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NCT06389058

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

Sponsor: San Diego State University

View on ClinicalTrials.gov

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.

Key Details

Gender

All

Age Range

6 Years - 85 Years

Study Type

OBSERVATIONAL

Enrollment

31795

Start Date

2023-05-01

Completion Date

2026-03

Last Updated

2026-02-27

Healthy Volunteers

No

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.

Locations (1)

San Diego State University

San Diego, California, United States