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

NCT06389058 | Active Not Recruiting

• 1 other identifier: G00014538
• Study Type: observational
• Target: 31,795
• Locations: 1 country
• Sites: 1

Brief 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

Severe Asthma

Outcome Measures

Primary Outcomes (1)

• Identification of Patients with Severe Asthma

  Identify patients with severe asthma and compare diagnoses to that of medical professionals

  4 years

Study Arms (1)

Severe Asthma

Patients with Severe or Uncontrolled Asthma

Other: Recommendation for the diagnoses and treatment of Severe Asthma

Interventions

Recommendation for the diagnoses and treatment of Severe Asthma OTHER

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

Severe Asthma

Eligibility Criteria

• Age: 6 Years - 85 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

De-identified EHR data from N=31,795 patients diagnosed with asthma at Scripps Health (San Diego, CA) were filtered and processed, adhering to strict inclusion and exclusion criteria designed to accurately isolate cases of asthma.

You may qualify if:

• \- Demographics: Males \~ 40%, Blacks \~ 5-10%, Hispanic \~15-30%, Urban \~80-90%

You may not qualify if:

• None

Sponsors & Collaborators

• San Diego State University: lead
• GlaxoSmithKline: collaborator
• Scripps Health: collaborator
• Modena Allergy + Asthma, La Jolla, CA: collaborator
• University of California, San Diego: collaborator

Study Sites (1)

San Diego State University

San Diego, California, 92182-1309, United States

Location

MeSH Terms

Conditions

Asthma

Condition Hierarchy (Ancestors)

Bronchial Diseases; Respiratory Tract Diseases; Lung Diseases, Obstructive; Lung Diseases; Respiratory Hypersensitivity; Hypersensitivity, Immediate; Hypersensitivity; Immune System Diseases

Study Officials

• yusuf Ozturk, Ph.D.

  San Diego State University

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: CROSS SECTIONAL
• Target Duration: 2 Years
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: April 24, 2024
• First Posted: April 29, 2024
• Study Start: May 1, 2023
• Primary Completion (Estimated): December 1, 2026
• Study Completion (Estimated): December 1, 2026
• Last Updated: April 22, 2026

Record last verified: 2026-04

Data Sharing

• IPD Sharing: Will not share

Locations

US (1)