超网络引导的多任务学习在患者预后和Zero-shot诊断预测中的应用
Patient Outcome and Zero-shot Diagnosis Prediction with Hypernetwork-guided Multitask Learning
摘要：
多任务深度学习已被应用于从文本预测患者结果，将临床笔记作为输入并训练具有多个任务的联合损失函数的深度神经网络。然而，多任务学习的联合训练方案受到任务间干扰的影响，并且由于罕见疾病或未知诊断，多个任务之间的诊断预测具有可推广性问题。为了解决这些挑战，我们提出了一种基于超网络的方法，该方法生成多任务预测头的任务条件参数和系数，以学习特定于任务的预测并平衡多任务学习。我们还将语义任务信息，以提高我们的任务条件多任务模型的泛化能力。从真实世界的MIMIC数据库中提取的早期和出院记录的实验表明，在大多数情况下，我们的方法在多任务患者结局预测上可以取得比强基线更好的性能。此外，我们的方法可以有效地处理的情况下，有限的信息和改善zero-shot预测看不见的诊断类别。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
