Patient hand-off and triage are two fundamental problems in health care.
Often doctors must painstakingly summarize complex findings to efficiently
communicate with specialists and quickly make decisions on which patients have
the most urgent cases. In pursuit of these challenges, we present (1) a model
with state-of-art radiology report summarization performance using (2) a novel
method for augmenting medical data, and (3) an analysis of the model
limitations and radiology knowledge gain. We also provide a data processing
pipeline for future models developed on the the MIMIC CXR dataset. Our best
performing model was a fine-tuned BERT-to-BERT encoder-decoder with 58.75/100
ROUGE-L F1, which outperformed specialized checkpoints with more sophisticated
attention mechanisms. We investigate these aspects in this work.

病人交接和分诊是医疗保健中的两个基本问题。为了高效地与专科医生沟通并快速决策哪些患者需要最紧急的医疗，医生们经常需要费力地总结复杂的发现。在探索这些挑战的过程中，我们提出了一种模型（1），该模型具有最先进的放射学报告摘要性能，并采用一种新的医疗数据增强方法（2），还对模型的局限性和放射学知识增长进行了分析（3）。此外，我们还为基于 MIMIC CXR 数据集开发的未来模型提供了数据处理流程。我们最好的模型是一个经过微调的 BERT-to-BERT 编码器 - 解码器，具有 58.75/100 的 ROUGE-L F1 得分，其表现优于带有更复杂的注意机制的专用检查点。我们在本文中研究了这些方面。