Attention-Based LSTM Network for COVID-19 Clinical Trial Parsing

• URL: http://arxiv.org/abs/2012.10063v1
• Date: Fri, 18 Dec 2020 05:55:52 GMT
• Title: Attention-Based LSTM Network for COVID-19 Clinical Trial Parsing
• Authors: Xiong Liu, Luca A. Finelli, Greg L. Hersch, Iya Khalil
• Abstract summary: We train attention-based bidirectional Long Short-Term Memory (Att-BiLSTM) models and use the optimal model to extract entities from the eligibility criteria of COVID-19 trials. We compare the performance of Att-BiLSTM with traditional ontology-based method. Our analyses demonstrate that Att-BiLSTM is an effective approach for characterizing patient populations in COVID-19 clinical trials.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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