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Khan M, Banerjee S, Muskawad S, Maity R, Chowdhury SR, Ejaz R, Kuuzie E, Satnarine T. The Impact of Artificial Intelligence on Allergy Diagnosis and Treatment. Curr Allergy Asthma Rep 2024; 24:361-372. [PMID: 38954325 DOI: 10.1007/s11882-024-01152-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/19/2024] [Indexed: 07/04/2024]
Abstract
PURPOSE OF REVIEW Artificial intelligence (AI), be it neuronal networks, machine learning or deep learning, has numerous beneficial effects on healthcare systems; however, its potential applications and diagnostic capabilities for immunologic diseases have yet to be explored. Understanding AI systems can help healthcare workers better assimilate artificial intelligence into their practice and unravel its potential in diagnostics, clinical research, and disease management. RECENT FINDINGS We reviewed recent advancements in AI systems and their integration in healthcare systems, along with their potential benefits in the diagnosis and management of diseases. We explored machine learning as employed in allergy diagnosis and its learning patterns from patient datasets, as well as the possible advantages of using AI in the field of research related to allergic reactions and even remote monitoring. Considering the ethical challenges and privacy concerns raised by clinicians and patients with regard to integrating AI in healthcare, we explored the new guidelines adapted by regulatory bodies. Despite these challenges, AI appears to have been successfully incorporated into various healthcare systems and is providing patient-centered solutions while simultaneously assisting healthcare workers. Artificial intelligence offers new hope in the field of immunologic disease diagnosis, monitoring, and management and thus has the potential to revolutionize healthcare systems.
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Affiliation(s)
- Maham Khan
- Fatima Jinnah Medical University, Lahore, Pakistan.
| | | | | | - Rick Maity
- Institute of Post Graduate Medical Education and Research, Kolkata, West Bengal, India
| | | | - Rida Ejaz
- Shifa College of Medicine, Islamabad, Pakistan
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Darsha Jayamini WK, Mirza F, Asif Naeem M, Chan AHY. Investigating Machine Learning Techniques for Predicting Risk of Asthma Exacerbations: A Systematic Review. J Med Syst 2024; 48:49. [PMID: 38739297 PMCID: PMC11090925 DOI: 10.1007/s10916-024-02061-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Accepted: 04/04/2024] [Indexed: 05/14/2024]
Abstract
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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Affiliation(s)
- Widana Kankanamge Darsha Jayamini
- School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland, 1010, New Zealand.
- Department of Software Engineering, Faculty of Computing and Technology, University of Kelaniya, Kelaniya, 11300, Sri Lanka.
| | - Farhaan Mirza
- School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland, 1010, New Zealand
| | - M Asif Naeem
- Department of Data Science & Artificial Intelligence, National University of Computer and Emerging Sciences (NUCES), Islamabad, 44000, Pakistan
| | - Amy Hai Yan Chan
- School of Pharmacy, Faculty of Medical and Health Sciences, University of Auckland, Auckland, 1142, New Zealand
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Bhargava H, Salomon C, Suresh S, Chang A, Kilian R, Stijn DV, Oriol A, Low D, Knebel A, Taraman S. Promises, Pitfalls, and Clinical Applications of Artificial Intelligence in Pediatrics. J Med Internet Res 2024; 26:e49022. [PMID: 38421690 PMCID: PMC10940991 DOI: 10.2196/49022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 09/01/2023] [Accepted: 01/22/2024] [Indexed: 03/02/2024] Open
Abstract
Artificial intelligence (AI) broadly describes a branch of computer science focused on developing machines capable of performing tasks typically associated with human intelligence. Those who connect AI with the world of science fiction may meet its growing rise with hesitancy or outright skepticism. However, AI is becoming increasingly pervasive in our society, from algorithms helping to sift through airline fares to substituting words in emails and SMS text messages based on user choices. Data collection is ongoing and is being leveraged by software platforms to analyze patterns and make predictions across multiple industries. Health care is gradually becoming part of this technological transformation, as advancements in computational power and storage converge with the rapid expansion of digitized medical information. Given the growing and inevitable integration of AI into health care systems, it is our viewpoint that pediatricians urgently require training and orientation to the uses, promises, and pitfalls of AI in medicine. AI is unlikely to solve the full array of complex challenges confronting pediatricians today; however, if used responsibly, it holds great potential to improve many aspects of care for providers, children, and families. Our aim in this viewpoint is to provide clinicians with a targeted introduction to the field of AI in pediatrics, including key promises, pitfalls, and clinical applications, so they can play a more active role in shaping the future impact of AI in medicine.
