Clinical Risk Prediction with Temporal Probabilistic Asymmetric Multi-Task Learning

TL;DR

This paper introduces a novel temporal asymmetric multi-task learning model for clinical risk prediction that leverages feature-level uncertainty to improve knowledge transfer across tasks and timepoints, enhancing prediction accuracy without negative transfer.

Contribution

The paper proposes a new temporal asymmetric multi-task learning approach that dynamically transfers knowledge based on feature uncertainty, addressing limitations of existing methods in time-series clinical data.

Findings

Significantly outperforms existing models on clinical risk prediction tasks.

Effectively avoids negative transfer in multi-task learning.

Provides interpretable knowledge graphs validated by clinicians.

Abstract

Although recent multi-task learning methods have shown to be effective in improving the generalization of deep neural networks, they should be used with caution for safety-critical applications, such as clinical risk prediction. This is because even if they achieve improved task-average performance, they may still yield degraded performance on individual tasks, which may be critical (e.g., prediction of mortality risk). Existing asymmetric multi-task learning methods tackle this negative transfer problem by performing knowledge transfer from tasks with low loss to tasks with high loss. However, using loss as a measure of reliability is risky since it could be a result of overfitting. In the case of time-series prediction tasks, knowledge learned for one task (e.g., predicting the sepsis onset) at a specific timestep may be useful for learning another task (e.g., prediction of mortality) at a later timestep, but lack of loss at each timestep makes it difficult to measure the reliability at each timestep. To capture such dynamically changing asymmetric relationships between tasks in time-series data, we propose a novel temporal asymmetric multi-task learning model that performs knowledge transfer from certain tasks/timesteps to relevant uncertain tasks, based on feature-level uncertainty. We validate our model on multiple clinical risk prediction tasks against various deep learning models for time-series prediction, which our model significantly outperforms, without any sign of negative transfer. Further qualitative analysis of learned knowledge graphs by clinicians shows that they are helpful in analyzing the predictions of the model. Our final code is available at https://github.com/anhtuan5696/TPAMTL.