International Conference on Technologies and Applications of
Artificial Intelligence (TAAI). 2022;143-148
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.
Kenichiro Ando, Mamoru Komachi, Takashi Okumura, Hiromasa Horiguchi, Yuji Matsumoto
2023-03-10
