Improving diagnosis of rare genetic immune diseases using advanced algorithms

Collaborative multi-site project to speed the identification and management of rare genetic immune diseases

NIH-funded research University of California Los Angeles · NIH-11009552

This study is working to make it easier and faster to diagnose rare immune disorders that can cause serious infections by using advanced computer technology to analyze health records, helping doctors find patients who need care sooner.

Quick facts

Grant typeR01 grant
Study typeNIH-funded research
Funding institutionUniversity of California Los Angeles NIH-funded
Lab location1 site (Los Angeles, United States)
Project IDNIH-11009552 on NIH RePORTER

What this research studies

This research focuses on enhancing the diagnosis of primary immunodeficiency diseases (PIDs), which are rare genetic disorders that can lead to severe infections and autoimmunity. By employing machine-learning algorithms, the project aims to identify patients with these conditions through their electronic health records. A collaborative effort among multiple University of California medical centers and computational genomics teams will facilitate the development of a phenotype risk scoring system to expedite patient identification. This innovative approach seeks to address the long diagnostic delays that many patients experience.

Who could benefit from this research

Good fit: Ideal candidates for this research include individuals with suspected primary immunodeficiency diseases, particularly those experiencing recurrent infections or autoimmune symptoms.

Not a fit: Patients with common immune disorders or those without significant immune-related symptoms may not benefit from this research.

Why it matters

Potential benefit: If successful, this research could significantly reduce the time to diagnosis for patients with rare genetic immune diseases, leading to earlier treatment and improved health outcomes.

How similar studies have performed: Previous research has demonstrated success in using machine-learning approaches for patient identification in other medical fields, suggesting a promising potential for this novel application in immunology.

Where this research is happening

Los Angeles, United States

Researchers

About this research

  1. This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
  2. Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
  3. For full project details, budget, and progress reports, visit the official NIH RePORTER page below.
Last reviewed 2026-06-13 by the Find a Trial editorial team. Information on this page is for educational purposes and is not medical advice. Always consult qualified healthcare professionals about clinical trial participation.