Predicting Practice Setting Using Topic Modeling

Liwei Wang, Yanshan Wang, Feichen Shen, Majid Rastegar-Mojarad, Hongfang D Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The implementation of problem lists in EHRs has a potential to help practitioners to provide customized care to patients. However, it remains an open question on how to leverage problem lists in different practice settings to provide tailored care, of which the bottleneck lies in the associations between problem list and practice setting. In this study, we investigated their association and predicted practice setting based on problem list using topic modeling.

Original languageEnglish (US)
Title of host publicationProceedings - 2018 IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages62-63
Number of pages2
ISBN (Electronic)9781538667774
DOIs
StatePublished - Jul 16 2018
Externally publishedYes
Event6th IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018 - New York, United States
Duration: Jun 4 2018Jun 7 2018

Other

Other6th IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018
CountryUnited States
CityNew York
Period6/4/186/7/18

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Keywords

  • latent dirichlet allocation
  • practice setting
  • problem list
  • topic modeling

ASJC Scopus subject areas

  • Information Systems and Management
  • Health Informatics

Cite this

Wang, L., Wang, Y., Shen, F., Rastegar-Mojarad, M., & Liu, H. D. (2018). Predicting Practice Setting Using Topic Modeling. In Proceedings - 2018 IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018 (pp. 62-63). [8411806] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICHI-W.2018.00020