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

Running, not enrolling

Conditions studied: Severe Asthma

In brief

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 facts

Study ID

NCT06389058

Run by

San Diego State University

People needed

31795

Starts

2023-05-01

Expected to finish

2026-12-01

Last updated by the study team

2026-04-22

Who can join

Age: 6 and older, up to 85. Sex: any. Healthy volunteers: not accepted.

You may qualify if…

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

You may not qualify if…

• None

Where it is running

• San Diego State University — San Diego, California, United States

Full record on ClinicalTrials.gov

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