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https://www.selleckchem.com/products/cid755673.html Integration of heterogenous resources is key for Rare Disease research. Within the EJP RD, common Application Programming Interface specifications are proposed for discovery of resources and data records. This is not sufficient for automated processing between RD resources and meeting the FAIR principles. To design a solution to improve FAIR for machines for the EJP RD API specification. A FAIR Data Point is used to expose machine-actionable metadata of digital resources and it is configured to store its content to a semantic database to be FAIR at the source. A solution was designed based on grlc server as middleware to implement the EJP RD API specification on top of the FDP. grlc reduces potential API implementation overhead faced by maintainers who use FAIR at the source. grlc reduces potential API implementation overhead faced by maintainers who use FAIR at the source. Patients with major adverse cardiovascular events (MACE) such as myocardial infarction or stroke suffer from frequent hospitalizations and have high mortality rates. By identifying patients at risk at an early stage, MACE can be prevented with the right interventions. The aim of this study was to develop machine learning-based models for the 5-year risk prediction of MACE. The data used for modelling included electronic medical records of more than 128,000 patients including 29,262 patients with MACE. A feature selection based on filter and embedded methods resulted in 826 features for modelling. Different machine learning methods were used for modelling on the training data. A random forest model achieved the best calibration and discriminative performance on a separate test data set with an AUROC of 0.88. The developed risk prediction models achieved an excellent performance in the test data. Future research is needed to determine the performance of these models and their clinical benefit in prospective settings. The developed risk prediction

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