Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress 2023 EHA Congress 2023 ASCO Annual Meeting
Clinical Collections
Advances in Alzheimer’s

At a glance: The ONWARDS insulin icodec trials

A round-up of the ONWARDS phase 3 clinical trials evaluating the novel weekly insulin icodec in people with diabetes.
ADVANCED SEARCH
Log in
Top
BMC Medical Research Methodology
Published in: BMC Medical Research Methodology 1/2019

Open Access 01-12-2019 | Research article

Current approaches to identify sections within clinical narratives from electronic health records: a systematic review

Authors: Alexandra Pomares-Quimbaya, Markus Kreuzthaler, Stefan Schulz

Published in: BMC Medical Research Methodology | Issue 1/2019

Login to get access

Abstract

Background

The identification of sections in narrative content of Electronic Health Records (EHR) has demonstrated to improve the performance of clinical extraction tasks; however, there is not yet a shared understanding of the concept and its existing methods. The objective is to report the results of a systematic review concerning approaches aimed at identifying sections in narrative content of EHR, using both automatic or semi-automatic methods.

Methods

This review includes articles from the databases: SCOPUS, Web of Science and PubMed (from January 1994 to September 2018). The selection of studies was done using predefined eligibility criteria and applying the PRISMA recommendations. Search criteria were elaborated by using an iterative and collaborative keyword enrichment.

Results

Following the eligibility criteria, 39 studies were selected for analysis. The section identification approaches proposed by these studies vary greatly depending on the kind of narrative, the type of section, and the application. We observed that 57% of them proposed formal methods for identifying sections and 43% adapted a previously created method. Seventy-eight percent were intended for English texts and 41% for discharge summaries. Studies that are able to identify explicit (with headings) and implicit sections correspond to 46%. Regarding the level of granularity, 54% of the studies are able to identify sections, but not subsections. From the technical point of view, the methods can be classified into rule-based methods (59%), machine learning methods (22%) and a combination of both (19%). Hybrid methods showed better results than those relying on pure machine learning approaches, but lower than rule-based methods; however, their scope was more ambitious than the latter ones. Despite all the promising performance results, very few studies reported tests under a formal setup. Almost all the studies relied on custom dictionaries; however, they used them in conjunction with a controlled terminology, most commonly the UMLSⓇ metathesaurus.

