机器学习在医疗和公共卫生中应用

张景奇; 史文宝; 纪秀娟

doi:10.11847/zgggws1125035

机器学习在医疗和公共卫生中应用

Application of machine learning in medical and public health researches: a review

摘要

摘要:
目的对机器学习在医疗和公共卫生研究中的应用方向进行梳理，为制定相应政策和策略提供科学依据。
方法通过Citespace科研文献可视化工具对Web of Science数据库有关机器学习在医疗和公共卫生中应用的2 014篇文献进行聚类，找到当今的研究热点。
结果机器学习在医疗和公共卫生中的应用主要为辅助诊疗、监测和预防疾病、机器人辅助手术、药物警戒和研发以及医疗体系质量管理5个方向。
结论机器学习在医疗和公共卫生相关研究中具有较强的适用性和十分广阔的应用前景，在机器学习模型的帮助下根据医学相关数据做出的诊断和决策，不仅能够为个人带来健康，更有助于国家战略的实现。未来在打破医疗数据孤岛以及机器学习的医学伦理等方向可进一步加强探索。

Abstract:
Objective To review the applicability and application directions of machine learning in medical and public health research and to provide references for developing relevant policies and strategies.
Methods Via Web of Science, we retrieved 2 014 literatures on application of machine learning in medical and public health researches and conducted cluster analysis using visualization tool Citespace to identify hotspots in the researches.
Results The retrieved research literatures mainly covered following five fields: assistant diagnosis and treatment, disease monitoring and prevention, robotic assisted surgery, drug alert and drug discovery, and quality management of medical system.
Conclusion Machine learning is of strong applicability and extensive application in medical and public health researches.

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