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At least 937 records · Page 52Linked to original sources
PubMed2026

Emerging multidimensional biomarker system for cardiovascular-kidney-metabolic syndrome: from multi-omics integration to clinical artificial intelligence.

Cardiovascular-kidney-metabolic (CKM) syndrome is an emerging clinical entity that highlights the complex, bidirectional interplay among cardiovascular disease, chronic kidney disease, and metabolic disorders, representing a substantial and growing global health burden. This conceptualization marks a paradigm shift from viewing these conditions in isolation to understanding them as an interconnected disease continuum. Traditional biomarkers face significant limitations in the early detection, risk stratification, and precise management of CKM, necessitating a transition towards an integrated framework that captures its multisystem nature. This review systematically outlines an emerging multidimensional biomarker system encompassing key pathological axes such as metabolism, immuno-inflammation, oxidative stress, and biological aging, offering refined risk assessment beyond conventional metrics. The development of this system is propelled by revolutionary platforms, including accessible sampling techniques (e.g., dried blood spots), advanced in vitro models (e.g., multi-organ-on-a-chip), and multi-omics technologies. These platforms not only facilitate a deeper dissection of the heterogeneous origins and inter-organ crosstalk in CKM but also accelerate the discovery and validation of novel biomarkers. Concurrently, artificial intelligence serves as a pivotal tool for clinical translation, effectively integrating high-dimensional data to transform complex molecular profiles into actionable clinical insights. By enabling the construction of dynamic risk prediction and decision-support systems, this review charts a pathway toward proactive, individualized, and precise prevention and management of CKM syndrome.

Humans↗
PubMedDate not supplied

Hycones: a hybrid approach to designing decision support systems.

Hycones II is a tool that facilitates the construction of hybrid connectionist expert systems to solve problems in classification. Hycones II provides a hybrid system that integrates frames with three types of neural network: the "combinatorial" neural model (CNM), the "Fuzzy ARTMAP" model, and the "Semantic ART" (SMART) model, a combination of the CNM and Fuzzy ART-MAP. We will compare the ability of these three models to solve diagnostic problems in two medical domains.

Artificial Intelligence↗
PubMed1985

Aging in the nervous system.

This review of the effects of aging on the nervous system covers functional changes, such as slowing of reaction time; behavioral and psychological changes; physiological changes in the brain; and pathological changes. The author discusses the sensory processes in relation to the neurological examination. Dementia due to Alzheimer's disease, normal pressure hydrocephalus, and other causes is also discussed.

Aged↗
PubMed1989

A computer based handbook and atlas of pathology.

The Diagnostic Encyclopaedia Workstation (DEW) is a computerized handbook of pathology intended for use in diagnostic practice. It consists of a combination of a personal computer (PC), a video disc player (VDP), for which a specially developed disc is used, two monitors, a mouse and software. The hard disc of the computer contains textual information on diagnoses in categories such as macroscopy, common histology, immunopathology, clinical observations and prognosis and case histories. This information is frequently illustrated by pictures on a video disc which is automatically addressed by the computer software. All pictures, at present some 3000, pertain to case histories which are included in the system. Also integrated are classification aids in two categories: diagnostic criteria and differential diagnosis. Advantages of DEW over the use of conventional manuals are 1) the extensive volume of text, 2) the large number of high quality illustrations, 3) the immediate access to cross references and 4) the potential for continuous revision.

Anatomy, Artistic↗
PubMed2005

Online selection of discriminative tracking features.

This paper presents an online feature selection mechanism for evaluating multiple features while tracking and adjusting the set of features used to improve tracking performance. Our hypothesis is that the features that best discriminate between object and background are also best for tracking the object. Given a set of seed features, we compute log likelihood ratios of class conditional sample densities from object and background to form a new set of candidate features tailored to the local object/background discrimination task. The two-class variance ratio is used to rank these new features according to how well they separate sample distributions of object and background pixels. This feature evaluation mechanism is embedded in a mean-shift tracking system that adaptively selects the top-ranked discriminative features for tracking. Examples are presented that demonstrate how this method adapts to changing appearances of both tracked object and scene background. We note susceptibility of the variance ratio feature selection method to distraction by spatially correlated background clutter and develop an additional approach that seeks to minimize the likelihood of distraction.

Algorithms↗
PubMed1996

A real time patient monitoring system on the World Wide Web.

World Wide Web (Web) technology has become increasingly popular and successful, because it uses standard communication protocols and Hyper Text Markup Language(HTML), and is supported on multiple platforms. Innovations such as server push, secure socket layer and Java make it possible to use the Web as the basis for creating monitoring systems of dynamic processes. This paper presents a project, whose goal is to develop a Web based Intelligent on-line Monitoring system for Intensive Care Units (IMI). IMI has been tested and evaluated in an intensive care unit since October 1995, and the results are promising. After presenting the motivations of using Web technology, we present the system structure and the functionality of IMI as well as the testing and evaluation results. Security issues are also addressed.

