KG-LIME:预测多发性硬化症疾病修饰疗法的药物不良事件的个性化风险
Jason Patterson1, Nicholas Tatonetti1,2
1Department of Biomedical Informatics, Columbia University, New York, NY 10032, United States.
概括
这项研究开发了人工智能模型来预测多发性硬化症患者的不良药物事件,实现了良好的准确性,并使用一种新的知识图法为预测提供了可解释的解释.
科学领域:
- 药物监督和人工智能 药物监督和人工智能
- 临床信息学 临床信息学
- 计算医学是一种计算医学.
背景情况:
- 药物不良事件 (ADEs) 对接受多发性硬化症 (MS) 治疗的患者构成重大风险.
- 预测这些事件需要复杂的模型,这些模型可以解释患者数据中的时间关系.
- 现有的方法往往缺乏解释性,阻碍了临床的信任和应用.
研究的目的:
- 开发具有时间意识的个体级风险评分模型,用于MS患者治疗疾病修饰疗法 (DMT) 的ADE.
- 为这些风险模型所做的预测提供可解释的解释.
- 利用知识图 (KG) 来增强模型理解和特征重要性.
主要方法:
- 利用了电子健康记录 (EHR) 数据的时间序列,这些数据以观察医疗结果伙伴关系共同数据模型 (OMOP CDM) 进行结构化.
- 使用图形卷积网络 (GCNs) 来从OMOP KG中学习概念嵌入.
- 集成概念嵌入到长期短期记忆 (LSTM) 网络中,用于1年的ADE预测.
- 实施了一种新的知识图局部可解释模型-不可知论解释 (KG-LIME) 方法,用于可解释的预测.
主要成果:
- 这些模型在4859名患者的队列中有效预测了56种不良事件类型中的32种 (P < .05).
- 通过0.77 ± 0.15的曲线下的面积 (AUC) 和0.31 ± 0.27的精度回忆曲线下的面积 (AUC-PR) 实现了强烈的歧视.
- 证明了良好的校准,屏障评分 (BS) 为 0.04 ± 0.04.
- KG-LIME成功地生成了对预测至关重要的概念的可解释,经文献验证的列表.
结论:
- 开发的风险模型显示高校准性和歧视性,用于预测MS患者的ADEs.
- 新的KG-LIME方法有效地使用KG来突出重要预测概念,提高模型的可解释性.
- 未来的研究应该探索不同的时间窗口来预测ADE,特别是短期和长期事件.
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