模仿Bradford Hill因果关系考虑的监督药物不良反应信号框架
Supervised Adverse Drug Reaction Signalling Framework Imitating Bradford Hill's Causality Considerations
摘要：
大型纵向观察性医学数据可能包含丰富的信息，并被认为是获得新药安全性知识的潜在来源。不幸的是，在分析纵向观测数据时存在许多复杂性和潜在问题。由于这些复杂性，使用观察数据大规模检测负面副作用的现有方法都倾向于区分关联和因果关系。需要开发能够更好地区分因果关系和非因果关系的新方法，以充分利用数据。在本文中，我们建议使用一组因果关系的考虑因素开发的流行病学家布拉德福德山的基础上，工程功能，使应用监督学习的问题，检测负面的副作用。布拉德福德山的考虑着眼于药物和结果关系的各种角度，以确定它是否显示因果特征。我们教了一个分类器在这些视角中寻找模式，它学会了区分关联和因果关系。本研究的新颖之处在于结合监督学习和Bradford Hill因果关系的考虑，自动化Bradford Hill的因果关系评估。我们评估了药物安全性金标准框架，即观察性医疗结果伙伴关系的非特异性关联参考集。该方法具有良好的区分能力，曲线下面积范围为0.792-0.940（现有方法最佳值：0.73），平均精密度为0.640（现有方法最佳值：0.141）。所提出的特征可以有效地计算并随时更新，使框架适用于大的观测数据。
英文摘要：
Big longitudinal observational medical data potentially hold a wealth of information and have been recognised as potential sources for gaining new drug safety knowledge. Unfortunately there are many complexities and underlying issues when analysing longitudinal observational data. Due to these complexities, existing methods for large-scale detection of negative side effects using observational data all tend to have issues distinguishing between association and causality. New methods that can better discriminate causal and non-causal relationships need to be developed to fully utilise the data. In this paper we propose using a set of causality considerations developed by the epidemiologist Bradford Hill as a basis for engineering features that enable the application of supervised learning for the problem of detecting negative side effects. The Bradford Hill considerations look at various perspectives of a drug and outcome relationship to determine whether it shows causal traits. We taught a classifier to find patterns within these perspectives and it learned to discriminate between association and causality. The novelty of this research is the combination of supervised learning and Bradford Hill's causality considerations to automate the Bradford Hill's causality assessment. We evaluated the framework on a drug safety gold standard know as the observational medical outcomes partnership's nonspecified association reference set. The methodology obtained excellent discriminate ability with area under the curves ranging between 0.792-0.940 (existing method optimal: 0.73) and a mean average precision of 0.640 (existing method optimal: 0.141). The proposed features can be calculated efficiently and be readily updated, making the framework suitable for big observational data.
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