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Detecting Earlier Indicators of Homelessness in the Free Text of Medical Records
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2014
Year
EngineeringEarly Warning IndicatorsCorpus LinguisticsLanguage ProcessingText MiningNatural Language ProcessingInformation RetrievalData ScienceComputational LinguisticsDocument ClassificationLanguage StudiesPublic HealthFree TextContent AnalysisNamed-entity RecognitionStatisticsHealth Services ResearchVulnerable Patient PopulationHousingNlp TaskUs VeteransMedical Language ProcessingInformation ExtractionMedical RecordsHealth DataVulnerable PopulationText ProcessingLinguisticsHealth InformaticsHomelessness
Early warning indicators to identify US Veterans at risk of homelessness are currently only inferred from administrative data. References to indicators of risk or instances of homelessness in the free text of medical notes written by Department of Veterans Affairs (VA) providers may precede formal identification of Veterans as being homeless. This represents a potentially untapped resource for early identification. Using natural language processing (NLP), we investigated the idea that concepts related to homelessness written in the free text of the medical record precede the identification of homelessness by administrative data. We found that homeless Veterans were much higher utilizers of VA resources producing approximately 12 times as many documents as non-homeless Veterans. NLP detected mentions of either direct or indirect evidence of homelessness in a significant portion of Veterans earlier than structured data.