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Affiliation(s)
- Hansa Bhargava
- Children's Hospital of Atlanta, Atlanta, GA, United States
- School of Medicine, Emory University, Atlanta, GA, United States
- Healio, South New Jersey, NJ, United States
| | | | - Srinivasan Suresh
- Division of Health Informatics, Department of Pediatrics, University of Pittsburgh, Pittsburgh, PA, United States
- UPMC Children's Hospital of Pittsburgh, Pittsburgh, PA, United States
| | - Anthony Chang
- Fowler School of Engineering, Chapman University, Orange, CA, United States
| | | | | | - Albert Oriol
- Rady Children's Hospital, San Diego, CA, United States
| | | | | | - Sharief Taraman
- Cognoa, Inc, Palo Alto, CA, United States
- Children's Hospital of Orange County, Orange, CA, United States
- University of California Irvine School of Medicine, Irvine, CA, United States
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Budiarto A, Tsang KCH, Wilson AM, Sheikh A, Shah SA. Machine Learning-Based Asthma Attack Prediction Models From Routinely Collected Electronic Health Records: Systematic Scoping Review. JMIR AI 2023; 2:e46717. [PMID: 38875586 PMCID: PMC11041490 DOI: 10.2196/46717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 09/28/2023] [Accepted: 10/09/2023] [Indexed: 06/16/2024]
Abstract
BACKGROUND An early warning tool to predict attacks could enhance asthma management and reduce the likelihood of serious consequences. Electronic health records (EHRs) providing access to historical data about patients with asthma coupled with machine learning (ML) provide an opportunity to develop such a tool. Several studies have developed ML-based tools to predict asthma attacks. OBJECTIVE This study aims to critically evaluate ML-based models derived using EHRs for the prediction of asthma attacks. METHODS We systematically searched PubMed and Scopus (the search period was between January 1, 2012, and January 31, 2023) for papers meeting the following inclusion criteria: (1) used EHR data as the main data source, (2) used asthma attack as the outcome, and (3) compared ML-based prediction models' performance. We excluded non-English papers and nonresearch papers, such as commentary and systematic review papers. In addition, we also excluded papers that did not provide any details about the respective ML approach and its result, including protocol papers. The selected studies were then summarized across multiple dimensions including data preprocessing methods, ML algorithms, model validation, model explainability, and model implementation. RESULTS Overall, 17 papers were included at the end of the selection process. There was considerable heterogeneity in how asthma attacks were defined. Of the 17 studies, 8 (47%) studies used routinely collected data both from primary care and secondary care practices together. Extreme imbalanced data was a notable issue in most studies (13/17, 76%), but only 38% (5/13) of them explicitly dealt with it in their data preprocessing pipeline. The gradient boosting-based method was the best ML method in 59% (10/17) of the studies. Of the 17 studies, 14 (82%) studies used a model explanation method to identify the most important predictors. None of the studies followed the standard reporting guidelines, and none were prospectively validated. CONCLUSIONS Our review indicates that this research field is still underdeveloped, given the limited body of evidence, heterogeneity of methods, lack of external validation, and suboptimally reported models. We highlighted several technical challenges (class imbalance, external validation, model explanation, and adherence to reporting guidelines to aid reproducibility) that need to be addressed to make progress toward clinical adoption.
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Affiliation(s)
- Arif Budiarto
- Asthma UK Center for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, United Kingdom
- Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia
| | - Kevin C H Tsang
- Asthma UK Center for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - Andrew M Wilson
- Norwich Medical School, University of East Anglia, Norwich, United Kingdom
- Norfolk and Norwich University Hospital NHS Foundation Trust, Norwich, United Kingdom
| | - Aziz Sheikh
- Asthma UK Center for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - Syed Ahmar Shah
- Asthma UK Center for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, United Kingdom
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Yao H, Wang L, Zhou X, Jia X, Xiang Q, Zhang W. Predicting the therapeutic efficacy of AIT for asthma using clinical characteristics, serum allergen detection metrics, and machine learning techniques. Comput Biol Med 2023; 166:107544. [PMID: 37866086 DOI: 10.1016/j.compbiomed.2023.107544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 09/07/2023] [Accepted: 09/28/2023] [Indexed: 10/24/2023]
Abstract
Bronchial asthma is a prevalent non-communicable disease among children. The study collected clinical data from 390 children aged 4-17 years with asthma, with or without rhinitis, who received allergen immunotherapy (AIT). Combining these data, this paper proposed a predictive framework for the efficacy of mite subcutaneous immunotherapy in asthma based on machine learning techniques. Introducing the dispersed foraging strategy into the Salp Swarm Algorithm (SSA), a new improved algorithm named DFSSA is proposed. This algorithm effectively alleviates the imbalance between search speed and traversal caused by the fixed partitioning pattern in traditional SSA. Utilizing the fusion of boosting algorithm and kernel extreme learning machine, an AIT performance prediction model was established. To further investigate the effectiveness of the DFSSA-KELM model, this study conducted an auxiliary diagnostic experiment using the immunotherapy predictive medical data collected by the hospital. The findings indicate that selected indicators, such as blood basophil count, sIgE/tIgE (Der p) and sIgE/tIgE (Der f), play a crucial role in predicting treatment outcome. The classification results showed an accuracy of 87.18% and a sensitivity of 93.55%, indicating that the prediction model is an effective and accurate intelligent tool for evaluating the efficacy of AIT.
