The Analysis of Head Injury Data Using Decision Tree Techniques
AIMDM '99 Proceedings of the Joint European Conference on Artificial Intelligence in Medicine and Medical Decision Making
Exploratory Under-Sampling for Class-Imbalance Learning
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
A review of feature selection techniques in bioinformatics
Bioinformatics
Heterogeneous data fusion for alzheimer's disease study
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
MEDiSN: medical emergency detection in sensor networks
Proceedings of the 6th ACM conference on Embedded network sensor systems
An integrated machine learning approach to stroke prediction
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
A multi-task learning formulation for predicting disease progression
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Density-based logistic regression
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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Clinical study found that early detection and intervention are essential for preventing clinical deterioration in patients, for patients both in intensive care units (ICU) as well as in general wards but under real-time data sensing (RDS). In this paper, we develop an integrated data mining approach to give early deterioration warnings for patients under real-time monitoring in ICU and RDS. Existing work on mining real-time clinical data often focus on certain single vital sign and specific disease. In this paper, we consider an integrated data mining approach for general sudden deterioration warning. We synthesize a large feature set that includes first and second order time-series features, detrended fluctuation analysis (DFA), spectral analysis, approximative entropy, and cross-signal features. We then systematically apply and evaluate a series of established data mining methods, including forward feature selection, linear and nonlinear classification algorithms, and exploratory undersampling for class imbalance. An extensive empirical study is conducted on real patient data collected between 2001 and 2008 from a variety of ICUs. Results show the benefit of each of the proposed techniques, and the final integrated approach significantly improves the prediction quality. The proposed clinical warning system is currently under integration with the electronic medical record system at Barnes-Jewish Hospital in preparation for a clinical trial. This work represents a promising step toward general early clinical warning which has the potential to significantly improve the quality of patient care in hospitals.