A comparative study of pre-trained language models for named entity recognition in clinical trial eligibility criteria from multiple corpora

被引:0
作者
Jianfu Li
Qiang Wei
Omid Ghiasvand
Miao Chen
Victor Lobanov
Chunhua Weng
Hua Xu
机构
[1] The University of Texas Health Science Center at Houston,School of Biomedical Informatics
[2] German National Library of Economics,Department of Biomedical Informatics
[3] Covance by Labcorp,undefined
[4] Columbia University,undefined
来源
BMC Medical Informatics and Decision Making | / 22卷
关键词
Clinical trial; Eligibility criteria; Named entity recognition; Pre-trained language model;
D O I
暂无
中图分类号
学科分类号
摘要
引用
收藏
相关论文
共 107 条
[1]  
Weng C(2011)Formal representations of eligibility criteria: a literature review J Biomed Inform 43 451-467
[2]  
Tu SW(2016)Characterizing treatment pathways at scale using the OHDSI network Proc Natl Acad Sci 113 7329-7336
[3]  
Sim I(2015)Assessing the collective population representativeness of related type 2 diabetes trials by combining public data from Clinical Trials.gov and NHANES Stud Health Technol Inform. 216 569-1071
[4]  
Richesson R(2017)EliIE: an open-source information extraction system for clinical trial eligibility criteria J Am Med Inf Assoc 24 1062-1240
[5]  
Hripcsak G(2020)BioBERT: a pre-trained biomedical language representation model for biomedical text mining Bioinformatics 36 1234-23
[6]  
Ryan PB(2021)Domain-specific language model pretraining for biomedical natural language processing ACM Trans Comput Healthc (HEALTH) 3 1-77
[7]  
Duke JD(2019)SpanBERT: Improving pre-training by representing and predicting spans Trans Assoc Comput Linguist 8 64-49
[8]  
Shah NH(2017)Clinical information extraction applications: a literature review J Biomed Inform 2018 34-21
[9]  
Park RW(2020)A study of deep learning approaches for medication and adverse drug event extraction from clinical text J Am Med Inform Assoc 27 13-556
[10]  
Huser V(2011)2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text J Am Med Inform Assoc 18 552-12