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International Journal of Chronic Obstructive Pulmonary Disease Volume 10, 2015 - Issue

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Original Research

Forecasting COPD hospitalization in the clinic: optimizing the chronic respiratory questionnaire

Beatriz Abascal-Bolado1 Pulmonary Division, Instituto de Investigación Sanitaria Valdecilla (IDIVAL), Santander, Spain

Paul J Novotny2 Department of Cancer Center Statistics, Health Science Research, Mayo Clinic, Rochester, MN, USA

Jeff A Sloan2 Department of Cancer Center Statistics, Health Science Research, Mayo Clinic, Rochester, MN, USA

Craig Karpman3 Mindful Breathing Laboratory, Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN, USA

Megan M Dulohery3 Mindful Breathing Laboratory, Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN, USA

Roberto P Benzo3 Mindful Breathing Laboratory, Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN, USACorrespondence[email protected]

Pages 2295-2301 | Published online: 22 Oct 2015

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Articles from other publishers (3)

Siyang Zeng, Mehrdad Arjomandi & Gang Luo. (2022) Automatically Explaining Machine Learning Predictions on Severe Chronic Obstructive Pulmonary Disease Exacerbations: Retrospective Cohort Study. JMIR Medical Informatics 10:2, pages e33043.
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Siyang Zeng, Mehrdad Arjomandi, Yao Tong, Zachary C Liao & Gang Luo. (2022) Developing a Machine Learning Model to Predict Severe Chronic Obstructive Pulmonary Disease Exacerbations: Retrospective Cohort Study. Journal of Medical Internet Research 24:1, pages e28953.
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Gang Luo, Bryan L Stone, Xiaoming Sheng, Shan He, Corinna Koebnick & Flory L Nkoy. (2021) Using Computational Methods to Improve Integrated Disease Management for Asthma and Chronic Obstructive Pulmonary Disease: Protocol for a Secondary Analysis. JMIR Research Protocols 10:5, pages e27065.
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