Automated identification of relevant new information in clinical narrative

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

6 Scopus citations

Abstract

The ability to explore and visualize clinical information is important for clinicians when reviewing and cognitively synthesizing electronic clinical documents for new patients contained in electronic health record (EHR) systems. In this study, we explore the use of language models for detecting new and potentially relevant information within an individual patient's collection of clinical documents using an expert-based reference standard for evaluation. We achieved good accuracy with a heterogeneous system based on a modified n-gram language model with statistically-derived and classic stop word removal and lexical normalization, as well as heuristic rules. This technique also identified relevant new information not identified with the expert-derived reference standard. These methods appear promising for providing an automated means to improve the use of electronic documents by clinicians.

Original languageEnglish (US)
Title of host publicationIHI'12 - Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium
Pages837-841
Number of pages5
DOIs
StatePublished - Mar 8 2012
Event2nd ACM SIGHIT International Health Informatics Symposium, IHI'12 - Miami, FL, United States
Duration: Jan 28 2012Jan 30 2012

Publication series

NameIHI'12 - Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium

Other

Other2nd ACM SIGHIT International Health Informatics Symposium, IHI'12
CountryUnited States
CityMiami, FL
Period1/28/121/30/12

Keywords

  • Electronic health record
  • Information redundancy
  • Information retrieval
  • N-gram model
  • Natural language processing

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