Medication based machine learning to identify subpopulations of pediatric hemodialysis patients in an electronic health record database.

ABSTRACT: Electronic health records (EHRs) have given rise to large and complex databases of medical information that have the potential to become powerful tools for clinical research. However, differences in coding systems and the detail and accuracy of the information within EHRs can vary across institutions. This makes it challenging to identify subpopulations of patients and limits the widespread use of multi-institutional databases. In this study, we leveraged machine learning to identify patterns in medication usage among hospitalized pediatric patients receiving renal replacement therapy and created a predictive model that successfully differentiated between intermittent (iHD) and continuous renal replacement therapy (CRRT) hemodialysis patients. We trained six machine learning algorithms (lo

SUBMITTER: McKnite AM 

PROVIDER: S-EPMC9674326 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature