Representation of EHR data for predictive modeling: a comparison between UMLS and other terminologies.

ABSTRACT:

Objective

Predictive disease modeling using electronic health record data is a growing field. Although clinical data in their raw form can be used directly for predictive modeling, it is a common practice to map data to standard terminologies to facilitate data aggregation and reuse. There is, however, a lack of systematic investigation of how different representations could affect the performance of predictive models, especially in the context of machine learning and deep learning.

Materials and methods

We projected the input diagnoses data in the Cerner HealthFacts database to Unified Medical Language System (UMLS) and 5 other terminologies, including CCS, CCSR, ICD-9, ICD-10, and PheWAS, and evaluated the prediction performances of these terminologies on 2 different tasks

SUBMITTER: Rasmy L 

PROVIDER: S-EPMC7647355 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature