使用人工智能和预测建模,使学习医疗保健系统 (LHS) 能够为流行病做好准备
Anshu Ankolekar1, Lisanne Eppings1, Fabio Bottari2
1Department of Precision Medicine, GROW School for Oncology, Maastricht University Medical Centre+, Maastricht, the Netherlands.
Computational and structural biotechnology journal
|June 4, 2024
概括
人工智能 (AI) 和预测模型可以通过支持患者和临床医生来增强未来的流行病反应. 利用人工智能利用COVID-19数据可以改善学习医疗保健系统 (LHS) 以更好地管理传染病.
科学领域:
- 公共卫生 公共卫生
- 医疗信息学 医疗信息学
- 流行病学 流行病学
背景情况:
- 随着COVID-19的流行,传染病管理面临着新的挑战.
- 及时采用学习医疗保健系统 (LHS) 支柱"知识,数据,实践"可以减轻全球医疗保健危机.
- 在COVID-19期间生成的各种数据集为高级分析提供了机会.
研究的目的:
- 检查COVID-19为未来的流行病准备所带来的挑战和机会.
- 提出利用人工智能和预测建模来增强LHS功能的策略.
- 突出政策制定者在支持人工智能驱动的医疗保健计划中的作用.
主要方法:
- 分析COVID-19流行病的发展轨迹和数据生成.
- 建议利用人工智能和预测建模来利用数据的策略.
- 审查AI在传染病管理和LHS实施中的潜在应用.
主要成果:
- 人工智能和预测模型可以显著支持患者和临床医生的传染病管理.
- 人工智能工具可以帮助早期发现疾病,对患者进行分拣,预测结果,以及监测病毒演变和治疗疗效.
- 以患者为中心的方法对于有效的疾病缓解至关重要.
结论:
- 实施人工智能和预测分析对于加强学习医疗保健系统 (LHS) 至关重要.
- 这些技术可以通过启用积极的疾病管理和对干预措施的实时评估来提高对未来流行病的准备.
- 立法和政策支持对于成功将人工智能纳入医疗保健系统至关重要.
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