Affiliation:
1. Washington University in St. Louis, USA, and Xidian University, China
2. Washington University in St. Louis, USA
3. Washington University School of Medicine, USA
Abstract
Data mining on medical data has great potential to improve the treatment quality of hospitals and increase the survival rate of patients. Every year, 4-17% of patients undergo cardiopulmonary or respiratory arrest while in hospitals. Clinical study has found early detection and intervention to be essential for preventing clinical deterioration in patients at general hospital units. This paper proposes an early warning system (EWS) designed to identify the signs of clinical deterioration and provide early warning for serious clinical events. The EWS is designed to provide reliable early alarms for patients at the general hospital wards (GHWs). The main task of EWS is a challenging classification problem on high-dimensional stream data with irregular, multi-scale data gaps, measurement errors, outliers, and class imbalance. This paper proposes a novel data mining framework for analyzing such medical data streams. The authors assess the feasibility of the proposed EWS approach through retrospective study that includes data from 41,503 visits at a major hospital. Finally, the system is applied in a clinical trial at a major hospital and obtains promising results. This project is an example of multidisciplinary cyber-physical systems involving researchers in clinical science, data mining, and nursing staff.
Reference21 articles.
1. Bloodgood, M., & Vijay-Shanker, K. (2009). Taking into account the differences between actively and passively acquired data: the case of active learning with support vector machines for imbalanced datasets. In Proceedings of Human Language Technologies: The Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers (pp. 137-140).
2. The use of the area under the ROC curve in the evaluation of machine learning algorithms
3. Cui, Y., Dy, J. G., Sharp, G. C., Alexander, B. M., & Jiang, S. B. (2008). Learning methods for lung tumor markerless gating in image-guided radiotherapy. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 902-910).
4. Toward the early diagnosis of neonatal sepsis and sepsis-like illness using novel heart rate analysis.;M. P.Griffin;American Academy of Pediatrics,2001
5. Hackmann, G., Chen, M., Chipara, O., Lu, C., Chen, Y., Koleff, M., & Bailey, T. C. (2010). Toward a two-tier clinical warning system for hospitalized patients. In Proceedings of the American Medical Informatics Association Annual Symposium.
Cited by
5 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献