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However, in the Electronic Health Record, data related to many social risk factors are primarily recorded in free-text clinical notes, rather than as more readily computable structured data, and hence cannot currently be easily incorporated into automated assessments of health. In this paper, we present\n                      <jats:italic>Moonstone<\/jats:italic>\n                      , a new, highly configurable rule-based clinical natural language processing system designed to automatically extract information that requires inferencing from clinical notes. Our initial use case for the tool is focused on the automatic extraction of social risk factor information \u2014 in this case,\n                      <jats:italic>housing situation<\/jats:italic>\n                      ,\n                      <jats:italic>living alone<\/jats:italic>\n                      , and\n                      <jats:italic>social support<\/jats:italic>\n                      \u2014 from clinical notes. Nursing notes, social work notes, emergency room physician notes, primary care notes, hospital admission notes, and discharge summaries, all derived from the Veterans Health Administration, were used for algorithm development and evaluation.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      An evaluation of Moonstone demonstrated that the system is highly accurate in extracting and classifying the three variables of interest (\n                      <jats:italic>housing situation<\/jats:italic>\n                      ,\n                      <jats:italic>living alone<\/jats:italic>\n                      , and\n                      <jats:italic>social support<\/jats:italic>\n                      ). The system achieved positive predictive value (i.e. precision) scores ranging from 0.66 (\n                      <jats:italic>homeless\/marginally housed<\/jats:italic>\n                      ) to 0.98 (\n                      <jats:italic>lives at home\/not homeless<\/jats:italic>\n                      ), accuracy scores ranging from 0.63 (\n                      <jats:italic>lives in facility<\/jats:italic>\n                      ) to 0.95 (\n                      <jats:italic>lives alone<\/jats:italic>\n                      ), and sensitivity (i.e. recall) scores ranging from 0.75 (\n                      <jats:italic>lives in facility<\/jats:italic>\n                      ) to 0.97 (\n                      <jats:italic>lives alone<\/jats:italic>\n                      ).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>\n                      The Moonstone system is \u2014 to the best of our knowledge \u2014 the first freely available, open source natural language processing system designed to extract social risk factors from clinical text with good (\n                      <jats:italic>lives in facility<\/jats:italic>\n                      ) to excellent (\n                      <jats:italic>lives alone<\/jats:italic>\n                      ) performance. Although developed with the social risk factor identification task in mind, Moonstone provides a powerful tool to address a range of clinical natural language processing tasks, especially those tasks that require nuanced linguistic processing in conjunction with inference capabilities.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s13326-019-0198-0","type":"journal-article","created":{"date-parts":[[2019,4,11]],"date-time":"2019-04-11T01:20:24Z","timestamp":1554945624000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":65,"title":["Moonstone: a novel natural language processing system for inferring social risk from clinical narratives"],"prefix":"10.1186","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3209-8108","authenticated-orcid":false,"given":"Mike","family":"Conway","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Salomeh","family":"Keyhani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lee","family":"Christensen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brett R.","family":"South","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marzieh","family":"Vali","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Louise C.","family":"Walter","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Danielle L.","family":"Mowery","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Samir","family":"Abdelrahman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wendy W.","family":"Chapman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2019,4,11]]},"reference":[{"issue":"21","key":"198_CR1","doi-asserted-by":"publisher","first-page":"815","DOI":"10.1136\/amiajnl-2013-001934","volume":"5","author":"Y Ye","year":"2014","unstructured":"Ye Y, Tsui FR, Wagner M, Espino JU, Li Q. 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