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

arXiv: https://arxiv.org/abs/2012.10063 · HTML: https://mlsanity.com/paper/2012.10063/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Dec 2020.

## Built on by (most cited first, 5 of 5)

- [Clinical Trial Information Extraction with BERT](https://mlsanity.com/paper/2110.10027/) (2021)
- [Applications of Explainable AI for 6G: Technical Aspects, Use Cases, and Research Challenges](https://mlsanity.com/paper/2112.04698/) (2021)
- [ACR: A Benchmark for Automatic Cohort Retrieval](https://mlsanity.com/paper/2406.14780/) (2024)
- [A Scalable AI Approach for Clinical Trial Cohort Optimization](https://mlsanity.com/paper/2109.02808/) (2021)
- [Cohort Retrieval using Dense Passage Retrieval](https://mlsanity.com/paper/2507.01049/) (2025)

## Abstract (arXiv)

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.