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Multi-task transfer learning for in hospital-death prediction of ICU patients

Version 2 2024-06-04, 04:21
Version 1 2016-05-12, 14:07
conference contribution
posted on 2024-06-04, 04:21 authored by C Karmakar, B Saha, M Palaniswami, Svetha VenkateshSvetha Venkatesh
Multi-Task Transfer Learning (MTTL) is an efficient approach for learning from inter-related tasks with small sample size and imbalanced class distribution. Since the intensive care unit (ICU) data set (publicly available in Physionet) has subjects from four different ICU types, we hypothesize that there is an underlying relatedness amongst various ICU types. Therefore, this study aims to explore MTTL model for in-hospital mortality prediction of ICU patients. We used singletask learning (STL) approach on the augmented data as well as individual ICU data and compared the performance with the proposed MTTL model. As a performance measurement metrics, we used sensitivity (Sens), positive predictivity (+Pred), and Score. MTTL with class balancing showed the best performance with score of 0.78, 0.73, o.52 and 0.63 for ICU type 1 (Coronary care unit), 2 (Cardiac surgery unit), 3 (Medical ICU) and 4 (Surgical ICU) respectively. In contrast the maximum score obtained using STL approach was 0.40 for ICU type 1 & 2. These results indicates that the performance of in-hospital mortality can be improved using ICU type information and by balancing the ’non-survivor’ class. The findings of the study may be useful for quantifying the quality of ICU care, managing ICU resources and selecting appropriate interventions.

History

Pagination

3321-3324

Location

Orlando, Florida

Start date

2016-08-16

End date

2016-08-20

Language

eng

Publication classification

E Conference publication, E1 Full written paper - refereed

Copyright notice

2016, IEEE

Title of proceedings

EMBC 2016: Proceedings of the 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Event

Engineering in Medicine and Biology Society. Annual International Conference (38th : 2016 : Orlando, Florida)

Publisher

IEEE

Place of publication

Piscataway, N.J.

Series

Annual International Conference of the IEEE Engineering in Medicine and Biology Society