重症监护室患者病情预测方法研究进展

Advances inprediction methods of the ICU patients condition

重症监护室(intensive care unit,ICU)患者病情预测对帮助医生制定医疗方案、配置医疗资源、评估医疗效果具有重要意义。本文从临床和机器学习两个领域介绍了国内外ICU患者病情预测方法的研究和应用进展,主要包括急性生理和慢性健康状况评分(acute physiology and chronic health evaluation,APACHE)、简明急性生理功能评分(simplified acute physiology score,SAPS)、逻辑回归、贝叶斯、人工神经网络、支持向量机(support vector machine,SVM)和Adaboost等方法,分析了各种方法的预测模型、预测结果和不足,并对ICU患者病情预测方法的未来发展趋势进行展望。

The prediction of the ICU patients condition plays an important role in helping doctors make treatment plans,distributing medical resources and assessing medical effects. This paper introduces the research and application advances of the methods used to predicting ICU patients＇ condition at home and abroad from two fields： clinic and machine learning, including acute physiology and chronic health evaluation（APACHE）,simplified acute physiology score（SAPS）,logistic regression,Bayes,artificial neural network,support vector machine（SVM）,and Adaboost,analyses the predicting models,results and shortcomings of different methods and looks into the future of the prediction methods of ICU patients condition.