关键词: ICU, 不确定时间序列, 预测, 机器学习, 混合框架

Abstract: The mortality prediction of ICU patient has been an active topic in the past decades. Machine learning algorithms have been proved to have preliminary effects in this domain and still have room for improvement. In order to deal with the ICU time series which is both high dimensional and uncertain sampling interval, this paper proposes the idea that the unequal sampling frequency phenomenon in time series can be transferred to the empty value under the regular sampling frequency and corresponding strategies. Then, this paper proposes a two-step hybrid framework which combines the time series clustering and machine learning algorithm. In the first step, the dimension and uncertainty are reduced; in the second step, classical machine learning algorithms are conducted for mortality prediction of ICU patient. The experiments on a public data set show that the results of classifying the minority death patients are more efficient than the traditional solutions and the elastic interval is better. The selection for best time interval is validated by the experiments meanwhile.

Key words: ICU, uncertain time series, prediction, machine learning, hybrid framework