Automatic detection of inconsistencies between free text and coded data in sarcoma discharge letters

Ruty Rinott, Michele Torresani, Rossella Bertulli, Abigail Goldsteen, Paolo Casali, Boaz Carmeli, Noam Slonim

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

Abstract

Discordance between data stored in Electronic Health Records (EHR) may have a harmful effect on patient care. Automatic identification of such situations is an important yet challenging task, especially when the discordance involves information stored in free text fields. Here we present a method to automatically detect inconsistencies between data stored in free text and related coded fields. Using EHR data we train an ensemble of classifiers to predict the value of coded fields from the free text fields. Cases in which the classifiers predict with high confidence a code different from the clinicians' choice are marked as potential inconsistencies. Experimental results over discharge letters of sarcoma patients, verified by a domain expert, demonstrate the validity of our method.

Original languageEnglish
Title of host publicationStudies in Health Technology and Informatics
Pages661-666
Number of pages6
Volume180
DOIs
Publication statusPublished - 2012
Event24th Medical Informatics in Europe Conference, MIE 2012 - Pisa, Italy
Duration: Aug 26 2012Aug 29 2012

Other

Other24th Medical Informatics in Europe Conference, MIE 2012
Country/TerritoryItaly
CityPisa
Period8/26/128/29/12

Keywords

  • Clinical decision support
  • EHR
  • Machine learning
  • NLP

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management

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