Abstract
In recent years, there has been an increase in the automatic analysis of electronic health records (EHR) for the screening of diseases or the outcomes measurement. However, little has been done to assess the diversity of algorithms, databases, and analysis protocols. We aim to determine the global extend of machile learning (ML) use in screening diseasses by analyzing structure, demographics and clinical characteristics of EHR employed in previous analysis
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