Summary
The research work presents an exploration into the potential of multi-task learning (MTL) within the context of electronic health record (EHR) data analysis and clinical prediction tasks. This study innovatively addresses the critical challenges of task grouping and model architecture design, which are essential for optimizing MTL frameworks.
Strengths
S1. By proposing an automated approach, AutoDP, the paper contributes to reducing human intervention in the configuration process, thereby streamlining the identification of optimal task combinations and neural architectures tailored for EHR data.
S2. The employment of surrogate model-based optimization and a progressive sampling strategy demonstrates a novel and efficient methodology for navigating the vast search space, leading to improved performance with a feasible computational cost.
S3. The experimental results on the real-world EHR dataset, MIMIC-IV, validate the efficacy of the proposed AutoDP framework, showcasing substantial performance improvements over both hand-crafted and existing automated methods.
S4. This work not only advances the state-of-the-art in automated machine learning for healthcare but also provides valuable insights and tools for the broader research community working on multi-task learning problems.
Weaknesses
Q1. Figure Clarity and Comprehensibility: The overview of the proposed AutoDP in Figure 1 lacks self-evidency, making it challenging for readers, particularly those who are not specialists in electronic health record (EHR) modeling and analysis, to grasp the main insights at a glance. The figure is dense with symbols and elements that are not immediately interpretable without additional context. It is recommended that the authors enhance the figure's presentation to improve readability and comprehension. This could involve breaking down complex elements, using clearer labeling, and providing a more detailed legend or accompanying text that guides the reader through the visualization.
Q2. Evaluation Metric Consideration: For many clinical prediction tasks on datasets like MIMIC, where label balance may not be achievable, the authors might consider the use of AUPRC (Area Under the Precision-Recall Curve) as an alternative or complementary evaluation metric to AUC-ROC (Area Under the Receiver Operating Characteristic Curve). AUPRC can be a more informative measure when dealing with imbalanced classes, as it evaluates the model's ability to rank samples correctly, independent of the classification threshold. Precision-recall metrics are less sensitive to the choice of threshold compared to accuracy or precision alone, which is a critical consideration in clinical settings where the cost of false positives and negatives can vary significantly.
Q3. Generalizability and Extensibility: The current experiments are limited to the MIMIC dataset, and the baseline models have been fine-tuned for this specific dataset in the level of model-design. While the results on MIMIC are promising, readers may be interested in the general applicability of the proposed method to other datasets and clinical scenarios. It would be beneficial for the authors to provide experiments on additional datasets to demonstrate the robustness and generalizability of their approach. Furthermore, an exploration of how the method performs when integrated with different models or when adapted to various clinical prediction tasks would strengthen the paper's contribution and credibility. Given the focus on EHR data, it would be insightful if the authors could discuss the transferability of their model to other EHR datasets, potentially from different geographical regions or healthcare systems. This discussion could include challenges related to data heterogeneity, variations in clinical practices, and how these factors might affect the performance of the proposed AutoDP framework.