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Using NLP and Neural Networks to Autonomously Identify Severe Asthma and Determine Study Eligibility in a Large Healthcare System

NCT06389058 · Status: ACTIVE_NOT_RECRUITING · Type: OBSERVATIONAL · Enrollment: 31795

Last updated 2026-04-22

No results posted yet for this study

Summary

The study aims to to use new technologies (ML, AI, NLP), to autonomously identify moderate to severe asthma populations within an EHR system, describe differences in treatment patterns across different populations, and determine trial eligibility.

Primary Objectives Please ensure you detail primary objectives Aim 1. Determine and validate a diagnosis of severe asthma (SA) using predictive features obtained from the Scripps Health EHR.

* Aim 1a: Use ML applied to structured EHR data to predict SA. Use the opinion of 2 specialty-trained physicians and ATS guidelines to determine model accuracy.
* Aim 1b: Use NLP applied to unstructured text to predict SA. Determine model accuracy as above in Aim 1a.
* Aim 1c: Use a combination of ML applied to structured data to predict SA. Determine model accuracy as above in Aim 1a.

Conditions

Interventions

OTHER

Recommendation for the diagnoses and treatment of Severe Asthma

No intervention planned in this phase for the patients. Recommendations to be developed for healthcare and condition.

Sponsors & Collaborators

Principal Investigators

  • yusuf Ozturk, Ph.D. · San Diego State University

Eligibility

Min Age
6 Years
Max Age
85 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2023-05-01
Primary Completion
2026-12-31
Completion
2026-12-31

Countries

  • United States

Study Locations

More Related Trials

Entities

Diseases
Companies

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06389058 on ClinicalTrials.gov