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Affiliation(s)
- Hao Yao
- Department of Pediatric Allergy and Immunology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325027, China
| | - Lingya Wang
- Department of Pediatric Allergy and Immunology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325027, China
| | - Xinyu Zhou
- Department of Pediatric Allergy and Immunology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325027, China
| | - Xiaoxiao Jia
- Department of Pediatric Allergy and Immunology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325027, China
| | - Qiangwei Xiang
- Department of Pediatric Allergy and Immunology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325027, China.
| | - Weixi Zhang
- Department of Pediatric Allergy and Immunology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325027, China.
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Xu S, Deo RC, Soar J, Barua PD, Faust O, Homaira N, Jaffe A, Kabir AL, Acharya UR. Automated detection of airflow obstructive diseases: A systematic review of the last decade (2013-2022). COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 241:107746. [PMID: 37660550 DOI: 10.1016/j.cmpb.2023.107746] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Revised: 07/07/2023] [Accepted: 08/02/2023] [Indexed: 09/05/2023]
Abstract
BACKGROUND AND OBJECTIVE Obstructive airway diseases, including asthma and Chronic Obstructive Pulmonary Disease (COPD), are two of the most common chronic respiratory health problems. Both of these conditions require health professional expertise in making a diagnosis. Hence, this process is time intensive for healthcare providers and the diagnostic quality is subject to intra- and inter- operator variability. In this study we investigate the role of automated detection of obstructive airway diseases to reduce cost and improve diagnostic quality. METHODS We investigated the existing body of evidence and applied Preferred Reporting Items for Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to search records in IEEE, Google scholar, and PubMed databases. We identified 65 papers that were published from 2013 to 2022 and these papers cover 67 different studies. The review process was structured according to the medical data that was used for disease detection. We identified six main categories, namely air flow, genetic, imaging, signals, and miscellaneous. For each of these categories, we report both disease detection methods and their performance. RESULTS We found that medical imaging was used in 14 of the reviewed studies as data for automated obstructive airway disease detection. Genetics and physiological signals were used in 13 studies. Medical records and air flow were used in 9 and 7 studies, respectively. Most papers were published in 2020 and we found three times more work on Machine Learning (ML) when compared to Deep Learning (DL). Statistical analysis shows that DL techniques achieve higher Accuracy (ACC) when compared to ML. Convolutional Neural Network (CNN) is the most common DL classifier and Support Vector Machine (SVM) is the most widely used ML classifier. During our review, we discovered only two publicly available asthma and COPD datasets. Most studies used private clinical datasets, so data size and data composition are inconsistent. CONCLUSIONS Our review results indicate that Artificial Intelligence (AI) can improve both decision quality and efficiency of health professionals during COPD and asthma diagnosis. However, we found several limitations in this review, such as a lack of dataset consistency, a limited dataset and remote monitoring was not sufficiently explored. We appeal to society to accept and trust computer aided airflow obstructive diseases diagnosis and we encourage health professionals to work closely with AI scientists to promote automated detection in clinical practice and hospital settings.
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Affiliation(s)
- Shuting Xu
- School of Mathematics Physics and Computing, University of Southern Queensland, Springfield Central, QLD 4300, Australia; Cogninet Australia, Sydney, NSW 2010, Australia
| | - Ravinesh C Deo
- School of Mathematics Physics and Computing, University of Southern Queensland, Springfield Central, QLD 4300, Australia
| | - Jeffrey Soar
- School of Business, University of Southern Queensland, Australia
| | - Prabal Datta Barua
- Cogninet Australia, Sydney, NSW 2010, Australia; School of Business, University of Southern Queensland, Australia; Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia; Australian International Institute of Higher Education, Sydney, NSW 2000, Australia; School of Science Technology, University of New England, Australia; School of Biosciences, Taylor's University, Malaysia; School of Computing, SRM Institute of Science and Technology, India; School of Science and Technology, Kumamoto University, Japan; Sydney School of Education and Social Work, University of Sydney, Australia.
