Sunyang Fu1, David Chen2, Huan He3, Sijia Liu4, Sungrim Moon5, Kevin J Peterson6, Feichen Shen7, Liwei Wang8, Yanshan Wang9, Andrew Wen10, Yiqing Zhao11, Sunghwan Sohn12, Hongfang Liu13. 1. Department of Health Sciences Research, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, United States; University of Minnesota - Twin Cities, Minneapolis, MN 55455, United States. Electronic address: Fu.Sunyang@mayo.edu. 2. Department of Health Sciences Research, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, United States. Electronic address: Chen.David@mayo.edu. 3. Department of Health Sciences Research, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, United States. Electronic address: He.Huan@mayo.edu. 4. Department of Health Sciences Research, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, United States. Electronic address: Liu.Sijia@mayo.edu. 5. Department of Health Sciences Research, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, United States. Electronic address: Moon.Sungrim@mayo.edu. 6. Department of Information Technology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, United States; University of Minnesota - Twin Cities, Minneapolis, MN 55455, United States. Electronic address: Peterson.Kevin@mayo.edu. 7. Department of Health Sciences Research, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, United States. Electronic address: Shen.Feichen@mayo.edu. 8. Department of Health Sciences Research, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, United States. Electronic address: Wang.Liwei@mayo.edu. 9. Department of Health Sciences Research, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, United States. Electronic address: Wang.Yanshan@mayo.edu. 10. Department of Health Sciences Research, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, United States. Electronic address: Wen.Andrew@mayo.edu. 11. Department of Health Sciences Research, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, United States. Electronic address: Zhao.Yiqing@mayo.edu. 12. Department of Health Sciences Research, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, United States. Electronic address: Sohn.Sunghwan@mayo.edu. 13. Department of Health Sciences Research, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, United States; University of Minnesota - Twin Cities, Minneapolis, MN 55455, United States. Electronic address: Liu.Hongfang@mayo.edu.
Abstract
BACKGROUND: Concept extraction, a subdomain of natural language processing (NLP) with a focus on extracting concepts of interest, has been adopted to computationally extract clinical information from text for a wide range of applications ranging from clinical decision support to care quality improvement. OBJECTIVES: In this literature review, we provide a methodology review of clinical concept extraction, aiming to catalog development processes, available methods and tools, and specific considerations when developing clinical concept extraction applications. METHODS: Based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a literature search was conducted for retrieving EHR-based information extraction articles written in English and published from January 2009 through June 2019 from Ovid MEDLINE In-Process & Other Non-Indexed Citations, Ovid MEDLINE, Ovid EMBASE, Scopus, Web of Science, and the ACM Digital Library. RESULTS: A total of 6,686 publications were retrieved. After title and abstract screening, 228 publications were selected. The methods used for developing clinical concept extraction applications were discussed in this review.
BACKGROUND: Concept extraction, a subdomain of natural language processing (NLP) with a focus on extracting concepts of interest, has been adopted to computationally extract clinical information from text for a wide range of applications ranging from clinical decision support to care quality improvement. OBJECTIVES: In this literature review, we provide a methodology review of clinical concept extraction, aiming to catalog development processes, available methods and tools, and specific considerations when developing clinical concept extraction applications. METHODS: Based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a literature search was conducted for retrieving EHR-based information extraction articles written in English and published from January 2009 through June 2019 from Ovid MEDLINE In-Process & Other Non-Indexed Citations, Ovid MEDLINE, Ovid EMBASE, Scopus, Web of Science, and the ACM Digital Library. RESULTS: A total of 6,686 publications were retrieved. After title and abstract screening, 228 publications were selected. The methods used for developing clinical concept extraction applications were discussed in this review.
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