Conclusions

Identification of sections in EHR narratives is gaining popularity for improving clinical extraction projects. This study enabled the community working on clinical NLP to gain a formal analysis of this task, including the most successful ways to perform it.
Literature
1.
Apostolova E, Channin DS, Demner-Fushman D, Furst J, Lytinen S, Raicu D. Automatic segmentation of clinical texts. In: Conf Proc IEEE Eng Med Biol Soc.: 5905-8. New York: IEEE: 2009.
2.
3.
Bramsen P, Deshpande P, Lee YK, Barzilay R. Finding temporal order in discharge summaries. In: AMIA Annual Symposium. USA: American Medical Informatics Association: 2006.
4.
Chapman WW, Savova GK, Zheng J, Tharp M, Crowley R. Anaphoric reference in clinical reports: characteristics of an annotated corpus. J Biomed Inform. 2012; 45(3):507–21.PubMedCrossRef
5.
Chen C, Chang N, Chang Y, Dai H. Section heading recognition in electronic health records using conditional random fields. In: TAAI, volume 8916 of LNCS. Springer: 2014. p. 47–55.
6.
Cho PS, Taira RK, Kangarloo H. Automatic section segmentation of medical reports. In: AMIA Annual Symposium. USA: American Medical Informatics Association; 2003;2003:155.
7.
Dai H-J, Syed-Abdul S, Chen C-W, Wu C-C. Recognition and evaluation of clinical section headings in clinical documents using token-based formulation with conditional random fields. BioMed Res Int. 2015; 2015:10.
8.
Deléger L, Névéol A. Identification automatique de zones dans des documents pour la constitution d’un corpus médical en français). In: Traitement Automatique des Langues Naturelles, Marseille, France: Association pour le Traitement Automatique des Langues: 2014. p. 568–73.
9.
Denny JC, Miller RA, Johnson KB, Spickard-III A. Development and evaluation of a clinical note section header terminology. In: AMIA Annual Symposium, Washington, DC, USA, November 8-12. USA: American Medical Informatics Association: 2008.
10.
Denny JC, Spickard-III A, Johnson K, Peterson N, Peterson J, Miller R. Evaluation of a method to identify and categorize section headers in clinical documents. JAMIA. 2009; 16(6):806–15.PubMed
11.
Denny JC, Spickard-III A, Speltz P, Porier R, Rosenstiel DE, Powers JS. Using natural language processing to provide personalized learning opportunities from trainee clinical notes. J Biomed Inform. 2015; 56:292–9.PubMedCrossRef
12.
Doan S, Bastarache L, Klimkowski S, Denny JC, Xu H. Integrating existing natural language processing tools for medication extraction from discharge summaries. J Am Med Inform Assoc. 2010; 17(5):528–31.PubMedPubMedCentralCrossRef
13.
Dolin RH, Alschuler L, Boyer S, Beebe C, Behlen FM, Biron PV, Shabo (Shvo) A. Hl7 clinical document architecture, release 2. J Am Med Inform Assoc. 2006; 13(1):30–9.PubMedPubMedCentralCrossRef
14.
Edinger T, Demner-Fushman D, Cohen AM, Bedrick S, Hersh W. Evaluation of clinical text segmentation to facilitate cohort retrieval. AMIA Annu Symp. 2017; 2017:660–9.
15.
Friedman C, Shagina L, Lussier YA, Hripcsak G. Automated encoding of clinical documents based on natural language processing. J Am Med Inform Assoc. 2004; 11(5):392–402.PubMedPubMedCentralCrossRef
16.
Ganesan K, Subotin M. A general supervised approach to segmentation of clinical texts. In: IEEE International Conference on Big Data, Washington, DC, USA, October 27-30. New York: IEEE: 2014. p. 33–40.
17.
Haug PJ, Wu X, Ferraro JP, Savova G, Huff SM, Chute CG. Developing a section labeler for clinical documents. AMIA Ann Symp. 2014; 2014:636–44.
18.
Hsu W, Han SX, Arnold CW, Bui AAT, Enzmann DR. A data-driven approach for quality assessment of radiologic interpretations. J Am Med Inform Assoc. 2016; 23(e1):e152–6.PubMedCrossRef
19.
Häyrinen K, Saranto K, Nykänen P. Definition, structure, content, use and impacts of electronic health records: A review of the research literature. Int J Med Inform. 2008; 77(5):291–304.PubMedCrossRef
20.
Jancsary J, Matiasek J, Trost H. Revealing the structure of medical dictations with conditional random fields. In: Conference on Empirical Methods in Natural Language Processing, 25-27 October. Honolulu: Association for Computational Linguistics: 2008. p. 1–10.
21.
Jancsary JM. Recognizing structure in report transcripts. Master’s thesis, Vienna University of Technology. 2008.
22.
Johnson SB, Bakken S, Dine D, Hyun S, Mendonça E, Morrison F, Bright T, Van Vleck T, Wrenn J, Stetson P. An electronic health record based on structured narrative. JAMIA. 2008; 15(1):54–64.PubMed
23.
Kozier B, Erb G. Kozier & Erb’s Techniques in Clinical Nursing: Basic to Intermediate Skills. New Jersey: Pearson/Prentice Hall; 2004.
24.
Kreuzthaler M, Schulz S. Detection of sentence boundaries and abbreviations in clinical narratives. BMC Med Inform Decis Making. 2015; 2(15 Suppl):S4.CrossRef
25.
