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.

使用患者访问纪录的多模态数据，即先前的患者访问 X 线图像、报告，以填充电子医疗报告中的 “发现” 部分，该方法使用转换器模型和多模式融合模块，以优于先前方法的 F1 分数和 BLEU-4、METEOR 和 ROUGE-L 的结果进行训练。