During the patient's hospitalization, the physician must record daily observations of the patient and summarize them into a brief document called "discharge summary" when the patient is discharged. Automated generation of discharge summary can greatly relieve the physicians' burden, and has been addressed recently in the research community. Most previous studies of discharge summary generation using the sequence-to-sequence architecture focus on only inpatient notes for input. However, electric health records (EHR) also have rich structured metadata (e.g., hospital, physician, disease, length of stay, etc.) that might be useful. This paper investigates the effectiveness of medical meta-information for summarization tasks. We obtain four types of meta-information from the EHR systems and encode each meta-information into a sequence-to-sequence model. Using Japanese EHRs, meta-information encoded models increased ROUGE-1 by up to 4.45 points and BERTScore by 3.77 points over the vanilla Longformer. Also, we found that the encoded meta-information improves the precisions of its related terms in the outputs. Our results showed the benefit of the use of medical meta-information.
翻译:在病人住院期间,医生必须每天记录病人的观察结果,并将这些观察结果归纳成一份简短的文件,在病人出院时称为“放行摘要”,自动生成出产摘要可以大大减轻医生的负担,最近已在研究界得到处理。以前对使用顺序到顺序结构的放行摘要生成进行的大多数研究只侧重于住院记录,然而,电力健康记录(EHR)也有丰富的结构化元数据(如医院、医生、疾病、停留时间等),这些元数据可能有用。本文调查医疗元信息对总结任务的效果。我们从EHR系统获得四类元信息,并将每一种元信息编码成一个顺序到顺序的模式。使用日文的 EHR,元信息编码模型将ROUGE-1增加了4.45个点,BERTScore增加了3.77个点。此外,我们发现已编码的元信息改善了其相关术语在产出中的精确性。我们的成果显示了使用元信息的好处。</s>