  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.
