A major impediment to successful drug development is the complexity, cost, and scale of clinical trials. The detailed internal structure of clinical trial data can make conventional optimization difficult to achieve. Recent advances in machine learning, specifically graph-structured data analysis, have the potential to enable significant progress in improving the clinical trial design. TrialGraph seeks to apply these methodologies to produce a proof-of-concept framework for developing models which can aid drug development and benefit patients. In this work, we first introduce a curated clinical trial data set compiled from the CT.gov, AACT and TrialTrove databases (n=1191 trials; representing one million patients) and describe the conversion of this data to graph-structured formats. We then detail the mathematical basis and implementation of a selection of graph machine learning algorithms, which typically use standard machine classifiers on graph data embedded in a low-dimensional feature space. We trained these models to predict side effect information for a clinical trial given information on the disease, existing medical conditions, and treatment. The MetaPath2Vec algorithm performed exceptionally well, with standard Logistic Regression, Decision Tree, Random Forest, Support Vector, and Neural Network classifiers exhibiting typical ROC-AUC scores of 0.85, 0.68, 0.86, 0.80, and 0.77, respectively. Remarkably, the best performing classifiers could only produce typical ROC-AUC scores of 0.70 when trained on equivalent array-structured data. Our work demonstrates that graph modelling can significantly improve prediction accuracy on appropriate datasets. Successive versions of the project that refine modelling assumptions and incorporate more data types can produce excellent predictors with real-world applications in drug development.
翻译:临床试验数据的详细内部结构使得难以实现常规优化。最近机器学习的进展,特别是图表结构数据分析的进展,有可能使改进临床试验设计取得显著进展。TrialGraph试图应用这些方法,为开发能够帮助药物发展和使病人受益的模型建立一个概念验证框架。在这项工作中,我们首先采用由CT.gov、AACT和Treatorve数据库(n=1191试验;代表100万病人)汇编的临床试验数据集,并描述将这一数据转换为图表结构格式。我们然后详细列出数学基础和采用图表机学习算法,这些算法通常使用标准机器分类方法,用于开发有助于药物发展和使病人受益的模型。我们培训这些模型是为了预测临床试验的附带效果,提供疾病、现有医疗条件和治疗等信息。MetPath2Vec算法表现得非常好,标准物流回归模型、定型森林、支持Vecormoral-al-al-allistal ASirmal ASirmal ASirmal ASiral ASal ASirmal ASirmal ASirmal ASyal ASyal ASirmal ASal ASmal ASmal ASyal ASour ASour ASirmal ASoursal ASmal ASmal ASyal ASyal ASU ASal ASal ASal ASal ASal ASal ASl AS ASal ASmal ASir ASir ASm ASm ASm ASm ASir ASir ASm ASir ASir 能够能够可以预测 ASir ASir ASAL ASAL ASAL ASAL ASAL ASAL ASAL AS ASAL ASAL AS ASl SA AS AS AS AS AS ASAL ASAL ASl ASAL ASl ASl AS ASl AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS AS SA AS AS AS AS AS AS AS AS AS AS AS