| | - Oliver Faust
- School of Computing and Information Science, Anglia Ruskin University Cambridge Campus, UK
| | - Nusrat Homaira
- School of Clinical Medicine, University of New South Wales, Australia; Sydney Children's Hospital, Sydney, Australia; James P. Grant School of Public Health, Dhaka, Bangladesh
| | - Adam Jaffe
- School of Clinical Medicine, University of New South Wales, Australia; Sydney Children's Hospital, Sydney, Australia
| | | | - U Rajendra Acharya
- School of Mathematics Physics and Computing, University of Southern Queensland, Springfield Central, QLD 4300, Australia; School of Science and Technology, Kumamoto University, Japan
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Yudkin JS, Allicock MA, Atem FD, Galeener CA, Messiah SE, Barlow SE. The Use of Electronic Health Record Data to Identify Variation in Referral, Consent, and Engagement in a Pediatric Intervention for Overweight and Obesity: A Cross-Sectional Study. Popul Health Manag 2023; 26:365-377. [PMID: 37792388 DOI: 10.1089/pop.2023.0120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/05/2023] Open
Abstract
Clinical weight management programs face low participation. The authors assessed whether using electronic health record (EHR) data can identify variation in referral, consent, and engagement in a pediatric overweight and obesity (OW/OB) intervention. Using Epic EHR data collected between August 2020 and April 2021, sociodemographic and clinical diagnostic data (ie, International Classification of Disease [ICD] codes from visit and problem list [PL]) were analyzed to determine their association with referral, consent, and engagement in an OW/OB intervention. Bivariate analyses and multivariable logistic regression modeling were performed, with Bayesian inclusion criterion score used for model selection. Compared with the 581 eligible patients, referred patients were more likely to be boys (60% vs. 54%, respectively; P = 0.04) and have a higher %BMIp95 (119% vs. 112%, respectively; P < 0.01); consented patients were more likely to have a higher %BMIp95 (120% vs. 112%, respectively; P < 0.01) and speak Spanish (71% vs. 59%, respectively; P = 0.02); and engaged patients were more likely to have a higher %BMIp95 (117% vs. 112%, respectively; P = 0.03) and speak Spanish (78% vs. 59%, respectively; P < 0.01). The regression model without either ICD codes or PL diagnoses was the best fit across all outcomes, which were associated with baseline %BMIp95 and health clinic location. Neither visit nor PL diagnoses helped to identify variation in referral, consent, and engagement in a pediatric OW/OB intervention, and their role in understanding participation in such interventions remains unclear. However, additional efforts are needed to refer and engage younger girls with less extreme cases of OW/OB, and to support non-Hispanic families to consent.
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Affiliation(s)
- Joshua S Yudkin
- The University of Texas Health Science Center at Houston (UTHealth) School of Public Health, Dallas, Texas, USA
| | - Marlyn A Allicock
- The University of Texas Health Science Center at Houston (UTHealth) School of Public Health, Dallas, Texas, USA
- Center for Pediatric Population Health, The University of Texas Health Science Center at Houston (UTHealth) School of Public Health, Dallas, Texas, USA
| | - Folefac D Atem
- The University of Texas Health Science Center at Houston (UTHealth) School of Public Health, Dallas, Texas, USA
- Center for Pediatric Population Health, The University of Texas Health Science Center at Houston (UTHealth) School of Public Health, Dallas, Texas, USA
| | - Carol A Galeener
- The University of Texas Health Science Center at Houston (UTHealth) School of Public Health, Dallas, Texas, USA
| | - Sarah E Messiah
- The University of Texas Health Science Center at Houston (UTHealth) School of Public Health, Dallas, Texas, USA
- Center for Pediatric Population Health, The University of Texas Health Science Center at Houston (UTHealth) School of Public Health, Dallas, Texas, USA
- Department of Pediatrics, McGovern Medical School, Houston, Texas, USA
| | - Sarah E Barlow
- Division of Pediatric Gastroenterology, Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, Texas, USA
- Children's Health, Children's Medical Center Dallas, Dallas, Texas, USA
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van Breugel M, Fehrmann RSN, Bügel M, Rezwan FI, Holloway JW, Nawijn MC, Fontanella S, Custovic A, Koppelman GH. Current state and prospects of artificial intelligence in allergy. Allergy 2023; 78:2623-2643. [PMID: 37584170 DOI: 10.1111/all.15849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 07/08/2023] [Accepted: 07/31/2023] [Indexed: 08/17/2023]
Abstract
The field of medicine is witnessing an exponential growth of interest in artificial intelligence (AI), which enables new research questions and the analysis of larger and new types of data. Nevertheless, applications that go beyond proof of concepts and deliver clinical value remain rare, especially in the field of allergy. This narrative review provides a fundamental understanding of the core concepts of AI and critically discusses its limitations and open challenges, such as data availability and bias, along with potential directions to surmount them. We provide a conceptual framework to structure AI applications within this field and discuss forefront case examples. Most of these applications of AI and machine learning in allergy concern supervised learning and unsupervised clustering, with a strong emphasis on diagnosis and subtyping. A perspective is shared on guidelines for good AI practice to guide readers in applying it effectively and safely, along with prospects of field advancement and initiatives to increase clinical impact. We anticipate that AI can further deepen our knowledge of disease mechanisms and contribute to precision medicine in allergy.
