Predicting Adverse Neonatal Outcomes for Preterm Neonates with Multi-Task Learning
This work addresses the need for more accurate and interpretable diagnosis tools for preterm neonates, though it is incremental as it builds on existing machine learning methods by incorporating multi-task learning.
The paper tackled the problem of predicting multiple adverse neonatal outcomes for preterm neonates by proposing a multi-task learning framework that leverages correlations between outcomes, achieving effective results as demonstrated through experiments on EHRs from 121 preterm neonates.
Diagnosis of adverse neonatal outcomes is crucial for preterm survival since it enables doctors to provide timely treatment. Machine learning (ML) algorithms have been demonstrated to be effective in predicting adverse neonatal outcomes. However, most previous ML-based methods have only focused on predicting a single outcome, ignoring the potential correlations between different outcomes, and potentially leading to suboptimal results and overfitting issues. In this work, we first analyze the correlations between three adverse neonatal outcomes and then formulate the diagnosis of multiple neonatal outcomes as a multi-task learning (MTL) problem. We then propose an MTL framework to jointly predict multiple adverse neonatal outcomes. In particular, the MTL framework contains shared hidden layers and multiple task-specific branches. Extensive experiments have been conducted using Electronic Health Records (EHRs) from 121 preterm neonates. Empirical results demonstrate the effectiveness of the MTL framework. Furthermore, the feature importance is analyzed for each neonatal outcome, providing insights into model interpretability.