Prediciton of emergency events: a multi-task multi-label learning approach
Saha, Budhaditya, Gupta, Sunil K. and Venkatesh, Svetha 2015, Prediciton of emergency events: a multi-task multi-label learning approach. In Cao, Tru, Lim, Ee-Peng, Zhou, Zhi-Hua, Ho, Tu-Bao, Cheung, David and Motoda, Hiroshi (ed), Advances in knowledge discovery and data mining 19th Pacific-Asia Conference, PAKDD 2015, Ho Chi Minh City, Vietnam, May 19-22, 2015, Proceedings, Part I, Springer, Berlin, Germany, pp.226-238, doi: 10.1007/978-3-319-18038-0_18.
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Prediciton of emergency events: a multi-task multi-label learning approach
Advances in knowledge discovery and data mining 19th Pacific-Asia Conference, PAKDD 2015, Ho Chi Minh City, Vietnam, May 19-22, 2015, Proceedings, Part I
Prediction of patient outcomes is critical to plan resources in an hospital emergency department. We present a method to exploit longitudinal data from Electronic Medical Records (EMR), whilst exploiting multiple patient outcomes. We divide the EMR data into segments where each segment is a task, and all tasks are associated with multiple patient outcomes over a 3, 6 and 12 month period. We propose a model that learns a prediction function for each task-label pair, interacting through two subspaces: the first subspace is used to impose sharing across all tasks for a given label. The second subspace captures the task-specific variations and is shared across all the labels for a given task. The proposed model is formulated as an iterative optimization problems and solved using a scalable and efficient Block co-ordinate descent (BCD) method. We apply the proposed model on two hospital cohorts - Cancer and Acute Myocardial Infarction (AMI) patients collected over a two year period from a large hospital emergency department. We show that the predictive performance of our proposed models is significantly better than those of several state-of-the-art multi-task and multi-label learning methods.
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