A review of statistical and machine learning methods for modeling cancer risk using structured clinical data
作者：
Highlights：
• A review of literature using analytical techniques to predict cancer risk is performed.
• This research can improve patient outcomes and reduce healthcare costs.
• Clinical data must be structured, frequently captured, and clinically relevant.
• Advanced modeling methods are lacking in present studies.
摘要
•A review of literature using analytical techniques to predict cancer risk is performed.•This research can improve patient outcomes and reduce healthcare costs.•Clinical data must be structured, frequently captured, and clinically relevant.•Advanced modeling methods are lacking in present studies.
论文关键词：Cancer prediction,Cancer recurrence,Cancer relapse,Data mining,Machine learning,Electronic health records
论文评审过程：Received 7 January 2017, Revised 8 September 2017, Accepted 13 June 2018, Available online 14 July 2018, Version of Record 23 August 2018.
论文官网地址：https://doi.org/10.1016/j.artmed.2018.06.002
