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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Batal, I. Valizadegan, H. Cooper, G.F. Hauskrecht, M. |
| Copyright Year | 2011 |
| Abstract | We study the problem of learning classification models from complex multivariate temporal data encountered in electronic health record systems. The challenge is to define a good set of features that are able to represent well the temporal aspect of the data. Our method relies on temporal abstractions and temporal pattern mining to extract the classification features. Temporal pattern mining usually returns a large number of temporal patterns, most of which may be irrelevant to the classification task. To address this problem, we present the minimal predictive temporal patterns framework to generate a small set of predictive and non-spurious patterns. We apply our approach to the real-world clinical task of predicting patients who are at risk of developing heparin induced thrombocytopenia. The results demonstrate the benefit of our approach in learning accurate classifiers, which is a key step for developing intelligent clinical monitoring systems. |
| Starting Page | 358 |
| Ending Page | 365 |
| File Size | 402473 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781457717994 |
| DOI | 10.1109/BIBM.2011.39 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-11-12 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Databases temporal pattern mining Time series analysis Prediction algorithms Silicon Data models electronic health records Data mining patient monitoring multivariate time series classification Testing minimal predictive temporal patterns |
| Content Type | Text |
| Resource Type | Article |
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