Detecting asthma exacerbations using daily home monitoring and machine learning

Zhang, Olivier; Minku, Leandro L.; Gonem, Sherif

doi:10.1080/02770903.2020.1802746

Cited by 22 publications

(39 citation statements)

References 30 publications

(28 reference statements)

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“…Attack prediction refers to studies that use machine learning to predict an asthma event (typically an attack) usually using mHealth data. [34][35][36][37][38][39][40][41][42] Patient clustering refers to studies which subtype the asthma population using unsupervised learning algorithms. 43,44 See Table 2 for a summary of the papers.…”

Section: Search Resultsmentioning

confidence: 99%

“…Many methods and devices for monitoring different aspects of a person have been studied individually and in combination. Machine learning can be applied to breath monitoring, 37,41 sleep monitoring, 23,[34][35][36]38,39,42 cough and wheeze, 24,26,27,[29][30][31]36 lung function monitoring, 23,25,[33][34][35]38,40 adherence monitoring, 32,35,38,43 and environment monitoring. 39,40,44 However, studies had different outcome measures; hence, it is difficult to conduct a direct comparison between studies.…”

Section: Search Resultsmentioning

confidence: 99%

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Application of Machine Learning Algorithms for Asthma Management with mHealth: A Clinical Review

Tsang¹,

Pinnock²,

Wilson³

et al. 2022

JAA

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Background: Asthma is a variable long-term condition. Currently, there is no cure for asthma and the focus is, therefore, on longterm management. Mobile health (mHealth) is promising for chronic disease management but to be able to realize its potential, it needs to go beyond simply monitoring. mHealth therefore needs to leverage machine learning to provide tailored feedback with personalized algorithms. There is a need to understand the extent of machine learning that has been leveraged in the context of mHealth for asthma management. This review aims to fill this gap. Methods: We searched PubMed for peer-reviewed studies that applied machine learning to data derived from mHealth for asthma management in the last five years. We selected studies that included some human data other than routinely collected in primary care and used at least one machine learning algorithm. Results: Out of 90 studies, we identified 22 relevant studies that were then further reviewed. Broadly, existing research efforts can be categorized into three types: 1) technology development, 2) attack prediction, 3) patient clustering. Using data from a variety of devices (smartphones, smartwatches, peak flow meters, electronic noses, smart inhalers, and pulse oximeters), most applications used supervised learning algorithms (logistic regression, decision trees, and related algorithms) while a few used unsupervised learning algorithms. The vast majority used traditional machine learning techniques, but a few studies investigated the use of deep learning algorithms. Discussion: In the past five years, many studies have successfully applied machine learning to asthma mHealth data. However, most have been developed on small datasets with internal validation at best. Small sample sizes and lack of external validation limit the generalizability of these studies. Future research should collect data that are more representative of the wider asthma population and focus on validating the derived algorithms and technologies in a real-world setting.

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Section: Search Resultsmentioning

confidence: 99%

Section: Search Resultsmentioning

confidence: 99%

Application of Machine Learning Algorithms for Asthma Management with mHealth: A Clinical Review

Tsang¹,

Pinnock²,

Wilson³

et al. 2022

JAA

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“…These simple screening tests could be adapted to a telemedicine format. The importance of lung function and respiratory symptoms was recently studied with peak expiratory flow and asthma symptom scores in home telemonitoring to predict asthma exacerbations using machine learning algorithms [18]. As patient-reported outcomes, respiratory symptoms are gaining interest in clinical research [19].…”

Section: Discussionmentioning

confidence: 99%

Factors Predicting Treatment of World Trade Center-Related Lung Injury: A Longitudinal Cohort Study

Putman

Lahousse

Goldfarb

et al. 2020

IJERPH

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The factors that predict treatment of lung injury in occupational cohorts are poorly defined. We aimed to identify patient characteristics associated with initiation of treatment with inhaled corticosteroid/long-acting beta-agonist (ICS/LABA) >2 years among World Trade Center (WTC)-exposed firefighters. The study population included 8530 WTC-exposed firefighters. Multivariable logistic regression assessed the association of patient characteristics with ICS/LABA treatment for >2 years over two-year intervals from 11 September 2001–10 September 2017. Cox proportional hazards models measured the association of high probability of ICS/LABA initiation with actual ICS/LABA initiation in subsequent intervals. Between 11 September 2001–1 July 2018, 1629/8530 (19.1%) firefighters initiated ICS/LABA treatment for >2 years. Forced Expiratory Volume in 1 s (FEV1), wheeze, and dyspnea were consistently and independently associated with ICS/LABA treatment. High-intensity WTC exposure was associated with ICS/LABA between 11 September 2001–10 September 2003. The 10th percentile of risk for ICS/LABA between 11 September 2005–10 Septmeber 2007 was associated with a 3.32-fold increased hazard of actual ICS/LABA initiation in the subsequent 4 years. In firefighters with WTC exposure, FEV1, wheeze, and dyspnea were independently associated with prolonged ICS/LABA treatment. A high risk for treatment was identifiable from routine monitoring exam results years before treatment initiation.

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“…Decision tree is easy to understanding, but unstable and prone to overfitting. [47,49,60], [67,68,78], [79,97,100], [102] Lasso regression Lasso regression is a linear regression method using L1-regularization. L1-regularization can compress the coefficients of variables and change some coefficients to zero, so as to achieve the purpose of variable selection.…”

Section: Decision Treementioning

confidence: 99%

“…The application of four ML algorithms (logistic regression, decision tree, naïve Bayes, and perceptron algorithm) to predict severe exacerbations of asthma was recently reported based on daily monitoring data of 576 severe exacerbation events in 2,010 asthma patients. The logistic regression-based model yielded an optimal AUC of 0.85, sensitivity of 90% and specificity of 83% [60]. Given the close correlation between severe exacerbations and asthma mortality, the model may be useful to physicians as a reference, but the research data were collected from paper diaries that may be inaccurate.…”

Section: Application Of Ai/ml To the Monitoring And Management Of Asthmamentioning

confidence: 99%

Artificial Intelligence and Machine Learning in Chronic Airway Diseases: Focus on Asthma and Chronic Obstructive Pulmonary Disease

Feng¹,

Wang²,

Zeng³

et al. 2021

Int. J. Med. Sci.

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Chronic airway diseases are characterized by airway inflammation, obstruction, and remodeling and show high prevalence, especially in developing countries. Among them, asthma and chronic obstructive pulmonary disease (COPD) show the highest morbidity and socioeconomic burden worldwide. Although there are extensive guidelines for the prevention, early diagnosis, and rational treatment of these lifelong diseases, their value in precision medicine is very limited. Artificial intelligence (AI) and machine learning (ML) techniques have emerged as effective methods for mining and integrating large-scale, heterogeneous medical data for clinical practice, and several AI and ML methods have recently been applied to asthma and COPD. However, very few methods have significantly contributed to clinical practice. Here, we review four aspects of AI and ML implementation in asthma and COPD to summarize existing knowledge and indicate future steps required for the safe and effective application of AI and ML tools by clinicians.

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Detecting asthma exacerbations using daily home monitoring and machine learning

Cited by 22 publications

References 30 publications

Application of Machine Learning Algorithms for Asthma Management with mHealth: A Clinical Review

Application of Machine Learning Algorithms for Asthma Management with mHealth: A Clinical Review

Factors Predicting Treatment of World Trade Center-Related Lung Injury: A Longitudinal Cohort Study

Artificial Intelligence and Machine Learning in Chronic Airway Diseases: Focus on Asthma and Chronic Obstructive Pulmonary Disease

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