Patient Outcome and Zero-shot Diagnosis Prediction with Hypernetwork-guided Multitask Learning
This addresses multitask learning challenges in healthcare for predicting patient outcomes, with incremental improvements in handling rare diseases and unseen diagnoses.
The paper tackled inter-task interference and generalizability issues in multitask deep learning for patient outcome prediction from clinical notes by proposing a hypernetwork-based approach that generates task-conditioned parameters and coefficients, achieving better performance than baselines on the MIMIC database and improving zero-shot prediction on unseen diagnoses.
Multitask deep learning has been applied to patient outcome prediction from text, taking clinical notes as input and training deep neural networks with a joint loss function of multiple tasks. However, the joint training scheme of multitask learning suffers from inter-task interference, and diagnosis prediction among the multiple tasks has the generalizability issue due to rare diseases or unseen diagnoses. To solve these challenges, we propose a hypernetwork-based approach that generates task-conditioned parameters and coefficients of multitask prediction heads to learn task-specific prediction and balance the multitask learning. We also incorporate semantic task information to improves the generalizability of our task-conditioned multitask model. Experiments on early and discharge notes extracted from the real-world MIMIC database show our method can achieve better performance on multitask patient outcome prediction than strong baselines in most cases. Besides, our method can effectively handle the scenario with limited information and improve zero-shot prediction on unseen diagnosis categories.