开发一个概率布尔网络 (PBN) 来建模手术内血压管理
Chamara Gunaratne1, Ron Ison1, Catherine C Price2
1Department of Anesthesiology, University of Florida College of Medicine, Gainesville, FL, USA.
Computer methods and programs in biomedicine
|March 29, 2024
概括
这项研究开发了一种概率布尔网络 (PBN),用于指导外科手术期间的血压管理. 通过选择干预措施,PBN可以稳定血液动力学,尽管药物选择的准确性需要进一步改进.
科学领域:
- 麻醉学 麻醉学
- 人工智能的人工智能
- 计算生物学 计算生物学
背景情况:
- 麻醉师在手术期间监测血压,这是器官 perfusion 的重要标志.
- 目前的术后血压管理依赖于临床医生的经验,导致治疗变化.
- 机器学习为预测模型提供了潜在的潜力,以指导外科手术期间的决策.
研究的目的:
- 将血压管理的临床决策转化为使用概率布尔网络 (PBNs) 的算法框架.
- 通过选择临床干预来维持血压在目标范围内,假设和开发一种能够纠正血液动力学不稳定的PBN.
主要方法:
- 利用了65岁以上接受全身麻醉手术 (2018-2020) 的患者的数据.
- 提取的参数包括心率,血压和药物的频率.
- 使用Python 3开发和验证了一个PBN模型,将数据分割成训练和验证集 (66/33分割).
主要成果:
- PBN成功模拟了血液动力学不稳定的患者,并通过药物管理纠正了不平衡.
- 该模型在所有代中始终实现稳定,稳态矩阵.
- 在模拟临床药物选择方面,PBN表现出了可变的准确性,在血管扩展剂方面表现出色,但在血管压缩剂方面扎.
结论:
- 开发的PBN在根据患者的生命体征选择适当的干预措施以纠正血液动力学不稳定性方面表现有前途.
- 需要进一步的研究来完善PBN模拟临床实践中观察到的药物选择频率的能力.
- 目前的PBN模型为开发更强大的算法提供了有价值的见解,用于外科手术期间的血液动力学管理.
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