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Impact of train/test sample regimen on performance estimate stability of machine learning in cardiovascular imaging, , , , , and . Sci Rep, 11 (1): 14490 (July 2021)Time and event-specific deep learning for personalized risk assessment after cardiac perfusion imaging., , , , , , , , , and 11 other author(s). npj Digit. Medicine, (2023)Mining Concepts for a COVID Interface Terminology for Annotation of EHRs., , , , , , and . IEEE BigData, page 3753-3760. IEEE, (2020)Using annotation for computerized support for fast skimming of cardiology electronic health record notes., , , , , , , and . BIBM, page 4043-4050. IEEE, (2023)Mining of EHR for interface terminology concepts for annotating EHRs of COVID patients., , , , , , , and . BMC Medical Informatics Decis. Mak., 23 (1): 40 (December 2023)Handling missing values in machine learning to predict patient-specific risk of adverse cardiac events: Insights from REFINE SPECT registry., , , , , , , , , and 10 other author(s). Comput. Biol. Medicine, (2022)AI-derived epicardial fat measurements improve cardiovascular risk prediction from myocardial perfusion imaging., , , , , , , , , and 6 other author(s). npj Digit. Medicine, (2024)Skimming of Electronic Health Records Highlighted by an Interface Terminology Curated with Machine Learning Mining., , , , , , , , and . BIOSTEC (2), page 498-505. SCITEPRESS, (2024)Generating Training Data for Concept-Mining for an 'Interface Terminology' Annotating Cardiology EHRs., , , , , , and . BIBM, page 1728-1735. IEEE, (2020)