机器学习:一种新的剂量个性化方法
Qiu-Yue Li1, Bo-Hao Tang1, Yue-E Wu1
1Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education),NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China.
Clinical pharmacology and therapeutics
|September 15, 2023
概括
机器学习 (ML) 对个性化药物剂量有希望,但需要进一步研究. 目前的研究往往缺乏质量和外部验证,这阻碍了ML用于个性化药物剂量的临床使用.
科学领域:
- 精准医学是一门精准的医学.
- 计算生物学 计算生物学
- 药学指标 (Pharmacometrics) 是一个指标.
背景情况:
- 机器学习 (ML) 擅长分析复杂的数据,为个性化医疗提供了潜力.
- 使用ML的个性化剂量是一个新兴的领域,需要系统的评估.
- 现有的研究尚未充分探索ML在优化药物剂量的能力.
研究的目的:
- 在个性化药物剂量中系统地审查ML应用的研究设计和建模细节.
- 总结当前的实践,确定局限性,并提出在药量学中对ML的改进.
- 评估在应用ML用于剂量个性化研究中的质量和偏差风险.
主要方法:
- 进行了对使用ML用于个性化药物剂量的研究的系统审查.
- 分析了研究群体,预测目标,数据源,ML算法和特征选择.
- 评估预测性能并使用预测模型风险偏差评估工具 (PROBAST) 进行质量评估.
主要成果:
- ML适用于先验和后续剂量选择,优化和治疗药物监测.
- 研究主要集中于狭窄的治疗指数药物,如免疫抑制剂和抗感染药物.
- 对特殊人群 (例如儿童) 的关注有限,大多数研究都表现出不良的方法质量和偏见的高风险.
结论:
- 用于剂量个性化的ML的临床实施受到外部验证和临床实用性评估的缺乏所阻碍.
- 改善研究设计,方法严格性和验证对于将ML模型转化为临床实践至关重要.
- 需要进一步的研究来提高ML驱动的个性化剂量策略的临床相关性和适用性.
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