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Affiliation(s)
- Merlijn van Breugel
- Department of Pediatric Pulmonology and Pediatric Allergology, Beatrix Children's Hospital, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
- Groningen Research Institute for Asthma and COPD (GRIAC), University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
- MIcompany, Amsterdam, the Netherlands
| | - Rudolf S N Fehrmann
- Department of Medical Oncology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | | | - Faisal I Rezwan
- Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, UK
- Department of Computer Science, Aberystwyth University, Aberystwyth, UK
| | - John W Holloway
- Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, UK
- National Institute for Health and Care Research Southampton Biomedical Research Centre, University Hospitals Southampton NHS Foundation Trust, Southampton, UK
| | - Martijn C Nawijn
- Groningen Research Institute for Asthma and COPD (GRIAC), University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
- Department of Pathology and Medical Biology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Sara Fontanella
- National Heart and Lung Institute, Imperial College London, London, UK
- National Institute for Health and Care Research Imperial Biomedical Research Centre (BRC), London, UK
| | - Adnan Custovic
- National Heart and Lung Institute, Imperial College London, London, UK
- National Institute for Health and Care Research Imperial Biomedical Research Centre (BRC), London, UK
| | - Gerard H Koppelman
- Department of Pediatric Pulmonology and Pediatric Allergology, Beatrix Children's Hospital, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
- Groningen Research Institute for Asthma and COPD (GRIAC), University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
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MacMath D, Chen M, Khoury P. Artificial Intelligence: Exploring the Future of Innovation in Allergy Immunology. Curr Allergy Asthma Rep 2023:10.1007/s11882-023-01084-z. [PMID: 37160554 PMCID: PMC10169188 DOI: 10.1007/s11882-023-01084-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/21/2023] [Indexed: 05/11/2023]
Abstract
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. 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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Affiliation(s)
- Derek MacMath
- Department of Medicine, Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA
| | - Meng Chen
- Division of Pulmonary, Allergy & Critical Care Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Paneez Khoury
- National Institutes of Allergic and Infectious Diseases, National Institutes of Health, 9000 Rockville Pike, Bethesda, MD, USA.
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Bhardwaj P, Tyagi A, Tyagi S, Antão J, Deng Q. Machine learning model for classification of predominantly allergic and non-allergic asthma among preschool children with asthma hospitalization. J Asthma 2023; 60:487-495. [PMID: 35344453 DOI: 10.1080/02770903.2022.2059763] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
OBJECTIVE Asthma is the most frequent chronic airway illness in preschool children and is difficult to diagnose due to the disease's heterogeneity. This study aimed to investigate different machine learning models and suggested the most effective one to classify two forms of asthma in preschool children (predominantly allergic asthma and non-allergic asthma) using a minimum number of features. METHODS After pre-processing, 127 patients (70 with non-allergic asthma and 57 with predominantly allergic asthma) were chosen for final analysis from the Frankfurt dataset, which had asthma-related information on 205 patients. The Random Forest algorithm and Chi-square were used to select the key features from a total of 63 features. Six machine learning models: random forest, extreme gradient boosting, support vector machines, adaptive boosting, extra tree classifier, and logistic regression were then trained and tested using 10-fold stratified cross-validation. RESULTS Among all features, age, weight, C-reactive protein, eosinophilic granulocytes, oxygen saturation, pre-medication inhaled corticosteroid + long-acting beta2-agonist (PM-ICS + LABA), PM-other (other pre-medication), H-Pulmicort/celestamine (Pulmicort/celestamine during hospitalization), and H-azithromycin (azithromycin during hospitalization) were found to be highly important. The support vector machine approach with a linear kernel was able to diffrentiate between predominantly allergic asthma and non-allergic asthma with higher accuracy (77.8%), precision (0.81), with a true positive rate of 0.73 and a true negative rate of 0.81, a F1 score of 0.81, and a ROC-AUC score of 0.79. Logistic regression was found to be the second-best classifier with an overall accuracy of 76.2%. CONCLUSION Predominantly allergic and non-allergic asthma can be classified using machine learning approaches based on nine features. Supplemental data for this article is available online at at www.tandfonline.com/ijas .
