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Prediciton of emergency events: a multi-task multi-label learning approach

Version 2 2024-06-03, 17:12
Version 1 2015-08-31, 13:36
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posted on 2024-06-03, 17:12 authored by B Saha, Sunil GuptaSunil Gupta, Svetha VenkateshSvetha Venkatesh
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.

History

Volume

9077

Chapter number

18

Pagination

226-238

Location

Vietnam

Start date

2015-05-19

End date

2015-07-22

ISSN

0302-9743

eISSN

1611-3349

ISBN-13

9783319180380

Language

eng

Publication classification

B Book chapter, B1 Book chapter

Copyright notice

2015, IEEE

Extent

58

Editor/Contributor(s)

Cao T, Lim EP, Zhou ZH, Ho TB, Cheung D, Motoda H

Publisher

Springer

Place of publication

Berlin, Germany

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

Series

Lecture notes in computer science; v.9077

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