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

TL;DR

This paper presents an attention-based LSTM model that effectively extracts eligibility criteria variables from COVID-19 clinical trials, outperforming traditional ontology-based methods in accuracy and aiding trial analysis.

Contribution

The study introduces an attention-based bidirectional LSTM approach for extracting trial eligibility variables, demonstrating superior performance over ontology-based methods.

Findings

Att-BiLSTM achieves higher precision, recall, and F1 scores than ontology models.

The model effectively characterizes patient populations in COVID-19 trials.

Results support the use of deep learning for clinical trial data extraction.

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.