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

Brief summary (registry, in English)

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.

What you commit to

• Study type: Observational
• Participants enrolled: 31795
• From 1 May 2023 to the primary completion date, Dec 2026
• 1 site in 1 country
• Visits and tests: ask the trial team, the registry does not list them

Built from the registry fields of ClinicalTrials.gov. It describes the study, not your situation.

Who cannot take part

Copied verbatim from ClinicalTrials.gov and grouped by topic. Only the trial team decides who can take part.

Who the study is looking for

Age in the registry: 6 years to 85 years · Sex: All · Does not accept healthy volunteers

Full criteria on ClinicalTrials.gov

Sites (1)

Sites in registry order. Pick your city on the disease page to sort them by distance.