机器学习在缺血性卒中诊治中的应用与展望

Application and prospect of machine learning in the diagnosis and treatment of ischemic stroke

缺血性卒中有高死亡率、高致残率和高复发率的特点,临床上常出现漏诊、误诊现象.而基于机器学习的计算机辅助技术能够通过对医学文本、影像等资料的特征挖掘,辅助医生快速、准确地提高诊疗效率.本文通过梳理机器学习的不同方法,及其在缺血性卒中预测、诊断、治疗和预后的应用,对未来机器学习在该领域的预测、诊断、制定个性化治疗方案以及医疗资源合理性配置等方面进行展望.

Ischemic stroke leads to high mortality,high disability rate,and high recurrence rate.Missed diagnoses and misdiagnoses are common.Computer-assisted technologies based on machine learning,by mining features from medical texts and images are suggested to help doctors improve diagnostic and treatment efficiency quickly and accurately.This review provides an overview of various machine learning methods and research progress in predicting,diagnosing,treating,and prognosis of ischemic stroke.The review is also summarized the future application of machine learning in the field of ischemic stroke for prediction,diagnosis,developing personalized treatment plans,and optimizing medical resource allocation.