Integration of Heterogeneous Medical Decision Support Systems Based on Web Services

The implementation of the medical decision support system (MDSS) in clinical practice requires careful monitoring to ensure patient safety and track the performance of artificial intelligence technologies.Goal: To assess the effectiveness of the TOP-3 MDSS in outpatient clinics of the Moscow Heath Care Department.Materials and methods: The Moscow Heath Care Department monitored the TOP-3 operation between October 1, 2020 and March 03, 2024 (n = 63,809,360 people). The Hit-3 metric was used to determine if the MDSS needs retraining. An additional study involving medical experts included a retrospective analysis of data from 3,000 patients and calculation of the diagnostic agreement rate among the MDSS, doctors and the experts.Results: The monitoring demonstrated mean Hit-3 of 63.5, 64.5 and 67.7 for MDSS ver. 1, 2, and 3, respectively. In cases where the clinicians disagreed with the MDSS (n = 2000), based on the patient complaints, experts agreed with the MDSS in 80.2% of cases, with clinicians in 11.5% of cases, and established a different diagnosis in 8.3% of cases. In cases where the clinicians’ and the MDSS’s conclusions matched (n = 1000), experts approved the diagnosis in 50.4% of cases, selected an alternative diagnosis suggested by the MDSS in 37.9% of cases, and established a different diagnosis for 11.7% of patients. Conclusion: The described monitoring methodology, supplemented by expert review, allowed for a comprehensive assessment of the MDSS to be implemented in the health care system. Based on the results of the TOP-3 effectiveness assessment, it was decided that the analyzed list of data from electronic health records should be expanded, which will be implemented in the upcoming version TOP-3+.

A multilayer perceptron-based medical decision support system for heart disease diagnosis

This volume is a result of the fruitful and vivid discussions during the MedDecSup'2012 International Workshop bringing together a relevant body of knowledge, and new developments in the increasingly important field of medical informatics. This carefully edited book presents new ideas aimed at the development of intelligent processing of various kinds of medical information and the perfection of the contemporary computer systems for medical decision support. The book presents advances of the medical information systems for intelligent archiving, processing, analysis and search-by-content which will improve the quality of the medical services for every patient and of the global healthcare system. The book combines in a synergistic way theoretical developments with the practicability of the approaches developed and presents the last developments and achievements in medical informatics to a broad range of readers: engineers, mathematicians, physicians, and PhD students.

The enormous volume of medical data has resulted in the development of sophisticated systems that facilitate information search and enable clinicians in decision-making process. Driven by artificial intelligence, knowledge graphs (KGs) provide a solid structure for organising and evaluating vast volumes of diverse medical data, therefore enabling wiser question development and improved decision-making. This article presents a whole strategy for integrating knowledge graphs with artificial intelligence-based approaches to improve medical information search and decision support systems performance. Graph-based reasoning, natural language processing (NLP), and machine learning all help the proposed approach to enhance semantic comprehension. It achieves this by tying together unorganised and organised medical data sources to provide pertinent analysis. Using predictive analytics, personalised healthcare recommendations, and real-time clinical decision support, the AI-powered knowledge graph architecture helps you It achieves this by continuously shifting the relationships among illnesses, symptoms, therapies, pasts of patients. This approach also ensures that many healthcare systems may cooperate better, which facilitates information search and reduces the diagnostic error count. Including reinforcement learning techniques enhances question results depending on user interaction, therefore enhancing the search process. The results of experiments show that KGs with AI work better than traditional database-driven methods when it comes to getting medical information quickly, correctly, and usefully. The suggested method helps healthcare workers a lot by making it easier for them to get accurate, evidence-based information more quickly. This will eventually lead to better patient results. This study shows that knowledge graphs driven by AI have the ability to completely change how medical information is managed and how decisions are made. This could lead to smarter and more flexible healthcare systems.

Neural network modeling for surgical decisions on traumatic brain injury patients

Purpose Improving healthcare services by developing assistive technologies includes both the health aid devices and the analysis of the data collected by them. The acquired data modeled as a knowledge base give more insight into each patient’s health status and needs. Therefore, the ultimate goal of a health-care system is obtaining recommendations provided by an assistive decision support system using such knowledge base, benefiting the patients, the physicians and the healthcare industry. This paper aims to define the knowledge flow for a medical assistive decision support system by structuring raw medical data and leveraging the knowledge contained in the data proposing solutions for efficient data search, medical investigation or diagnosis and medication prediction and relationship identification. Design/methodology/approach The solution this paper proposes for implementing a medical assistive decision support system can analyze any type of unstructured medical documents which are processed by applying Natural Language Processing (NLP) tasks followed by semantic analysis, leading to the medical concept identification, thus imposing a structure on the input documents. The structured information is filtered and classified such that custom decisions regarding patients’ health status can be made. The current research focuses on identifying the relationships between medical concepts as defined by the REMed (Relation Extraction from Medical documents) solution that aims at finding the patterns that lead to the classification of concept pairs into concept-to-concept relations. Findings This paper proposed the REMed solution expressed as a multi-class classification problem tackled using the support vector machine classifier. Experimentally, this paper determined the most appropriate setup for the multi-class classification problem which is a combination of lexical, context, syntactic and grammatical features, as each feature category is good at representing particular relations, but not all. The best results we obtained are expressed as F1-measure of 74.9 per cent which is 1.4 per cent better than the results reported by similar systems. Research limitations/implications The difficulty to discriminate between TrIP and TrAP relations revolves around the hierarchical relationship between the two classes as TrIP is a particular type (an instance) of TrAP. The intuition behind this behavior was that the classifier cannot discern the correct relations because of the bias toward the majority classes. The analysis was conducted by using only sentences from electronic health record that contain at least two medical concepts. This limitation was introduced by the availability of the annotated data with reported results, as relations were defined at sentence level. Originality/value The originality of the proposed solution lies in the methodology to extract valuable information from the medical records via semantic searches; concept-to-concept relation identification; and recommendations for diagnosis, treatment and further investigations. The REMed solution introduces a learning-based approach for the automatic discovery of relations between medical concepts. We propose an original list of features: lexical – 3, context – 6, grammatical – 4 and syntactic – 4. The similarity feature introduced in this paper has a significant influence on the classification, and, to the best of the authors’ knowledge, it has not been used as feature in similar solutions.

In this study, a medical decision support system is presented to assist physicians in epileptic focus detection by correlating MRI and EEG data of temporal lobe epilepsy patients. By exploiting the asymmetry in the hippocampus in MRI images and using voxel-based morphometry analysis, gray matter reduction in the temporal and limbic lobes is detected, and epileptic focus prediction is realized. In addition, an epileptic focus is also determined by calculating the asymmetry score from EEG channels. Finally, epileptic focus detection was performed by associating MRI and EEG data with a decision tree. The results obtained from the proposed algorithm provide 100% overlap with the physician's finding on the EEG data. MRI and EEG correlation in epileptic focus detection was improved compared with physicians. The proposed algorithm can be used as a medical decision support system for epilepsy diagnosis, treatment, and surgery planning.

It is almost impossible to try to represent and analyze clinical data in the absence of a temporal dimension. The temporal aspect is especially important when automated decision support is used for patient care over substantial periods. This paper emphasizes the crucial role that tasks of temporal reasoning and temporal maintenance play in modern medical information and decision support systems; it also discusses the implications of providing automated support to clinicians who must perform such tasks as part of broader clinical tasks (for example, monitoring and therapy). Temporal reasoning tasks mainly involve intelligent analysis of time-oriented clinical data, and temporal maintenance tasks focus on effective storage and retrieval of these data. Both types of tasks, however, are highly relevant for such applications as patient monitoring, proper use of therapeutic guidelines, assessment of the quality of guideline use, and visualization and exploration of time-oriented biomedical data. Scientists in medical informatics should view the integration of these two areas as a major research and development goal. This paper demonstrates one approach to integration by presenting the concept of a temporal mediator, which combines temporal reasoning and temporal maintenance. Use of the temporal mediator in several clinical tasks is also presented and discussed.

Does Bayes have it? Decision Support Systems in diagnostic medicine

The health care process involves a set of massive data that can be of different types and placed in different places. The digitization of information, if properly analysed, leads to an increase in the quality of processes and services in the Healthcare sector. In this context, Machine Learning (ML) is the key enabling technology to reduce the costs of health care extracting knowledge from data. The goal of ML is to improve a system without any continuous human intervention. Medical Decision Support Systems (MDSS) are an added value on the one hand by speeding up diagnosis and care processes, on the other hand reducing the time of medical staff or specialized learning. The paper proposes a new method to enable medical decision making improving collaboration between machine learning experts and clinicians in order to model individual treatments. The idea is to adapt machine learning models and decision support systems to address the challenging task of the decision of individual treatments in order to make more efficient decision-making and more effective therapies.

Mechanical ventilation is a life-saving intervention, which, despite being routinely used in ICUs, poses the risk of causing further damage to the lung tissue if the ventilator is set inappropriately. Medical decision support systems may help in optimizing ventilator settings according to therapy goals given by the clinician. Before using the decision support algorithms in commercially available systems, extensive tests are necessary to ensure patient safety and correct decision making. Model-based patient simulators can assist in evaluating such decision support systems by creating different clinical scenarios. We propose a new Java based patient simulator that implements various models of respiratory mechanics, gas exchange and cardiovascular dynamics to form a complex patient model. The implemented models interact with one another to allow simulation of the ventilators influence on various physiological processes. Model simulations are running in real-time and simulation results can be extracted via multiple interfaces. Each of the implemented models has been validated to exhibit physiologically correct behavior. Results of the combined model system also showed to be physiologically plausible.

