Gao Yuhe, Bao Runxue, Ji Yuelyu, Sun Yiming, Song Chenxi, Ferraro Jeffrey P, Ye Ye
University of Pittsburgh.
GE Healthcare.
AMIA Jt Summits Transl Sci Proc. 2025 Jun 10;2025:167-176. eCollection 2025.
Knowledge exchange is crucial in healthcare, particularly when leveraging data from multiple clinical sites to address data scarcity, reduce costs, and enable timely interventions. Transfer learning can facilitate cross-site knowledge transfer, yet a significant challenge is the heterogeneity in clinical concepts across different sites. Recently, Large Language Models (LLMs) have shown significant potential in capturing the semantic meanings of clinical concepts and mitigating heterogeneity in biomedicine. This study analyzed electronic health records from two large healthcare systems to assess the impact of semantic embeddings from LLMs on local models, shared models, and transfer learning models. The results indicate that domain-specific LLMs, such as Med-BERT, consistently outperform in local and direct transfer scenarios, whereas generic models like OpenAI embeddings may need fine-tuning for optimal performance. This study emphasizes the importance of domain-specific embeddings and meticulous model tuning for effective knowledge transfer in healthcare. It remains essential to investigate the balance the balance between the complexity of downstream tasks, the size of training samples, and the extent of model tuning.
知识交流在医疗保健中至关重要，特别是在利用来自多个临床站点的数据来解决数据稀缺、降低成本并实现及时干预时。迁移学习可以促进跨站点知识转移，但一个重大挑战是不同站点临床概念的异质性。最近，大语言模型（LLMs）在捕捉临床概念的语义含义和缓解生物医学中的异质性方面显示出巨大潜力。本研究分析了来自两个大型医疗系统的电子健康记录，以评估大语言模型的语义嵌入对本地模型、共享模型和迁移学习模型的影响。结果表明，特定领域的大语言模型，如Med-BERT，在本地和直接迁移场景中始终表现出色，而像OpenAI嵌入这样的通用模型可能需要进行微调以实现最佳性能。本研究强调了特定领域嵌入和精心模型调整对于医疗保健中有效知识转移的重要性。研究下游任务的复杂性、训练样本的大小和模型调整的程度之间的平衡仍然至关重要。
