一个可解释的人工智能辅助的网络应用程序,用于癌症药物价值预测
Sonali Kothari1, Shivanandana Sharma1, Sanskruti Shejwal1
1Symbiosis Institute of Technology - Pune Campus, Symbiosis International (Deemed University), Pune, India.
MethodsX
|April 18, 2024
概括
这项研究引入了一种可解释的人工智能 (XAI) 模型,用于预测癌症药物价值,为医疗决策和个性化瘤治疗提供准确,可理解的见解.
科学领域:
- 计算生物学 计算生物学
- 人工智能在医学中的应用
- 药物发现 药物发现 药物发现
背景情况:
- 越来越多的人对医疗保健应用的可解释人工智能 (XAI) 感兴趣.
- 在医疗决策中需要可解释的模型,特别是在瘤学中.
研究的目的:
- 为准确的癌症药物价值预测开发一个XAI模型.
- 为癌细胞的化学组成和药物相互作用提供可解释的见解.
- 为个性化治疗建议创建癌症不可知框架.
主要方法:
- 在大量癌症药物化学化合物的数据集上训练的XAI模型的开发.
- 对模型的预测性能进行了广泛的培训和评估.
- 癌细胞化学成分中的因果关系和相关关系的分析.
主要成果:
- 拟议的XAI模型与现有模型相比,表现出优越的性能.
- 该模型提供了准确的药物价值预测,并提供了明确的解释.
- 根据化学性质确定了影响药物疗效的关键因素.
结论:
- 将XAI与大型数据集集集成为定制癌症治疗提供了一个有希望的方法.
- 癌症不可知的架构允许可扩展到各种癌症类型.
- 开发的框架有助于明智的临床决策和改善患者的治疗结果.
关键词:
癌症AIAI 癌症AI癌症XAI是一个基于人工智能的可解释的Web应用程序,它解释了肺癌数据集中的化学实体和药物的细节.组合学习学习 组合学习可解释的人工智能 (XAI)医疗保健 医疗保健 医疗保健 医疗保健机器学习是机器学习.更多相关视频
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