Discovering de facto diagnosis specialties

Xun Lu, Aston Zhang, Carl Gunter, Daniel Fabbri, David Liebovitz, Bradley Malin

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

Abstract

In health care institutions, medical specialty information may be lacking or inaccurate. Diagnosis histories offer information on which medical specialties may exist in practice, regardless of whether they have official codes. We refer to such specialties that are predicted with high certainty by diagnosis histories de facto diagnosis specialties. We aim to discover de facto diagnosis specialties under a general discovery-evaluation framework. Specifically, we employ a semi-supervised learning model and an unsupervised learning method for discovery. We further employ four supervised learning models for evaluation. We use one year of diagnosis histories from a major medical center, which consists of two data sets: one is fine-grained and the other is general. The semi-supervised learning model discovers a specialty for Breast Cancer on the fine-grained data set; while the unsupervised learning method confirms this discovery and suggests another specialty for Obesity on the larger general data set. The evaluation results reinforce that these two specialties can be recognized accurately by supervised learning models in comparison with 12 common diagnosis specialties defined by the Health Care Provider Taxonomy Code Set.

Original languageEnglish (US)
Title of host publicationBCB 2015 - 6th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics
PublisherAssociation for Computing Machinery, Inc
Pages7-16
Number of pages10
ISBN (Electronic)9781450338530
DOIs
StatePublished - Sep 9 2015
Event6th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, BCB 2015 - Atlanta, United States
Duration: Sep 9 2015Sep 12 2015

Publication series

NameBCB 2015 - 6th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics

Other

Other6th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, BCB 2015
CountryUnited States
CityAtlanta
Period9/9/159/12/15

Keywords

  • Data mining
  • Electronic health record
  • Medical informatics

ASJC Scopus subject areas

  • Software
  • Health Informatics
  • Computer Science Applications
  • Biomedical Engineering

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  • Cite this

    Lu, X., Zhang, A., Gunter, C., Fabbri, D., Liebovitz, D., & Malin, B. (2015). Discovering de facto diagnosis specialties. In BCB 2015 - 6th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (pp. 7-16). (BCB 2015 - 6th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics). Association for Computing Machinery, Inc. https://doi.org/10.1145/2808719.2808720