Extracting Concepts Related to Homelessness from the Free Text of VA Electronic Medical Records

Adi V Gundlapalli, Marjorie E Carter, Guy Divita, Shuying Shen, Miland Palmer, Brett South, B S Begum Durgahee, Andrew Redd, Matthew Samore
2014 AMIA Annual Symposium Proceedings  
Mining the free text of electronic medical records (EMR) using natural language processing (NLP) is an effective method of extracting information not always captured in administrative data. We sought to determine if concepts related to homelessness, a non-medical condition, were amenable to extraction from the EMR of Veterans Affairs (VA) medical records. As there were no off-the-shelf products, a lexicon of terms related to homelessness was created. A corpus of free text documents from
more » ... nt encounters was reviewed to create the reference standard for NLP training and testing. V3NLP Framework was used to detect instances of lexical terms and was compared to the reference standard. With a positive predictive value of 77% for extracting relevant concepts, this study demonstrates the feasibility of extracting positively asserted concepts related to homelessness from the free text of medical records.
pmid:25954364 pmcid:PMC4419940 fatcat:73gwq2ugcrdkjhnydi3j63pr44