Application of Data-Driven Approaches for Identifying Asthmatic Children with Suboptimal Asthma Care

Methods Among 6,901 children aged 5-17 years receiving primary care at Mayo Clinic Rochester, we identified those requiring asthma care and categorized them into 3 groups by utilizing data-mining tools such as a natural language processing algorithm: 1) Persistent asthmatics enrolled in Asthma Manag...

Full description

Saved in:
Bibliographic Details
Published in:Journal of allergy and clinical immunology 2017-02, Vol.139 (2), p.AB102-AB102
Main Authors: Ryu, Euijung, PhD, Wi, Chung-Il, MD, Sohn, Sunghwan, PhD, Moon, Sungrim, PhD, Liu, Hongfang, PhD, Green, Joy A., APRN, CNP, Ihrke, Kathy D., RN, Unterborn, Rhonda G, Wheeler, Philip H, Juhn, Young J., MD, MPH
Format: Article
Language:English
Subjects:
Allergy and Immunology
Asthma
Data mining
Citations: Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Methods Among 6,901 children aged 5-17 years receiving primary care at Mayo Clinic Rochester, we identified those requiring asthma care and categorized them into 3 groups by utilizing data-mining tools such as a natural language processing algorithm: 1) Persistent asthmatics enrolled in Asthma Management Program (AMP) (Group1); 2) persistent asthmatics not enrolled in AMP (Group2); and 3) children who met predetermined asthma criteria (PAC) without asthma diagnosis (Group 3).
ISSN:0091-6749
1097-6825
DOI:10.1016/j.jaci.2016.12.333