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Using NLP and Neural Networks to Autonomously Identify Severe Asthma and Determine Study Eligibility in a Large Healthcare System
Sponsor: San Diego State University
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
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