Advertisement

Automatic Identification of Substance Abuse from Social History in Clinical Text

  • Meliha YetisgenEmail author
  • Lucy Vanderwende
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10259)

Abstract

Substance abuse poses many negative health risks. Tobacco use increases the rates of many diseases such as coronary heart disease and lung cancer. Clinical notes contain rich information detailing the history of substance abuse from caregivers perspective. In this work, we present our work on automatic identification of substance abuse from clinical text. We created a publicly available dataset that has been annotated for three types of substance abuse including tobacco, alcohol, and drug, with 7 entity types per event, including status, type, method, amount, frequency, exposure-history and quit-history. Using a combination of machine learning and natural language processing approaches, our results on an unseen test set range from 0.51–0.58 F1 on stringent, full event, identification, and from 0.80–0.91 F1 for identification of the substance abuse event and status. These results indicate the feasibility of extracting detailed substance abuse information from clinical records.

Keywords

Clinical NLP Machine learning Information extraction 

References

  1. 1.
    Anand, P., Kunnumakara, A.B., Sundaram, C., et al.: Cancer is a preventable disease that requires major lifestyle changes. Pharm. Res. 25(9), 2097–2116 (2008)CrossRefGoogle Scholar
  2. 2.
    Srivastava, R.: Complicated lives – taking the social history. NEJM 265(7), 587–589 (2011)CrossRefGoogle Scholar
  3. 3.
    Melton, G.B., Manaktala, S., Sarkar, I.N., Chen, E.S.: Social and behavioral history information in public health datasets. In: AMIA Annual Symposium Proceedings 2012, pp. 625–634 (2012)Google Scholar
  4. 4.
    Uzuner, Ö., Goldstein, I., Luo, Y., Kohane, I.: Identifying patient smoking status from medical discharge records. J. Am. Med. Inform. Assoc. 15(1), 15–24 (2008)CrossRefGoogle Scholar
  5. 5.
    Cohen, A.M.: Five-way smoking status classification using text hot-spot identification and error-correcting output codes. J. Am. Med. Inform. Assoc. 15(1), 32–35 (2008)CrossRefGoogle Scholar
  6. 6.
    Clark, C., Good, K., Jezierny, L., Macpherson, M., Wilson, B., Chajewska, U.: Identifying smokers with a medical extraction system. J. Am. Med. Inform. Assoc. 15(1), 36–39 (2008)CrossRefGoogle Scholar
  7. 7.
    Jonnagaddala, J., Dai, H.J., Ray, P., Liaw, S.T.: A preliminary study on automatic identification of patient smoking status in unstructured electronic health records. In: ACL-IJCNLP 2015, pp. 147–151, 30 July 2015Google Scholar
  8. 8.
    Carter, E.W., Sarkar, I.N., Melton, G.B., Chen, E.S.: Representation of drug use in biomedical standards, clinical text, and research measures. In: AMIA Annual Symposium Proceeding 2015, pp. 376–385 (2015)Google Scholar
  9. 9.
    Chen, E., Garcia-Webb, M.: An analysis of free-text alcohol use documentation in the electronic health record: early findings and implications. Appl. Clin. Inform. 5(2), 402–415 (2014)CrossRefGoogle Scholar
  10. 10.
    Wang, Y., Chen, E.S., Pakhomov, S., Arsoniadis, E., Carter, E.W., Lindemann, E., Sarkar, I.N., Melton, G.B.: Automated extraction of substance use information from clinical texts. In: AMIA Annual Symposium Proceeding 2015, pp. 2121–2130, 5 November 2015Google Scholar
  11. 11.
    Tepper, M., Capurro, D., Xia, F., Vanderwende, L., Yetisgen-Yildiz, M.: Statistical section segmentation in free-text clinical records. In: Proceedings of LREC, Istanbul, May 2012Google Scholar
  12. 12.
    Millet, G.A.: WordNet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995)CrossRefGoogle Scholar
  13. 13.
    Bejan, C.A., Vanderwende, L., Xia, F., Yetisgen-Yildiz, M.: Assertion modeling and its role in clinical phenotype identification. J. Biomed. Inform. 46(1), 68–74 (2013)CrossRefGoogle Scholar
  14. 14.
    McCallum, A., Li, W.: Early results for named entity recognition with conditional random fields, feature induction and web-enhanced lexicons. In: Proceedings of CONLL at HLT-NAACL, pp. 188–191 (2003)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Biomedical and Health Informatics, School of MedicineUniversity of WashingtonSeattleUSA
  2. 2.Department of LinguisticsUniversity of WashingtonSeattleUSA
  3. 3.Microsoft ResearchRedmondUSA

Personalised recommendations