通过深度学习改变药物治疗:个性化医学的未来
Altaf Osman Mulani1, Minal Deshmukh2, Vaishali Jadhav3
1Department of Electronics and Telecommunication, SKN Sinhgad College of Engineering, Pandharpur, Maharashtra, India.
Drug research
|August 29, 2025
概括
通过改善药物反应预测和治疗策略, 深度学习模型,特别是变形金刚, 显著增强了个性化医疗. 这种人工智能方法提高了药物与患者的匹配效率,为精确的药物治疗铺平了道路.
科学领域:
- 药理学和计算生物学
- 医疗保健中的人工智能
背景情况:
- 个性化医疗根据患者的个性特征 (遗传,环境,生活方式) 进行治疗.
- 人工智能 (AI),特别是深度学习,为分析复杂的生物和患者数据提供了先进的功能.
研究的目的:
- 研究深度学习技术的应用,以优化个性化治疗策略.
- 评估用于药物反应预测,生物标志物识别和药物不良反应预测的深度学习模型.
主要方法:
- 使用深度学习模型,包括卷积神经网络 (CNN),循环神经网络 (RNN),变压器架构和生成对抗网络 (GAN).
- 在电子健康记录 (EHR),基因组序列和临床指标的各种数据集上训练模型.
- 对药物反应预测,生物标志物识别和不良药物反应 (ADR) 预测的模型性能进行评估.
主要成果:
- 基于变压器的架构在药物反应预测中获得了91.2%的准确性和0.92的AUC- ROC.
- 与传统方法相比,深度学习的整合提高了20-30%的药物-患者匹配效率.
- 证明了提高临床决策和准确药物治疗的潜力.
结论:
- 深度学习,特别是变压器模型, 显示出对推进个性化医疗和精确药物治疗的巨大希望.
- 需要解决包括数据隐私,模型解释性和监管合规性在内的挑战,以实现广泛的临床采用.
- 未来的研究方向包括可解释的人工智能 (XAI) 和联合学习,以克服局限性并促进临床整合.
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