Classification Based on Compressive Multivariate Time Series
Prediction of critical condition in intensive care unit (ICU) becomes one of the current major focuses in hospital healthcare delivery. Most of existing data mining methods only considered single time series signal and worked in original dimension. Consequently, they performed poorly for extended dataset of patient records. The main challenge of ICU prediction is the data too big to be stored and processed in timely manner. The problem in this study is how to compressed the original data into as small as possible while preserved prediction performance. In this paper, we propose multivariate compressed representation (MultiCoRe). Each recorded vital signal is transformed in frequency domain then reduced in low dimensional space. Multivariate distance measurement (MultiDist) is introduced to compute similarity between two patient records directly in MultiCoRe. Experimental results using MIMIC-II dataset show that our proposed method improved prediction accuracy and run hundreds times more efficient than other baseline methods.
KeywordsTime series representation Machine learning Medical prediction
This paper is partially supported by ARC Discovery Project, with Grant ID: DP140100104, Effective Recommendations based on Multi-Source Data.
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