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Optimizing Clinical Trial Eligibility Design Using Natural Language Processing Models and Real-World Data: Algorithm

Kyeryoung Lee¹, Zongzhi Liu¹, Yun Mai¹

¹GendDx (Sema4), Stamford, CT, United States.

|July 29, 2024

Summary

This summary is machine-generated.

Natural language processing (NLP) streamlines clinical trials by creating a knowledge base of eligibility criteria. This data-driven approach enhances patient identification and optimizes trial design for faster drug development.

Keywords:

clinical trial eligibility criteria clinical trial protocol optimization data-driven approach eligibility criteria–specific ontology natural language processing real-world data

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Area of Science:

Computational linguistics
Biomedical informatics
Clinical trial management

Background:

Clinical trials are essential for new therapies but often face delays.
Efficient data management, protocol optimization, and patient identification are key to reducing trial timelines.
Natural Language Processing (NLP) offers potential solutions for these challenges.

Purpose of the Study:

To evaluate data-driven methods for optimizing clinical trial protocols and identifying eligible patients.
To develop a comprehensive eligibility criteria knowledge base integrated with electronic health records using deep learning-based NLP.

Main Methods:

Extracted eligibility criteria from 3281 clinical trials (2013-2020) using a custom NLP pipeline (bidirectional LSTM-CRF).
Converted hypernym concepts to computable hyponyms for a knowledge base.
Utilized a subset of 2775 non-small cell lung cancer patients for pilot simulation.

Main Results:

Manually annotated 14.78% of trials, creating an eligibility criteria ontology.
Achieved high precision (0.91), recall (0.79), and F1-score (0.83) with the NLP pipeline.
Developed a standardized, EHR-compatible knowledge base and a prototype interface for trial optimization and patient identification.

Conclusions:

The NLP pipeline successfully created a standardized, machine-readable eligibility criteria knowledge base.
The prototype interface demonstrates the feasibility of using real-world data to assess criterion impact on patient eligibility.
Integrating NLP and real-world data offers a promising approach to streamline clinical trials and improve patient identification efficiency.