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
Open accessAbstract
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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“The resulting underlying databases created by EHR systems contain large heterogeneous data sets that combine structured and formatted data elements such as diagnoses (International Classification of Diseases-10), procedures (Current Procedural Terminology® code), and medications (RxNorm), but also rich unstructured data such as clinical narratives, which represent over 80% of the data in EHRs (5).”
From Accessing Artificial Intelligence for Clinical Decision-Making (2021) · cites this paper as [5]
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“clinical narratives, which represent over 80% of the data in EHRs (5).”
From Accessing Artificial Intelligence for Clinical Decision-Making (2021) · cites this paper as [5]
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Savova, G. K., Masanz, J. J., Ogren, P. V., Zheng, J., Sohn, S., Kipper-Schuler, K. C., & Chute, C. G. (2010) · Journal of the American Medical Informatics Association · doi:10.1136/jamia.2009.001560 · Cited by 1,967
Self-citationShickel, B., Tighe, P. J., Bihorac, A., & Rashidi, P. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2017.2767063 · Cited by 1,688
Mining electronic health records: towards better research applications and clinical care
Jensen, P. B., Jensen, L. J., & Brunak, S. (2012) · Nature Reviews Genetics · doi:10.1038/nrg3208 · Cited by 1,680
Registries for Evaluating Patient Outcomes: A User’s Guide
(2020) · doi:10.23970/ahrqepcregistries4 · Cited by 974
A large language model for electronic health records
Yang, X., Chen, A., PourNejatian, N., Shin, H. C., Smith, K. E., Parisien, C., Compas, C., Martin, C., Costa, A. B., Flores, M. G., et al. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00742-2 · Cited by 847
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
- Publisher
- Georg Thieme Verlag KG
- 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
- 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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