Deakin University

File(s) under permanent embargo

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

conference contribution
posted on 2016-08-18, 00:00 authored by Chandan KarmakarChandan Karmakar, Budhaditya 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.



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


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


3321 - 3324




Orlando, Florida

Place of publication

Piscataway, N.J.

Start date


End date




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

Usage metrics

    Research Publications


    No categories selected