Machine learning for COPD exacerbation prediction

Esteban, Cristóbal; Moraza, Javier; Sancho, Fernando; Aburto, Myriam; Arámburu, Amaia; Goiria, Begoña; García-Loizaga, Amaia; Capelastegui, Alberto

doi:10.1183/13993003.congress-2015.oa3282

Cited by 11 publications

(12 citation statements)

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“…Previous studies explored using computed tomography (CT) images COPD patients and controls for disease classification [63]. Some studies also used patient reported data (such as heart rate, respiratory rate) to predict disease exacerbation and resulted in an ROC of 0.87 [64] and another with 70% sensitivity and 71% specificity [65].…”

Section: Discussionmentioning

confidence: 99%

Gene expression microarray public dataset reanalysis in chronic obstructive pulmonary disease

2019

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Chronic obstructive pulmonary disease (COPD) was classified by the Centers for Disease Control and Prevention in 2014 as the 3rd leading cause of death in the United States (US). The main cause of COPD is exposure to tobacco smoke and air pollutants. Problems associated with COPD include under-diagnosis of the disease and an increase in the number of smokers worldwide. The goal of our study is to identify disease variability in the gene expression profiles of COPD subjects compared to controls, by reanalyzing pre-existing, publicly available microarray expression datasets. Our inclusion criteria for microarray datasets selected for smoking status, age and sex of blood donors reported. Our datasets used Affymetrix, Agilent microarray platforms (7 datasets, 1,262 samples). We re-analyzed the curated raw microarray expression data using R packages, and used Box-Cox power transformations to normalize datasets. To identify significant differentially expressed genes we used generalized least squares models with disease state, age, sex, smoking status and study as effects that also included binary interactions, followed by likelihood ratio tests (LRT). We found 3,315 statistically significant (Storey-adjusted q-value <0.05) differentially expressed genes with respect to disease state (COPD or control). We further filtered these genes for biological effect using results from LRT q-value <0.05 and model estimates’ 10% two-tailed quantiles of mean differences between COPD and control), to identify 679 genes. Through analysis of disease, sex, age, and also smoking status and disease interactions we identified differentially expressed genes involved in a variety of immune responses and cell processes in COPD. We also trained a logistic regression model using the common array genes as features, which enabled prediction of disease status with 81.7% accuracy. Our results give potential for improving the diagnosis of COPD through blood and highlight novel gene expression disease signatures.

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Section: Discussionmentioning

confidence: 99%

Gene expression microarray public dataset reanalysis in chronic obstructive pulmonary disease

2019

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“…The abundant recorded data that can be stored from the vast array of patients can help the training of accurate AI models, particularly through deep learning approaches. It is recognised that early detection of exacerbations in COPD can increase positive outcomes and reduce hospital admissions; telehealth-based systems interventions can decrease the costs associated with COPD patients [18] and promote better self-management [19,20].…”

Section: Strengthsmentioning

confidence: 99%

Artificial Intelligence for Long-term Respiratory Disease Management

Catherwood

Rafferty

McLaughlin

2018

Electronic Workshops in Computing

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This paper presents the strengths, weaknesses, opportunities, and threats for Artificial Intelligence (AI) as applied to long-term respiratory disease management. This analysis will help to identify, understand, and evaluate key aspects of the technology as well as the various internal/external forces which influence its success in this application space. Such understanding is instrumental to ensure judicial planning and implementation with suitable safeguards being considered. AI has the potential to radically change how respiratory disease management is conducted and may help clinicians to realise new treatment paradigms. The application of AI is clearly not specific to respiratory disease management; however it is a chronic disease that requires ongoing monitoring and well evidenced decision making regarding treatment pathways or medication modification. This work emphasises the current position of AI as applied to respiratory disease management and identifies the issues to help develop strategic directions to ensure successful implementation, evidenced by ubiquitous acceptance and uptake.

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“…Random forest classifiers were reportedly used in [12] and [13]. A range of methods including k -means clustering, radial basis functions, and probabilistic neural networks were tested in [14] and [15].…”

Section: Introductionmentioning

confidence: 99%

Classification of Exacerbation Frequency in the COPDGene Cohort Using Deep Learning With Deep Belief Networks

Ying

Dutta

Guo

et al. 2020

IEEE J. Biomed. Health Inform.

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This study aims to develop an automatic classifier based on deep learning for exacerbation frequency in patients with chronic obstructive pulmonary disease (COPD). A three-layer deep belief network (DBN) with two hidden layers and one visible layer was employed to develop classification models and the models’ robustness to exacerbation was analyzed. Subjects from the COPDGene cohort were labeled with exacerbation frequency, defined as the number of exacerbation events per year. 10,300 subjects with 361 features each were included in the analysis. After feature selection and parameter optimization, the proposed classification method achieved an accuracy of 91.99%, using a 10-fold cross validation experiment. The analysis of DBN weights showed that there was a good visual spatial relationship between the underlying critical features of different layers. Our findings show that the most sensitive features obtained from the DBN weights are consistent with the consensus showed by clinical rules and standards for COPD diagnostics. We thus demonstrate that DBN is a competitive tool for exacerbation risk assessment for patients suffering from COPD.

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Machine learning for COPD exacerbation prediction

Cited by 11 publications

References 0 publications

Gene expression microarray public dataset reanalysis in chronic obstructive pulmonary disease

Gene expression microarray public dataset reanalysis in chronic obstructive pulmonary disease

Artificial Intelligence for Long-term Respiratory Disease Management

Classification of Exacerbation Frequency in the COPDGene Cohort Using Deep Learning With Deep Belief Networks

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