Department of Computer & Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, 33431, USA.
Sci Rep. 2021 Feb 10;11(1):3446. doi: 10.1038/s41598-021-82840-x.
In this study, we propose to use machine learning to understand terminated clinical trials. Our goal is to answer two fundamental questions: (1) what are common factors/markers associated to terminated clinical trials? and (2) how to accurately predict whether a clinical trial may be terminated or not? The answer to the first question provides effective ways to understand characteristics of terminated trials for stakeholders to better plan their trials; and the answer to the second question can direct estimate the chance of success of a clinical trial in order to minimize costs. By using 311,260 trials to build a testbed with 68,999 samples, we use feature engineering to create 640 features, reflecting clinical trial administration, eligibility, study information, criteria etc. Using feature ranking, a handful of features, such as trial eligibility, trial inclusion/exclusion criteria, sponsor types etc., are found to be related to the clinical trial termination. By using sampling and ensemble learning, we achieve over 67% Balanced Accuracy and over 0.73 AUC (Area Under the Curve) scores to correctly predict clinical trial termination, indicating that machine learning can help achieve satisfactory prediction results for clinical trial study.
在这项研究中，我们提议使用机器学习来理解已终止的临床试验。我们的目标是回答两个基本问题：（1）与已终止临床试验相关的常见因素/标志物有哪些？（2）如何准确预测一项临床试验是否可能终止？第一个问题的答案为利益相关者提供了了解终止试验特征的有效方法，以便更好地规划他们的试验；第二个问题的答案可以直接估计临床试验成功的机会，以最小化成本。我们使用 311260 项试验构建了一个包含 68999 个样本的测试平台，使用特征工程创建了 640 个特征，反映了临床试验管理、资格、研究信息、标准等。通过特征排名，发现一些特征，如试验资格、试验纳入/排除标准、赞助商类型等，与临床试验终止有关。通过采样和集成学习，我们实现了超过 67%的平衡准确率和超过 0.73 的 AUC（曲线下面积）分数，以正确预测临床试验终止，表明机器学习可以帮助实现对临床试验研究的令人满意的预测结果。
