急性冠脉综合征不良心血管事件机器学习预测模型的研究进展
Research progress of machine learning prediction model for unintentional vascular events in acute coronary syndrome
周仟慧 1古满平1
作者信息
- 1. 重庆医科大学附属第一医院护理部,重庆 400010
- 折叠
摘要
急性冠状动脉(冠脉)综合征患者并发不良心血管事件会严重影响其预后.近年来,人工智能和大数据在医学领域的发展为该类人群的风险预测提供了新思路,目前,已有多种机器学习预测模型用于预测急性冠脉综合征患者的各类不良心血管事件.该文对机器学习预测模型的研究与应用现状进行了综述,并分析了模型的构建方法、数据来源和特点、模型验证方法和危险因素,为患者风险评估、早期预防与预后评估提供参考依据.
Abstract
The adverse cardiovascular events in patients with acute coronary syndrome can seriously af-fect the prognosis.In recent years,the development of artificial intelligence and big data in the medical fields has provided new ideas for the risk prediction of such people.At present,a variety of machine learning predic-tion models have been used to predict various adverse cardiovascular events in patients with acute coronary syndrome.This article reviewed the research and application status of these machine learning prediction mod-els,and reviewed the model construction methods,data sources and characteristics,model validation and risk factors,so as to provide references for patient risk assessment,early prevention and prognosis assessment.
关键词
急性冠状动脉综合征/机器学习/主要不良心血管事件/预测模型/综述Key words
Acute coronary syndrome/Machine learning/Major adverse cardiovascular events/Risk prediction model/Review引用本文复制引用
基金项目
重庆市重点专科建设(临床护理)精品建设项目(0203[2023]47号202336)
重庆医科大学研究生智慧医学专项研发计划项目(YJSZHYX202219)
重庆医科大学附属第一医院2024年度护理科研创新项目(HLPY2024-14)
出版年
2024
国家科技期刊平台
NSTL
万方数据