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Affiliation(s)
- Piyush Bhardwaj
- Centre for Advanced Computational Solutions (C-fACS), Department of Molecular Biosciences, Lincoln University, Lincoln, Christchurch, New Zealand
| | - Ashish Tyagi
- Department of Forensic Medicine & Toxicology, SHKM Govt. Medical College, Nuh, Haryana, India
| | - Shashank Tyagi
- Department of Forensic Medicine & Toxicology, Lady Hardinge Medical College & Associated Hospitals, New Delhi, India
| | - Joana Antão
- Lab3R-Respiratory Research and Rehabilitation Laboratory, School of Health Sciences (ESSUA), Department of Medical Sciences, Institute of Biomedicine (iBiMED), University of Aveiro, Aveiro, Portugal.,Department of Research and Education, CIRO, Horn, The Netherlands
| | - Qichen Deng
- Department of Research and Education, CIRO, Horn, The Netherlands.,Department of Respiratory Medicine, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Centre, Maastricht, The Netherlands.,Faculty of Health, Medicine and Life Sciences, Maastricht University Medical Centre, Limburg, The Netherlands
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11
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Ekpo RH, Osamor VC, Azeta AA, Ikeakanam E, Amos BO. Machine learning classification approach for asthma prediction models in children. HEALTH AND TECHNOLOGY 2023. [DOI: 10.1007/s12553-023-00732-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
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12
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Perrem L, Subbarao P. Moving the dial on identifying endotypes of asthma from early life. Eur Respir J 2022; 60:60/3/2201031. [PMID: 36175027 DOI: 10.1183/13993003.01031-2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 08/15/2022] [Indexed: 11/05/2022]
Affiliation(s)
- Lucy Perrem
- Division of Respiratory Medicine, The Hospital for Sick Children, Toronto, ON, Canada.,Department of Pediatrics, University of Toronto, Toronto, ON, Canada.,Translational Medicine Program, SickKids Research Institute, Toronto, ON, Canada
| | - Padmaja Subbarao
- Division of Respiratory Medicine, The Hospital for Sick Children, Toronto, ON, Canada .,Department of Pediatrics, University of Toronto, Toronto, ON, Canada.,The Department of Medicine, McMaster University, Hamilton, ON, Canada
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13
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Nuutinen M, Haukka J, Virkkula P, Torkki P, Toppila-Salmi S. Using machine learning for the personalised prediction of revision endoscopic sinus surgery. PLoS One 2022; 17:e0267146. [PMID: 35486626 PMCID: PMC9053825 DOI: 10.1371/journal.pone.0267146] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 04/03/2022] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Revision endoscopic sinus surgery (ESS) is often considered for chronic rhinosinusitis (CRS) if maximal conservative treatment and baseline ESS prove insufficient. Emerging research outlines the risk factors of revision ESS. However, accurately predicting revision ESS at the individual level remains uncertain. This study aims to examine the prediction accuracy of revision ESS and to identify the effects of risk factors at the individual level. METHODS We collected demographic and clinical variables from the electronic health records of 767 surgical CRS patients ≥16 years of age. Revision ESS was performed on 111 (14.5%) patients. The prediction accuracy of revision ESS was examined by training and validating different machine learning models, while the effects of variables were analysed using the Shapley values and partial dependence plots. RESULTS The logistic regression, gradient boosting and random forest classifiers performed similarly in predicting revision ESS. Area under the receiving operating characteristic curve (AUROC) values were 0.744, 0.741 and 0.730, respectively, using data collected from the baseline visit until six months after baseline ESS. The length of time during which data were collected improved the prediction performance. For data collection times of 0, 3, 6 and 12 months after baseline ESS, AUROC values for the logistic regression were 0.682, 0.715, 0.744 and 0.784, respectively. The number of visits before or after baseline ESS, the number of days from the baseline visit to the baseline ESS, patient age, CRS with nasal polyps (CRSwNP), asthma, non-steroidal anti-inflammatory drug exacerbated respiratory disease and immunodeficiency or suspicion of it all associated with revision ESS. Patient age and number of visits before baseline ESS carried non-linear effects for predictions. CONCLUSIONS Intelligent data analysis identified important predictors of revision ESS at the individual level, such as the frequency of clinical visits, patient age, Type 2 high diseases and immunodeficiency or a suspicion of it.
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Affiliation(s)
- Mikko Nuutinen
- Haartman Institute, University of Helsinki, Helsinki, Finland
- Nordic Healthcare Group, Helsinki, Finland
| | - Jari Haukka
- Department of Public Health, University of Helsinki, Helsinki, Finland
| | - Paula Virkkula
- Department of Otorhinolaryngology-Head and Neck Surgery, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Paulus Torkki
- Department of Public Health, University of Helsinki, Helsinki, Finland
| | - Sanna Toppila-Salmi
- Haartman Institute, University of Helsinki, Helsinki, Finland
- Skin and Allergy Hospital, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
- * E-mail:
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14
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Filipow N, Main E, Sebire NJ, Booth J, Taylor AM, Davies G, Stanojevic S. Implementation of prognostic machine learning algorithms in paediatric chronic respiratory conditions: a scoping review. BMJ Open Respir Res 2022; 9:9/1/e001165. [PMID: 35297371 PMCID: PMC8928277 DOI: 10.1136/bmjresp-2021-001165] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Accepted: 03/06/2022] [Indexed: 11/23/2022] Open
Abstract
Machine learning (ML) holds great potential for predicting clinical outcomes in heterogeneous chronic respiratory diseases (CRD) affecting children, where timely individualised treatments offer opportunities for health optimisation. This paper identifies rate-limiting steps in ML prediction model development that impair clinical translation and discusses regulatory, clinical and ethical considerations for ML implementation. A scoping review of ML prediction models in paediatric CRDs was undertaken using the PRISMA extension scoping review guidelines. From 1209 results, 25 articles published between 2013 and 2021 were evaluated for features of a good clinical prediction model using the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guidelines. Most of the studies were in asthma (80%), with few in cystic fibrosis (12%), bronchiolitis (4%) and childhood wheeze (4%). There were inconsistencies in model reporting and studies were limited by a lack of validation, and absence of equations or code for replication. Clinician involvement during ML model development is essential and diversity, equity and inclusion should be assessed at each step of the ML pipeline to ensure algorithms do not promote or amplify health disparities among marginalised groups. As ML prediction studies become more frequent, it is important that models are rigorously developed using published guidelines and take account of regulatory frameworks which depend on model complexity, patient safety, accountability and liability.
