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

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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

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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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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.
ORCID: Savova, G. K.
Hurdle, J. F.
Venue: Yearbook of Medical Informatics
Publisher: Georg Thieme Verlag KG
Published: 2008-08
Volume: 17
Issue: 01
Pages: 128-144
ISSN: 0943-4747 (ISSN-L)
2364-0502
DOI: 10.1055/s-0038-1638592
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: bronze
doi.org
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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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

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

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