Automatic evidence quality prediction to support evidence-based decision making
作者：
Highlights：
• We present a classification model for the automatic quality grading of clinical evidence.
• We propose NLP-based approaches for extraction of informative features from text.
• We present a supervised learning approach using SVM classifiers for evidence grading.
• We show that the performance of our approach is comparable to human performance.
• Our quality grading approach can significantly reduce practitioners’ time needs.
摘要
Highlights•We present a classification model for the automatic quality grading of clinical evidence.•We propose NLP-based approaches for extraction of informative features from text.•We present a supervised learning approach using SVM classifiers for evidence grading.•We show that the performance of our approach is comparable to human performance.•Our quality grading approach can significantly reduce practitioners’ time needs.
论文关键词：Automatic text classification,Automatic medical evidence classification,Decision support system,Medical natural language processing,Evidence-based medicine
论文评审过程：Received 3 July 2014, Revised 31 March 2015, Accepted 15 April 2015, Available online 22 April 2015, Version of Record 9 June 2015.
论文官网地址：https://doi.org/10.1016/j.artmed.2015.04.001
