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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.
