Extracting Information on Lifestyle Issues from Clinical Narratives in EHR

Our team has been involved in developing a clinical decision support system (CDSS), which requires information about patients’ lifestyle. However, patients’ lifestyle issues are usually encoded in clinician generated narrative texts, which poses significant barriers to their information accessibilit...

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Bibliographic Details
Published inJapan Journal of Medical Informatics Vol. 37; no. 6; pp. 313 - 321
Main Authors Ma, Xiaojun, Shinohara, Emiko, Han, Hao, Ishii, Masamichi, Imai, Takeshi, Ohe, Kazuhiko
Format Journal Article
LanguageEnglish
Published Japan Association for Medical Informatics 2017
Subjects
Lifestyle issues
Machine learning
Natural language processing
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Summary:Our team has been involved in developing a clinical decision support system (CDSS), which requires information about patients’ lifestyle. However, patients’ lifestyle issues are usually encoded in clinician generated narrative texts, which poses significant barriers to their information accessibility. In this paper, we propose an approach to identifying lifestyle issues of obesity, smoking and drinking in electronic health records (EHR) using machine learning and natural language processing techniques. To evaluate our approach, we conduct experiments using clinical narratives from The University of Tokyo Hospital which were generated in 2015 and saved in SS-MIX2 extended storage. The experimental results show that the proposed approach achieves equivalent high performance compared to previous studies focusing on English discharge summaries.
ISSN:0289-8055
2188-8469
DOI:10.14948/jami.37.313