Extraction of temporal relations from clinical free text: A systematic review of current approaches.
Background: Temporal relations between clinical events play an important role in clinical assessment and decision making. Extracting such relations from free text data is a challenging task because it lies on between medical natural language processing, temporal representation and temporal reasoning...
| Published in: | Journal of Biomedical Informatics Vol. 108 |
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| Main Authors: | , , |
| Format: | research Journal Article |
| Published: |
Academic Press Inc.
Aug2020
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=145213116&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 145213116 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15320464 OMB jtl: Journal of Biomedical Informatics issn: 15320464 maglogo: N pubinfo: dt: Aug2020 vid: 108 pid: 735 pub: Academic Press Inc. place: Burlington, Massachusetts artinfo: ui: 145213116 145213116 NLM32673788 145213116 10.1016/j.jbi.2020.103488 NLM32673788 145213116 ppct: 1 formats: tig: atl: Extraction of temporal relations from clinical free text: A systematic review of current approaches. aug: au: Alfattni, Ghada Peek, Niels Nenadic, Goran affil: Department of Computer Science, University of Manchester, Manchester, UK sug: subj: Natural Language Processing Information Retrieval Data Mining Time Scales Ferrans and Powers Quality of Life Index ab: Background: Temporal relations between clinical events play an important role in clinical assessment and decision making. Extracting such relations from free text data is a challenging task because it lies on between medical natural language processing, temporal representation and temporal reasoning.Objectives: To survey existing methods for extracting temporal relations (TLINKs) between events from clinical free text in English; to establish the state-of-the-art in this field; and to identify outstanding methodological challenges.Methods: A systematic search in PubMed and the DBLP computer science bibliography was conducted for studies published between January 2006 and December 2018. The relevant studies were identified by examining the titles and abstracts. Then, the full text of selected studies was analyzed in depth and information were collected on TLINK tasks, TLINK types, data sources, features selection, methods used, and reported performance.Results: A total of 2834 publications were identified for title and abstract screening. Of these publications, 51 studies were selected. Thirty-two studies used machine learning approaches, 15 studies used a hybrid approaches, and only four studies used a rule-based approach. The majority of studies use publicly available corpora: THYME (28 studies) and the i2b2 corpus (17 studies).Conclusion: The performance of TLINK extraction methods ranges widely depending on relation types and events (e.g. from 32% to 87% F-score for identifying relations between clinical events and document creation time). A small set of TLINKs (before, after, overlap and contains) has been widely studied with relatively good performance, whereas other types of TLINK (e.g., started by, finished by, precedes) are rarely studied and remain challenging. Machine learning classifiers (such as Support Vector Machine and Conditional Random Fields) and Deep Neural Networks were among the best performing methods for extracting TLINKs, but nearly all the work has been carried out and tested on two publicly available corpora only. The field would benefit from the availability of more publicly available, high-quality, annotated clinical text corpora. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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