PLM-ICD: Automatic ICD Coding with Pretrained Language Models
This work addresses a domain-specific challenge in medical NLP for improving diagnostic coding efficiency, but it is incremental as it builds on existing methods with pretrained models.
The paper tackled the problem of automatically classifying electronic health records into diagnostic codes by analyzing why pretrained language models underperform and developing a framework to address issues like large label space and domain mismatch, achieving state-of-the-art performance on the MIMIC benchmark.
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