利用人工智能预测癌症的易感性、复发性和生存期

癌症具有较高的发病率和致死率，对人类健康具有重大威胁。癌症预后分析可以有效避免过度治疗及医疗资源的浪费，为医务人员及家属进行医疗决策提供科学依据，已成为癌症研究的必要条件。随着近年来人工智能技术的迅速发展，对癌症患者的预后情况进行自动化分析成为可能。此外，随着医疗信息化的发展，智慧医疗的理念受到广泛关注。癌症患者作为智慧医疗的重要组成部分，对其进行有效的智能预后分析十分必要。本文综述现有基于机器学习的癌症预后方法。首先，对机器学习与癌症预后进行概述，介绍癌症预后及相关的机器学习方法，分析机器学习在癌症预后中的应用；然后，对基于机器学习的癌症预后方法进行归纳，包括癌症易感性预测、癌症复发性预测、癌症生存期预测，梳理了它们的研究现状、涉及到的癌症类型与数据集、用到的机器学习方法及预后性能、特点、优势与不足；最后，对癌症预后方法进行总结与展望。

Artificial Intelligence-based Prediction for Cancer Susceptibility, Recurrence and Survival

Cancer has a high incidence and mortality and is a major threat to human health. Cancer prognosis analysis can effectively avoid excessive treatment and waste of medical resources and provide a scientific basis for medical staff and their families to make medical decisions. It has become a necessary condition for cancer research. With the rapid development of artificial intelligence technology and medical informatization in recent years, smart medicine has received widespread attention. It has become possible to analyze the prognosis of cancer patients automatically. As an essential part of smart medicine, cancer patients need to conduct effective intelligent prognostic analysis. This article reviews the existing machine learning-based cancer prognosis methods. Firstly, it provides an overview of machine learning and cancer prognosis, introduces cancer prognosis and related machine learning methods, and analyzes the application of machine learning in cancer prognosis. Then, it summarizes cancer prognosis methods based on machine learning, including cancer susceptibility prediction, cancer recurrence prediction, and cancer survival prediction, and sorts out their research status, cancer types and data sets involved, and machine learning methods used and prediction performance. Finally, the cancer prognosis methods are summarized and prospected, and the aspects that should be explored and improved are proposed: (1) include other high-fatal cancers in the prognostic analysis; (2) comprehensive analysis of cancer expression data and image data to improve prognostic performance; (3) optimize the prognostic model to improve prognostic performance.