Despite the reduction in turn-around times in radiology reports with the use of speech recognition software, persistent communication errors can significantly impact the interpretation of the radiology report. Pre-filling a radiology report holds promise in mitigating reporting errors, and despite efforts in the literature to generate medical reports, there exists a lack of approaches that exploit the longitudinal nature of patient visit records in the MIMIC-CXR dataset. To address this gap, we propose to use longitudinal multi-modal data, i.e., previous patient visit CXR, current visit CXR, and previous visit report, to pre-fill the 'findings' section of a current patient visit report. We first gathered the longitudinal visit information for 26,625 patients from the MIMIC-CXR dataset and created a new dataset called Longitudinal-MIMIC. With this new dataset, a transformer-based model was trained to capture the information from longitudinal patient visit records containing multi-modal data (CXR images + reports) via a cross-attention-based multi-modal fusion module and a hierarchical memory-driven decoder. In contrast to previous work that only uses current visit data as input to train a model, our work exploits the longitudinal information available to pre-fill the 'findings' section of radiology reports. Experiments show that our approach outperforms several recent approaches by >=3% on F1 score, and >=2% for BLEU-4, METEOR and ROUGE-L respectively. The dataset and code will be made publicly available.

翻译：尽管语音识别软件的使用缩短了放射学报告的周转时间，但持续的沟通错误仍可能显著影响放射学报告的解读。预填充放射学报告有望减少报告错误，尽管已有文献致力于生成医学报告，但目前仍缺乏利用MIMIC-CXR数据集中患者就诊记录的纵向特性的方法。为填补这一空白，我们提出利用纵向多模态数据（即患者既往就诊的胸部X光片、当前就诊的胸部X光片以及既往就诊报告）来预填充当前就诊报告中“发现”部分。我们首先从MIMIC-CXR数据集中收集了26,625名患者的纵向就诊信息，并创建了一个名为Longitudinal-MIMIC的新数据集。基于该新数据集，我们训练了一个基于Transformer的模型，通过基于交叉注意力的多模态融合模块和层级化记忆驱动的解码器，捕获包含多模态数据（胸部X光图像与报告）的纵向患者就诊记录中的信息。与以往仅使用当前就诊数据作为模型输入的工作不同，本研究利用可获取的纵向信息来预填充放射学报告中的“发现”部分。实验表明，我们的方法在F1分数上优于近期多种方法至少3%，在BLEU-4、METEOR和ROUGE-L指标上分别至少提升2%。数据集和代码将公开提供。