Centre for Big Data Research in Health, The University of New South Wales, Sydney, Australia.
Stud Health Technol Inform. 2024 Sep 24;318:24-29. doi: 10.3233/SHTI240886.
Encapsulating a patient's clinical narrative into a condensed, informative summary is indispensable to clinical coding. The intricate nature of the clinical text makes the summarisation process challenging for clinical coders. Recent developments in large language models (LLMs) have shown promising performance in clinical text summarisation, particularly in radiology and echocardiographic reports, after adaptation to the clinical domain. To explore the summarisation potential of clinical domain adaptation of LLMs, a clinical text dataset, consisting of electronic medical records paired with "Brief Hospital Course" from the MIMIC-III database, was curated. Two open-source LLMs were then fine-tuned, one pre-trained on biomedical datasets and another on a general-content domain on the curated clinical dataset. The performance of the fine-tuned models against their base models were evaluated. The model pre-trained on biomedical data demonstrated superior performance after clinical domain adaptation. This finding highlights the potential benefits of adapting LLMs pre-trained on a related domain over a more generalised domain and suggests the possible role of clinically adapted LLMs as an assistive tool for clinical coders. Future work should explore adapting more advanced models to enhance model performance in higher-quality clinical datasets.
将患者的临床叙述纳入简洁、信息丰富的摘要对于临床编码是必不可少的。由于临床文本的复杂性，临床编码员在对其进行总结时面临挑战。最近，大型语言模型 (LLM) 的发展在临床文本总结方面表现出了很有前景的性能，特别是在经过临床领域的调整后，在放射学和超声心动图报告中。为了探索临床领域调整 LLM 进行总结的潜力，我们整理了一个临床文本数据集，该数据集由电子病历和 MIMIC-III 数据库中的“Brief Hospital Course”配对而成。然后，我们对两个开源的 LLM 进行了微调，一个是在生物医学数据集上预训练的，另一个是在经过整理的临床数据集上的通用内容领域预训练的。我们评估了微调模型相对于其基础模型的性能。在经过临床领域调整后，基于生物医学数据预训练的模型表现出了更好的性能。这一发现突出了在更相关的领域而不是更一般的领域调整 LLM 的潜在好处，并表明经过临床调整的 LLM 可能作为临床编码员的辅助工具。未来的工作应该探索调整更先进的模型，以提高高质量临床数据集的模型性能。
