Multi-label clinical document classification: Impact of label-density
作者：
Highlights：
• Expert clinicians assign, manually, codes from the ICD-10 to health records.
• Neural Networks (NNs) are well suited for multi-label classication tasks.
• Challenges: infer models from data with low label-density and capture dependencies.
• Experiments: three NNs with label-consistency rectification on two corpora.
• Contribution: (1) show dependence of performance on label density (2) release software.
摘要
•Expert clinicians assign, manually, codes from the ICD-10 to health records.•Neural Networks (NNs) are well suited for multi-label classication tasks.•Challenges: infer models from data with low label-density and capture dependencies.•Experiments: three NNs with label-consistency rectification on two corpora.•Contribution: (1) show dependence of performance on label density (2) release software.
论文关键词：Multi-label classification,Document classification,Electronic health records,ICD-10 classification
论文评审过程：Received 26 March 2019, Revised 4 July 2019, Accepted 21 July 2019, Available online 22 July 2019, Version of Record 25 July 2019.
论文官网地址：https://doi.org/10.1016/j.eswa.2019.112835
