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Combination Therapies and Personalized Medicine02:50

Combination Therapies and Personalized Medicine

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Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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针对个性化医疗建议的可解释机器学习:基于LIME的方法

Yuanyuan Wu1, Linfei Zhang1, Uzair Aslam Bhatti1

  • 1School of Information and Communication Engineering, Hainan University, Haikou 570100, China.

Diagnostics (Basel, Switzerland)
|August 26, 2023
PubMed
概括

本研究介绍了一个深度学习模型,用于解释健康建议的局部可解释模型-不可知解释 (LIME). 它通过解释老年人心脏病和糖尿病等慢性疾病的预测来提高信任.

科学领域:

  • 医疗保健中的人工智能
  • 医疗信息学 医疗信息学
  • 老年医学 老年医学

背景情况:

  • 慢性疾病对老年人的健康和福祉构成重大威胁.
  • 医院拥有大量的健康数据,对于了解疾病进展和个性化治疗至关重要.
  • 现有的健康推系统往往缺乏透明度,未能为其建议提供解释.

研究的目的:

  • 通过深度学习和局部可解释模型-不可知解释 (LIME) 提出一种新的可解释推系统.
  • 增强针对老年人慢性疾病的个性化患者治疗建议.
  • 通过阐明医疗建议背后的推理,增加患者的信任.

主要方法:

  • 应用了深度学习方法,与LIME集成,以提供可解释的建议.
  • 利用六个深度学习算法进行数据预处理后的解释.
  • 专注于老年人中两种常见的慢性疾病:心脏病和糖尿病.
  • 分析特征重要性和贡献系数,以解释模型预测.

主要成果:

  • 对于心脏病,CholCheck,GenHith和HighBP被确定为关键预测特征.
  • 对于糖尿病,葡萄糖,BMI和年龄被发现是最重要的特征.
关键词:
在 LIME 时代,射频算法 射频算法 射频算法深度学习是一种深度学习.梯度增强可以提高梯度.医疗推系统是医疗推系统.

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  • LIME有效地近似模型预测,并确定了两个数据集的特征重要性.
  • 该系统成功地阐明了患者特征对推的影响.

    • 结论:

    • 拟议的基于LIME的深度学习系统为慢性疾病提供可解释的健康建议.
    • 这种方法提高了对医疗推系统的透明度和患者的信任.
    • 这些发现对改善老年人医疗保健决策具有重要意义.