# A Scalable AI Approach for Clinical Trial Cohort Optimization

arXiv: https://arxiv.org/abs/2109.02808 · HTML: https://mlsanity.com/paper/2109.02808/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 17 references that are in the map.
- First posted on arXiv 7 Sep 2021.

## Builds on (most cited first, 3 of 3)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Applications of artificial intelligence in drug development using real-world data](https://mlsanity.com/paper/2101.08904/) (2021)
- [Attention-Based LSTM Network for COVID-19 Clinical Trial Parsing](https://mlsanity.com/paper/2012.10063/) (2020)

## Built on by (most cited first, 1 of 1)

- [Artificial Intelligence for In Silico Clinical Trials: A Review](https://mlsanity.com/paper/2209.09023/) (2022)

## Abstract (arXiv)

FDA has been promoting enrollment practices that could enhance the diversity of clinical trial populations, through broadening eligibility criteria. However, how to broaden eligibility remains a significant challenge. We propose an AI approach to Cohort Optimization (AICO) through transformer-based natural language processing of the eligibility criteria and evaluation of the criteria using real-world data. The method can extract common eligibility criteria variables from a large set of relevant trials and measure the generalizability of trial designs to real-world patients. It overcomes the scalability limits of existing manual methods and enables rapid simulation of eligibility criteria design for a disease of interest. A case study on breast cancer trial design demonstrates the utility of the method in improving trial generalizability.