Predicting falls using electronic health records: a time series approach.

ABSTRACT:

Objective

To develop a more accurate fall prediction model within the Veterans Health Administration.

Materials and methods

The cohort included Veterans admitted to a Veterans Health Administration acute care setting from July 1, 2020, to June 30, 2022, with a length of stay between 1 and 7 days. Demographic and clinical data were obtained through electronic health records. Veterans were identified as having a documented fall through clinical progress notes. A transformer model was used to obtain features of this data, which was then used to train a Light Gradient-Boosting Machine for classification and prediction. Area under the precision-recall curve assisted in model tuning, with geometric mean used to define an optimal classification threshold.

Results

Among 242,8

SUBMITTER: Hoover PJ 

PROVIDER: S-EPMC12492486 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature