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PubMed2005

Decision support system for the diagnosis of schizophrenia disorders.

Clinical decision support systems are useful tools for assisting physicians to diagnose complex illnesses. Schizophrenia is a complex, heterogeneous and incapacitating mental disorder that should be detected as early as possible to avoid a most serious outcome. These artificial intelligence systems might be useful in the early detection of schizophrenia disorder. The objective of the present study was to describe the development of such a clinical decision support system for the diagnosis of schizophrenia spectrum disorders (SADDESQ). The development of this system is described in four stages: knowledge acquisition, knowledge organization, the development of a computer-assisted model, and the evaluation of the system's performance. The knowledge was extracted from an expert through open interviews. These interviews aimed to explore the expert's diagnostic decision-making process for the diagnosis of schizophrenia. A graph methodology was employed to identify the elements involved in the reasoning process. Knowledge was first organized and modeled by means of algorithms and then transferred to a computational model created by the covering approach. The performance assessment involved the comparison of the diagnoses of 38 clinical vignettes between an expert and the SADDESQ. The results showed a relatively low rate of misclassification (18-34%) and a good performance by SADDESQ in the diagnosis of schizophrenia, with an accuracy of 66-82%. The accuracy was higher when schizophreniform disorder was considered as the presence of schizophrenia disorder. Although these results are preliminary, the SADDESQ has exhibited a satisfactory performance, which needs to be further evaluated within a clinical setting.

Decision Support Systems, Clinical↗
PubMed2006

How do experts recognize schizophrenia: the role of the disorganization symptom.

OBJECTIVE: Research on clinical reasoning has been useful in developing expert systems. These tools are based on Artificial Intelligence techniques which assist the physician in the diagnosis of complex diseases. The development of these systems is based on a cognitive model extracted through the identification of the clinical reasoning patterns applied by experts within the clinical decision-making context. This study describes the method of knowledge acquisition for the identification of the triggering symptoms used in the reasoning of three experts for the diagnosis of schizophrenia. METHOD: Three experts on schizophrenia, from two University centers in Sao Paulo, were interviewed and asked to identify and to represent the triggering symptoms for the diagnosis of schizophrenia according to the graph methodology. RESULTS: Graph methodology showed a remarkable disagreement on how the three experts established their diagnosis of schizophrenia. They differed in their choice of triggering-symptoms for the diagnosis of schizophrenia: disorganization, blunted affect and thought disturbances. CONCLUSIONS: The results indicate substantial differences between the experts as to their diagnostic reasoning patterns, probably under the influence of different theoretical tendencies. The disorganization symptom was considered to be the more appropriate to represent the heterogeneity of schizophrenia and also, to further develop an expert system for the diagnosis of schizophrenia.

Decision Support Systems, Clinical↗
PubMed2007

Neuro-fuzzy technology as a predictor of parathyroid hormone level in hemodialysis patients.

Measuring the plasma parathyroid hormone (PTH) concentration is crucial to evaluate renal bone disease in patients with renal failure. Although frequent measurement is needed to avoid inadequate prescription of phosphate binders and vitamin D preparations, artificial intelligence can repeatedly perform the forecasting tasks and may be a satisfactory substitute for laboratory tests. Neuro-fuzzy technology represents a promising forecasting application in clinical medicine. We therefore constructed a coactive neuro-fuzzy inference system (CANFIS) to predict plasma PTH concentrations in hemodialysis patients. The CANFIS was constructed with clinical parameters (patient age, plasma albumin, calcium, phosphorus, alkaline phosphatase, and calcium-phosphorus product) from a cohort of hemodialysis patients, and plasma PTH concentration measured by radioimmunoassay (RIA) was the supervised outcome. The accuracy of the CANFIS was prospectively compared with RIA in another hospital. Plasma PTH concentrations measured by RIA and predicted by CANFIS were 179.04 +/- 38.18 ng/l and 179.34 +/- 37.76 ng/l, respectively (p = 0.15). The CANFIS was able to precisely estimate plasma PTH concentrations in hemodialysis patients. These results suggest that the neuro-fuzzy technology, based on limited clinical parameters, is an excellent alternative to RIA for accurately predicting plasma PTH concentration in hemodialysis patients.

Aged↗
PubMedDate not supplied

Qualitative profiles of disability.

This study identified profiles of functional disability (FD) paralleled by increasing levels of disability. We assessed 96 subjects using the World Health Organization Disability Assessment Schedule II (WHODAS II). Clustering Based on Rules (ClBR) (a hybrid technique of Statistics and Artificial Intelligence) was used in the analysis. Four groups of subjects with different profiles of FD were ordered according to an increasing degree of disability: "Low," self-dependent subjects with no physical or emotional problems; "Intermediate I," subjects with low or moderate physical and emotional disability, with high perception of disability; "Intermediate II," subjects with moderate or severe disability concerning only physical problems related to self-dependency, without emotional problems; and "High," subjects with the highest degree of disability, both physical and emotional. The order of the four classes is paralleled by a significant difference (<0.001) in the WHODAS II standardized global score. In this paper, a new ontology for the knowledge of FD, based on the use of ClBR, is proposed. The definition of four classes, qualitatively different and with an increasing degree of FD, helps to appropriately place each patient in a group of individuals with a similar profile of disability and to propose standardized treatments for these groups.

Disability Evaluation↗
PubMed1989

Connectionist models of conditioning: A tutorial.

Models containing networks of neuron-like units have become increasingly prominent in the study of both cognitive psychology and artificial intelligence. This article describes the basic features of connectionist models and provides an illustrative application to compound-stimulus effects in respondent conditioning. Connectionist models designed specifically for operant conditioning are not yet widely available, but some current learning algorithms for machine learning indicate that such models are feasible. Conversely, designers for machine learning appear to have recognized the value of behavioral principles in producing adaptive behavior in their creations.

Journal Article↗
PubMed2026

Machine Learning in Hyperlipidaemia Research: Screening and Experimental Insights into Lipid Metabolism Modulators.

Hyperlipidemia, characterized by elevated blood lipid levels, represents a major global health concern due to its strong association with cardiovascular disease, diabetes, and metabolic syndrome. While current therapies - such as statins, fibrates, bile acid sequestrants, and PCSK9 inhibitors - are effective in controlling hyperlipidemia, they are often associated with adverse effects, potential drug resistance, and suboptimal efficacy in certain patient populations. All of the above underscore the urgent need for safer and more effective therapeutic alternatives. Among the major molecular targets involved in the regulation of lipid metabolism are HMG-CoA reductase, PCSK9, peroxisome proliferator-activated receptors (PPARs), cholesteryl ester transfer protein (CETP), and nuclear receptors, including the liver X receptor (LXR) and farnesoid X receptor (FXR), which are also targets for future antihyperlipidemic drug development. Recent advancements in artificial intelligence (AI) and machine learning (ML) have significantly transformed and accelerated drug discovery by enabling the processing of vast amounts of genomic, proteomic, and chemical data. Furthermore, ML tools such as quantitative structure-activity relationship (QSAR) modelling, deep learning, random forest, and support vector machines (SVM) have proven predictive and effective in identifying novel lipid metabolism modulators, thereby enhancing the efficacy and accuracy of virtual screening. Meanwhile, molecular docking has become an integral part of structure-based drug design (SBDD), and software such as AutoDock, Glide, and GOLD have proven effective in generating accurate ligand-target docking models. Molecular docking, together with ML-based approaches, enables the identification of potent and selective drug candidates. Overall, the combination of ML and molecular docking offers an efficient and accurate platform for antihyperlipidemic drug discovery, helping to overcome the limitations of currently available therapeutic strategies.

HMG-CoA reductase↗
PubMed2026

The Progress of Gout Prediction Models Based on Multi-source Data.

INTRODUCTION: Gout, a highly serious inflammatory disease that is caused by monosodium urate crystals, is becoming an increasingly significant health concern. Artificial Intelligence and multi-omics-based research have made significant gains for the early detection and prevention of gout based on diverse approaches. This review intends to summarize current advances in forecasting gout susceptibility and gout-related symptoms, evaluate the predictive efficacy of different features, and ascertain which clinical and omics characteristics are most effective in these prediction models. METHODS: We explored the PubMed database after 2010 using keywords such as "gout", "predictive model", "risk prediction", and "machine learning", and confined our search to Englishlanguage articles. The original peer-reviewed research articles that developed gout models were selected. Research that was not original or lacked internal validation was excluded. RESULTS: Clinical features, genomics, microbiomics, radiomics, and metabolomics have been utilized to construct models related to gout and have demonstrated excellent predictive performance. Multisource data prediction models usually exhibit better effectiveness. DISCUSSION: Gout-oriented models performed excellently in predictive performance but present limitations in certain clinical and omics domains. However, if they are to affect actual patient care, they must overcome some external confirmation roadblocks and the fiscal and practical implications they will face ahead of time. CONCLUSION: This review indicates that clinical and multi-omics models of gout are significant instruments for clinical decision-making. The models constructed in these studies may be crucial for the treatment of gout and its practical benefits.

Gout↗
PubMed2026

Immunoinformatics Approach for Optimization of Targeted Vaccine Design: New Paradigm in Clinical Trials and Healthcare Management.

INTRODUCTION: The immunoinformatics approach combines bioinformatics and computational tools, offering a revolutionary method for improving vaccine development by analyzing immune responses at the molecular level. Immunoinformatics enables the creation of customized vaccines designed for specific infections or cancer cells. OBJECTIVE: The primary objective of immunoinformatics is to enhance the vaccine development process by predicting and boosting the body's immune response. It aims to identify potential immunogenic epitopes and biomarkers that are important for creating vaccines with greater specificity and efficacy, especially when dealing with large-scale data. METHODS: Immunoinformatics utilizes a combination of proteomic, genomic, and epigenomic data, as well as machine learning algorithms and artificial intelligence techniques. These tools predict how various immunological components, e.g., T-cell and B-cell epitopes, interact with the immune system. This approach allows researchers to avoid traditional trial-and-error methods, enabling the efficient identification of potential vaccine candidates. Additionally, personalized vaccines can be developed by considering individual genetic and immunological characteristics. RESULTS: The use of immunoinformatics techniques accelerates the screening of vaccine candidates, enhances patient stratification, and optimizes formulations for clinical trials. This approach has been shown to improve vaccine safety, efficacy, and development speed. It also holds promise for managing healthcare on a large scale by producing vaccines tailored to specific populations, thereby improving the overall effectiveness of vaccination programs. CONCLUSION: Immunoinformatics represents a transformative approach to vaccine research, improving clinical trial efficiency and enabling the development of more reliable, flexible, and personalized vaccines. This approach has the potential to significantly enhance global healthcare outcomes by accelerating the vaccine development process and optimizing vaccination strategies.

Immunoinformatics↗
PubMed2004

Machine learning based pattern recognition applied to microarray data.

MOTIVATION: Microarrays have allowed the expression level of thousands of genes or proteins to be measured simultaneously. Data sets generated by these arrays consist of a small number of observations (e.g., 20-100 samples) on a very large number of variables (e.g., 10,000 genes or proteins). The observations in these data sets often have other attributes associated with them such as a class label denoting the pathology of the subject. Finding the genes or proteins that are correlated to these attributes is often a difficult task since most of the variables do not contain information about the pathology and as such can mask the identity of the relevant features. We describe a genetic algorithm (GA) that employs both supervised and unsupervised learning to mine gene expression and proteomic data. The pattern recognition GA selects features that increase clustering, while simultaneously searching for features that optimize the separation of the classes in a plot of the two or three largest principal components of the data. Because the largest principal components capture the bulk of the variance in the data, the features chosen by the GA contain information primarily about differences between classes in the data set. The principal component analysis routine embedded in the fitness function of the GA acts as an information filter, significantly reducing the size of the search space since it restricts the search to feature sets whose principal component plots show clustering on the basis of class. The algorithm integrates aspects of artificial intelligence and evolutionary computations to yield a smart one pass procedure for feature selection, clustering, classification, and prediction.

Algorithms↗
PubMed2005

Emerging technologies for cancer prevention and other population health challenges.

Emerging technologies, such as information and communication technologies (including future versions of the Internet), microelectromechanical systems, nanotechnologies, genomics, robotics, artificial intelligence, and sensors, provide enormous opportunities for enhancing health and quality of life. Population health technologies (PHTs) encompass the various applications of emerging technologies to improve the health of populations and communities. These technologies may change many population health paradigms, including those related to cancer prevention and control. In the future, emerging technologies will allow true customization of health communication to individuals, and existing tailoring approaches will be considered very crude. Environmental monitoring systems based on emerging technologies could also provide real-time information that health officials and community residents could use immediately to ameliorate potential carcinogenic or unhealthy exposures. Accelerating the application and diffusion of emerging technologies to population health challenges will require a multipronged approach, including new transdisciplinary programs, increased funding, supportive infrastructure, and policy changes.

Communication↗
PubMed2026

Adenoviral Vectors in Gene Therapy: A Detailed Overview.

Adenoviral vectors (AdVs) represent one of the most extensively researched platforms in the realm of gene therapy, providing advantages such as high transduction efficiency, large transgene capacity, and broad tropism. This review provides a detailed and structured overview of AdVs, highlighting their biology, gene delivery mechanisms, clinical applications, and challenges limiting their broader therapeutic applicability. The study also explores recent progress in vector engineering, such as rare serotypes, capsid modifications, third-generation vectors, as well as strategies for immune modulation and toxicity reduction. AdVs are used in therapies for genetic disorders, oncology, and vaccinology, alongside innovations such as CRISPR-Cas9, nanotechnology, and artificial intelligence design. Nevertheless, persistent hurdles, including vector immunogenicity, hepatotoxicity, scalability, and the lack of durable expression, prevent widespread clinical use. This review consolidates current knowledge and presents a future perspective on how AdVs may evolve as powerful, adaptable, and precise tools in modern gene therapy. By contextualizing strengths and unresolved challenges, this work aims to give researchers and clinicians a balanced foundation for evaluating their future roles in translational medicine.

Humans↗
PubMed2005

Latest advances in antiaging medicine.

Rapid progress is being made in our ability to modify the aging process. Rather than serving as a period of debility and decreasing health, for many people, the later years of life are becoming a period of continued productivity, independence and good health. Progress is also being made in increasing average lifespan. The leading causes of death (cardiovascular disease, cancer, lung disease, diabetes) are the end result of decades-long processes. With current knowledge, it is possible to delay the onset of these diseases. This can be assisted by lifestyle choices incorporating healthful diet, exercise, stress management, and nutritional supplementation. Emerging genomics technology will allow individuals to establish personalized programs, while early detection of heart disease and cancer will contribute to longevity. Biotechnological therapies involving stem cells, recombinant DNA, proteomics, therapeutic cloning and gene-based therapies are expected to play major roles in promoting successful aging. We are at the threshold of artificial intelligence (AI) and nanotechnology (NT). AI will allow for a merging of our biological thinking with advanced forms of non-biological intelligence to vastly expand our ability to think, create and experience. NT will ultimately allow us to build devices able to build molecules much like our current cellular machinery does, one atom at a time. It is the goal of today's antiaging medicine to forestall disease and aging long enough for people to utilize the powerful biotechnology and nanotechnology therapies that will be developed over the decades ahead. These future therapies have the potential to greatly extend longevity.

Aging↗
PubMed1992

Description, evaluation and clinical decision making according to various fetal heart rate patterns. Inter-observer and regional variability.

At 10 Danish obstetrical departments, 116 residents (42 senior and 74 junior) participated in a study to assess inter-observer and regional variability in the description and evaluation of and clinical decision regarding 11 fetal heart rate patterns. The 11 traces included normal as well as pathological patterns, and normal as well as clinically asphyxiated babies. Five antepartum and six intrapartum patterns were included. A total of 1,276 descriptions and evaluations were obtained. The degree of agreement in description of fetal heart rate changes was high regarding the baseline and the presence of silent or sinusoidal pattern (87-94% on an arbitrary 0-100% scale), and low regarding the assessment of variability and type of deceleration (50-72%). The degree of agreement in interpreting heart rate patterns was 59% (on an arbitrary 0-100% scale). Senior residents generally interpreted the changes as indicative of less serious fetal stress than did their junior colleagues, explaining why junior residents 30% more frequently than their older colleagues found an indication for Cesarean section. Relatively low regional inter-observer agreement scores were primarily due to low agreement between departments, especially between departments far apart. It is concluded that we still need a scientific clarification of which specific heart rate changes are the best predictors of fetal stress. Artificial intelligence programs for interpreting fetal cardiotocograms and ECG signals constitute one promising prospect.

Cesarean Section↗
PubMedDate not supplied

'Carrusel': an expert system for vestibular diagnosis.

