北京生物医学工程2017，Vol.36Issue(5)：524-529,534,7.DOI:10.3969/j.issn.1002-3208.2017.05.016
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
卜小轩 1诸强1
作者信息
- 1. 北京交通大学生物医学工程系 北京 100044
- 折叠
摘要
Abstract
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.关键词
重症监护室/病情/预测/急性生理和慢性健康状况评分/支持向量机Key words
intensive care unit/patients condition/prediction/acute physiology and chronic health evaluation/support vector machine分类
医药卫生引用本文复制引用
卜小轩,诸强..重症监护室患者病情预测方法研究进展[J].北京生物医学工程,2017,36(5):524-529,534,7.
