Abstract: Infectious diseases are one of the major threats to global public health. Inconvenience of diagnosis and treatment frequently causes misdiagnosis, missing diagnosis or overtreatment, resulting in serious clinical outcomes. As an important branch of artificial intelligence, machine learning has been widely used in multiple fields. Predictive models created based on patients’ clinical characteristics, laboratory tests, and imaging examinations are effective for prediction and evaluation of clinical diagnosis, therapeutic efficacy and prognosis, as well as detection of outbreaks. Machine learning modeling has the advantages of high efficiency, high accuracy and interpretability as compared to traditional modeling approaches, which provides a new tool for diagnosis and treatment of infectious diseases. This review summarizes the advances of applications of machine learning in clinical predictive models for infectious diseases.

Key words: Infectious disease, Machine learning, Predictive model

摘要： 感染性疾病是全球公共卫生重大威胁之一。由于其诊治的不便捷性，常常造成误诊、漏诊或过度治疗等，引起严重临床后果。作为人工智能的一个重要分支，机器学习已被广泛应用于多个领域。通过对患者临床特征、实验室检查、影像学检查等数据进行分析并建立预测模型，可预测和评估疾病临床诊断、治疗效果、预后转归及暴发侦测。与传统建模方式相比，机器学习建模具有高效、高精度和可解释等优点，这为感染性疾病诊治提供了新方法。本文对机器学习在感染性疾病临床预测模型中的应用进展进行综述。

关键词: 感染性疾病, 机器学习, 预测模型