Medical Decision Support System Research Articles

Expert gaze as a usability indicator of medical AI decision support systems: a preliminary study

Given the current state of medical artificial intelligence (AI) and perceptions towards it, collaborative systems are becoming the preferred choice for clinical workflows. This work aims to address expert interaction with medical AI support systems to gain insight towards how these systems can be better designed with the user in mind. As eye tracking metrics have been shown to be robust indicators of usability, we employ them for evaluating the usability and user interaction with medical AI support systems. We use expert gaze to assess experts’ interaction with an AI software for caries detection in bitewing x-ray images. We compared standard viewing of bitewing images without AI support versus viewing where AI support could be freely toggled on and off. We found that experts turned the AI on for roughly 25% of the total inspection task, and generally turned it on halfway through the course of the inspection. Gaze behavior showed that when supported by AI, more attention was dedicated to user interface elements related to the AI support, with more frequent transitions from the image itself to these elements. When considering that expert visual strategy is already optimized for fast and effective image inspection, such interruptions in attention can lead to increased time needed for the overall assessment. Gaze analysis provided valuable insights into an AI’s usability for medical image inspection. Further analyses of these tools and how to delineate metrical measures of usability should be developed.

Trust me if you can: a survey on reliability and interpretability of machine learning approaches for drug sensitivity prediction in cancer.

With the ever-increasing number of artificial intelligence (AI) systems, mitigating risks associated with their use has become one of the most urgent scientific and societal issues. To this end, the European Union passed the EU AI Act, proposing solution strategies that can be summarized under the umbrella term trustworthiness. In anti-cancer drug sensitivity prediction, machine learning (ML) methods are developed for application in medical decision support systems, which require an extraordinary level of trustworthiness. This review offers an overview of the ML landscape of methods for anti-cancer drug sensitivity prediction, including a brief introduction to the four major ML realms (supervised, unsupervised, semi-supervised, and reinforcement learning). In particular, we address the question to what extent trustworthiness-related properties, more specifically, interpretability and reliability, have been incorporated into anti-cancer drug sensitivity prediction methods over the previous decade. In total, we analyzed 36 papers with approaches for anti-cancer drug sensitivity prediction. Our results indicate that the need for reliability has hardly been addressed so far. Interpretability, on the other hand, has often been considered for model development. However, the concept is rather used intuitively, lacking clear definitions. Thus, we propose an easily extensible taxonomy for interpretability, unifying all prevalent connotations explicitly or implicitly used within the field.

Prediction of diabetes mellitus using XGBoost model

With the number of health-threatening diseases and deaths on the rise, medical decision support systems continue to prove effective in improving the efficiency of physicians and other healthcare providers and supporting clinical decisions. Diabetes remains one of the leading diseases responsible for many deaths worldwide. Diabetes is characterized by elevated blood glucose levels, which can have serious consequences for other organs in the body. According to the International Diabetes Alliance (IDA), 382 million people currently have diabetes and this number is expected to double to 592 million by 2035. This paper proposes a clinical prediction model for diabetes based on machine learning (ML).As traditional ML models, we consider the most commonly used classifiers, the Logic Tree (LR) and XGBoost. We compare these ML models. On the other hand, we also use deep learning (DL) models and apply a fully convolutional neural network (DNN) for diabetes prediction and recognition. The proposed models were evaluated in the public Kaggle database: prediction performance at each level was analyzed using the Precision, Recall, AUC, accuracy and F1 metrics, and overall prediction efficiency was assessed using accuracy and its macro-average: DL, LR, The overall accuracy obtained by XGBoost was 91.88%, 88.75% and 97.12% respectively. The experimental results show that XGBoost is more effective at predicting diabetes than deep learning and LR methods.

