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Public Health Genomics and Precision Health Knowledge Base (v9.0)
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Precision Health Database|Search|Public Health Genomics and Precision Health Knowledge Base (PHGKB)
Heart, Lung, Blood and Sleep Disorders
Last data update: Apr 25, 2024
. (Total: 63675 Documents since 2012)
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Collaboration between explainable artificial intelligence and pulmonologists improves the accuracy of pulmonary function test interpretation.
Nilakash Das et al. Eur Respir J
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Detection and staging of chronic obstructive pulmonary disease using a computed tomography-based weakly supervised deep learning approach.
Sun Jiaxing et al. European radiology 2022
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A Machine Learning Application to Predict Early Lung Involvement in Scleroderma: A Feasibility Evaluation.
Murdaca Giuseppe et al. Diagnostics (Basel, Switzerland) 2021 11(10)
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Machine learning for lung texture analysis on thin-section CT: Capability for assessments of disease severity and therapeutic effect for connective tissue disease patients in comparison with expert panel evaluations.
Ohno Yoshiharu et al. Acta radiologica (Stockholm, Sweden : 1987) 2021 2841851211044973
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Application of Machine Learning in Pulmonary Function Assessment Where Are We Now and Where Are We Going?
Giri Paresh C et al. Frontiers in physiology 2021 12678540
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Artificial intelligence/machine learning in respiratory medicine and potential role in asthma and COPD diagnosis.
Kaplan Alan et al. The journal of allergy and clinical immunology. In practice 2021
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Novel machine learning can predict acute asthma exacerbation.
Zein Joe G et al. Chest 2021 Jan
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Expert artificial intelligence-based natural language processing characterises childhood asthma.
Seol Hee Yun et al. BMJ open respiratory research 2020 Feb 7(1)
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Elastic Registration-driven Deep Learning for Longitudinal Assessment of Systemic Sclerosis Interstitial Lung Disease at CT.
Chassagnon Guillaume et al. Radiology 2020 Oct 200319
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Does an mHealth system reduce health service use for asthma?
To Teresa et al. ERJ open research 2020 Jul 6(3)
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The value of preoperative spirometry testing for predicting postoperative risk in upper abdominal and thoracic surgery assessed using big-data analysis.
Park Hyung Jun et al. Journal of thoracic disease 2020 Aug 12(8) 4157-4167
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Deep Learning Enables Automatic Classification of Emphysema Pattern at CT.
Humphries Stephen M et al. Radiology 2019 Dec 191022
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Artificial intelligence outperforms pulmonologists in the interpretation of pulmonary function tests.
Topalovic Marko et al. The European respiratory journal 2019 Feb
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Page last reviewed:
Feb 1, 2024
Page last updated:
Apr 25, 2024
Content source:
Public Health Genomics Branch in the Division of Blood Disorders and Public Health Genomics
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National Center on Birth Defects and Developmental Disabilities
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