Machine Learning Based Risk Prediction for Major Adverse Cardiovascular Events for ELGA-Authorized Clinics1.

Autor:	Polat Erdeniz S; Steiermärkische Krankenanstaltengesellschaft m. b. H., Graz, Austria.; Medical University of Graz, Graz, Austria.; Graz University of Technology, Graz, Austria., Kramer D; Steiermärkische Krankenanstaltengesellschaft m. b. H., Graz, Austria., Schrempf M; Steiermärkische Krankenanstaltengesellschaft m. b. H., Graz, Austria.; Medical University of Graz, Graz, Austria., Rainer PP; Medical University of Graz, Graz, Austria., Felfernig A; Graz University of Technology, Graz, Austria., Tran TNT; Graz University of Technology, Graz, Austria., Burgstaller T; Graz University of Technology, Graz, Austria., Lubos S; Graz University of Technology, Graz, Austria.
Jazyk:	angličtina
Zdroj:	Studies in health technology and informatics [Stud Health Technol Inform] 2023 May 02; Vol. 301, pp. 20-25.
DOI:	10.3233/SHTI230006
Abstrakt:	Background: Artificial Intelligence (AI) has had an important impact on many industries as well as the field of medical diagnostics. In healthcare, AI techniques such as case-based reasoning and data driven machine learning (ML) algorithms have been used to support decision-making processes for complex tasks. This is used to assist medical professionals in making clinical decisions. A way of supporting clinicians is providing predicted prognoses of various ML models. Objectives: Training an ML model based on the data of a hospital and using it on another hospital have some challenges. Methods: In this research, we applied data analysis to discover required data filters on a hospital's EHR data for training a model for another hospital. Results: We applied experiments on real-world data of ELGA (Austrian health record system) and KAGes (a public healthcare provider of 20+ hospitals in Austria). In this scenario, we train the prediction model for ELGA- authorized health service providers using the KAGes data since we do not have access to the complete ELGA data. Conclusion: Finally, we observed that filtering the data with both feature and value selection increases the classification performance of the prediction model, which is trained for another system.
Databáze:	MEDLINE
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