A prediction model for asthma exacerbations after stopping asthma biologics

Inselman, Jonathan; Jeffery, Molly Moore; Maddux, Jacob T.; Lam, Regina; Shah, Nilay D.; Rank, Matthew A.; Ngufor, Che

doi:10.1016/j.anai.2022.11.025

Cited by 6 publications

(22 citation statements)

References 23 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%

“…A set of studies defined their prediction window size as greater than or equal to a one-month period. 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.…”

Section: Resultsmentioning

confidence: 99%

“…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 ]. One study [ 29 ] identified clinical factors such as obesity, atopy, medication, asthma controller plan and patient service utilisation history as important asthma risk predictors.…”

Section: Resultsmentioning

confidence: 99%

“…Asthma medication also played an important role in the research by [ 33 ]. Further, previous asthma exacerbations and length of treatment with biologics were key predictors of asthma risk in [ 30 ]. Meanwhile, age, hospital stay, blight prevalence, and neighbourhood inequality are important predictors, according to another study [ 32 ].…”

Section: Resultsmentioning

confidence: 99%

“…Meanwhile, age, hospital stay, blight prevalence, and neighbourhood inequality are important predictors, according to another study [ 32 ]. In developing prediction models, the most common ML algorithms utilised by these studies are LR [ 29 , 30 , 33 ], RF [ 29 , 30 , 32 , 33 ], and XGB [ 29 , 31 , 33 ]. Only one study [ 32 ] in this category applied random undersampling to handle data imbalance.…”

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%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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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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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Primary Care Asthma Attack Prediction Models for Adults: A Systematic Review of Reported Methodologies and Outcomes

Ma,

Tibble

2024

JAA

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Prognostic models hold great potential for predicting asthma exacerbations, providing opportunities for early intervention, and are a popular area of current research. However, it is unclear how models should be compared and contrasted, given their differences in both design and performance, particularly with a view to potential implementation in routine practice. This systematic review aimed to identify novel predictive models of asthma attacks in adults and compare differences in construction related to populations, outcome definitions, prediction time horizons, algorithms, validation, and performance estimation. Twenty-five studies were identified for comparison, with varying definitions of asthma attacks and prediction event time horizons ranging from 15 days to 30 months. The most commonly used algorithm was logistic regression (20/25 studies); however, none of the six which tested multiple algorithms identified it as highest performing algorithm. The effect of various study design characteristics on performance was evaluated in order to provide context to the limitations of highly performing models. Models used a variety of constructs, which affected both their performance and their viability for implementation in routine practice. Consultation with stakeholders is necessary to identify priorities for model refinement and to create a benchmark of acceptable performance for implementation in clinical practice.

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A prediction model for asthma exacerbations after stopping asthma biologics

Cited by 6 publications

References 23 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

Artificial Intelligence: Exploring the Future of Innovation in Allergy Immunology

Primary Care Asthma Attack Prediction Models for Adults: A Systematic Review of Reported Methodologies and Outcomes

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