The introduced information model allows separating medical knowledge and presentation knowledge. The additional presentation layer will enrich the graphical user interface's flexibility and will allow an optimal presentation of medical data considering the different users' perspectives and different media used for data presentation.

This paper is an extension of work originally presented to pHealth 2019—16th International Conference on Wearable, Micro and Nano Technologies for Personalized Health. To provide an efficient decision support, it is necessary to integrate clinical decision support systems (CDSSs) in information systems routinely operated by healthcare professionals, such as hospital information systems (HISs), or by patients deploying their personal health records (PHR). CDSSs should be able to use the semantics and the clinical context of the data imported from other systems and data repositories. A CDSS platform was developed as a set of separate microservices. In this context, we implemented the core components of a CDSS platform, namely its communication services and logical inference components. A fast healthcare interoperability resources (FHIR)-based CDSS platform addresses the ease of access to clinical decision support services by providing standard-based interfaces and workflows. This type of CDSS may be able to improve the quality of care for doctors who are using HIS without CDSS features. The HL7 FHIR interoperability standards provide a platform usable by all HISs that are FHIR enabled. The platform has been implemented and is now productive, with a rule-based engine processing around 50,000 transactions a day with more than 400 decision support models and a Bayes Engine processing around 2000 transactions a day with 128 Bayesian diagnostics models.

Background: It could be seen in the previous decades that Machine Learning (ML) has a huge variety of possible implementations in medicine and can be of great use. Nevertheless, cardiovascular diseases cause about a third of the total global deaths. Does ML work in the cardiology domain and what is the current progress in this regard? To answer this question, we present a systematic review aiming at 1) identifying studies where machine learning algorithms were applied in the domain of cardiology; 2) providing an overview based on the existing literature about the state-of-the-art ML algorithms applied in cardiology. Methods: For organizing this review, we adopted the PRISMA statement. We used PubMed as the search engine and identified the search keywords as “Machine Learning”, “Data Mining”, “Cardiology”, and “Cardiovascular” in combinations. Scientific articles and conference papers published between 2013-2017 reporting about implementations of ML algorithms in the domain of cardiology have been included in this review. Results: In total, 27 relevant papers were included. We examined four aspects: the aims of ML systems, the methods, datasets, and evaluation metrics. The major part of the paper was aimed at predicting the risk of mortality. A promising branch of Machine Learning, the ‘Reinforcement Learning’, was also never proposed in the observed papers. Tree-based ensembles are common and show good results, whereas deep neural networks are poorly represented. Most papers (20 of 27) have used datasets that are hardly available for other researchers, e.g. unpublished local registries. We also identified 28 different metrics for model evaluation. This variety of metrics makes it difficult to compare the results of different researches. Conclusion: We suppose that this systematic review will be helpful for researchers developing medical machine learning systems and for cardiology in particular.

Background: Methods of data mining and analytics can be efficiently applied in medicine to develop models that use patient-specific data to predict the development of diabetic polyneuropathy. However, there is room for improvement in the accuracy of predictive models. Existing studies of diabetes polyneuropathy considered a limited number of predictors in one study to enable a comparison of efficiency of different machine learning methods with different predictors to find the most efficient one. The purpose of this study is the implementation of machine learning methods for identifying the risk of diabetes polyneuropathy based on structured electronic medical records collected in databases of medical information systems. Methods: For the purposes of our study, we developed a structured procedure for predictive modelling, which includes data extraction and preprocessing, model adjustment and performance assessment, selection of the best models and interpretation of results. The dataset contained a total number of 238,590 laboratory records. Each record 27 laboratory tests, age, gender and presence of retinopathy or nephropathy). The records included information about 5846 patients with diabetes. Diagnosis served as a source of information about the target class values for classification. Results: It was discovered that inclusion of two expressions, namely "nephropathy" and "retinopathy" allows to increase the performance, achieving up to 79.82% precision, 81.52% recall, 80.64% F1 score, 82.61% accuracy, and 89.88% AUC using the neural network classifier. Additionally, different models showed different results in terms of interpretation significance: random forest confirmed that the most important risk factor for polyneuropathy is the increased neutrophil level, meaning the presence of inflammation in the body. Linear models showed linear dependencies of the presence of polyneuropathy on blood glucose levels, which is confirmed by the clinical interpretation of the importance of blood glucose control. Conclusion: Depending on whether one needs to identify pathophysiological mechanisms for one's prospective study or identify early or late predictors, the choice of model will vary. In comparison with the previous studies, our research makes a comprehensive comparison of different decisions using a large and well-structured dataset applied to different decision support tasks.