血液学中的深度学习:从分子到患者
1Division of Hematology, Department of Medicine The Ohio State University Comprehensive Cancer Center.
Clinical hematology international
|October 17, 2024
概括
深度学习 (DL) 彻底改变了血液学,从分子分析到患者护理. 虽然有希望,但普遍性和可解释性方面的挑战仍然存在,以实现更广泛的采用.
科学领域:
- 计算生物学 计算生物学
- 医疗信息学 医疗信息学
- 血液学 血液学 血液学
背景情况:
- 深度学习 (DL) 是机器学习的一个子集,证明了医学上的重大进步.
- DL的血液学应用范围从基本分子研究到临床患者管理.
- 了解DL基础知识,架构和培训对于其医学实施至关重要.
研究的目的:
- 审查深度学习在血液学中的多样化应用.
- 在血液学上分析DL模型的架构,性能和局限性.
- 为该领域的非专家提供一个易于访问的DL介绍.
主要方法:
- 对血液学中的深度学习应用现有文献的审查.
- 对DL模型架构,性能指标和局限性的分析.
- 在分子,细胞,组织和患者层面对应用的分类.
主要成果:
- DL增强了分子分析 (多组学,蛋白质结构) 和细胞诊断 (细胞形态学,流细胞计,整片成像).
- 大型语言模型 (LLM) 能够分析临床数据,电子健康记录和临床笔记.
- 观察到有前途的结果,但模型通用性和可解释性的挑战仍然存在.
结论:
- 深度学习在血液学中提供了变革性的潜力,改善了诊断和数据分析.
- 血液学中DL的整合落后于其他医学领域,需要进一步的研究和开发.
- 解决概括性和可解释性方面的挑战是更广泛的临床采用的关键.
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