Integrating data-driven and knowledge-driven approaches to analyze clinical notes with structured data for sarcopenia detection.

ABSTRACT: Background: Patients with sarcopenia often go undetected in busy clinical practices since the muscle measurements are not easily incorporated into routine clinical practice. The current research fills the gap by utilizing unstructured clinical notes combined with structured data from electronic health records (EHR), to increase sarcopenia detection. Methods: We developed and evaluated four approaches to first extract clinical note features, then integrate with structured data for sarcopenia detection models. Case studies were used to demonstrate the interpretation of the results and show the important association between predictors and outcomes. Results: Out of 1304 participants, 1055 were controls, 249 met at least one criterion for Sarcopenia. The best performing mod

SUBMITTER: Luo X 

PROVIDER: S-EPMC12091225 | biostudies-literature | 2024 Oct-Dec

REPOSITORIES: biostudies-literature