预测个体治疗效应:机器学习和人工智能的挑战和机遇
Thomas Jaki1,2, Chi Chang3, Alena Kuhlemeier4
1University of Regensburg, Bajuwarenstraße 4, 93055 Regenburg, Germany.
概括
使用机器学习 (ML) 和人工智能 (AI) 预测个体治疗效果可以个性化医疗. 这种方法旨在将正确的治疗与正确的患者相匹配,以改善结果.
科学领域:
- 生物医学信息学 生物医学信息学
- 计算生物学 计算生物学
- 临床决策支持 临床决策支持
背景情况:
- 个性化医疗旨在为个体患者量身定制治疗,以获得最佳的疗效.
- 预测患者特异性治疗反应对于推进医疗保健至关重要.
- 目前的方法往往缺乏确定个别治疗益处的精度.
研究的目的:
- 展示机器学习 (ML) 和人工智能 (AI) 在预测个人治疗效应 (ITE) 中的潜力.
- 介绍和说明预测个体治疗效应 (PITE) 框架.
- 突出ITE预测方面的研究机会和挑战.
主要方法:
- 在PITE框架内使用的基线共变量 (特征).
- 采用ML和AI方法来预测个体患者的治疗益处.
- 将预测的治疗效果与其他干预措施进行比较.
主要成果:
- 展示了使用ML/AI来预测ITE的可行性.
- 证明了PITE框架在识别潜在治疗益处方面的能力.
- 为进一步研究个性化治疗预测提供了基础.
结论:
- 在预测个体治疗效果方面,ML和AI具有显著的前景.
- PITE框架为个性化医疗提供了一种可行的方法.
- 需要进一步的研究来应对现有挑战,并完善ITE预测方法.
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