人工智能协助药物治疗决策:可行性研究
Michael Bücker1, Kreshnik Hoti2, Olaf Rose3
1Münster School of Business -FH Münster - University of Applied Sciences, Münster, Germany.
Exploratory research in clinical and social pharmacy
|September 10, 2024
概括
人工智能 (AI) 在指导老年患者药物治疗决策方面表现有前途. 虽然存在挑战,但人工智能模型可以推药物,但人类监督对于安全有效的患者护理至关重要.
科学领域:
- 医疗信息学 医疗信息学
- 计算医学是一种计算医学.
- 药物治疗 药物治疗
背景情况:
- 人工智能 (AI) 提供了分析复杂医疗数据的潜力.
- 它在药物治疗决策中的应用受到患者特异性和动态因素的限制.
研究的目的:
- 研究人工智能在指导药物治疗决策中的实用性.
- 利用临床数据,包括诊断,实验室结果和生命体征.
主要方法:
- 从之前的药物治疗优化研究中调整的数据.
- 使用R和tidymodels的决策树,将数据分成74%的培训和26%的测试集.
- 使用引导和超参数调整来防止过拟合;计算性能指标,如准确性.
主要成果:
- 在101名老年患者中,人工智能模型在心血管药物类别中实现了38%至100%的预测准确度.
- 该模型对实验室和生命体征数据的解释不清楚.
- 人工智能滞后时间问题可以通过手动的决策树调整来解决.
结论:
- 人工智能模型展示了个性化药物推的潜力.
- 人工智能模型开发中的障碍在很大程度上被克服了.
- 未来的研究应该将药物效应与药物数据一起纳入,以便更好地进行实验室解释;人类监督对于AI药物治疗支持至关重要.
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