Learning and diSentangling patient static information from time-series Electronic hEalth Records (STEER).

ABSTRACT: Recent work in machine learning for healthcare has raised concerns about patient privacy and algorithmic fairness. Previous work has shown that self-reported race can be predicted from medical data that does not explicitly contain racial information. However, the extent of data identification is unknown, and we lack ways to develop models whose outcomes are minimally affected by such information. Here we systematically investigated the ability of time-series electronic health record data to predict patient static information. We found that not only the raw time-series data, but also learned representations from machine learning models, can be trained to predict a variety of static information with area under the receiver operating characteristic curve as high as 0.851 for biological sex, 0

SUBMITTER: Liao W 

PROVIDER: S-EPMC11493250 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature