Semantic categorization of Chinese eligibility criteria in clinical trials using machine learning methods.
Background: Semantic categorization analysis of clinical trials eligibility criteria based on natural language processing technology is crucial for the task of optimizing clinical trials design and building automated patient recruitment system. However, most of related researches focused on English...
| Published in: | BMC Medical Informatics & Decision Making Vol. 21; no. 1; pp. 1 - 13 |
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| Main Authors: | , , , , |
| Format: | research Journal Article |
| Published: |
BioMed Central
4/15/2021
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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=149847856&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 149847856 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14726947 1CI0 jtl: BMC Medical Informatics & Decision Making issn: 14726947 maglogo: N pubinfo: dt: 4/15/2021 vid: 21 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 149847856 149847856 NLM33858409 149847856 10.1186/s12911-021-01487-w NLM33858409 149847856 ppf: 1 ppct: 12 formats: tig: atl: Semantic categorization of Chinese eligibility criteria in clinical trials using machine learning methods. aug: au: Zong, Hui Yang, Jinxuan Zhang, Zeyu Li, Zuofeng Zhang, Xiaoyan affil: Research Center for Translational Medicine, Shanghai East Hospital, School of Life Sciences and Technology, Tongji University, 200092, Shanghai, China sug: subj: Semantics Unified Medical Language System China Pilot Studies Natural Language Processing Human Comparative Studies Multicenter Studies Evaluation Research Validation Studies Scales ab: Background: Semantic categorization analysis of clinical trials eligibility criteria based on natural language processing technology is crucial for the task of optimizing clinical trials design and building automated patient recruitment system. However, most of related researches focused on English eligibility criteria, and to the best of our knowledge, there are no researches studied the Chinese eligibility criteria. Thus in this study, we aimed to explore the semantic categories of Chinese eligibility criteria.Methods: We downloaded the clinical trials registration files from the website of Chinese Clinical Trial Registry (ChiCTR) and extracted both the Chinese eligibility criteria and corresponding English eligibility criteria. We represented the criteria sentences based on the Unified Medical Language System semantic types and conducted the hierarchical clustering algorithm for the induction of semantic categories. Furthermore, in order to explore the classification performance of Chinese eligibility criteria with our developed semantic categories, we implemented multiple classification algorithms, include four baseline machine learning algorithms (LR, NB, kNN, SVM), three deep learning algorithms (CNN, RNN, FastText) and two pre-trained language models (BERT, ERNIE).Results: We totally developed 44 types of semantic categories, summarized 8 topic groups, and investigated the average incidence and prevalence in 272 hepatocellular carcinoma related Chinese clinical trials. Compared with the previous proposed categories in English eligibility criteria, 13 novel categories are identified in Chinese eligibility criteria. The classification result shows that most of semantic categories performed quite well, the pre-trained language model ERNIE achieved best performance with macro-average F1 score of 0.7980 and micro-average F1 score of 0.8484.Conclusion: As a pilot study of Chinese eligibility criteria analysis, we developed the 44 semantic categories by hierarchical clustering algorithms for the first times, and validated the classification capacity with multiple classification algorithms. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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