Use of machine learning techniques in the development and refinement of a predictive model for early diagnosis of ankylosing spondylitis.

Journal: Clinical rheumatology

Published Date: Apr 1, 2020

Abstract

OBJECTIVE: To develop a predictive mathematical model for the early identification of ankylosing spondylitis (AS) based on the medical and pharmacy claims history of patients with and without AS.

Authors

Atul Deodhar

The Oregon Health & Science University, 3181 SW Sam Jackson Park Rd., Portland, OR, 97239, USA. deodhara@ohsu.edu.
Martin Rozycki

Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia.
Cody Garges

ProMetrics, LLC, King of Prussia, PA, USA.
Oodaye Shukla

HVH Precision Analytics, Wayne, PA, USA.
Theresa Arndt

HVH Precision Analytics, Wayne, PA, USA.
Tara Grabowsky

HVH Precision Analytics, Wayne, PA, USA.
Yujin Park

Novartis Pharmaceuticals Corporation, East Hanover, NJ, USA.

Keywords

Adolescent Adult Aged Child Databases, Factual Early Diagnosis Female Humans Machine Learning Male Middle Aged Models, Theoretical Retrospective Studies Risk Assessment Spondylitis, Ankylosing United States Young Adult

External Resources

View on PubMed Access via DOI PubMed (31044386)

Use of machine learning techniques in the development and refinement of a predictive model for early diagnosis of ankylosing spondylitis.

Abstract

Authors

Keywords

External Resources

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