Computer Communication Networks↗
PubMed1993

Computational logic: a method for formal analysis of the ICU knowledge base.

The object of clinical computing is to support the making of clinical decisions. This takes place in an integrated context of care that involves processes of three different kinds: (1) the physiologic processes in the patient, (2) the processes of physiologic data as stored in, and moved within, a distributed computing system, and (3) the therapeutic and decision-making processes of the clinical staff. The clinical computing system is complex, and as we press it further to work as a partner in the complete care context, it tends to become unmanageably so. In this article, we try to suggest how one might begin using a formal mathematical theory, called 'computational logic,' to give theoretical analyses that might help one to understand the computing system and thereby to try to increase its ability to meet demands on it, and might help one to understand the care context as a whole. We examine the three levels, one at a time, looking for structural analogies between them, and trying to identify the ways that they are linked together. Computational logic provides us with a common set of notions for doing so.

Artificial Intelligence↗
PubMed1991

Modeling all dialogue system participants to generate empathetic responses.

A dialogue system between an expert system and its users is described which combines two recent hypotheses. First, that the dialogue system should explicitly model both the person directly interacting with the dialogue system (the agent) and the person reasoned about by the expert system (the patient) in order to communicate meaningfully with both people. Second, that a dialogue system can model the domain-related beliefs, preferences and concerns of both its users and generate responses empathetic to both. This dialogue system is called SERUM, standing for 'System for Empathetic Responses with User Models.' SERUM generates natural-language responses about attribute values of domain objects, via three transformations. First, the system converts properties of the agent and patient, and domain knowledge, into a pragmatic objective like empathy. Second, SERUM converts the pragmatic objectives into surface structure cues like object emphasis and level of technicality. Finally, SERUM converts the surface structure cues to realize text that is natural, appropriately technical and emotionally empathetic. SERUM is demonstrated in describing tests and treatments for lung disease in AIDS patients, a sensitive domain where empathetic responses may be needed.

Algorithms↗
PubMed2000

Hierarchical rule-based monitoring and fuzzy logic control for neuromuscular block.

OBJECTIVE: The important task for anaesthetists is to provide an adequate degree of neuromuscular block during surgical operations, so that it should not be difficult to antagonize at the end of surgery. Therefore, this study examined the application of a simple technique (i.e., fuzzy logic) to an almost ideal muscle relaxant (i.e., rocuronium) at general anaesthesia in order to control the system more easily, efficiently, intelligently and safely during an operation. METHODS: The characteristics of neuromuscular blockade induced by rocuronium were studied in 10 ASA I or II adult patients anaesthetized with inhalational (i.e., isoflurane) anaesthesia. A Datex Relaxograph was used to monitor neuromuscular block. And, ulnar nerve was stimulated supramaximally with repeated train-of-four via surface electrodes at the wrist. Initially a notebook personal computer was linked to a Datex Relaxograph to monitor electromyogram (EMG) signals which had been pruned by a three-level hierarchical structure of filters in order to design a controller for administering muscle relaxants. Furthermore, a four-level hierarchical fuzzy logic controller using the fuzzy logic and rule of thumb concept has been incorporated into the system. The Student's test was used to compare the variance between the groups. p < 0.05 was considered significant. RESULTS: The system achieved stable control of muscle relaxation with a mean T1% error of -0.19 (SD 0.66) % accommodating a range in mean infusion rate (MIR) of 0.21-0.49 mg x kg(-1) x h(-1). When these results were compared with our previous ones using the same hierarchical structure applied to mivacurium, less variation in the T1% error (p < 0.05) but the same variation in infusion rate were observed. The controller activity of these two drugs showed no significant difference (p > 0.5). However, the consistent medium coefficient variance (CV) of the MIR of both rocuronium (i.e., 36.13 (SD 9.35) %) and mivacurium (i.e., 34.03 (SD 10.76) %) indicated a good controller activity. CONCLUSIONS: The results showed that a hierarchical rule-based monitoring and fuzzy logic control architecture can provide stable control of neuromuscular block despite the considerable individual variation in neuromuscular block required among patients. Also, there was less variation in T1% error compared with that of previous study on mivacurium. Meanwhile, the consistent medium CV of the MIR of both rocuronium and mivacurium indicated a good controller activity which is able to withstand noise, diathermy effect, artifacts and surgical disturbances.

Adolescent↗
PubMed2001

Learning from noisy information in FasArt and FasBack neuro-fuzzy systems.

