Extraction and validation of patient housing and food insecurity status in a large electronic health records database using selective prediction and active learning

Objective Information on patient social determinants of health is frequently recorded in unstructured clinical notes, making it inaccessible for researchers and policymakers. We aimed to extract and validate food and housing insecurity status on a large electronic health record-derived patient cohort by combining selective prediction and active learning. Materials and Methods Manually labeled charts selected via active learning were used to train L1-regularized logistic regression models to identify the presence of food insecurity (N=372, 42% event rate) and housing insecurity (N=559, 36% event rate) in clinical notes. In addition to validating predictions against labeled data, we further validated predictions on an additional unlabeled dataset through associative studies with demographic, clinical, and environmental variables with known associations with food and housing insecurity. Results The food insecurity model had AUC=0.83, sensitivity=0.90, PPV=0.90, and undetermined rate=0.59 (n=149); the housing insecurity model had AUC=0.81, sensitivity=0.50, PPV=1, and undetermined rate=0.65 (n=224). Out of 4,337 unlabeled patients, the 395 (9%) patients predicted to have food insecurity were more likely to be Hispanic/Latino (48% vs 24%, p<0.001) and have diabetes (34% vs 12%), hypertension (43% vs 11%), and heart disease (12% vs 0.7%) (p<0.001 for all). Discussion Selective prediction and active learning can facilitate efficient labeling of social determinants of health from unstructured EHR data to identify vulnerable populations and targets for healthcare system and policy intervention. Conclusion Machine learning can be used to extract high-fidelity information on patient food and housing insecurity status. ### Competing Interest Statement AS reports stock ownership in Roche (RHHVF). JHC reports royalties from Reaction Explorer LLC; consulting fees from National Institute of Drug Abuse Clinical Trials Network, Tuolc Inc, Roche Inc; and payment for expert testimony from Younker Hyde MacFarlane PLLC and Sutton Pierce. ### Funding Statement This study did not receive any funding ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Ethics approval was granted through Stanford University IRB (#65834). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes All data needed to evaluate the conclusions are present in the paper and in the Supplementary Materials. The datasets generated analyzed during the current study are not publicly available due to patient privacy but are available from the corresponding author (JHC) on reasonable request.