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A generalized Bayesian inference nets model (GBINM) is proposed to aid researchers to construct Bayesian inference nets for various applications. The benefit of such a model is well demonstrated by applying GBINM in constructing a hierarchical Bayesian fuzzy inference nets (HBFIN) to diagnose five important types of cardiovascular diseases (CVD). The patients' medical records with doctors' confirmed diagnostic results obtained from two hospitals in China are used to design and verify HBFIN. Bayesian theorem is used to calculate the propagation of probability and address the uncertainties involved in each sequential stage of inference nets to deduce the disease(s). The validity and effectiveness of proposed approach is witnessed clearly from testing results obtained.

DESIREE is a European-funded project to improve the management of primary breast cancer. We have developed three decision support systems (DSSs), a guideline-based, an experience-based, and a case-based DSSs, resp. GL-DSS, EXP-DSS, and CB-DSS, that operate simultaneously to offer an enriched multimodal decision support to clinicians. A breast cancer knowledge model has been built to describe within a common ontology the data model and the terminoontological knowledge used for representing breast cancer patient cases. It allows for rule-based and subsumption-based reasoning in the GL-DSS to provide best patient-centered reconciled care plans. It also allows for using semantic similarity in the retrieval algorithm implemented in the CB-DSS. Rainbow boxes are used to display patient cases similar to a given query patient. This innovative visualization technique translates the question of deciding the most appropriate treatment into a question of deciding the colour dominance among boxes. [ABSTRACT FROM AUTHOR]

A fused hierarchical neural networks (FHNNs) is proposed for applications mainly related to diagnosis and fault detection. The benefit of such a model is well demonstrated by applying FHNNs for cardiovascular disease (CVD) diagnosis hierarchically using hemodynamic parameters (HDPs) derived from non-invasive sphygmogram (SPG). Patients' medical records with diagnostic results confirmed by doctors obtained from two hospitals in China are used to test and verify FHNNs. Variance analysis is used to categorize HDPs according to the importance/relevance based on their influence on discriminating diseases. Different neural networks structures are tested in diagnosing CVD so as to choose the optimal sub neural networks (sub-NNs) for the proposed FHNNs. Finally FHNNs with fused sub-NNs for CVD diagnosis is presented. The validity and effectiveness in the improvement of accuracy is witnessed clearly from the testing results. [ABSTRACT FROM PUBLISHER]

The DESIREE project has developed a platform offering several complementary therapeutic decision support systems (DSSs) to improve care quality for breast cancer patients. A first assessment of the system was carried out in close-to-real tumor boards (TBs). Fourteen TB sessions were organized corresponding to a total of 125 exploitable decisions previously made without the system and re-played with the system after a washout period in three pilot sites. Results show an overestimation of declared compliance with guidelines when not using the system as compared to measured compliance with the recommendations issued from the guideline-based DSS of DESIREE. After using the system, measured compliance rate of decisions with guidelines was significantly improved from 74.4% to 89.6%. Most of the changes in decisions when using the guideline-based DSS were associated with non-compliant decisions that became compliant. Qualitative analysis and interviews showed that despite maturity issues, clinicians found DESIREE DSSs innovative and promising., (©2020 AMIA - All rights reserved.)

Breast cancer is the most common cancer among women. DESIREE is a European project which aims at developing web-based services for the management of primary breast cancer by multidisciplinary breast units (BUs). We describe the guideline-based decision support system (GL-DSS) of the project. Various breast cancer clinical practice guidelines (CPGs) have been selected to be concurrently applied to provide state-of-the-art patient-specific recommendations. The aim is to reconcile CPG recommendations with the objective of complementarity to enlarge the number of clinical situations covered by the GL-DSS. Input and output data exchange with the GL-DSS is performed using FHIR. We used a knowledge model of the domain as an ontology on which relies the reasoning process performed by rules that encode the selected CPGs. Semantic web tools were used, notably the Euler/EYE inference engine, to implement the GL-DSS. "Rainbow boxes" are a synthetic tabular display used to visualize the inferred recommendations.