多任务重校准聚合网络用于医学代码预测
Multitask Recalibrated Aggregation Network for Medical Code Prediction
摘要：
医疗编码将专业书面的医疗报告转换为标准化代码，是医疗信息系统和健康保险报销的重要组成部分。由受过训练的人类编码人员进行手动编码是耗时且容易出错的。因此，已经开发了自动编码算法，尤其是建立在机器学习和深度神经网络的最新进展上。为了解决编码冗长和嘈杂的临床文档和捕获代码关联的挑战，我们提出了一个多任务重新校准的聚合网络。特别是，多任务学习跨不同编码方案共享信息，并捕获不同医疗代码之间的依赖关系。共享模块中的特征重新校准和聚合增强了冗长笔记的表示学习。使用真实世界的MIMIC-III数据集进行的实验表明，预测性能得到了显着提高。
英文摘要：
Medical coding translates professionally written medical reports into standardized codes, which is an essential part of medical information systems and health insurance reimbursement. Manual coding by trained human coders is time-consuming and error-prone. Thus, automated coding algorithms have been developed, building especially on the recent advances in machine learning and deep neural networks. To solve the challenges of encoding lengthy and noisy clinical documents and capturing code associations, we propose a multitask recalibrated aggregation network. In particular, multitask learning shares information across different coding schemes and captures the dependencies between different medical codes. Feature recalibration and aggregation in shared modules enhance representation learning for lengthy notes. Experiments with a real-world MIMIC-III dataset show significantly improved predictive performance.
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