Abstract: Using the massive clinical data in the field of health care to support the decision making of auxiliary medicine, is the core technology and an inevitable development trend of intelligent medicine. Medical decision support mainly includes disease risk prediction and intelligent diagnosis. Based on clinical accumulation and real-time acquisition of multiple data sources, it realizes classification of patients’ disease types or prediction of disease risks through multiple machine learning algorithms. Based on the concept and method framework of medical decision support, this paper summarizes the current machine learning diagnosis and prediction methods according to different disease types, introduces the characteristics and differences of these methods, and analyzes the existing challenges and future development.

Key words: medical decision support, machine learning, intelligent diagnosis of disease, disease risk prediction

摘要： 利用健康医疗领域的海量临床数据进行辅助医疗决策支持是智慧医疗的核心技术和必然的发展趋势。医疗决策支持主要包括疾病风险预测与疾病智能诊断两方面，以临床积累和实时获取的多种数据来源为基础，通过多种机器学习算法实现对患者疾病类型的分类或者对患病风险的预测。从医疗决策支持的概念和方法框架出发，按照不同疾病种类，总结了当前采用的机器学习诊断和预测方法，着重介绍这些方法的特点和区别，并对存在的挑战和未来发展进行分析。

关键词: 医疗决策支持, 机器学习, 疾病智能诊断, 疾病风险预测