Health AI Central
Share:
Peer-Reviewed Publication
Int J Chron Obstruct Pulmon Dis2025;203451-3459.January 1, 2025Journal Article

Leveraging Machine Learning and Real-World Data to Predict Chronic Obstructive Pulmonary Disease Exacerbations.

Reynold A Panettieri1, Jason Roy2, Natalia Gontarczyk Uczkowski1, Allison Tyler3, Jason Attanucci3, Thomas G O'Riordan4, Kristin Kahle-Wrobleski4
1Rutgers Institute for Translational Medicine and Science, New Brunswick, NJ, USA.
2Rutgers School of Public Health, New Brunswick, NJ, USA.
3Deep 6 AI, Inc, Pasadena, CA, USA.
4US Value Evidence and Outcomes, GSK, Philadelphia, PA, USA.
PubMed PMC DOI PDF

Abstract

BACKGROUND: Previously, we reported that applying artificial intelligence and natural language processing to electronic health record (EHR) data can identify patients at risk of chronic obstructive pulmonary disease (COPD) exacerbations, based on clinical attributes identified in COPDGene. PURPOSE: Building on these data and using real-world data, we established a predictive model for identifying…

Create a free account to keep reading

Free members get 10 full research views every month across publications, clinical trials, FDA clearances, adverse events, and NIH grants. No credit card required.

Create Free AccountSign In

Want unlimited research access? See Pro plans

Data Accuracy Notice: Research intelligence on Health AI Central is aggregated from public sources (PubMed, ClinicalTrials.gov, FDA, NIH, CMS, and others) and refreshed nightly. Classifications and derived metrics are produced by automated methods described in our Methodology. We recommend verifying critical data points against the primary sources before making decisions.