Effectiveness and safety of an artificial intelligence-based medical decision support system for adjusting insulin pump settings in children with type 1 diabetes mellitus: randomized controlled trial

BACKGROUND: Previously, we presented the process of developing a clinical decision support system (CDSS) for adjusting insulin pump (IP) settings in children with type 1 diabetes mellitus (T1D) and assessing the agreement of the recommendations it generates with the expert opinion. The CDSS demonstrated satisfactory forecasting of glucose profile and agreement rates between recommendations CDSS and experts.AIM: To evaluate the effectiveness and safety of using CDSS in children with T1D, testing the hypothesis of non-inferiority (with a limit of -5%) of relative increase of glucose time in range (TIR) over 6 months.MATERIALS AND METHODS: The trial included 80 children with T1D, divided into two comparable groups of 40 children using the minimization method. Patients in the main group received recommendations for adjusting the IP settings from a physician who uses the CDSS; patients in the control group received recommendations from a physician (control group). Patients were observed for 6 months with remote consultations once a month (7 consultations in total) and monitoring of glycated hemoglobin (HbA1c) at 1, 4 and 7 consultations. The primary outcome is the difference in group mean relative changes in TIR (%), secondary outcomes are TIR (%), HbA1c concentration. RESULTS: The trial was completed by 63 patients 32 in the main group, 31 in the control group. The difference in the mean relative increase in TIR at the 7th consultation in the groups was 3.02%, one-sided 95% CI (-4.55%; inf ). Thus, the lower bound of this CI is greater than the noninferiority limit of -5%, and the noninferiority hypothesis can be accepted. There were no statistically significant differences between groups for all outcomes. The dynamics of the indicators were positive in the main group and had a statistical tendency towards positive changes in the control group.CONCLUSION: The use of CDSS was no less effective in terms of the TIR than the management of the patient by a physician. The use of CDSS in clinical practice can help in regular and frequent monitoring of children with T1D, and standardize at a high level the approach to correction of IP parameters, supplemented with CGM.

The effectiveness of endoscopic diagnostics of colon tumors using artificial intelligence: prospective tandem study

AIM: to estimate the effectiveness of a medical decision support system based on artificial intelligence in the endoscopic diagnosis of benign tumors during tandem study.PATIENTS AND METHODS: from October to December 2023, a single-center comparative tandem study of medical decision support system based on artificial intelligence “ArtInCol” was done. The first stage was a traditional colonoscopy under sedation, the second one — colonoscopy using AI. A pairwise comparison of the main indicators of the effectiveness was made.RESULTS: in the primary endpoint, the polyp detection rate (PDR) in the traditional colonoscopy group was 40.6% vs 56.4% in the AI group, p = 0.0001 (RR = 1.39; 95% CI: 1.04–1.87). The mean number of lesions detected (MPP) was 1.63 (± 1.2) vs 2.47 (± 1.8) in the AI group (mean difference = 0.84; (95% CI: 1.07–0.61).CONCLUSION: the study demonstrated the effectiveness of the original medical decision support system for benign colon tumors detection in real clinical practice. The further stage, a multicenter randomized trial is needed.

Prevalence of signs of professional burnout among medical workers involved in providing medical care for circulatory system diseases