Neuro-otology is the only field of otolaryngology in which diagnoses are mainly deductive. Although many technological advances have indeed occurred, the patient history still remains the most important part of the evaluation of a patient complaining of vertigo and/or dizziness. The use of artificial intelligence methods as an aid for the solution of clinical problems is not new. "Carrusel" is a Prolog build-up expert system dealing with the diagnosis of vestibular disorders that achieves a success rate of 97% when compared to the human experts involved in its design.

Expert Systems↗
PubMed1997

Neural networks in neurotologic expert systems.

Artificial intelligence donates new possibilities to neurotologic research. Neural networks are a computer-based reasoning method which can be applied in expert systems created for clinical decision support. Neural networks have been used in medical imaging, in medical signal processing and to analyze both clinical and laboratory data. Principally, neural networks simulate the function of the brain. They have to be taught to make correct decisions from the input data. This learning process can be either supervised or unsupervised. The decision making is based on mathematical transformations and it occurs on a hidden level. Calculations are made on parallel manner and the decision making simulates pattern recognition method. Neural networks suit well in medical problems which cannot be defined in simple rules. A drawback of neural networks is that the decisions are irrational and cannot be motivated to the user. Another problem is neural networks' difficulty to handle incomplete input data, i.e., how to define some default or expected values for unknown input parameters. In a complex medical area, which would require multilayered neural networks, the neural networks require a large amount of solved cases for the learning process. In our experience neural networks seem not suitable for diagnosing vertigo and a better choice would be either case-based reasoning or possibly genetic algorithms or a combination of these.

Diagnosis, Computer-Assisted↗
PubMed1986

Mental phenomena as changes of state in a finite-state machine.

This paper examines the classical functions of the psyche--cognition, affection and conation--from a phenomenological viewpoint and finds them to be defined each in terms of the other. It is held that this circularity of definition reflects the fundamental unity of conscious experience and, as a consequence, that the search for the biological substrate underlying individual functions too readily degenerates into a morphological and biochemical phrenology. An alternative approach, based on considerations from the field of artificial intelligence, is discussed. This approach provides a description of mental phenomena as changes of state in a finite-state machine, the next state being determined by both previous states and current inputs. This concept is shown to be compatible with the descriptions of conscious experience outlined in the phenomenology of Karl Jaspers. It is suggested that neurobiological investigations should be directed towards defining the processes by which state changes occur and further seeking to define mental pathology as aberrations of these dynamic processes.

Affect↗
PubMed1987

Computation, rhythm, and cerebral slow waves.

The trajectories of data and modelling from formerly divergent research efforts now seem to be converging to an unexpected region of the phase space of neuroscience. Computational network theory and simulation assume that temporal rhythm may be a significant parameter for the successful organization of nonlinear analog computation effected by hierarchical sets of biological neurons and of nonbiological circuitry alike. For neurobiology, the apparently chaotic rhythms of cerebral compound field potentials--the electroencephalogram (EEG) and slow waves of event related brain potentials (ERBP)--have long been a phenomenological embarrassment, of only marginal clinical utility. But recent data from molecular biophysics, nonlinear dynamics, artificial intelligence, and scalp-conducted human electrocorticography suggest a possible functional role in the serial gating of neural network computations for the familiar theta-alpha-beta rhythms of the EEG clinic.

Auditory Perception↗
PubMed1989

The introduction of personal computers into clinical cancer research in Norway.

Present and estimated future use of personal computers (PCs) in clinical cancer research was assessed after the distribution of 24 personal computers to clinicians and scientists engaged in clinical cancer research. Two questionnaires were sent to the clinicians with an interval of six months. The clinicians were divided into two main groups, 'Experts' and 'Non-experts', based on their background knowledge of and previous experience with computers. Word processing and recording of patient details were the main application fields for the PC. Requested tasks for future were the performance of statistical analyses and graphics. The clinicians, especially the Non-experts, met the following problems upon the reception of the PC: Lack of time and help to become acquainted with the PC, as well as lack of appropriate software. Half of the clinicians were reluctant to admit any usefulness of artificial intelligence for clinical cancer care. It is concluded that the introduction of personal computers will probably improve the facilities for clinical cancer research. However, clinicians need sufficient time and help to get started, and appropriate software must also be provided.

Humans↗