论文标题
COVID-19临床试验解析基于注意的LSTM网络
Attention-Based LSTM Network for COVID-19 Clinical Trial Parsing
论文作者
论文摘要
Covid-19临床试验设计是开发预防和治疗Covid-19的治疗剂的关键任务。在这项研究中，我们采用了深度学习方法来提取来自COVID-19试验的资格标准变量，以实现试验设计和优化的定量分析。具体而言，我们训练基于注意力的双向长期记忆（ATT-BILSTM）模型，并使用最佳模型从COVID-19试验的资格标准中提取实体（即变量）。我们将ATT-BILSTM的性能与传统的基于本体的方法进行了比较。基准数据集的结果表明，ATT-BILSTM的表现优于本体模型。 ATT-BILSTM的精度为0.942，召回0.810，F1为0.871，而本体学模型仅达到0.715的精度，召回0.659，F1的精度为0.686。我们的分析表明，ATT-BILSTM是表征COVID-19临床试验中患者人群的有效方法。
COVID-19 clinical trial design is a critical task in developing therapeutics for the prevention and treatment of COVID-19. In this study, we apply a deep learning approach to extract eligibility criteria variables from COVID-19 trials to enable quantitative analysis of trial design and optimization. Specifically, we train attention-based bidirectional Long Short-Term Memory (Att-BiLSTM) models and use the optimal model to extract entities (i.e., variables) from the eligibility criteria of COVID-19 trials. We compare the performance of Att-BiLSTM with traditional ontology-based method. The result on a benchmark dataset shows that Att-BiLSTM outperforms the ontology model. Att-BiLSTM achieves a precision of 0.942, recall of 0.810, and F1 of 0.871, while the ontology model only achieves a precision of 0.715, recall of 0.659, and F1 of 0.686. Our analyses demonstrate that Att-BiLSTM is an effective approach for characterizing patient populations in COVID-19 clinical trials.
