Medical progress notes play a crucial role in documenting a patient's hospital journey, including his or her condition, treatment plan, and any updates for healthcare providers. Automatic summarisation of a patient's problems in the form of a problem list can aid stakeholders in understanding a patient's condition, reducing workload and cognitive bias. BioNLP 2023 Shared Task 1A focuses on generating a list of diagnoses and problems from the provider's progress notes during hospitalisation. In this paper, we introduce our proposed approach to this task, which integrates two complementary components. One component employs large language models (LLMs) for data augmentation; the other is an abstractive summarisation LLM with a novel pre-training objective for generating the patients' problems summarised as a list. Our approach was ranked second among all submissions to the shared task. The performance of our model on the development and test datasets shows that our approach is more robust on unknown data, with an improvement of up to 3.1 points over the same size of the larger model.

本文提出了一种整合了两个互补组件的方法，其中一个使用大型语言模型为数据增强，另一个则使用新颖的预训练目标作为诊断和问题清单的生成模型，其表现在BioNLP 2023共享任务1A上名列第二。

PULSAR: 基于提取的医疗术语的预训练，用于患者问题和数据增广的黑盒大型语言模型摘要