Clinical text classification research trends: Systematic literature review and open issues
作者：
Highlights：
• To review free-text clinical text classification approaches from six aspects.
• In selected studies, mostly content-based and concept-based features were used.
• The datasets used in selected studies were categorized into four distinct types.
• Selected studies used either supervised machine learning or rule-based approaches.
• Ten open research challenges are presented in clinical text classification domain.
摘要
•To review free-text clinical text classification approaches from six aspects.•In selected studies, mostly content-based and concept-based features were used.•The datasets used in selected studies were categorized into four distinct types.•Selected studies used either supervised machine learning or rule-based approaches.•Ten open research challenges are presented in clinical text classification domain.
论文关键词：Clinical text classification,Feature engineering,Supervised machine learning,Rule-based text classification,Performance metrics
论文评审过程：Received 6 January 2018, Revised 14 September 2018, Accepted 15 September 2018, Available online 15 September 2018, Version of Record 25 September 2018.
论文官网地址：https://doi.org/10.1016/j.eswa.2018.09.034