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Affiliation(s)
- Nicole Filipow
- UCL Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Eleanor Main
- UCL Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Neil J Sebire
- Population, Policy and Practice Research and Teaching Department, UCL Great Ormond Street Institute of Child Health, University College London, London, UK.,GOSH NIHR BRC, Great Ormond Street Hospital for Children, London, UK
| | - John Booth
- Population, Policy and Practice Research and Teaching Department, UCL Great Ormond Street Institute of Child Health, University College London, London, UK.,GOSH NIHR BRC, Great Ormond Street Hospital for Children, London, UK
| | - Andrew M Taylor
- GOSH NIHR BRC, Great Ormond Street Hospital for Children, London, UK.,Institute of Cardiovascular Science, University College London, London, UK
| | - Gwyneth Davies
- Population, Policy and Practice Research and Teaching Department, UCL Great Ormond Street Institute of Child Health, University College London, London, UK.,GOSH NIHR BRC, Great Ormond Street Hospital for Children, London, UK
| | - Sanja Stanojevic
- Community Health and Epidemiology, Dalhousie University, Halifax, Nova Scotia, Canada
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15
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Patel D, Hall GL, Broadhurst D, Smith A, Schultz A, Foong RE. Does machine learning have a role in the prediction of asthma in children? Paediatr Respir Rev 2022; 41:51-60. [PMID: 34210588 DOI: 10.1016/j.prrv.2021.06.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 06/03/2021] [Indexed: 02/07/2023]
Abstract
Asthma is the most common chronic lung disease in childhood. There has been a significant worldwide effort to develop tools/methods to identify children's risk for asthma as early as possible for preventative and early management strategies. Unfortunately, most childhood asthma prediction tools using conventional statistical models have modest accuracy, sensitivity, and positive predictive value. Machine learning is an approach that may improve on conventional models by finding patterns and trends from large and complex datasets. Thus far, few studies have utilized machine learning to predict asthma in children. This review aims to critically assess these studies, describe their limitations, and discuss future directions to move from proof-of-concept to clinical application.
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Affiliation(s)
- Dimpalben Patel
- Wal-yan Respiratory Research Centre, Telethon Kids Institute, University of Western Australia, Perth, Australia; School of Allied Health, Faculty of Health Sciences, Curtin University, Perth, Australia.
| | - Graham L Hall
- Wal-yan Respiratory Research Centre, Telethon Kids Institute, University of Western Australia, Perth, Australia; School of Allied Health, Faculty of Health Sciences, Curtin University, Perth, Australia.
| | - David Broadhurst
- Centre for Integrative Metabolomics & Computational Biology, Edith Cowan University, Joondalup, Australia.
| | - Anne Smith
- School of Allied Health, Faculty of Health Sciences, Curtin University, Perth, Australia.
| | - André Schultz
- Wal-yan Respiratory Research Centre, Telethon Kids Institute, University of Western Australia, Perth, Australia; Department of Respiratory Medicine, Child and Adolescent Health Service, Perth, Australia; Division of Paediatrics, Faculty of Medicine, University of Western Australia, Perth, Australia.
| | - Rachel E Foong
- Wal-yan Respiratory Research Centre, Telethon Kids Institute, University of Western Australia, Perth, Australia; School of Allied Health, Faculty of Health Sciences, Curtin University, Perth, Australia.
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16
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Cilluffo G, Fasola S, Ferrante G, Licari A, Marseglia GR, Albarelli A, Marseglia GL, La Grutta S. Machine learning: A modern approach to pediatric asthma. Pediatr Allergy Immunol 2022; 33 Suppl 27:34-37. [PMID: 35080316 PMCID: PMC9303472 DOI: 10.1111/pai.13624] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/03/2021] [Revised: 07/25/2021] [Accepted: 08/06/2021] [Indexed: 11/30/2022]
Abstract
Among modern methods of statistical and computational analysis, the application of machine learning (ML) to healthcare data has been gaining recognition in helping us understand the heterogeneity of asthma and predicting its progression. In pediatric research, ML approaches may provide rapid advances in uncovering asthma phenotypes with potential translational impact in clinical practice. Also, several accurate models to predict asthma and its progression have been developed using ML. Here, we provide a brief overview of ML approaches recently proposed to characterize pediatric asthma.
