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Multi-Task Training with In-Domain Language Models for Diagnostic Reasoning
- Publication Year :
- 2023
- Publisher :
- arXiv, 2023.
-
Abstract
- Generative artificial intelligence (AI) is a promising direction for augmenting clinical diagnostic decision support and reducing diagnostic errors, a leading contributor to medical errors. To further the development of clinical AI systems, the Diagnostic Reasoning Benchmark (DR.BENCH) was introduced as a comprehensive generative AI framework, comprised of six tasks representing key components in clinical reasoning. We present a comparative analysis of in-domain versus out-of-domain language models as well as multi-task versus single task training with a focus on the problem summarization task in DR.BENCH (Gao et al., 2023). We demonstrate that a multi-task, clinically trained language model outperforms its general domain counterpart by a large margin, establishing a new state-of-the-art performance, with a ROUGE-L score of 28.55. This research underscores the value of domain-specific training for optimizing clinical diagnostic reasoning tasks.<br />Comment: Accepted to the Proceedings of the 5th Clinical NLP Workshop at ACL
Details
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....dc694b781978cff94bcfde4858736a3e
- Full Text :
- https://doi.org/10.48550/arxiv.2306.04551