Kropf S, Krücken P, Mueller W, Denecke K. Structuring legacy pathology reports by openEHR archetypes to enable semantic querying. Methods Inf Med. 2017; 56(3):230–7.PubMedCrossRef
26.
Lai K, Topaz M, Goss F, Zhou L. Automated misspelling detection and correction in clinical free-text records. J Biomed Inform. 2015; 55:188–95.PubMedCrossRef
27.
Lee W, Choi J. Temporal segmentation for capturing snapshots of patient histories in korean clinical narrative. Healthc Inform Res. 2018; 24(3):179–86.PubMedPubMedCentralCrossRef
28.
Lei J, Tang B, Lu X, Gao K, Jiang M, Xu H. A comprehensive study of named entity recognition in chinese clinical text. JAMIA. 2014; 21(5):808–14.PubMed
29.
Li Y, Lipsky Gorman S, Elhadad N. Section classification in clinical notes using supervised hidden markov model. In: 1st ACM International Health Informatics Symposium. New York: ACM: 2010. p. 744–50.
30.
Lin C, Karlson EW, Dligach D, Ramirez MP, Miller TA, Mo H, Braggs NS, Cagan A, Gainer VS, Denny JC, Savova GK. Automatic identification of methotrexate-induced liver toxicity in patients with rheumatoid arthritis from the electronic medical record. J Am Med Inform Assoc. 2015; 22(e1):e151–61.PubMedCrossRef
31.
Lohr C, Luther S, Matthies F, Modersohn L, Ammon D, Saleh K, Henkel AG, Kiehntopf M, Hahn U. Cda-compliant section annotation of german-language discharge summaries: Guideline development, annotation campaign, section classificationl. AMIA Annu Symp. 2018; 2018:770–9.
32.
Ludwick DA, Doucette J. Adopting electronic medical records in primary care: Lessons learned from health information systems implementation experience in seven countries. I J Med Inf. 2009; 78(1):22–31.CrossRef
33.
McDonald CJ, Huff SM, Suico JG, Hill G, Leavelle D, Aller R, Forrey A, Mercer K, DeMoor G, Hook J, Williams W, Case J, Maloney P. Loinc, a universal standard for identifying laboratory observations: A 5-year update. Clin Chem. 2003; 49(4):624–33.PubMedCrossRef
34.
Mehrabi S, Krishnan A, Roch AM, Schmidt H, Li D, Kesterson J, Beesley C, Dexter P, Schmidt M, Palakal M, Liu H. Identification of Patients with Family History of Pancreatic Cancer–Investigation of an NLP System Portability. Stud Health Technol Inform. 2015; 216:604–8.PubMedPubMedCentral
35.
Melton GB, Wang Y, Arsoniadis EG, Pakhomov SVS, Adam TJ, Kwaan MR, Rothenberger DA, Chen ES. Analyzing operative note structure in development of a section header resource. In: MedInfo, volume 216 of Studies in Health Technology and Informatics. IOS Press: 2015. p. 821–6.
36.
Meystre S, Haug P. Automation of a problem list using natural language processing. BMC Med Inform Decis Making. 2005; 5:30.CrossRef
37.
Miller GA. Wordnet: A lexical database for english. Commun ACM. 1995; 38(11):39–41. ISSN 0001-0782.CrossRef
38.
Miller R, Masarie FE, Myers JD. Quick medical reference (qmr) for diagnostic assistance. MD Comput. 1986; 3(5):34–48.PubMed
39.
Mowery DL, Wiebe J, Visweswaran S, Harkema H, Chapman WW. Building an automated SOAP classifier for emergency department reports. J Biomed Inform. 2012; 45(1):71–81.PubMedCrossRef
40.
Ni J, Delaney B, Florian R. Fast model adaptation for automated section classification in electronic medical records. In: MedInfo, volume 216 of Studies in Health Technology and Informatics. Netherlands: IOS Press: 2015. p. 35–9.
41.
Orosz G, Novȧk A, Prȯszėky G. Hybrid text segmentation for hungarian clinical records. In: Advances in Artificial Intelligence and Its Applications - 12th Mexican International Conference on Artificial Intelligence, Mexico City, Mexico, November 24-30: 2013. p. 306–17.
42.
Phuong ND, Chau VTN. Automatic de-identification of medical records with a multilevel hybrid semi-supervised learning approach. In: RIVF. New York: IEEE: 2016. p. 43–8.
43.
Pomares-Quimbaya A, Sierra-Múnera A, González RA, Daza-Rodríguez JC, Muñoz-Velandia OM, Garcia-Peña AA, Labbé C. Named entity recognition over electronic health records through a combined dictionary-based approach. Procedia Comput Sci. 2016; 100:55–61. ISSN 1877-0509.CrossRef
44.
Rokach L, Romano R, Maimon O. Negation recognition in medical narrative reports. Inf Retr. 2008; 11(6):499–538.CrossRef
45.
Rubin DL, Desser TS. A data warehouse for integrating radiologic and pathologic data. J Am Coll Radiol. 2008; 5(3):210–7.PubMedCrossRef
46.
Sadoughi N, Finley GP, Edwards E, Robinson A, Korenevsky M, Brenndoerfer M, Axtmann N, Miller M, Suendermann-Oeft D. Detecting section boundaries in medical dictations: Toward real-time conversion of medical dictations to clinical reports. In: SPECOM, volume 11096 of LNCS. Springer: 2018. p. 563–73.
47.
Safran C, Bloomrosen M, Hammond W, Labkoff S, Markel-Fox S, Tang P, Detmer D. Toward a national framework for the secondary use of health data: An american medical informatics association white paper. JAMIA. 2007; 14(1):1–9.PubMed
48.
Savova GK, Masanz JJ, Ogren PV, Zheng J, Sohn S, Schuler KK, Chute CG. Mayo clinical text analysis and knowledge extraction system (ctakes): architecture, component evaluation and applications. J Am Med Inform Assoc. 2010; 17(5):507–13.PubMedPubMedCentralCrossRef
49.
Schadow G, McDonald CJ. Extracting structured information from free text pathology reports. In: AMIA Annual Symposium, Washington, DC, USA, November 8-12. USA: American Medical Informatics Association: 2003.
50.
Schuemie M, Trieschnigg D, Meij E. Dutchhattrick: Semantic query modeling, context, section detection, and match score maximization. In: Proceedings of the Twentieth Text REtrieval Conference (TREC 2011). US: National Institute of Standards and Technology: 2011. p. 26.
51.
Shivade CP, Malewadkar P, Fosler-Lussier E, Lai AM. Comparison of UMLS terminologies to identify risk of heart disease using clinical notes. J Biomed Inform. 2015; 58:S103–10.PubMedPubMedCentralCrossRef
52.
53.
Suominen H, Salakoski T. Supporting communication and decision making in finnish intensive care with language technology. J Healthc Eng. 2010; 1(4):595–614.CrossRef
54.
Suominen H, Lundgrén-Laine H, Salanterä S, Karsten H, Salakoski T. Information flow in intensive care narratives. In: IEEE International Conference on Bioinformatics and Biomedicine Workshop. New York: IEEE: 2009. p. 325–40.
55.
Taira RK, Soderland SG, Jakobovits RM. Automatic structuring of radiology free-text reports. Radiographics Rev Publ Radiol Soc N Am Inc. 2001; 21(1):237–45.
56.
Tchraktchiev D, Angelova G, Boytcheva S, Angelov Z, Zacharieva S. Completion of structured patient descriptions by semantic mining. Stud Health Technol Inf. 2011; 166:260–9.
57.
Tepper M, Capurro D, Xia F, Vanderwende L, Yetisgen-Yildiz M. Statistical section segmentation in free-text clinical records. In: LREC. Istanbul: European Language Resources Association (ELRA): 2012. p. 2001–8.
58.
Tran LT, Divita G, Redd A, Carter ME, Samore MH, Gundlapalli AV. Scaling out and evaluation of obsecan, an automated section annotator for semi-structured clinical documents, on a large VA clinical corpus. In: AMIA Annual Symposium: 2015.
59.
60.
Wang X, Chase HS, Markatou M, Hripcsak G, Friedman C. Selecting information in electronic health records for knowledge acquisition. J Biomed Inform. 2010; 43(4):595–601.PubMedPubMedCentralCrossRef
61.
Wang Y, Wang L, Rastegar-Mojarad M, Moon S, Shen F, Afzal N, Liu S, Zeng Y, Mehrabi S, Sohn S, Liu H. Clinical information extraction applications: A literature review. J Biomed Inform. 2018; 77:34–49.PubMedCrossRef
62.
Waranusast R, Haddawy P, Dailey M. Segmentation of text and non-text in on-line handwritten patient record based on spatio-temporal analysis. In: 12th Conference on Artificial Intelligence in Medicine. Berlin: Springer-Verlag: 2009. p. 345–54.
63.
64.
Weiskopf NG, Hripcsak G, Swaminathan S, Weng C. Defining and measuring completeness of electronic health records for secondary use. J Biomed Inform. 2013; 46(5).PubMedCrossRef
65.
Xu Y, Liu J, Wu J, Wang Y, Tu Z, Sun J, Tsujii J, Chang EI. A classification approach to coreference in discharge summaries: 2011 i2b2 challenge. J Am Med Inform Assoc. 2012; 19(5):897–905.PubMedPubMedCentralCrossRef
66.
Zheng J, Chapman W, Crowley R, Savova G. Coreference resolution: A review of general methodologies and applications in the clinical domain. J Biomed Inform. 2011; 44(6):1113–22.PubMedPubMedCentralCrossRef
67.
Zweigenbaum P, Deléger L, Lavergne T, Névéol A, Bodnari A. A supervised abbreviation resolution system for medical text. In: Working Notes for CLEF Conference, Valencia, Spain, September 23-26, volume 1179 of CEUR Workshop Proceedings: 2013.
Metadata
Title
Current approaches to identify sections within clinical narratives from electronic health records: a systematic review
Authors
Alexandra Pomares-Quimbaya
Markus Kreuzthaler
Stefan Schulz
Publication date
01-12-2019
Publisher
BioMed Central
Published in
BMC Medical Research Methodology / Issue 1/2019
Electronic ISSN: 1471-2288
DOI
https://doi.org/10.1186/s12874-019-0792-y

Other articles of this Issue 1/2019

BMC Medical Research Methodology 1/2019 Go to the issue

Research article

Aims, design and preliminary findings of the Hellenic National Nutrition and Health Survey (HNNHS)

Technical advance

A Bayesian hierarchical logistic regression model of multiple informant family health histories

Research article

A community-based intervention to increase participation in cervical cancer screening among immigrants in Norway

Research article

How do studies assess the preventability of readmissions? A systematic review with narrative synthesis

Research article

An innovative method for clinical practice guideline contextualisation for chronic musculoskeletal pain in the South African context

Research article

Graphical comparisons of relative disease burden across multiple risk factors