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机器学习在1型和2型糖尿病的负担得起和可访问的胰岛素管理中的应用:综合性审查
Maryam Eghbali-Zarch1, Sara Masoud1
1Department of Industrial and Systems Engineering, Wayne State University, Detroit, MI 48202, USA.
Artificial intelligence in medicine
|April 17, 2024
概括
机器学习 (ML) 可以通过优化剂量和预测需求来改善糖尿病患者负担得起的胰岛素管理. 进一步的研究应该探索保险,生物相似性影响和与成本相关的遵守问题,以改善可访问性.
科学领域:
- 医疗信息学 医疗信息学
- 糖尿病管理 糖尿病管理
- 医疗保健中的人工智能
背景情况:
- 对于糖尿病患者来说,有效的胰岛素管理对于维持血糖稳定性和预防并发症至关重要.
- 高昂的胰岛素成本在美国对持续且负担得起的糖尿病护理构成了重大障碍.
研究的目的:
- 审查机器学习 (ML) 的应用,以提高糖尿病患者胰岛素管理的负担能力和可访问性.
- 确定当前ML应用的局限性,并提出未来的研究方向,以实现成本效益高的胰岛素治疗.
主要方法:
- 在糖尿病胰岛素管理中对机器学习应用的综合文献综述.
- 分析ML在剂量计算,葡萄糖预测,坚持和并发症预测中的作用.
- 识别研究缺口和未来的研究途径.
主要成果:
- 机器学习在胰岛素管理的各个方面都有潜力,包括剂量,预测和坚持.
- 目前ML的使用存在关键限制,特别是关于成本和可访问性.
- 未来的研究应侧重于保险,生物相似性影响,与成本相关的遵守和患者的成本共享.
结论:
- 机器学习为改善负担得起和可访问的胰岛素管理提供了有希望的途径.
- 解决已识别的局限性和追求拟议的研究方向可以带来具有成本效益的解决方案.
- 在ML的创新可以显著有利于依赖胰岛素治疗的患者.
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