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首页> 外文会议>Computing in Cardiology 2012.;vol. 39. >CinC Challenge: Cluster analysis of multi-granular time-series data for mortality rate prediction
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CinC Challenge: Cluster analysis of multi-granular time-series data for mortality rate prediction

机译：CinC挑战：多粒度时间序列数据的聚类分析以预测死亡率

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摘要

The goal of this research is to develop novel cluster analysis techniques to identify similarity between ICU time-series data. The results generated by cluster analysis are further used for ICU mortality prediction. To preprocess multi-granular ICU time-series, we proposed a segmentation-based method to divide time-series into several segments. The minimal and maximal values within each segment were captured to maintain the statistical feature of the segment. A weighted Euclidean distance function was in place to evaluate the similarity between two instances and clustering was later used to convert each time-series into a corresponding cluster number. This way, we turned the high dimensional ICU time series data into a 2-dimensional matrix. A rule-based classification model was developed from this 2-dimensional matrix, and the model was used to predict the in-hospital mortality for test cases. The experiments show that above approach is effective in handling ICU time-series data.
机译：这项研究的目的是开发新颖的聚类分析技术，以识别ICU时间序列数据之间的相似性。聚类分析产生的结果可进一步用于ICU死亡率预测。为了对多颗粒ICU时间序列进行预处理，我们提出了一种基于分段的方法，将时间序列划分为多个段。捕获每个段内的最小值和最大值，以保持段的统计特征。设置了加权欧几里得距离函数来评估两个实例之间的相似性，随后使用聚类将每个时间序列转换为相应的聚类数。这样，我们将高维ICU时间序列数据转换为二维矩阵。从此二维矩阵开发了基于规则的分类模型，该模型用于预测测试案例的院内死亡率。实验表明，上述方法在处理ICU时间序列数据方面是有效的。

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