Abstract
This paper presents the results of the Data Science for Digital Health (DS4DH) group in the MEDIQA-Chat Tasks at ACL-ClinicalNLP 2023. Our study combines the power of a classical machine learning method, Support Vector Machine, for classifying medical dialogues, along with the implementation of one-shot prompts using GPT-3.5. We employ dialogues and summaries from the same category as prompts to generate summaries for novel dialogues. Our findings exceed the average benchmark score, offering a robust reference for assessing performance in this field.- Anthology ID:
- 2023.clinicalnlp-1.57
- Volume:
- Proceedings of the 5th Clinical Natural Language Processing Workshop
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Venue:
- ClinicalNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 536–545
- Language:
- URL:
- https://aclanthology.org/2023.clinicalnlp-1.57
- DOI:
- Cite (ACL):
- Boya Zhang, Rahul Mishra, and Douglas Teodoro. 2023. DS4DH at MEDIQA-Chat 2023: Leveraging SVM and GPT-3 Prompt Engineering for Medical Dialogue Classification and Summarization. In Proceedings of the 5th Clinical Natural Language Processing Workshop, pages 536–545, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- DS4DH at MEDIQA-Chat 2023: Leveraging SVM and GPT-3 Prompt Engineering for Medical Dialogue Classification and Summarization (Zhang et al., ClinicalNLP 2023)
- PDF:
- https://preview.aclanthology.org/starsem-semeval-split/2023.clinicalnlp-1.57.pdf