应用机器学习技术来预测与药物相关的副作用:政策简报
Esmaeel Toni1, Haleh Ayatollahi2
1Student Research Committee, Iran University of Medical Sciences, Tehran, Islamic Republic of Iran.
概括
机器学习 (ML) 可以早期预测药物副作用,改善公共卫生. 政策建议侧重于数据标准化,验证,整合,教育和公平性法规,以便在药物开发中负责任地采用ML.
科学领域:
- 药物监督 药物监督 药物监督
- 医疗信息学 医疗信息学
- 监管科学 监管科学
背景情况:
- 传统的药物安全监测可能无法检测罕见或长期副作用.
- 机器学习 (ML) 显示了早期预测药物相关不良事件的潜力.
研究的目的:
- 提出基于证据的政策选择来利用ML来预测与药物相关的副作用.
- 解决药物安全的ML采用的障碍和机会.
主要方法:
- 相关研究的范围审查.
- 对政策制定障碍和机会的二次分析.
- 政策建议的综合.政策建议的综合.
主要成果:
- 发现的挑战包括数据标准化,模型解释性和监管调整.
- 可解释的ML和跨部门的协作可以提高预测的准确性和公平性.
- 关于数据收集,模型验证,整合,公众意识和公平性法规,提出了五项政策建议.
结论:
- ML为提高药物安全性和患者治疗结果提供了巨大的潜力.
- 为了有效实施ML,必须解决道德,监管和技术方面的挑战.
- 跨学科协调和基于证据的政策制定对于在药物开发中负责任地采用ML至关重要.
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