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

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Akshay Swaminathan , Wasan Kumar , Iván López , Edward Tran , Ujwal Srivastava , William Yang Wang , Max K Clary , Willemijn H. van Deursen , Jonathan G. Shaw , Olivier Gevaert , Jonathan H. Chen ·

YOU? · · 2022 · Open Access · · DOI: https://doi.org/10.1101/2022.12.06.22283140

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.

Related Topics

Food Security

Electronic Health Record

Machine Learning

Artificial Intelligence

Concepts

Food insecurity Logistic regression Electronic health record Cohort Machine learning Artificial intelligence Health records Random forest Healthy food Medicine Environmental health Computer science Health care Food security Geography Internal medicine Political science Food science Law Agriculture Archaeology Chemistry

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Type: preprint
Language: en
Landing Page: https://doi.org/10.1101/2022.12.06.22283140
PDF: https://www.medrxiv.org/content/medrxiv/early/2022/12/06/2022.12.06.22283140.full.pdf
OA Status: green
References: 23
Related Works: 10
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DOI: https://doi.org/10.1101/2022.12.06.22283140

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

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Type: preprint

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Language: en

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Publication year: 2022

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Publication date: 2022-12-06

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Authors: Akshay Swaminathan, Wasan Kumar, Iván López, Edward Tran, Ujwal Srivastava, William Yang Wang, Max K Clary, Willemijn H. van Deursen, Jonathan G. Shaw, Olivier Gevaert, Jonathan H. Chen

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Landing page: https://doi.org/10.1101/2022.12.06.22283140

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Concepts: Food insecurity, Logistic regression, Electronic health record, Cohort, Machine learning, Artificial intelligence, Health records, Random forest, Healthy food, Medicine, Environmental health, Computer science, Health care, Food security, Geography, Internal medicine, Political science, Food science, Law, Agriculture, Archaeology, Chemistry

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