during the patient's stay. - Difficult for other physicians to read - A short summary is created at the patient's discharge. - Important document, sharing information with other institutions - Creation is a hard work
applied an abstractive summarization method to a portion of the largest multi-institutional healthcare data in Japan held by the National Hospital Organization. 6 Our motivation 144 hospitals in Japan Security room Server National Hospital Organization (NHO)
> 0VSPCTFSWBUJPOT • %JGGFSFOUIPTQJUBMTBOEQIZTJDJBOTIBWFEJGGFSFOUQBUUFSOT PG DMJOJDBMSFDPSET • 4FWFSFEJTFBTFTIBWFMBSHFSTVNNBSJFT Meta-information may be useful for discharge summary generation models? 8 Research question
meta-information generates higher quality summaries. 2. We found that a model encoding disease information can produce proper disease and symptom words following the source. In addition, we found that the model using physician and hospital information can generate proper symbols. 10 Contributions
meta-information generates higher quality summaries. 2. We found that a model encoding disease information can produce proper disease and symptom words following the source. In addition, we found that the model using physician and hospital information can generate proper symbols. - /PTUVEJFTBCTUSBDUEJTDIBSHFTVNNBSZJO+BQBOFTF 3. We are the first to apply the abstractive summarization method to generate Japanese discharge summaries. 11 Contributions
Supporting Transition of Care. Diana Diaz et al. 2020.] They tried to introduce meta-information into a model, but its impact has not been investigated. 12 Prior work Encode patient health records with tf-idf grouping Encode the writer's information
- We employ Longformer because both input and output are long texts. - To introduce meta-information, a new feature embedding layer is added to the model's input following previous work (BERT and XLM). 13 Methods: Our model Disease Hospital Physician Stay length 4 types
the experiment, free-text fields were extracted from both the inpatient record and the discharge summary. - The data are randomly split into 22,630, 1,000, and 1,000 for train, validation, and test, respectively. 16 Dataset
meta-information outperform vanilla in BS and BR. • 0VUQVUTPGNPEFMXJUINFUBJOGPSNBUJPOBSFNPSFOBUVSBMGPSIVNBOT 19 Results #FSU4DPSF #-&635 All features is all four types features are added to the embedding layer (4*Feature embeddings)
meta-information outperform vanilla in BS and BR. • 0VUQVUTPGNPEFMXJUINFUBJOGPSNBUJPOBSFNPSFOBUVSBMGPSIVNBOT - All features is the lowest in BS. • 5IJTNBZCFEVFUPMBDLPGUSBJOJOHEBUBCFDBVTFUIFBMMGFBUVSFTIBT RVBESVQMFQBSBNFUFS 20 Results #FSU4DPSF #-&635 All features is all four types features are added to the embedding layer (4*Feature embeddings)
• -BCFMJOHCZ.F$BC +.FEJD - A model encoded disease information generates more suitable disease and symptom words. - Models encoded with physician and hospital information improved the precision of symbol generation. • %JGGFSFOUIPTQJUBMTBOEEPDUPSTIBWFEJGGFSFOUXSJUJOHIBCJUT CVMMFU QPJOUTTVDIBT<・c * c - > QVODUVBUJPOTVDIBT<、c，c．c。> 21 Analysis: precisions of words
four types of information to Longformer and verified the impact of the meta-information. - All four types of information exceeded the performance of the vanilla Longformer model, with the highest performance achieved by encoding disease information. 22 Conclusion
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