BACKGROUND: Among 13 thousand doctors interviewed in 2021, 42% displayed signs of professional burnout. This phenomenon negatively affects the frequency of repeated hospitalizations, the number of adverse events, and increased employee turnover. However, studies concerning burnout in doctors are rare and do not focus on specialists who provide medical care for patients with circulatory system diseases (CSD). AIM: was to assess the level of burnout in medical workers involved in the provision of medical care for patients with CSD and to explore staff perceptions of its prevention. MATERIALS AND METHODS: A sociological survey (based on the standardized Maslach Burnout Inventory questionnaire) was conducted among medical personnel (women represented the majority of the sample — 76%, men — 24%). The sample is represented by doctors (n=89), nursing staff and auxillary nurses (NSAN) (n=128). The mean age for the whole group of respondents was 40.7±0.7, professional experience lasted 12.0±0.6 years. Standard methods of descriptive statistics were used for the data analysis. The age characteristics of the group had no significant differences by professional categories. Doctors had 39.0±1.2 years (min — 24, max — 63), NSAN had 41.0±0.6 years (min — 19, max — 67). Professional experience lasted differed doctors 39.0±1.2 years, in NSAN — 12.3±0.8. Men were characterized by a mean age of 36.7±1.6 years and professional experience lasted 10.0±1.2 years and women 42.0±0.7 years and 12.6±0.7 years, respectively. The character of distribution of quantitative data in the presented study was analyzed using the Kolmogorov–Smirnov and Shapiro–Wilk criterion. RESULTS: 80.2% of respondents presented with a high level of reduced professional accomplishments (RPA), 53.4% — a moderate average level of depersonalization. The emotional exhaustion was similar in all respondents, although average level of emotional exhaustion was the most prevalent (39.6%). 43.8% of doctors had an average level of emotional exhaustion, which is higher compared with NSAN — 37.5%. Clinical and paraclinical units are associated with a medium level of emotional exhaustion and a high RPA. At the same time, paraclinical units are characterized by a medium level of depersonalization, in contrast to clinical units, which are found to have a high level of this factor. According to the respondents, possible measures to prevent emotional burnout should include a set of measures including, rational distribution of responsibilities within departments, workload among the staff, as well as the formation of special skills to overcome stressful situations and optimization of activities with the help of digital solutions (support system for medical decisions, etc.). CONCLUSION: Working as medical staff and providing medical care for patients with CSD is strongly associated with a high level of reduced professional accomplishments (80.2% of respondents). Proper measures for the prevention of emotional burnout should primarily include organizational solutions focused on optimizing internal processes in medical staff’ work. According to the respondents, among the measures aimed at the prevention of emotional burnout, the most demanded may be: rational distribution of responsibilities within departments, workload among staff, as well as the formation of special skills to overcome stressful situations and optimization of activities with the help of digital solutions (medical decision support system, etc.).

Improved Kepler Optimization Algorithm for enhanced feature selection in liver disease classification

Liver diseases represent a significant healthcare challenge, impacting millions globally and posing complexities in diagnosis. To address this global health concern, this paper introduces a groundbreaking enhancement to the Kepler Optimization Algorithm, termed I-KOA, designed specifically for feature selection in high-dimensional datasets. By harnessing the synergies of Opposition-Based Learning and a Local Escaping Operator grounded in the k-nearest Neighbor (kNN) classifier, I-KOA asserts itself as a potent tool for local exploitation, balanced exploration, and evasion of local optima. To our knowledge, this is the first work to exploit KOA as a feature selection method. Pioneering the utilization of KOA as a feature selection method, the paper rigorously tests I-KOA in two extensive experiments, tackling the complex CEC’22 benchmark suite functions and the intricate landscape of five liver disease datasets. Results underscore I-KOA’s unparalleled performance, validated through the Friedman test, where it surpasses seven rival optimization algorithms. Achieving an outstanding overall classification accuracy of 93.46%, Feature selection size of 0.1042, sensitivity of 97.46%, precision of 94.37%, and F1-score of 90.35% across the liver disease datasets, I-KOA’s randomized algorithm ensures robust feature selection, striking a compelling balance between subset size and classification efficacy. Acknowledging computational demands and generalization nuances, I-KOA is a formidable tool ready to revolutionize medical diagnosis and decision support systems. The open source codes of the proposed I-KOA are available at https://www.mathworks.com/matlabcentral/fileexchange/161376-improved-kepler-optimization-algorithm.

Deep Learning Approaches for Medical Image Analysis and Diagnosis.

In addition to enhancing diagnostic accuracy, deep learning techniques offer the potential to streamline workflows, reduce interpretation time, and ultimately improve patient outcomes. The scalability and adaptability of deep learning algorithms enable their deployment across diverse clinical settings, ranging from radiology departments to point-of-care facilities. Furthermore, ongoing research efforts focus on addressing the challenges of data heterogeneity, model interpretability, and regulatory compliance, paving the way forseamless integration of deep learning solutions into routine clinical practice. As the field continues to evolve, collaborations between clinicians, data scientists, and industry stakeholders will be paramount in harnessing the full potential of deep learning for advancing medical image analysis and diagnosis. Furthermore, the integration of deep learning algorithms with other technologies, including natural language processing and computer vision, may foster multimodal medical data analysis and clinical decision support systems to improve patient care. The future of deep learning in medical image analysis and diagnosis is promising. With each success and advancement, this technology is getting closer to being leveraged for medical purposes. Beyond medical image analysis, patient care pathways like multimodal imaging, imaging genomics, and intelligent operating rooms or intensive care units can benefitfrom deep learning models.

Multimodal Machine Learning in Image-Based and Clinical Biomedicine: Survey and Prospects

AbstractMachine learning (ML) applications in medical artificial intelligence (AI) systems have shifted from traditional and statistical methods to increasing application of deep learning models. This survey navigates the current landscape of multimodal ML, focusing on its profound impact on medical image analysis and clinical decision support systems. Emphasizing challenges and innovations in addressing multimodal representation, fusion, translation, alignment, and co-learning, the paper explores the transformative potential of multimodal models for clinical predictions. It also highlights the need for principled assessments and practical implementation of such models, bringing attention to the dynamics between decision support systems and healthcare providers and personnel. Despite advancements, challenges such as data biases and the scarcity of “big data” in many biomedical domains persist. We conclude with a discussion on principled innovation and collaborative efforts to further the mission of seamless integration of multimodal ML models into biomedical practice.

Multi-criteria decision analysis method for differential diagnosis of tropical febrile diseases

This paper employs the Analytical Hierarchy Process (AHP) to enhance the accuracy of differential diagnosis for febrile diseases, particularly prevalent in tropical regions where misdiagnosis may have severe consequences. The migration of health workers from developing countries has resulted in frontline health workers (FHWs) using inadequate protocols for the diagnosis of complex health conditions. The study introduces an innovative AHP-based Medical Decision Support System (MDSS) incorporating disease risk factors derived from physicians’ experiential knowledge to address this challenge. The system’s aggregate diagnostic factor index determines the likelihood of febrile illnesses. Compared to existing literature, AHP models with risk factors demonstrate superior prediction accuracy, closely aligning with physicians’ suspected diagnoses. The model’s accuracy ranges from 85.4% to 96.9% for various diseases, surpassing physicians’ predictions for Lassa, Dengue, and Yellow Fevers. The MDSS is recommended for use by FHWs in communities lacking medical experts, facilitating timely and precise diagnoses, efficient application of diagnostic test kits, and reducing overhead expenses for administrators.

A medical decision support system using an artificial intelligence module for endoscopic examination of the stomach

A medical decision support system using an artificial intelligence module for endoscopic examination of the stomach

The Effect of an Electronic Medical Record-Based Clinical Decision Support System on Adherence to Clinical Protocols in Inflammatory Bowel Disease Care: Interrupted Time Series Study.

Clinical decision support systems (CDSSs) embedded in electronic medical records (EMRs), also called electronic health records, have the potential to improve the adoption of clinical guidelines. The University of Alberta Inflammatory Bowel Disease (IBD) Group developed a CDSS for patients with IBD who might be experiencing disease flare and deployed it within a clinical information system in 2 continuous time periods. This study aims to evaluate the impact of the IBD CDSS on the adherence of health care providers (ie, physicians and nurses) to institutionally agreed clinical management protocols. A 2-period interrupted time series (ITS) design, comparing adherence to a clinical flare management protocol during outpatient visits before and after the CDSS implementation, was used. Each interruption was initiated with user training and a memo with instructions for use. A group of 7 physicians, 1 nurse practitioner, and 4 nurses were invited to use the CDSS. In total, 31,726 flare encounters were extracted from the clinical information system database, and 9217 of them were manually screened for inclusion. Each data point in the ITS analysis corresponded to 1 month of individual patient encounters, with a total of 18 months of data (9 before and 9 after interruption) for each period. The study was designed in accordance with the Statement on Reporting of Evaluation Studies in Health Informatics (STARE-HI) guidelines for health informatics evaluations. Following manual screening, 623 flare encounters were confirmed and designated for ITS analysis. The CDSS was activated in 198 of 623 encounters, most commonly in cases where the primary visit reason was a suspected IBD flare. In Implementation Period 1, before-and-after analysis demonstrates an increase in documentation of clinical scores from 3.5% to 24.1% (P<.001), with a statistically significant level change in ITS analysis (P=.03). In Implementation Period 2, the before-and-after analysis showed further increases in the ordering of acute disease flare lab tests (47.6% to 65.8%; P<.001), including the biomarker fecal calprotectin (27.9% to 37.3%; P=.03) and stool culture testing (54.6% to 66.9%; P=.005); the latter is a test used to distinguish a flare from an infectious disease. There were no significant slope or level changes in ITS analyses in Implementation Period 2. The overall provider adoption rate was moderate at approximately 25%, with greater adoption by nurse providers (used in 30.5% of flare encounters) compared to physicians (used in 6.7% of flare encounters). This is one of the first studies to investigate the implementation of a CDSS for IBD, designed with a leading EMR software (Epic Systems), providing initial evidence of an improvement over routine care. Several areas for future research were identified, notably the effect of CDSSs on outcomes and how to design a CDSS with greater utility for physicians. CDSSs for IBD should also be evaluated on a larger scale; this can be facilitated by regional and national centralized EMR systems.

Extensive Review of Literature on Explainable AI (XAI) in Healthcare Applications

Abstract: Artificial Intelligence (AI) techniques are widely being used in the medical fields or various applications including diagnosis of diseases, prediction and classification of diseases, drug discovery, etc. However, these AI techniques are lacking in the transparency of the predictions or decisions made due to their black box-type operations. The explainable AI (XAI) addresses such issues faced by AI to make better interpretations or decisions by physicians. This article explores XAI techniques in the field of healthcare applications, including the Internet of Medical Things (IoMT). XAI aims to provide transparency, accountability, and traceability in AI-based systems in healthcare applications. It can help in interpreting the predictions or decisions made in medical diagnosis systems, medical decision support systems, smart wearable healthcare devices, etc. Nowadays, XAI methods have been utilized in numerous medical applications over the Internet of Things (IOT), such as medical diagnosis, prognosis, and explanations of the AI models, and hence, XAI in the context of IoMT and healthcare has the potential to enhance the reliability and trustworthiness of AI systems.

ORGANIZATION OF DISPENSARY OBSERVATION OF PATIENTS WITH PATHOLOGY OF THE MACULAR RETINA USING ARTIFICIAL INTELLIGENCE SYSTEMS

Background: Despite the mention in the provision of medical care for diseases of the organ of vision, adnexa and orbit about equipping the medical consultative and diagnostic department of the clinic with an optical coherence tomograph, dynamic monitoring of patients with retinal pathology after the start of treatment is most often carried out in a medical ophthalmological center, which reduces accessibility for patients with primary (first identified) morbidity requiring initiation of treatment. The required technology needs to be changed, intensified, incl. using artificial intelligence technologies.&#x0D; Aim: development of methodological foundations for organizational technology for dispensary observation of patients with pathology of the posterior segment of the eye using clinical decision support system (CDSS) based on artificial intelligence.&#x0D; Materials and methods: The assessment of the existing regulatory framework was carried out on the basis of an analysis of the Constitution of the Russian Federation, Federal laws, by-laws, and judicial practice. The creation of a methodology for a structured medical document describing an optical coherence tomography image was carried out using an expert method: a survey of 100 ophthalmologists with the appropriate level of education, incl. additional professional, engaged in the provision of medical services - specialized medical care (treatment) to patients with pathology of the posterior segment of the eye.&#x0D; Results: Using an expert method, 123 binary signs were selected and described to describe the structure of the macular region of the retina in normal and pathological conditions, of which 26 signs were identified that can be interpreted as predictors of worsening of the clinical course of the disease.&#x0D; Conclusion: The developed classifier made it possible to create and train a medical decision support system based on 60,000 medical images, which, as an information service, without making a diagnosis, can change the organization of the dynamic observation process: the formation of patient routing - a primary service using the developed CDSS; if there are signs of deterioration in the clinical picture - routing to a medical ophthalmological center to assess the dynamics, provide specialized services, incl. high-tech medical care.

Bridging the voice of healthcare to digital transformation in practice – a holistic approach

BackgroundDigital transformation is key for healthcare to meet future needs and expectations and compete equally with new actors on the market. Medical digital diagnosis tools and clinical decision support systems (CDSS) are improving and offer new opportunities. To introduce new technology in healthcare can however be a challenging mission, too often ending in failure, with excessive costs or the actual transformation work not being carried out at all. It is unclear how to drive the establishment to reach desired results in this environment, and how industrial experiences can be used to support healthcare.ObjectiveThe objective of this study was to develop a holistic approach for introducing new information technology (IT), such as a CDSS, into a primary healthcare organization supported by industry best practices for digital transformation.MethodsThis qualitative study used a combined inductive and deductive method where the perceptions and beliefs of selected primary healthcare stakeholders were used as directions for developing an approach that could utilize existing industry best practices for digital transformation.ResultsA holistic healthcare-ified approach including 20 requirements was developed, that meet the needs of healthcare. The voice of healthcare was used as prism to healthcare-ify the industry practices and adapt it to match specific healthcare conditions. An example was provided showing how the research could be put into practice.ConclusionsThis study proposed a holistic approach, based on industry best practices, but adapted to healthcare using the voice of healthcare as a bridge, that may be used to introduce CDSS and other IT into a primary healthcare organization and step up the needed digitalization.

Ethical considerations in AI-enhanced medical decision support systems: A review

As Artificial Intelligence (AI) continues to play an increasingly pivotal role in medical decision support systems, the ethical implications of its integration into healthcare practices demand comprehensive examination. This review delves into the ethical considerations surrounding AI-enhanced medical decision support systems, aiming to provide insights into the challenges, existing frameworks, exemplary practices, and emerging trends in this rapidly evolving field. The significance of ethical considerations is underscored by the patient-centric focus, emphasizing the impact of AI on patient outcomes and the delicate balance between technological advancements and patient welfare. Trust and transparency emerge as critical pillars, exploring the role of trust in medical decision-making and the imperative of ensuring transparency in AI algorithms to foster confidence among healthcare professionals and patients. Ethical challenges, including privacy and confidentiality concerns, biases in AI algorithms, and issues related to informed consent, are thoroughly examined. Strategies for safeguarding patient data, mitigating biases, and transparently communicating with patients are explored to address these challenges. The role of accountability and responsibility is delineated, defining the ethical responsibilities of both healthcare professionals and AI developers. The review surveys existing ethical frameworks in healthcare AI and evaluates their applicability and effectiveness. Additionally, it highlights recent proposals for ethical guidelines, emphasizing the need to integrate ethical considerations into the entire development life cycle of AI-enhanced medical decision support systems. Case studies and exemplary practices from healthcare institutions implementing ethical AI serve to illustrate real-world applications and offer insights into best practices. The evolving landscape of ethical AI research is explored, showcasing ongoing initiatives and potential innovations that hold promise for addressing ethical challenges in the future. This review underscores the paramount importance of ethical considerations in the integration of AI into medical decision support systems. It provides a comprehensive overview of current challenges, existing frameworks, exemplary practices, and emerging trends, emphasizing the ongoing need for vigilance and ethical governance to ensure the responsible and beneficial deployment of AI in healthcare.

Analyzing the impact of data visualization applications for diagnosing the health conditions through hesitant fuzzy-based hybrid medical expert system