Neuro-fuzzy systems have been in the focus of recent research as a solution to jointly exploit the main features of fuzzy logic systems and neural networks. Within the application literature, neuro-fuzzy systems can be found as methods for function identification. This approach is supported by theorems that guarantee the possibility of representing arbitrary functions by fuzzy systems. However, due to the fact that real data are often noisy, generation of accurate identifiers is presented as an important problem. Within the Adaptive Resonance Theory (ART), PROBART architecture has been proposed as a solution to this problem. After a detailed comparison of these architectures based on their design principles, the FasArt and FasBack models are proposed. They are neuro-fuzzy identifiers that offer a dual interpretation, as fuzzy logic systems or neural networks. FasArt and FasBack can be trained on noisy data without need of change in their structure or data preprocessing. In the simulation work, a comparative study is carried out on the performances of Fuzzy ARTMAP, PROBART, FasArt and FasBack, focusing on prediction error and network complexity. Results show that FasArt and FasBack clearly enhance the performance of other models in this important problem.

Artifacts↗
PubMed2000

TelFam: a telemedicine system for the family doctor practices.

The paper presents the TelFam telemedicine service system to support the daily practice of family doctors. The TelFam system covers two basic telemedicine services: telemonitoring of patients, and teleconsulting. The system architecture is comprised by three main components: server in the teleconsulting center, the doctor workstations and patient's home units. During a consultation a general practitioner and a specialist in the teleconsulting center exchange patient information, opinions, treatment suggestions and perform cooperative work on the same data in the WYSIWIS mode. Telemonitoring covers patient's vital signs (ECG, respiration, body temperature, SpO2, blood pressure) which next are processed to assess the patient's state.

Artificial Intelligence↗
PubMedDate not supplied

[Collective properties of the mutual-learned neuronal net systems in the information field].

Model of neural networks system, in which networks interact by transmission and associative recognition of signals, is studied by computer simulation and qualitative approach. System behavior depends on the value of learning parameter epsilon, which determines the weight of writing in memory of each network every transmissible signal. Two different regimes are found: regime of auto-governed behavior, which depends only on initial networks characteristics, and regime of collective recognition of initial signal in form of a certain stable signals cycle. Analogy of this model and Aigen's hypercycle, the problem of creation of some new information in this model are discussed, too.

Artificial Intelligence↗
PubMed1995

Acts and knowledge management in an open hospital information system.

This communication presents the management of a customizable patient medical dossier as implemented in the NUCLEUS project. After a brief reminder of the NUCLEUS hospital information system features, we discuss the two main innovative concepts underlying the intelligent management of the integrated patient dossier: acts management and knowledge management. The functionalities related to patient dossier are then introduced.

Artificial Intelligence↗
PubMed2006

Rule generation for protein secondary structure prediction with support vector machines and decision tree.

Support vector machines (SVMs) have shown strong generalization ability in a number of application areas, including protein structure prediction. However, the poor comprehensibility hinders the success of the SVM for protein structure prediction. The explanation of how a decision made is important for accepting the machine learning technology, especially for applications such as bioinformatics. The reasonable interpretation is not only useful to guide the "wet experiments," but also the extracted rules are helpful to integrate computational intelligence with symbolic AI systems for advanced deduction. On the other hand, a decision tree has good comprehensibility. In this paper, a novel approach to rule generation for protein secondary structure prediction by integrating merits of both the SVM and decision tree is presented. This approach combines the SVM with decision tree into a new algorithm called SVM_ DT, which proceeds in three steps. This algorithm first trains an SVM. Then, a new training set is generated through careful selection from the output of the SVM. Finally, the obtained training set is used to train a decision tree learning system and to extract the corresponding rule sets. The results of the experiments of protein secondary structure prediction on RS126 data set show that the comprehensibility of SVM_DT is much better than that of the SVM. Moreover, the generalization ability of SVM_DT is better than that of C4.5 decision trees and is similar to that of the SVM. Hence, SVM_DT can be used not only for prediction, but also for guiding biological experiments.

Algorithms↗
PubMed1995

Objects and domains for managing medical data and knowledge.

This paper assesses the object-oriented data paradigm, and describes an algebraic approach which permits the generation of data objects from relational data, based on the knowledge captured in a formal Entity-Relationship model, the Structural Model. The advantage is that now objects can be created that satisfy a variety of particular views, as long as the hierarchies represented by the views are subsumed in the network represented by the overall structural model. The disadvantage of creating view-objects dynamically is that the additional layering has performance implications, so that the speedup expected from object-oriented databases versus relational databases, due to their hierarchical object storage, cannot be realized. However, scalability of systems is increased since large systems tend to have multiple objectives, and hence often multiple valid hierarchical views over the data. This approach has been implemented in the Penguin project, and recently some commercial successors are emerging. In truly large systems new problems arise, namely that now not only multiple views will exist, but also that the domains to be covered by the data will be autonomous and hence heterogeneous. One result is that ontologies associated with the multiple domains will differ as well. This paper proposes a knowledge-based algebra over the ontologies, so that the domain knowledge can be partitioned for maintenance. Only the articulation points, where the domains intersect, have to be agreed upon as defined by matching rules which define the shared ontologies.

Artificial Intelligence↗