基于机器学习的院内心脏骤停预警模型研究进展

Research Progress of In-Hospital Cardiac Ar-rest Early Warning Model Based on Machine Learning

摘要: 早期识别心脏骤停(CA)的预警症状和指标对患者的生存起到重要作用，由异常预测因子构成的临床预测模型作为风险的量化工具，为早期识别心脏骤停提供证据，近年来得到普遍应用。基于机器学习的心脏骤停预警模型具有灵活的预测算法，比传统的早期预警评分预测方法更准确、预测效能更高。国内外学者通过各种方法进一步提高了其预测能力，并实现了模型实时预测心脏骤停的功能。本综述复习相关心脏骤停预警模型的发展历程、模型方法和预测性能与总结模型发展中的局限性，探讨基于机器学习的心脏骤停预警模型对预防心脏骤停和心脏骤停后提供决策的研究价值以及对具有高预测能力的预警模型进行展望。

Abstract: Early identification of early warning symptoms and indicators of cardiac arrest (CA) plays an im-portant role in the survival of patients, and the clinical prediction model composed of abnormal predictors is used as a risk quantitative tool to provide evidence for early identification of cardiac arrest, and has been widely used in recent years. The early warning model of cardiac arrest based on machine learning has a flexible prediction algorithm, which is more accurate and more efficient than the traditional early warning score prediction method. Scholars at home and abroad have fur-ther improved their prediction ability through various methods, and realized the function of the model to predict cardiac arrest in real time. This review reviews the development history, model methods and prediction performance of relevant cardiac arrest early warning models, summarizes the limitations of model development, discusses the research value of cardiac arrest early warning models based on machine learning in preventing cardiac arrest and providing decision-making af-ter cardiac arrest, and prospects early warning models with high predictive ability.