Effectively managing healthcare data is crucial for accurate diagnosis and personalized patient care. As the utilization of healthcare data grows for personalized care, concerns about reliability, privacy, and security have emerged. To address these issues, this research explores the fusion of analytical techniques with interactive visual representations, known as visual analytics, as a promising solution. The focus is on evaluating the trustworthiness of healthcare data in Kingdom of Saudi Arabia, particularly the capability of visual analytics tools in facilitating accurate and secure healthcare data analysis. This study tackles challenges such as the absence of specific evaluation criteria, the need to process vast healthcare datasets, the establishment of trust, and the necessity for automation. In response, a hybrid medical decision support system is introduced, leveraging hesitant fuzzy decision systems. The primary objective is to evaluate trustworthiness of visual analytics tools for disease diagnosis within healthcare data. Within the framework of hesitant fuzzy logic, the paper employs a medical multi-criteria decision-making system that integrates the analytic network process and the technique for order of preference by similarity to an ideal solution. Rigorous validation ensures the accuracy and reliability of the findings. The research not only provides valuable insights but also conducts comparative analyses of the proposed models against existing ones, demonstrating the practicality of optimal decision-making in Saudi Arabia environment of healthcare scenarios. Several popular alternatives of healthcare based tools have been used in this study such as Tableau, JupyteR, Zoho Reports, QlikView, Visual.ly, DOMO BI, SAS Visual Analytics. From the results achieved DOMO BI visual analytics tool is found to be most secure and robust tool for healthcare professionals. This effort aims to enhance patient care and outcomes, ultimately contributing to the improvement of the overall healthcare landscape in Saudi Arabia.

Enhancing Medical Decision Making: A Semantic Technology-Based Framework for Efficient Diagnosis Inference

In the dynamic landscape of healthcare, decision support systems (DSS) confront continuous challenges, especially in the era of big data. Background: This study extends a Q&amp;A-based medical DSS framework that utilizes semantic technologies for disease inference based on a patient’s symptoms. The framework inputs “evidential symptoms” (symptoms experienced by the patient) and outputs a ranked list of hypotheses, comprising an ordered pair of a disease and a characteristic symptom. Our focus is on advancing the framework by introducing ontology integration to semantically enrich its knowledgebase and refine its outcomes, offering three key advantages: Propagation, Hierarchy, and Range Expansion of symptoms. Additionally, we assessed the performance of the fully implemented framework in Python. During the evaluation, we inspected the framework’s ability to infer the patient’s disease from a subset of reported symptoms and evaluated its effectiveness in ranking it prominently among hypothesized diseases. Methods: We conducted the expansion using dedicated algorithms. For the evaluation process, we defined various metrics and applied them across our knowledge base, encompassing 410 patient records and 41 different diseases. Results: We presented the outcomes of the expansion on a toy problem, highlighting the three expansion advantages. Furthermore, the evaluation process yielded promising results: With a third of patient symptoms as evidence, the framework successfully identified the disease in 94% of cases, achieving a top-ranking accuracy of 73%. Conclusions: These results underscore the robust capabilities of the framework, and the enrichment enhances the efficiency of medical experts, enabling them to provide more precise and informed diagnostics.

Application of virtual reality technology in medical decision support systems

Application of virtual reality technology in medical decision support systems

Technologies of Bioimpedance Spectroscopy in Decision Support Systems for the Diagnosis of Socially Significant Diseases

The purpose of the research is to develop methods for the synthesis of hybrid classifiers to assess the risk of socially significant diseases using bioimpedance analysis.Methods. We developed a descriptor approach using impedance spectroscopy results, generating four amplitudephase-frequency responses from four quasi-orthogonal leads. They create the feature spaces necessary for our hybrid classifier in the diagnosis of pancreatic diseases, the autonomous intelligent agents of which are built on various paradigms: probabilistic neural networks, fuzzy logical inference, fully connected feedforward neural networks. We also presented a device structure for creating an informative feature space.Results. Experimental studies of the proposed methods and means of classifying medical rice were carried out on diagnostic tasks according to the classes "acute destructive pancreatitis" – "no acute destructive pancreatitis" and differential diagnosis tasks according to the classes "prostate cancer" ‒ "chronic pancreatitis". They showed that incorporating multi-frequency sensing into neural network-based classifiers allows the development of clinical decision support systems for disease diagnosis that are comparable in performance to existing clinical diagnostic methods. The results were confirmed in groups of male and female patients at different stages of cancer aged 25 to 80 years using a variety of diagnostic methods, including history, physical examination, assessment of comorbidities, laboratory tests, ultrasound, laparoscopy, intraoperative exploration and computed tomography.Conclusion. The use of bioimpedance spectroscopy and hybrid classifier models opens up new opportunities for accessible and objective diagnosis of pancreatic diseases, expanding the capabilities of intelligent medical decision support systems.