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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Title Prediciton of emergency events: a multi-task multi-label learning approach
Author(s) Saha, BudhadityaORCID iD for Saha, Budhaditya orcid.org/0000-0001-8011-6801
Gupta, Sunil K.ORCID iD for Gupta, Sunil K. orcid.org/0000-0002-3308-1930
Venkatesh, SvethaORCID iD for 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
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