Cited by 895Biomedical Text Mining and OntologiesMachine Learning in HealthcareElectronic Health Records Systems

The DOI matches its Crossref record; 173 of the 173 references it cites were found in a registry or an index.

Extracting Information from Textual Documents in the Electronic Health Record: A Review of Recent Research

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Savova, G. K., Kipper-Schuler, K. C., Hurdle, J. F., Meystre, S. M.·2008·Yearbook of Medical Informatics

Abstract

Summary: Objectives We examine recent published research on the extraction of information from textual documents in the Electronic Health Record (EHR). Methods Literature review of the research published after 1995, based on PubMed, conference proceedings, and the ACM Digital Library, as well as on relevant publications referenced in papers already included. Results 174 publications were selected and are discussed in this review in terms of methods used, pre-processing of textual documents, contextual features detection and analysis, extraction of information in general, extraction of codes and of information for decision-support and enrichment of the EHR, information extraction for surveillance, research, automated terminology management, and data mining, and de-identification of clinical text. Conclusions Performance of information extraction systems with clinical text has improved since the last systematic review in 1995, but they are still rarely applied outside of the laboratory they have been developed in. Competitive challenges for information extraction from clinical text, along with the availability of annotated clinical text corpora, and further improvements in system performance are important factors to stimulate advances in this field and to increase the acceptance and usage of these systems in concrete clinical and biomedical research contexts.

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Research works printed but not linked: 5

  1. Carbonell JG, Hayes PJ. Natural Language Under- standing. In: Shapiro SC, editor. Encyclopedia of Artificial Intelligence: Wiley;1992. p. 660-77.
  2. Sager N, Friedman C, Chi E. The analysis and processing of clinical narrative. In: Salamon R, Blum B, Jørgensen M, editors. Medinfo 86; 1986; Amsterdam (Holland): Elsevier; 1986. p. 1101-5.
  3. Uzuner O, Goldstein I, Luo Y, Kohane I. Identify- ing Patient Smoking Status from Medical Dis- charge Records. J Am Med Inform Assoc 2008 January-February;15(1):14-24. Epub 2007 Oct 18.
  4. Miller G. WordNet: a dictionary browser. Proc of the First International Conference on Information and Data; 1985; Ontario, Canada; 1985.
  5. Fellbaum C. WordNet: An electronic lexical data- base. Cambridge, MA: MIT Press; 1998.

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Metadata

Authors
Savova, G. K., Kipper-Schuler, K. C., Hurdle, J. F., & Meystre, S. M.
Affiliations
  • Mayo Clinic (US)
  • University of Utah (US)
Corresponding author
Meystre, S. M.
Venue
Yearbook of Medical Informatics
Published
2008-08
Volume
17
Issue
01
Pages
128-144
ISSN
  • 0943-4747 (ISSN-L)
  • 2364-0502
OpenAlex
W2114388055
Cited by
895
References
173
Citations by year
2026: 28 · 2025: 41 · 2024: 22 · 2023: 32 · 2022: 32 · 2021: 48 · 2020: 61 · 2019: 91 · 2018: 55 · 2017: 66 · 2016: 63 · 2015: 63 · 2014: 78 · 2013: 62 · 2012: 57
FWCI
13.15
Type
journal article
Language
en
Open access
Locations
  • Yearbook of Medical Informatics (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Medicine › Health Informatics › Biomedical Text Mining and Ontologies
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
  • Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
Keywords
information extraction, clinical text, electronic health records, clinical text mining, clinical decision support, clinical surveillance, de-identification, text preprocessing, contextual features
MeSH terms
Humans, Natural Language Processing, Population Surveillance, Information Storage and Retrieval, Medical Records Systems, Computerized, Vocabulary, Controlled, Biomedical Research

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