Attention-Based LSTM Network for COVID-19 Clinical Trial Parsing
This work provides an effective method for characterizing patient populations in COVID-19 clinical trials, which is beneficial for researchers and clinicians involved in trial design and optimization.
This paper developed an attention-based bidirectional Long Short-Term Memory (Att-BiLSTM) model to extract eligibility criteria variables from COVID-19 clinical trials. The Att-BiLSTM model achieved a precision of 0.942, recall of 0.810, and F1 of 0.871, outperforming a traditional ontology-based method which scored 0.715 precision, 0.659 recall, and 0.686 F1.
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.