Multi-task prediction of organ dysfunction in the ICU using sequential sub-network routing
Abstract
Introduction:
Multi-task learning (MTL) using electronic health records (EHRs) allows concurrent prediction of multiple endpoints. MTL has shown promise in improving model performance and training efficiency; however it often suffers from negative transfer - impaired learning if tasks are not appropriately selected. We introduce a sequential sub-network routing (SeqSNR) architecture which uses soft parameter sharing to find related tasks and encourage cross-learning between them.
Materials and Methods:
Using the Medical Information Mart for Intensive Care (MIMIC-III) dataset, we train deep neural network models to predict the onset of six endpoints including specific organ dysfunctions and general clinical outcomes: acute kidney injury, continuous renal replacement therapy, mechanical ventilation, vasoactive medications, mortality, and length of stay. We compare single task models (ST) with naive multi-task (shared bottom, SB) and SeqSNR in terms of discriminative performance and label efficiency.
Results:
SeqSNR showed a modest yet statistically significant performance boost across at least 4 out of 6 tasks compared to SB and ST. When the size of the training dataset was reduced for a given task, SeqSNR outperformed ST for all cases showing an average AU PRC boost of 2.1%, 2.9%, and 2.1% for tasks using 1%, 5%, and 10% of labels respectively.
Discussion and Conclusion:
Multi-task learning has variable performance compared to single-task learning, with the possibility for negative transfer. The SeqSNR architecture outperforms SB and ST in discriminative performance and shows superior performance in terms of label efficiency. SeqSNR should be considered for multi-task predictive modeling using EHR data.
Multi-task learning (MTL) using electronic health records (EHRs) allows concurrent prediction of multiple endpoints. MTL has shown promise in improving model performance and training efficiency; however it often suffers from negative transfer - impaired learning if tasks are not appropriately selected. We introduce a sequential sub-network routing (SeqSNR) architecture which uses soft parameter sharing to find related tasks and encourage cross-learning between them.
Materials and Methods:
Using the Medical Information Mart for Intensive Care (MIMIC-III) dataset, we train deep neural network models to predict the onset of six endpoints including specific organ dysfunctions and general clinical outcomes: acute kidney injury, continuous renal replacement therapy, mechanical ventilation, vasoactive medications, mortality, and length of stay. We compare single task models (ST) with naive multi-task (shared bottom, SB) and SeqSNR in terms of discriminative performance and label efficiency.
Results:
SeqSNR showed a modest yet statistically significant performance boost across at least 4 out of 6 tasks compared to SB and ST. When the size of the training dataset was reduced for a given task, SeqSNR outperformed ST for all cases showing an average AU PRC boost of 2.1%, 2.9%, and 2.1% for tasks using 1%, 5%, and 10% of labels respectively.
Discussion and Conclusion:
Multi-task learning has variable performance compared to single-task learning, with the possibility for negative transfer. The SeqSNR architecture outperforms SB and ST in discriminative performance and shows superior performance in terms of label efficiency. SeqSNR should be considered for multi-task predictive modeling using EHR data.
Research Areas
