OptimCLM:优化临床语言模型,通过知识蒸,修剪和量化来预测患者的结果
Mohammad Junayed Hasan1, Fuad Rahman2, Nabeel Mohammed1
1Apurba NSU R&D Lab, Department of Electrical and Computer Engineering, North South University, Dhaka, Bangladesh.
International journal of medical informatics
|December 21, 2024
概括
这项研究优化了医疗保健的临床语言模型 (CLM),实现了显著的压缩和加快速度,性能损失最小. OptimCLM框架允许在临床环境中高效地部署先进的CLM.
科学领域:
- 人工智能在医学中的应用
- 医疗保健的自然语言处理.
- 机器学习用于临床决策支持
背景情况:
- 临床语言模型 (CLM) 通过改进决策和资源管理,为医疗保健转型提供了潜力.
- 推断过程中的高计算成本目前限制了CLM的现实应用.
- 优化CLM对于在临床实践中实现广泛采用至关重要.
研究的目的:
- 开发和验证一个有效的框架来压缩CLM.
- 为了减少CLM的推断时间和存储要求,而不会影响性能.
- 为了促进在现实世界的临床环境中部署CLM.
主要方法:
- OptimCLM框架使用集体学习,知识蒸 (KD),修剪和量化.
- 作为教师组合,使用了域自适应的CLM (DischargeBERT,COReBERT).
- 知识被转移到较小的模型 (BERT-PKD,TinyBERT) 使用黑盒KD,修剪和8位量化.
主要成果:
- OptimCLM实现了高达22.88倍的压缩和28.7倍的推理速度.
- 观察到最小的性能降低:TinyBERT的AUROC损失<5%,BERT-PKD的AUROC损失<2%.
- 优化的模型在临床结果预测任务中胜过了几种最先进的模型.
结论:
- 领域特定的微调结合集体学习和KD优于知识转移的预培训.
- 该研究证明了在医疗保健中部署计算效率高的CLM的可行性.
- 可以使用减少的计算资源开发优化的CLM,为更广泛的临床整合铺平道路.
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