The objective of this study was to discover biopsychosocial concepts from primary care that were statistically related to inappropriate emergency room use by using natural language processing tools. De-identified free text was extracted from a clinic in Guelph, Ontario and analyzed with MetaMap and GATE. Over 10 million concepts were extracted from 13,836 patient records. There were 77 codes that fell within the realm of biopsychosocial, were very statistically significant (p < 0.001) and had an OR > 2.0. Thematically, these codes involved mental health and pain related biopsychosocial concepts. Similar to other literature, pain and mental health problems are seen to be important factors of inappropriate emergency room use. Despite sources error in the NLP procedure, the study demonstrates the feasibly of combining natural language processing and primary care data to analyze the issue of inappropriate emergency room use. This technique could be used to analyze other, more complex problems. / Graduate
| Identifer | oai:union.ndltd.org:uvic.ca/oai:dspace.library.uvic.ca:1828/3860 |
| Date | 28 March 2012 |
| Creators | St-Maurice, Justin |
| Contributors | Kuo, Hsing |
| Source Sets | University of Victoria |
| Language | English, English |
| Detected Language | English |
| Type | Thesis |
| Rights | Available to the World Wide Web |
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