of effective clinical reasoning have been formulated. Three reasoning strategies are considered here: probabilistic, causal, and deterministic. Probabilistic reasoning relies on the statistical relations between clinical variables and is frequently used in formal calculations of disease likelihoods. Probabilistic reasoning is especially useful in evoking diagnostic hypotheses and in assessing the significance of clinical findings and test results. Causal reasoning builds a physiologic model and assesses a patient's findings for coherency and completeness against the model; it functions especially effectively in verification of diagnostic hypotheses. Deterministic reasoning consists of sets of compiled rules generated from routine, well-defined practices. Much human problem solving may derive from activation and implementation of such rules. A deeper understanding of clinical cognition should enhance clinical teaching and patient care.

Cognition↗
PubMed1999

[RHEUMexpert: a documentation and expert system for rheumatic diseases].

A computer assisted documentation of signs and findings in rheumatic diseases is described. This documentation was developed by the Austrian Society for Rheumatology and thought to be a minimal standard for the use by general practitioners. In addition, a knowledge-based basic differential diagnosis support was developed, which differentiates between major groups of rheumatic diseases as inflammatory spine diseases, mechanical or metabolic reasons for spine disorders, inflammatory joint diseases, degenerative or metabolic joint diseases, soft tissue diseases. This presentation describes the results of an evaluation of 75 typical case histories and a second study where 252 case histories were documented retrospectively in this new system. The results of the first showed a pretty good discrimination between the described groups of different diagnoses (sensitivity between 71 and 100 percent for all groups with the exception of metabolic joint diseases, specificity between 75 and 94 percent). The second--retrospective--documentation and diagnostic support showed much weaker results (sensitivity for major groups 74-76 percent). The reasons for the different outcomes are discussed: On the one hand, signs and symptoms from case reports could not be transferred completely in the new documentation, as some findings retrospectively could not be defined sharp enough. On the other hand the study showed, that the sensitivity of well defined disorders as inflammatory joint diseases (exp. rheumatoid arthritis) reaches almost 100 percent, whereas it is as low as 50 percent in some other diseases (e.g. gout) whose characteristic findings and symptoms are suppressed by treatment (drug medication) in many cases. The results show that computer based documentation of rheumatic diseases facilitates the systematized and standardised documentation of patient data. However, a few modifications of the knowledge base as well as the knowledge representation formalisms are necessary to achieve a better performance in differential diagnostic support.

Arthritis, Rheumatoid↗
PubMed2002

Consistency and reproducibility of attribute extraction by different machine learning systems.

Machine learning systems as tools for intelligent data analysis are used for extracting attributes relevant for prediction of defined outcomes. The aim of the paper was to compare two machine learning (ML) systems and propose method for intra- and inter-testing of consistency and reproducibility in attributes extracted from real dataset. See5 and FMLS extracted relevant attributes from real medical dataset. Comparison of results of both systems shows that: accuracy and sensitivity are nearly the same for both FMLS and See5 when FMLS was forced by attributes extracted by See5, and little bit lower when FMLS used its own extracted attributes; specificity is nearly the same for both FMLS and See5 when FMLS was forced by attributes extracted by See5, and much more higher when FMLS used its own extracted attributes; both of ML systems show intra-testing consistency and does not show any inter-testing consistency.

Artificial Intelligence↗
PubMed1991

Applications of expert computer systems.

An expert system is a computer program which uses artificial intelligence to make logical decisions on the basis of input data. The rules the system uses to make its decisions are called heuristics which can be provided in the form of IF . . . THEN statements, or they can be learned by the system from examples. The term "expert" is used because the rules or examples come from human experts and the program is considered to have captured their expertise. Currently there are many expert systems in business and medical use but few, if any, are used in optometry. The rule-oriented nature of many ophthalmic procedures suggests a future role for these systems, but their cost-effectiveness may not yet be favorable enough to justify development of expert systems for optometric practice.

Diagnosis, Computer-Assisted↗
PubMedDate not supplied

Systemic organ assessment using computerized profiles.

Clinical monitoring of isolated parameters may lead to erroneous conclusions or conflicting speculations about the patient's general physiological status. Data derived from two or three primary parameters may provide a better understanding of the patient's overall status. Four automated profiles have been designed to aid in the management of critically ill patients. Data obtained from simple blood and urinary tests are used to compute parameters for various physiologic, renal, respiratory, and metabolic functions. The derived data are plotted in hard-copy form. A sequential series of each profile assists the physician in making intelligent and rapid therapeutic decisions and in evaluating the effectiveness of this therapy.

Computers↗
PubMed1993

Finding relevant biomolecular features.

Many methods for analyzing biological problems are constrained by problem size. The ability to distinguish between relevant and irrelevant features of a problem may allow a problem to be reduced in size sufficiently to make it tractable. The issue of learning in the presence of large numbers of irrelevant features is an important one in machine learning, and recently, several methods have been proposed to address this issue. A combination of machine learning approaches and statistical analysis methods can be used to identify a set of relevant attributes for currently intractable biological problems. We call our framework F/I/E (Focus-Induce-Extract). As an example of this methodology, this paper reports on the identification of the features of mutations in collagen that are likely to be relevant in the bone disease Osteogenesis imperfecta.

Amino Acid Sequence↗
PubMed1990

Eliciting principles of hazard identification from experts.

National experts in the field of developmental toxicology were interviewed in order to elicit the principles, or rules-of-thumb, they use in determining if a compound or agent is likely to be a developmental hazard during pregnancy. Several levels of individual and cumulative consensus activity were carried out that resulted in consensus in 71 rules and partial consensus in an additional 24 rules of 145 rules initially elicited. Rules could be divided generically into those affecting the expert's confidence in a piece of scientific evidence and those determining the weight of importance of that evidence in deciding about hazard identification. Topically, the rules also divided into those about the general nature or characteristics of a compound, animal studies testing for an effect of the compound, and human reports about the presence of absence of developmental effects associated with the compound. Several conclusions about the methodology include the following: 1) expert systems must be based on the knowledge of more than one expert; 2) considerable human effort is expended in evaluating the certainty of scientific evidence before combining the evidence for problem solving; 3) how experts use evidence of different degrees of uncertainty in their decisions is a major area that is yet to be determined and that may greatly affect subsequent efforts in artificial intelligence; and 4) knowledge elicitation by interview has limitations but is a workable methodology for medical decision making.

Animal Testing Alternatives↗
PubMed2000

Production of diagnostic rules from a neurotologic database with decision trees.

A decision tree is an artificial intelligence program that is adaptive and is closely related to a neural network, but can handle missing or nondecisive data in decision-making. Data on patients with Meniere's disease, vestibular schwannoma, traumatic vertigo, sudden deafness, benign paroxysmal positional vertigo, and vestibular neuritis were retrieved from the database of the otoneurologic expert system ONE for the development and testing of the accuracy of decision trees in the diagnostic workup. Decision trees were constructed separately for each disease. The accuracies of the best decision trees were 94%, 95%, 99%, 99%, 100%, and 100% for the respective diseases. The most important questions concerned the presence of vertigo, hearing loss, and tinnitus; duration of vertigo; frequency of vertigo attacks; severity of rotational vertigo; onset and type of hearing loss; and occurrence of head injury in relation to the timing of onset of vertigo. Meniere's disease was the most difficult to classify correctly. The validity and structure of the decision trees are easily comprehended and can be used outside the expert system.

Databases, Factual↗
PubMed2026

Artificial Intelligence and Machine Learning Applications in Fibromuscular Dysplasia: Transforming Diagnosis, Risk Stratification, and Clinical Decision-Making.

Fibromuscular dysplasia (FMD) is a non-atherosclerotic vascular disorder with heterogeneous presentations, making diagnosis and management highly dependent on imaging and clinical expertise. This narrative review examines how artificial intelligence (AI) and machine learning (ML) are transforming FMD care. AI-enhanced imaging, particularly convolutional neural network-based analysis, improves detection of the characteristic "string-of-beads" pattern on CT angiography, magnetic resonance angiography, and ultrasound, although FMD-specific validation remains limited. ML models facilitate risk stratification, prediction of disease progression, and early identification of complications such as aneurysms and stroke by integrating clinical, imaging, and genomic data. AI-driven clinical decision support systems further enable personalized treatment selection through pharmacogenomic insights and robot-assisted interventions. Despite promising real-world applications, challenges persist, including limited large-scale datasets, workflow integration, regulatory barriers, and algorithmic bias affecting underrepresented populations. Future advances in explainable AI, federated learning, and digital health integration may enable a shift toward predictive, patient-centered FMD management.

Humans↗
PubMed1994

Neural [correction of Neutral] networks for control, identification and diagnosis.

Advances in the theory and technology of artificial neural networks provide the potential for new approaches to the problems of control, identification, and diagnosis for large, complex systems. However, these approaches must be validated for specific applications before they can be exploited effectively. Because of the unique capabilities they offer, neural networks should play an important role in space exploration systems operations. After a brief introduction to neural networks is presented, some applications of neural networks to identification and control of space systems are described and discussed. They span the spectrum of relatively straightforward to rather complex applications. An explanation of how neural networks can be applied to such important tasks as fault diagnosis and accommodation is presented. Neural networks are shown to be part of the hierarchy of intelligent control where a higher order decision element monitors and supervises lower order elements for sensing and actuation.

Algorithms↗
PubMed2003

The role of soft computing in intelligent machines.

An intelligent machine relies on computational intelligence in generating its intelligent behaviour. This requires a knowledge system in which representation and processing of knowledge are central functions. Approximation is a 'soft' concept, and the capability to approximate for the purposes of comparison, pattern recognition, reasoning, and decision making is a manifestation of intelligence. This paper examines the use of soft computing in intelligent machines. Soft computing is an important branch of computational intelligence, where fuzzy logic, probability theory, neural networks, and genetic algorithms are synergistically used to mimic the reasoning and decision making of a human. This paper explores several important characteristics and capabilities of machines that exhibit intelligent behaviour. Approaches that are useful in the development of an intelligent machine are introduced. The paper presents a general structure for an intelligent machine, giving particular emphasis to its primary components, such as sensors, actuators, controllers, and the communication backbone, and their interaction. The role of soft computing within the overall system is discussed. Common techniques and approaches that will be useful in the development of an intelligent machine are introduced, and the main steps in the development of an intelligent machine for practical use are given. An industrial machine, which employs the concepts of soft computing in its operation, is presented, and one aspect of intelligent tuning, which is incorporated into the machine, is illustrated.

Algorithms↗
PubMed2000

DATA: a decision aid for management of the patient with stable angina pectoris.

OBJECTIVE: The patient with stable disabling angina must choose between bypass surgery, coronary angioplasty, or medical therapy. Estimation of comparative outcomes of these alternative therapies is difficult. DESIGN: Utilizing Artificial Intelligence, an expert-system computerized decision aid, DATA (Decision Aid for Therapy of Angina), was developed to run on a personal computer and to calculate the probability of each possible clinical event based on individual patient characteristics. For each therapeutic option, relative clinical outcomes and anticipated charges are computed. Ten patients were evaluated retrospectively by DATA and by a group of cardiologists. RESULTS: DATA agreed with the primary therapy given to all patients. The physician group underestimated the value of alternative therapies and underestimated charges for all therapies. CONCLUSIONS: This decision aid allows comparison of alternative therapies in terms of relative patient outcome and anticipated costs can be better estimated.

Angina Pectoris↗