Identifying asthma patients at high risk of exacerbation in a routine visit: A machine learning model

Jiao, Tianze; Schnitzer, Mireille E.; Forget, Amélie; Blais, Lucie

doi:10.1016/j.rmed.2022.106866

Cited by 10 publications

(19 citation statements)

References 44 publications

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“…Most of the studies exercised the k- fold cross-validation technique to validate the model on the training data. Different studies chose different k values such as 3 [ 38 ], 4 [ 27 , 28 ], 5 [ 16 , 23 , 24 , 32 , 34 ] and 10 [ 30 , 33 , 35 ].…”

Section: Resultsmentioning

confidence: 99%

“…One study [ 29 ] used a 1-month period while [ 30 ] used 6-months period as the prediction window size. All of the other studies [ 31 – 33 ] kept the prediction window size to 1 year. While [ 29 , 30 , 32 ] considered lookback windows similar in size to prediction windows, no clear details of lookback windows are provided in [ 31 , 33 ].…”

Section: Resultsmentioning

confidence: 99%

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Investigating Machine Learning Techniques for Predicting Risk of Asthma Exacerbations: A Systematic Review

Darsha Jayamini,

Mirza,

Asif Naeem

et al. 2024

J Med Syst

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Asthma, a common chronic respiratory disease among children and adults, affects more than 200 million people worldwide and causes about 450,000 deaths each year. Machine learning is increasingly applied in healthcare to assist health practitioners in decision-making. In asthma management, machine learning excels in performing well-defined tasks, such as diagnosis, prediction, medication, and management. However, there remain uncertainties about how machine learning can be applied to predict asthma exacerbation. This study aimed to systematically review recent applications of machine learning techniques in predicting the risk of asthma attacks to assist asthma control and management. A total of 860 studies were initially identified from five databases. After the screening and full-text review, 20 studies were selected for inclusion in this review. The review considered recent studies published from January 2010 to February 2023. The 20 studies used machine learning techniques to support future asthma risk prediction by using various data sources such as clinical, medical, biological, and socio-demographic data sources, as well as environmental and meteorological data. While some studies considered prediction as a category, other studies predicted the probability of exacerbation. Only a group of studies applied prediction windows. The paper proposes a conceptual model to summarise how machine learning and available data sources can be leveraged to produce effective models for the early detection of asthma attacks. The review also generated a list of data sources that other researchers may use in similar work. Furthermore, we present opportunities for further research and the limitations of the preceding studies.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Investigating Machine Learning Techniques for Predicting Risk of Asthma Exacerbations: A Systematic Review

Darsha Jayamini,

Mirza,

Asif Naeem

et al. 2024

J Med Syst

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“…Despite the prevalence of asthma and the urgency to optimally treat patients with severe asthma in a cost-effective manner, as far as we are aware, there is no externally validated risk prediction tool for exacerbations in severe asthma. In recent years, several complex machine-learning models have been developed, all based on electronic health records from local settings that cover an exhaustive list of comorbidity, medication use and occasionally lab test results, to predict asthma exacerbations and admissions in the general asthma population 25–27 49. Given the model complexity and data availability issues, those models are hardly generalisable to patients with severe asthma or general asthma patients in other routine care settings.…”

Section: Data Methods and Analysismentioning

confidence: 99%

“…Moreover, in recent years, several new prediction tools with the inclusion of biomarkers and/or the use of machine-learning algorithms on large-scale electronic medical records or health administrative data (including medication use and laboratory results) have been proposed. [22][23][24][25][26][27] However, there is no validated risk-scoring tool for patients with severe asthma.…”

Section: Strengths and Limitations Of This Studymentioning

confidence: 99%

Individualised risk prediction model for exacerbations in patients with severe asthma: protocol for a multicentre real-world risk modelling study

et al. 2023

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IntroductionSevere asthma is associated with a disproportionally high disease burden, including the risk of severe exacerbations. Accurate prediction of the risk of severe exacerbations may enable clinicians to tailor treatment plans to an individual patient. This study aims to develop and validate a novel risk prediction model for severe exacerbations in patients with severe asthma, and to examine the potential clinical utility of this tool.Methods and analysisThe target population is patients aged 18 years or older with severe asthma. Based on the data from the International Severe Asthma Registry (n=8925), a prediction model will be developed using a penalised, zero-inflated count model that predicts the rate or risk of exacerbation in the next 12 months. The risk prediction tool will be externally validated among patients with physician-assessed severe asthma in an international observational cohort, the NOVEL observational longiTudinal studY (n=1652). Validation will include examining model calibration (ie, the agreement between observed and predicted rates), model discrimination (ie, the extent to which the model can distinguish between high-risk and low-risk individuals) and the clinical utility at a range of risk thresholds.Ethics and disseminationThis study has obtained ethics approval from the Institutional Review Board of National University of Singapore (NUS-IRB-2021-877), the Anonymised Data Ethics and Protocol Transparency Committee (ADEPT1924) and the University of British Columbia (H22-01737). Results will be published in an international peer-reviewed journal.Trial registration numberEuropean Union electronic Register of Post-Authorisation Studies, EU PAS Register (EUPAS46088).

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“…A regularized logistic regression ML for the classification of problems with more variables than classification items [ 17 , 18 ]…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence: Exploring the Future of Innovation in Allergy Immunology

MacMath

Chen

Khoury

2023

Curr Allergy Asthma Rep

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Purpose of Review Artificial intelligence (AI) has increasingly been used in healthcare. Given the capacity of AI to handle large data and complex relationships between variables, AI is well suited for applications in healthcare. Recently, AI has been applied to allergy research. Recent Findings In this article, we review how AI technologies have been utilized in basic science and clinical allergy research for asthma, atopic dermatitis, rhinology, adverse reactions to drugs and vaccines, food allergy, anaphylaxis, urticaria, and eosinophilic gastrointestinal disorders. We discuss barriers for AI adoption to improve the care of patients with atopic diseases. Summary These studies demonstrate the utility of applying AI to the field of allergy to help investigators expand their understanding of disease pathogenesis, improve diagnostic accuracy, enable prediction for treatments and outcomes, and for drug discovery.

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Identifying asthma patients at high risk of exacerbation in a routine visit: A machine learning model

Cited by 10 publications

References 44 publications

Investigating Machine Learning Techniques for Predicting Risk of Asthma Exacerbations: A Systematic Review

Investigating Machine Learning Techniques for Predicting Risk of Asthma Exacerbations: A Systematic Review

Individualised risk prediction model for exacerbations in patients with severe asthma: protocol for a multicentre real-world risk modelling study

Artificial Intelligence: Exploring the Future of Innovation in Allergy Immunology

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