利用人工智能和机器学习促进儿童健康公平
C Ronny Cheung1,2, Mark Butler1,2, Carolyn Cooper3
1Evelina London Children's Hospital, London, United Kingdom.
人工智能 (AI) 和机器学习 (ML) 可以加剧健康差距, 这项研究探讨了ML的应用,以预测没有预约的情况,并确定针对性干预的高风险喘患者.
科学领域:
- 儿童健康
- 医疗公平
- 计算健康
背景情况:
- 如果不小心使用人工智能和机器学习,
- 机器学习提供了对影响健康的社会经济因素的潜在见解,使个人和人口水平的预测成为可能.
- 解决儿童健康中的健康公平需要仔细考虑AI/ML应用.
研究的目的:
- 概述ML在儿童健康方面的潜在应用,重点关注其对健康公平的影响.
- 描述在不同城市环境中促进人口健康公平的两种新型ML使用案例.
- 识别和减轻 ML 训练数据,模型和部署中潜在的不平等现象.
主要方法:
- 使用常规的人口数据预测门诊缺席的ML算法的开发和培训.
- 创建使用常规健康决定性指标的儿童喘风险预测工具.
- 在伦敦市中心的多元化人口中应用ML.
主要成果:
- 证明了ML在预测儿童健康预约的有用性.
- 开发了一种工具来识别高风险的喘患者进行预防性干预.
- 突出 ML 针对干预和改善健康公平的潜力.
结论:
- 通过深思熟虑地开发和部署,
- 需要采取积极的策略来防止不公平的行为无意中嵌入到ML系统中.
- 缓解策略对于确保所有儿童均能从AI/ML中受益至关重要.
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