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Affiliation(s)
- Giovanna Cilluffo
- Institute for Biomedical Research and Innovation, National Research Council, Palermo, Italy
| | - Salvatore Fasola
- Institute for Biomedical Research and Innovation, National Research Council, Palermo, Italy
| | - Giuliana Ferrante
- Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, University of Palermo, Palermo, Italy
| | - Amelia Licari
- Pediatric Clinic, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy.,Department of Clinical-Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy
| | | | - Andrea Albarelli
- Department of Environmental Sciences, Informatics and Statistics, Ca' Foscari University, Venice, Italy
| | - Gian Luigi Marseglia
- Pediatric Clinic, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy.,Department of Clinical-Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy
| | - Stefania La Grutta
- Institute for Biomedical Research and Innovation, National Research Council, Palermo, Italy
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17
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Mariani S, Metting E, Lahr MMH, Vargiu E, Zambonelli F. Developing an ML pipeline for asthma and COPD: The case of a Dutch primary care service. INT J INTELL SYST 2021. [DOI: 10.1002/int.22568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Stefano Mariani
- Department of Sciences and Methods for Engineering University of Modena and Reggio Emilia Reggio Emilia Italy
| | - Esther Metting
- Health Technology Assessment, Department of Epidemiology, University of Groningen University Medical Center Groningen The Netherlands
| | - Maarten M. H. Lahr
- Health Technology Assessment, Department of Epidemiology, University of Groningen University Medical Center Groningen The Netherlands
| | - Eloisa Vargiu
- EURECAT Technology Centre Digital Health Unit Barcelona Spain
| | - Franco Zambonelli
- Department of Sciences and Methods for Engineering University of Modena and Reggio Emilia Reggio Emilia Italy
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18
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Kothalawala DM, Murray CS, Simpson A, Custovic A, Tapper WJ, Arshad SH, Holloway JW, Rezwan FI. Development of childhood asthma prediction models using machine learning approaches. Clin Transl Allergy 2021; 11:e12076. [PMID: 34841728 PMCID: PMC9815427 DOI: 10.1002/clt2.12076] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 09/23/2021] [Accepted: 10/18/2021] [Indexed: 01/11/2023] Open
Abstract
BACKGROUND Respiratory symptoms are common in early life and often transient. It is difficult to identify in which children these will persist and result in asthma. Machine learning (ML) approaches have the potential for better predictive performance and generalisability over existing childhood asthma prediction models. This study applied ML approaches to predict school-age asthma (age 10) in early life (Childhood Asthma Prediction in Early life, CAPE model) and at preschool age (Childhood Asthma Prediction at Preschool age, CAPP model). METHODS Clinical and environmental exposure data was collected from children enrolled in the Isle of Wight Birth Cohort (N = 1368, ∼15% asthma prevalence). Recursive Feature Elimination (RFE) identified an optimal subset of features predictive of school-age asthma for each model. Seven state-of-the-art ML classification algorithms were used to develop prognostic models. Training was performed by applying fivefold cross-validation, imputation, and resampling. Predictive performance was evaluated on the test set. Models were further externally validated in the Manchester Asthma and Allergy Study (MAAS) cohort. RESULTS RFE identified eight and twelve predictors for the CAPE and CAPP models, respectively. Support Vector Machine (SVM) algorithms provided the best performance for both the CAPE (area under the receiver operating characteristic curve, AUC = 0.71) and CAPP (AUC = 0.82) models. Both models demonstrated good generalisability in MAAS (CAPE 8-year = 0.71, 11-year = 0.71, CAPP 8-year = 0.83, 11-year = 0.79) and excellent sensitivity to predict a subgroup of persistent wheezers. CONCLUSION Using ML approaches improved upon the predictive performance of existing regression-based models, with good generalisability and ability to rule in asthma and predict persistent wheeze.
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Affiliation(s)
- Dilini M. Kothalawala
- Human Development and HealthFaculty of MedicineUniversity of SouthamptonSouthamptonUK
- NIHR Southampton Biomedical Research CentreUniversity Hospital SouthamptonSouthamptonUK
| | - Clare S. Murray
- Division of Infection, Immunity, and Respiratory MedicineSchool of Biological SciencesUniversity of ManchesterManchester University Hospital NHS Foundation TrustManchester Academic Health Science CentreManchesterUK
| | - Angela Simpson
- Division of Infection, Immunity, and Respiratory MedicineSchool of Biological SciencesUniversity of ManchesterManchester University Hospital NHS Foundation TrustManchester Academic Health Science CentreManchesterUK
| | - Adnan Custovic
- National Heart and Lung InstituteImperial College of Science, Technology, and MedicineLondonUK
| | - William J. Tapper
- Human Development and HealthFaculty of MedicineUniversity of SouthamptonSouthamptonUK
| | - S. Hasan Arshad
- NIHR Southampton Biomedical Research CentreUniversity Hospital SouthamptonSouthamptonUK
- The David Hide Asthma and Allergy Research CentreSt. Mary's HospitalIsle of WightUK
- Clinical and Experimental SciencesFaculty of MedicineUniversity of SouthamptonSouthamptonUK
| | - John W. Holloway
- Human Development and HealthFaculty of MedicineUniversity of SouthamptonSouthamptonUK
- NIHR Southampton Biomedical Research CentreUniversity Hospital SouthamptonSouthamptonUK
| | - Faisal I. Rezwan
- Human Development and HealthFaculty of MedicineUniversity of SouthamptonSouthamptonUK
- Department of Computer ScienceAberystwyth UniversityAberystwythUK
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