Learning Tasks for Multitask Learning
Name
1806.02878.pdf
Description
Submitted version
Size
779.29 KB
Format
Adobe PDF
Checksum (MD5)
a3656df8f35067da57154e233ea32fcf
Author(s)
Suresh, Harini
Gong, Jen J.
Guttag, John V.
Alternative Title
Heterogenous Patient Populations in the ICU
Date Issued
July 19, 2018
Publisher
ACM
Citation
Suresh, Harini, Gong, Jen J. and Guttag, John V. 2018. "Learning Tasks for Multitask Learning."
Version
Original manuscript
Abstract
© 2018 Copyright held by the owner/author(s). Machine learning approaches have been effective in predicting adverse outcomes in different clinical settings. These models are often developed and evaluated on datasets with heterogeneous patient populations. However, good predictive performance on the aggregate population does not imply good performance for specific groups. In this work, we present a two-step framework to 1) learn relevant patient subgroups, and 2) predict an outcome for separate patient populations in a multi-task framework, where each population is a separate task. We demonstrate how to discover relevant groups in an unsupervised way with a sequence-to-sequence autoencoder. We show that using these groups in a multi-task framework leads to better predictive performance of in-hospital mortality both across groups and overall. We also highlight the need for more granular evaluation of performance when dealing with heterogeneous populations.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3219819.3219930
