Supervised extraction of diagnosis codes from EMRS: Role of feature selection, data selection, and probabilistic thresholding

Anthony Rios, Ramakanth Kavuluru

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

12 Scopus citations

Abstract

Extracting diagnosis codes from medical records is a complex task carried out by trained coders by reading all the documents associated with a patient's visit. With the popularity of electronic medical records (EMRs), computational approaches to code extraction have been proposed in the recent years. Machine learning approaches to multi-label text classification provide an important methodology in this task given each EMR can be associated with multiple codes. In this paper, we study the the role of feature selection, training data selection, and probabilistic threshold optimization in improving different multi-label classification approaches. We conduct experiments based on two different datasets: a recent gold standard dataset used for this task and a second larger and more complex EMR dataset we curated from the University of Kentucky Medical Center. While conventional approaches achieve results comparable to the state-of-the-art on the gold standard dataset, on our complex in-house dataset, we show that feature selection, training data selection, and probabilistic thresholding provide significant gains in performance.

Original languageEnglish
Title of host publicationProceedings - 2013 IEEE International Conference on Healthcare Informatics, ICHI 2013
Pages66-73
Number of pages8
DOIs
StatePublished - 2013
Event2013 1st IEEE International Conference on Healthcare Informatics, ICHI 2013 - Philadelphia, PA, United States
Duration: Sep 9 2013Sep 11 2013

Publication series

NameProceedings - 2013 IEEE International Conference on Healthcare Informatics, ICHI 2013

Conference

Conference2013 1st IEEE International Conference on Healthcare Informatics, ICHI 2013
Country/TerritoryUnited States
CityPhiladelphia, PA
Period9/9/139/11/13

ASJC Scopus subject areas

  • Health Informatics

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