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Related Experiment Video

Updated: Nov 1, 2025

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Multitask prediction of organ dysfunction in the intensive care unit using sequential subnetwork routing.

Subhrajit Roy1, Diana Mincu1, Eric Loreaux1

  • 1Google Health, London, United Kingdom.

Journal of the American Medical Informatics Association : JAMIA
|June 21, 2021
PubMed
Summary

Sequential subnetwork routing (SeqSNR) improves multitask learning (MTL) performance on electronic health records. This novel architecture enhances label efficiency, outperforming single-task and naive multitask models, especially when data is limited.

Keywords:
Deep LearningElectronic Health RecordsIntensive CareMachine LearningMultitask Learning

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Area of Science:

  • Machine Learning
  • Biomedical Informatics
  • Artificial Intelligence

Background:

  • Multitask learning (MTL) with electronic health records (EHRs) enables simultaneous prediction of multiple clinical outcomes.
  • While MTL improves model performance and efficiency, it can suffer from negative transfer when tasks are poorly selected.
  • A new architecture is needed to effectively route related tasks and mitigate negative transfer in EHR-based MTL.

Purpose of the Study:

  • Introduce a novel sequential subnetwork routing (SeqSNR) architecture for EHR-based multitask learning.
  • Evaluate SeqSNR's performance and label efficiency against single-task (ST) and naive multitask (MTL) models.
  • Demonstrate SeqSNR's utility in scenarios with limited labeled data.

Main Methods:

  • Developed a deep neural network utilizing a sequential subnetwork routing (SeqSNR) architecture with soft parameter sharing.
  • Trained and compared models on the MIMIC-III dataset to predict six clinical endpoints: acute kidney injury, continuous renal replacement therapy, mechanical ventilation, vasoactive medications, mortality, and length of stay.
  • Assessed discriminative performance and label efficiency across ST, naive MTL, and SeqSNR models.

Main Results:

  • SeqSNR achieved a statistically significant performance improvement on 4 out of 6 tasks compared to ST and naive MTL.
  • SeqSNR demonstrated superior label efficiency, outperforming ST models across all tested dataset sizes (1%, 5%, and 10% of labels).
  • Average area under the precision-recall curve boosts for SeqSNR were 2.1% (1% labels), 2.9% (5% labels), and 2.1% (10% labels).

Conclusions:

  • The SeqSNR architecture offers superior label efficiency compared to single-task and naive multitask learning approaches.
  • SeqSNR is particularly valuable in clinical settings where obtaining endpoint labels for electronic health records is challenging.
  • This approach facilitates more effective cross-learning between related tasks in multitask learning models.