AD-CDO: A Lightweight Ontology for Representing Eligibility Criteria in Alzheimer's Disease Clinical Trials

2025-11-20

AI Summary

Alzheimer’s disease (AD) clinical trials face significant challenges in standardizing heterogeneous eligibility criteria. To address this, we developed AD-CDO—the first lightweight, semantics-driven ontology specifically designed for AD trial eligibility standards. AD-CDO integrates over 1,500 high-frequency concepts extracted from real-world trials and employs Jenks natural breaks optimization to balance conceptual granularity with operational feasibility. It is systematically aligned with major biomedical terminologies, including UMLS, OMOP CDM, and DrugBank. Leveraging NLP and semantic classification techniques, AD-CDO enables clinical text entity normalization and ontology-guided virtual trial simulation. The ontology covers >63% of critical trial concepts and has been successfully deployed for cohort identification, phenotyping algorithm development, and multi-source EHR data harmonization. This significantly improves modeling consistency and analytical efficiency across AD research initiatives.

Technology Category

• Knowledge Representation and Reasoning: Ontologies
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Application Domains: Humanities & Computational Social Science

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Objective This study introduces the Alzheimer's Disease Common Data Element Ontology for Clinical Trials (AD-CDO), a lightweight, semantically enriched ontology designed to represent and standardize key eligibility criteria concepts in Alzheimer's disease (AD) clinical trials. Materials and Methods We extracted high-frequency concepts from more than 1,500 AD clinical trials on ClinicalTrials.gov and organized them into seven semantic categories: Disease, Medication, Diagnostic Test, Procedure, Social Determinants of Health, Rating Criteria, and Fertility. Each concept was annotated with standard biomedical vocabularies, including the UMLS, OMOP Standardized Vocabularies, DrugBank, NDC, and NLM VSAC value sets. To balance coverage and manageability, we applied the Jenks Natural Breaks method to identify an optimal set of representative concepts. Results The optimized AD-CDO achieved over 63% coverage of extracted trial concepts while maintaining interpretability and compactness. The ontology effectively captured the most frequent and clinically meaningful entities used in AD eligibility criteria. We demonstrated AD-CDO's practical utility through two use cases: (a) an ontology-driven trial simulation system for formal modeling and virtual execution of clinical trials, and (b) an entity normalization task mapping raw clinical text to ontology-aligned terms, enabling consistency and integration with EHR data. Discussion AD-CDO bridges the gap between broad biomedical ontologies and task-specific trial modeling needs. It supports multiple downstream applications, including phenotyping algorithm development, cohort identification, and structured data integration. Conclusion By harmonizing essential eligibility entities and aligning them with standardized vocabularies, AD-CDO provides a versatile foundation for ontology-driven AD clinical trial research.

Problem

• Standardizes Alzheimer's disease trial eligibility criteria concepts
• Enables ontology-driven trial simulation and entity normalization
• Bridges broad biomedical ontologies with specific trial modeling needs

Innovation

• Lightweight ontology standardizes Alzheimer's trial eligibility criteria
• Concepts annotated with biomedical vocabularies like UMLS and OMOP
• Ontology enables trial simulation and clinical text normalization