Exploring Optimal Granularity for Extractive Summarization of Unstructured Health Records: Analysis of the Largest Multi-Institutional Archive of Health Records in Japan

@article{Ando2022ExploringOG,
  title={Exploring Optimal Granularity for Extractive Summarization of Unstructured Health Records: Analysis of the Largest Multi-Institutional Archive of Health Records in Japan},
  author={Kenichiro Ando and Takashi Okumura and Mamoru Komachi and Hiromasa Horiguchi and Yuji Matsumoto},
  journal={ArXiv},
  year={2022},
  volume={abs/2209.10041}
}
Automated summarization of clinical texts can reduce the burden of medical professionals. “Discharge summaries” are one promising application of the summarization, because they can be generated from daily inpatient records. Our preliminary experiment suggests that 20–31% of the descriptions in discharge summaries overlap with the content of the inpatient records. However, it remains unclear how the summaries should be generated from the unstructured source. To decompose the physician’s… 
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