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. Attached Files Name Description MIMEType Size Downloads Title Prediciton of emergency events: a multi-task multi-label learning approach Author(s) Saha, Budhaditya orcid.org/0000-0001-8011-6801 Gupta, Sunil K. orcid.org/0000-0002-3308-1930 Venkatesh, Svetha orcid.org/0000-0001-8675-6631 Title 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 I Editor(s) Cao, Tru Lim, Ee-Peng Zhou, Zhi-Hua Ho, Tu-Bao Cheung, David Motoda, Hiroshi Publication date 2015 Series Lecture notes in computer science; v.9077 Chapter number 18 Total chapters 58 Start page 226 End page 238 Total pages 13 Publisher Springer Place of Publication Berlin, Germany Keyword(s) Science & Technology Technology Computer Science, Artificial Intelligence Computer Science, Information Systems Computer Science, Theory & Methods Computer Science Summary 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. ISBN 9783319180380 ISSN 0302-9743 1611-3349 Language eng DOI 10.1007/978-3-319-18038-0_18 Field of Research 080109 Pattern Recognition and Data Mining 08 Information And Computing Sciences Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences HERDC Research category B1 Book chapter ERA Research output type B Book chapter Copyright notice ©2015, IEEE Persistent URL http://hdl.handle.net/10536/DRO/DU:30077468 Document type: Book Chapter Collection: Centre for Pattern Recognition and Data Analytics Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Mon, 31 Aug 2015, 13:36:23 EST Tue, 01 Sep 2015, 05:03:01 EST Thu, 03 Mar 2016, 14:48:36 EST Thu, 03 Mar 2016, 15:16:42 EST Thu, 07 Apr 2016, 16:30:49 EST Fri, 08 Apr 2016, 06:07:20 EST Tue, 12 Apr 2016, 08:59:04 EST Tue, 12 Apr 2016, 09:03:14 EST Tue, 13 Mar 2018, 08:52:53 EST Tue, 13 Mar 2018, 08:54:02 EST Filtered Full Citation counts:   Cited 1 times in TR Web of Science Cited 1 times in Scopus Search Google Scholar Access Statistics: 299 Abstract Views, 6 File Downloads  -  Detailed Statistics Created: Thu, 07 Apr 2016, 16:30:49 EST

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