一种新的方法来建模CRT的多阶段决策过程,使用机器学习与不确定性量化
Kristoffer Larsen1, Chen Zhao2, Joyce Keyak3
1Department of Mathematical Sciences, Michigan Technological University, Houghton, MI, USA.
ArXiv
|March 11, 2024
概括
这项研究开发了一种多阶段机器学习模型,用于预测心力衰竭 (HF) 患者的心脏再同步治疗 (CRT) 反应. 该模型有效地减少了对昂贵的SPECT MPI数据采集的需求,而不会影响预测性能.
科学领域:
- 心脏病学 心脏病学
- 医疗成像医学成像
- 机器学习 机器学习
背景情况:
- 在心力衰竭 (HF) 中预测心脏再同步治疗 (CRT) 的患者结果通常涉及广泛的数据收集,包括单光子发射计算机断层扫描心肌 perfusion 成像 (SPECT MPI).
- 当前的机器学习 (ML) 模型通常利用所有可用的数据,而不考虑与数据采集相关的重大成本和时间.
结论:
- 拟议的多阶段ML模型有效地预测CRT响应,同时优化数据采集策略.
- 不确定性量化使智能决策成为可能,减少对昂贵的SPECT MPI数据的依赖.
- 这种方法提供了一种具有成本效益和高效的方法,用于预测HF管理中的患者结果.
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