Development and validation of computable social phenotypes for health-related social needs.

ABSTRACT:

Objective

Measurement of health-related social needs (HRSNs) is complex. We sought to develop and validate computable phenotypes (CPs) using structured electronic health record (EHR) data for food insecurity, housing instability, financial insecurity, transportation barriers, and a composite-type measure of these, using human-defined rule-based and machine learning (ML) classifier approaches.

Materials and methods

We collected HRSN surveys as the reference standard and obtained EHR data from 1550 patients in 3 health systems from 2 states. We followed a Delphi-like approach to develop the human-defined rule-based CP. For the ML classifier approach, we trained supervised ML (XGBoost) models using 78 features. Using surveys as the reference standard, we calculated sensitivity,

SUBMITTER: Gregory ME 

PROVIDER: S-EPMC11706536 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature