Phenotyping people with a history of injecting drug use within electronic medical records using an interactive machine learning approach.

ABSTRACT: People with a history of injecting drug use are a priority for eliminating blood-borne viruses and sexually transmissible infections. Identifying them for disease surveillance in electronic medical records (EMRs) is challenged by sparsity of predictors. This study introduced a novel approach to phenotype people who have injected drugs using structured EMR data and interactive human-in-the-loop methods. We iteratively trained random forest classifiers removing important features and adding new positive labels each time. The initial model achieved 92.7% precision and 93.5% recall. Models maintained >90% precision and recall after nine iterations, revealing combinations of less obvious features influencing predictions. Applied to approximately 1.7 million patients, the final model identified

SUBMITTER: El-Hayek C 

PROVIDER: S-EPMC11608217 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature