论文标题
PLM-ICD：使用验证语言模型的自动ICD编码
PLM-ICD: Automatic ICD Coding with Pretrained Language Models
论文作者
论文摘要
将电子健康记录（EHR）自动分为诊断代码对NLP社区的挑战。最先进的方法将此问题视为多标签分类问题，并提出了各种体系结构来模拟此问题。但是，这些系统并未利用验证的语言模型的出色性能，这在自然语言理解任务上实现了出色的性能。先前的工作表明，经常使用的填充方案在此任务上表现不佳。因此，本文旨在分析表现不佳的原因，并通过验证的语言模型为自动编码开发一个框架。我们通过实验发现了三个主要问题：1）大型标签空间，2）长输入序列和3）域之间的域不匹配和训练和微调。我们提出了PLMICD，该框架通过各种策略来应对挑战。实验结果表明，我们提出的框架可以在基准模拟数据上以多个指标来克服挑战并实现最先进的绩效。源代码可从https://github.com/miulab/plm-icd获得
Automatically classifying electronic health records (EHRs) into diagnostic codes has been challenging to the NLP community. State-of-the-art methods treated this problem as a multilabel classification problem and proposed various architectures to model this problem. However, these systems did not leverage the superb performance of pretrained language models, which achieved superb performance on natural language understanding tasks. Prior work has shown that pretrained language models underperformed on this task with the regular finetuning scheme. Therefore, this paper aims at analyzing the causes of the underperformance and developing a framework for automatic ICD coding with pretrained language models. We spotted three main issues through the experiments: 1) large label space, 2) long input sequences, and 3) domain mismatch between pretraining and fine-tuning. We propose PLMICD, a framework that tackles the challenges with various strategies. The experimental results show that our proposed framework can overcome the challenges and achieves state-of-the-art performance in terms of multiple metrics on the benchmark MIMIC data. The source code is available at https://github.com/MiuLab/PLM-ICD
