Medizinische Universität Graz Austria/Österreich - Forschungsportal - Medical University of Graz
Gewählte Publikation:
SHR Neuro Krebs Kardio Lipid Stoffw Microb
Polat, Erdeniz, S; Kramer, D; Schrempf, M; Rainer, PP; Felfernig, A; Tran, TNT; Burgstaller, T; Lubos, S.
Machine Learning Based Risk Prediction for Major Adverse Cardiovascular Events for ELGA-Authorized Clinics1.
Stud Health Technol Inform. 2023; 301: 20-25.
Doi: 10.3233/SHTI230006
PubMed
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- Führende Autor*innen der Med Uni Graz
- POLAT ERDENIZ Seda
- Co-Autor*innen der Med Uni Graz
- Rainer Peter
- Schrempf Michael
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- Abstract:
- 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.
- Find related publications in this database (using NLM MeSH Indexing)
- Humans - administration & dosage
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- Delivery of Health Care - administration & dosage
- Cardiovascular Diseases - diagnosis