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

NCT06389058

Active Not RecruitingN/A

Sponsor: San Diego State University

Conditions: Severe Asthma

Interventions: Recommendation for the diagnoses and treatment of Severe Asthma

Countries: United States

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.

Eligibility overview

Sex: ALL

Age: 6 Years to 85 Years

Healthy volunteers: No

Study type: OBSERVATIONAL

Eligibility criteria

Inclusion Criteria:

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

Exclusion Criteria:

* None

Locations (1)

San Diego, California, United States