EPFL-MAKE at “Discharge Me!”: An LLM System for Automatically Generating Discharge Summaries of Clinical Electronic Health Record
Haotian Wu, Paul Boulenger, Antonin Faure, Berta Céspedes, Farouk Boukil, Nastasia Morel, Zeming Chen, Antoine Bosselut
Abstract
This paper presents our contribution to the Streamlining Discharge Documentation shared task organized as part of the ACL’24 workshop. We propose MEDISCHARGE (Meditron-7B Based Medical Summary Generation System for Discharge Me), an LLM-based system to generate Brief Hospital Course and Discharge Instruction summaries based on a patient’s Electronic Health Record. Our system is build on a Meditron-7B with context window extension, ensuring the system can handle cases of variable lengths with high quality. When the length of the input exceeds the system input limitation, we use a dynamic information selection framework to automatically extract important sections from the full discharge text. Then, extracted sections are removed in increasing order of importance until the input length requirement is met. We demonstrate our approach outperforms tripling the size of the context window of the model. Our system obtains a 0.289 overall score in the leaderboard, an improvement of 183% compared to the baseline, and a ROUGE-1 score of 0.444, achieving a second place performance in the shared task.- Anthology ID:
- 2024.bionlp-1.61
- Volume:
- Proceedings of the 23rd Workshop on Biomedical Natural Language Processing
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand
- Editors:
- Dina Demner-Fushman, Sophia Ananiadou, Makoto Miwa, Kirk Roberts, Junichi Tsujii
- Venues:
- BioNLP | WS
- SIG:
- SIGBIOMED
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 696–711
- Language:
- URL:
- https://aclanthology.org/2024.bionlp-1.61
- DOI:
- 10.18653/v1/2024.bionlp-1.61
- Cite (ACL):
- Haotian Wu, Paul Boulenger, Antonin Faure, Berta Céspedes, Farouk Boukil, Nastasia Morel, Zeming Chen, and Antoine Bosselut. 2024. EPFL-MAKE at “Discharge Me!”: An LLM System for Automatically Generating Discharge Summaries of Clinical Electronic Health Record. In Proceedings of the 23rd Workshop on Biomedical Natural Language Processing, pages 696–711, Bangkok, Thailand. Association for Computational Linguistics.
- Cite (Informal):
- EPFL-MAKE at “Discharge Me!”: An LLM System for Automatically Generating Discharge Summaries of Clinical Electronic Health Record (Wu et al., BioNLP-WS 2024)
- PDF:
- https://preview.aclanthology.org/ingest-2024-clasp/2024.bionlp-1.61.pdf