Kavli Affiliate: Cheng Peng
| First 5 Authors: Mengxian Lyu, Cheng Peng, Daniel Paredes, Ziyi Chen, Aokun Chen
| Summary:
Automatic generation of discharge summaries presents significant challenges
due to the length of clinical documentation, the dispersed nature of patient
information, and the diverse terminology used in healthcare. This paper
presents a hybrid solution for generating discharge summary sections as part of
our participation in the "Discharge Me!" Challenge at the BioNLP 2024 Shared
Task. We developed a two-stage generation method using both extractive and
abstractive techniques, in which we first apply name entity recognition (NER)
to extract key clinical concepts, which are then used as input for a
prompt-tuning-based GatorTronGPT model to generate coherent text for two
important sections including "Brief Hospital Course" and "Discharge
Instructions". Our system was ranked 5th in this challenge, achieving an
overall score of 0.284. The results demonstrate the effectiveness of our hybrid
solution in improving the quality of automated discharge section generation.
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