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Prediciton of emergency events: a multi-task multi-label learning approach
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posted on 2015-01-01, 00:00 authored by Budhaditya Saha, Sunil GuptaSunil Gupta, Svetha VenkateshSvetha VenkateshPrediction 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.
History
Event
19th Pacific-Asia Conference in Knowledge Discovery and Data MiningTitle of book
Advances in knowledge discovery and data mining 19th Pacific-Asia Conference, PAKDD 2015, Ho Chi Minh City, Vietnam, May 19-22, 2015, Proceedings, Part IVolume
9077Series
Lecture notes in computer science; v.9077Chapter number
18Pagination
226 - 238Publisher
SpringerLocation
VietnamPlace of publication
Berlin, GermanyPublisher DOI
Start date
2015-05-19End date
2015-07-22ISSN
0302-9743eISSN
1611-3349ISBN-13
9783319180380Language
engPublication classification
B Book chapter; B1 Book chapterCopyright notice
2015, IEEEExtent
58Editor/Contributor(s)
T Cao, E Lim, Z Zhou, T Ho, D Cheung, H MotodaUsage metrics
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