将机器学习和人工智能纳入生命过程流行病学:通往创新公共卫生解决方案的途径
Shanquan Chen1, Jiazhou Yu2, Sarah Chamouni3
1Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK. Shanquan.chen@lshtm.ac.uk.
BMC medicine
|September 1, 2024
概括
机器学习 (ML) 和人工智能 (AI) 可以通过分析复杂的健康数据来推进生命过程流行病学. 这些技术的负责任整合为创新的公共卫生解决方案和公平的未来提供了机会.
科学领域:
- 流行病学 流行病学
- 公共卫生 公共卫生
- 计算生物学 计算生物学
背景情况:
- 生命过程流行病学研究了由生物,社会和环境因素影响的健康轨迹.
- 机器学习 (ML) 和人工智能 (AI) 为复杂的健康数据集提供了先进的分析能力.
- 现有的流行病学研究越来越多地利用ML和AI来识别模式并改善预测.
研究的目的:
- 总结ML和AI在生命过程流行病学中的当前应用.
- 讨论在这个领域整合ML和AI的未来潜力和挑战.
- 为开发使用ML和AI的创新公共卫生解决方案提供建议.
主要方法:
- 在流行病学研究中审查当前的ML和AI应用.
- 基于生命周期研究原则的ML和AI应用框架 (生命周期发展,代理,时间和地点,时间,连接生命).
- 讨论包括数据质量,可解释性,偏见,隐私和公平性在内的挑战.
主要成果:
- 机器学习和人工智能可以处理大型,复杂的数据集,识别复杂的模式,整合多式联络数据,提高预测准确度和增强因果推理.
- 这些技术可以识别关键干预窗口,模型风险因素相互作用,预测疾病轨迹,并加强因果推理.
- 成功的整合需要解决数据质量,可解释性,偏见,隐私和公平方面的挑战.
结论:
- 在生命周期流行病学中利用ML和AI为推进理解和开发公共卫生解决方案提供了重大机会.
- 通过跨学科合作,标准化准则和优先考虑公平性来应对挑战是必不可少的.
- 负责任地使用ML和AI可以在整个生命过程中带来更健康,更公平的结果.
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