Dataset Information

Mining 100 million notes to find homelessness and adverse childhood experiences: 2 case studies of rare and severe social determinants of health in electronic health records.

ABSTRACT: Objective:Understanding how to identify the social determinants of health from electronic health records (EHRs) could provide important insights to understand health or disease outcomes. We developed a methodology to capture 2 rare and severe social determinants of health, homelessness and adverse childhood experiences (ACEs), from a large EHR repository. Materials and Methods:We first constructed lexicons to capture homelessness and ACE phenotypic profiles. We employed word2vec and lexical associations to mine homelessness-related words. Next, using relevance feedback, we refined the 2 profiles with iterative searches over 100 million notes from the Vanderbilt EHR. Seven assessors manually reviewed the top-ranked results of 2544 patient visits relevant for homelessness and 1000 patients relevant for ACE. Results:word2vec yielded better performance (area under the precision-recall curve [AUPRC] of 0.94) than lexical associations (AUPRC?=?0.83) for extracting homelessness-related words. A comparative study of searches for the 2 phenotypes revealed a higher performance achieved for homelessness (AUPRC?=?0.95) than ACE (AUPRC?=?0.79). A temporal analysis of the homeless population showed that the majority experienced chronic homelessness. Most ACE patients suffered sexual (70%) and/or physical (50.6%) abuse, with the top-ranked abuser keywords being "father" (21.8%) and "mother" (15.4%). Top prevalent associated conditions for homeless patients were lack of housing (62.8%) and tobacco use disorder (61.5%), while for ACE patients it was mental disorders (36.6%-47.6%). Conclusion:We provide an efficient solution for mining homelessness and ACE information from EHRs, which can facilitate large clinical and genetic studies of these social determinants of health.

SUBMITTER: Bejan CA

PROVIDER: S-EPMC6080810 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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Mining 100 million notes to find homelessness and adverse childhood experiences: 2 case studies of rare and severe social determinants of health in electronic health records.

Bejan Cosmin A CA Angiolillo John J Conway Douglas D Nash Robertson R Shirey-Rice Jana K JK Lipworth Loren L Cronin Robert M RM Pulley Jill J Kripalani Sunil S Barkin Shari S Johnson Kevin B KB Denny Joshua C JC

Journal of the American Medical Informatics Association : JAMIA 20180101 1

<h4>Objective</h4>Understanding how to identify the social determinants of health from electronic health records (EHRs) could provide important insights to understand health or disease outcomes. We developed a methodology to capture 2 rare and severe social determinants of health, homelessness and adverse childhood experiences (ACEs), from a large EHR repository.<h4>Materials and methods</h4>We first constructed lexicons to capture homelessness and ACE phenotypic profiles. We employed word2vec a ...[more]

PMID: 29016793

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Dataset Information

Mining 100 million notes to find homelessness and adverse childhood experiences: 2 case studies of rare and severe social determinants of health in electronic health records.

Publications

Mining 100 million notes to find homelessness and adverse childhood experiences: 2 case studies of rare and severe social determinants of health in electronic health records.

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