Evaluating the impact of modeling choices on the performance of integrated genetic and clinical models.

ABSTRACT: The value of genetic information for improving the performance of clinical risk prediction models has yielded variable conclusions. Many methodological decisions have the potential to contribute to differential results across studies. Here, we performed multiple modeling experiments integrating clinical and demographic data from electronic health records (EHR) and genetic data to understand which decision points may affect performance. Clinical data in the form of structured diagnostic codes, medications, procedural codes, and demographics were extracted from two large independent health systems and polygenic risk scores (PRS) were generated across all patients with genetic data in the corresponding biobanks. Crohn's disease was used as the model phenotype based on its substantial genetic

SUBMITTER: Morley TJ 

PROVIDER: S-EPMC10635256 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature