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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.