Parkinson’s disease is world’s leading disorder in people aged above 60. It gradually affects the entire body and the brain losses control over the organs. Early detection of Parkinson’s disease can reduce the severity of the disease. Medical diagnosis decision support system (MDDSS) assists medical experts in accurate diagnosis of Parkinson’s disease and can also be useful in telemedicine. Data mining classification algorithms can diagnose Parkinson’s disease with the best possible accuracy when optimal numbers of features are selected. This work proposes a novel algorithm BEJA-V: an evolutionary version of Jaya Algorithm for feature selection in classification using scalarization method of solving multi-objective optimization problems. The continuous Jaya Algorithm is converted into binary encoded Jaya algorithm using V-shaped transformation function. The proposed BEJA-V algorithm improves the classification accuracy and ensures optimal size of features subset.

Liver failure is a common and life-threatening disease, and its early and accurate diagnosis plays a decisive role in improving the treatment process and the quality of life of patients. The complexity of the diagnostic process and the high costs of traditional methods highlight the necessity of utilizing intelligent and efficient systems. In this research, an innovative approach based on a multilayer perceptron (MLP) optimized by a genetic algorithm is presented. First, using principal component analysis (PCA), the data dimensions were reduced, and then the MLP network was trained with weights and biases adjusted by the genetic algorithm (GA). The performance of the proposed system was evaluated on two validated datasets, ILPD and BUPA, in the MATLAB environment and compared with reference methods. The results showed that this system achieved accuracies of 99.55% on the ILPD dataset and 97.85% on the BUPA dataset, demonstrating a significant superiority over previous approaches. This remarkable improvement is due to the optimal tuning of network parameters and can pave the way for the development of a new generation of medical decision support systems with high accuracy and stability.

In areas of medical diagnosis and decision-making, several uncertainty and ambiguity shrouded situations are most often imposed. In this regard, one may well assume that intuitionistic fuzzy sets (IFS) should stand as a potent technique useful for demystifying associated with the real healthcare decision-making situations. To this end, we are developing a prototype model helpful for detecting the patients risk degree in Intensive Care Unit (ICU). Based on the intuitionistic fuzzy sets, dubbed Medical Intuitionistic Fuzzy Expert Decision Support System (MIFEDSS), the shown work has its origins in the Modified Early Warning Score (MEWS) standard. It is worth noting that the proposed prototype effectiveness validation is associated through a real case study test at the Polyclinic ESSALEMA cited in Sfax, Tunisia. This paper does actually provide some practical initial results concerning the system as carried out in real life situations. Indeed, the proposed system turns out to prove that the MIFEDSS does actually display an imposing capability for an established handily ICU related uncertainty issues. The performance of the prototypes is compared with the MEWS standard which exposed that the IFS application appears to perform highly better in deferring accuracy than the expert MEWS score with higher degrees of sensitivity and specificity being recorded.

Aim: Large language models (LLMs) are increasingly integrated into medical education and decision support systems. However, their capabilities in acute care settings, such as emergency rheumatology, require systematic evaluation. This study aimed to compare the educational performance of four LLMs ChatGPT-4o, DeepSeek v3.2, Gemini 2.5 Pro, and Perplexity Academic across five domains: clinical accuracy, safety, diagnostic reasoning, realism, and use of evidence. Materials and Methods: Each model generated responses to 20 standardized emergency rheumatology scenarios. Two board-certified rheumatologists independently evaluated the outputs using a 10-point scoring rubric. Inter-rater reliability was calculated using the intraclass correlation coefficient (ICC). Differences among models were assessed using Friedman tests with post-hoc Wilcoxon signed-rank tests corrected for multiple comparisons. A Likert scale was also used to assess scenario complexity and educational utility. Results: ChatGPT-4o and DeepSeek v3.2 achieved the highest average total scores (mean: 7.75 each), significantly outperforming Perplexity Academic (mean: 6.70; p < 0.001). Although both models showed higher mean scores than Gemini 2.5 Pro (p ≈ 0.03), these differences were not statistically significant after correction. DeepSeek v3.2 showed slightly greater performance consistency. ICC analyses confirmed high inter-rater agreement across all domains (ICC>0.80). Conclusion: ChatGPT-4o and DeepSeek v3.2 demonstrated superior clinical reasoning, safety, and educational utility in emergency rheumatology scenarios. These findings support their potential role as adjunctive tools in medical training, provided expert oversight and validation mechanisms are in place.