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Wu CP, Sleiman J, Fakhry B, Chedraoui C, Attaway A, Bhattacharyya A, Bleecker ER, Erdemir A, Hu B, Kethireddy S, Meyers DA, Rashidi HH, Zein JG. Novel Machine Learning Identifies 5 Asthma Phenotypes Using Cluster Analysis of Real-World Data. THE JOURNAL OF ALLERGY AND CLINICAL IMMUNOLOGY. IN PRACTICE 2024:S2213-2198(24)00420-3. [PMID: 38685479 DOI: 10.1016/j.jaip.2024.04.035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 03/25/2024] [Accepted: 04/19/2024] [Indexed: 05/02/2024]
Abstract
BACKGROUND Asthma classification into different subphenotypes is important to guide personalized therapy and improve outcomes. OBJECTIVES To further explore asthma heterogeneity through determination of multiple patient groups by using novel machine learning (ML) approaches and large-scale real-world data. METHODS We used electronic health records of patients with asthma followed at the Cleveland Clinic between 2010 and 2021. We used k-prototype unsupervised ML to develop a clustering model where predictors were age, sex, race, body mass index, prebronchodilator and postbronchodilator spirometry measurements, and the usage of inhaled/systemic steroids. We applied elbow and silhouette plots to select the optimal number of clusters. These clusters were then evaluated through LightGBM's supervised ML approach on their cross-validated F1 score to support their distinctiveness. RESULTS Data from 13,498 patients with asthma with available postbronchodilator spirometry measurements were extracted to identify 5 stable clusters. Cluster 1 included a young nonsevere asthma population with normal lung function and higher frequency of acute exacerbation (0.8 /patient-year). Cluster 2 had the highest body mass index (mean ± SD, 44.44 ± 7.83 kg/m2), and the highest proportion of females (77.5%) and Blacks (28.9%). Cluster 3 comprised patients with normal lung function. Cluster 4 included patients with lower percent of predicted FEV1 of 77.03 (12.79) and poor response to bronchodilators. Cluster 5 had the lowest percent of predicted FEV1 of 68.08 (15.02), the highest postbronchodilator reversibility, and the highest proportion of severe asthma (44.9%) and blood eosinophilia (>300 cells/μL) (34.8%). CONCLUSIONS Using real-world data and unsupervised ML, we classified asthma into 5 clinically important subphenotypes where group-specific asthma treatment and management strategies can be designed and deployed.
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Affiliation(s)
- Chao-Ping Wu
- Respiratory Institute, Cleveland Clinic, Cleveland, Ohio
| | - Joelle Sleiman
- Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio
| | - Battoul Fakhry
- Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio
| | | | - Amy Attaway
- Respiratory Institute, Cleveland Clinic, Cleveland, Ohio; Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio
| | | | - Eugene R Bleecker
- Department of Medicine, Division of Pulmonary Medicine, Mayo Clinic, Scottsdale, Ariz
| | - Ahmet Erdemir
- Respiratory Institute, Cleveland Clinic, Cleveland, Ohio
| | - Bo Hu
- Respiratory Institute, Cleveland Clinic, Cleveland, Ohio
| | | | - Deborah A Meyers
- Department of Medicine, Division of Pulmonary Medicine, Mayo Clinic, Scottsdale, Ariz
| | - Hooman H Rashidi
- Pathology and Laboratory Medicine Institute, Cleveland Clinic, Ohio
| | - Joe G Zein
- Department of Medicine, Division of Pulmonary Medicine, Mayo Clinic, Scottsdale, Ariz.
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Lotfata A, Moosazadeh M, Helbich M, Hoseini B. Socioeconomic and environmental determinants of asthma prevalence: a cross-sectional study at the U.S. County level using geographically weighted random forests. Int J Health Geogr 2023; 22:18. [PMID: 37563691 PMCID: PMC10413687 DOI: 10.1186/s12942-023-00343-6] [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: 04/30/2023] [Accepted: 08/04/2023] [Indexed: 08/12/2023] Open
Abstract
BACKGROUND Some studies have established associations between the prevalence of new-onset asthma and asthma exacerbation and socioeconomic and environmental determinants. However, research remains limited concerning the shape of these associations, the importance of the risk factors, and how these factors vary geographically. OBJECTIVE We aimed (1) to examine ecological associations between asthma prevalence and multiple socio-physical determinants in the United States; and (2) to assess geographic variations in their relative importance. METHODS Our study design is cross sectional based on county-level data for 2020 across the United States. We obtained self-reported asthma prevalence data of adults aged 18 years or older for each county. We applied conventional and geographically weighted random forest (GWRF) to investigate the associations between asthma prevalence and socioeconomic (e.g., poverty) and environmental determinants (e.g., air pollution and green space). To enhance the interpretability of the GWRF, we (1) assessed the shape of the associations through partial dependence plots, (2) ranked the determinants according to their global importance scores, and (3) mapped the local variable importance spatially. RESULTS Of the 3059 counties, the average asthma prevalence was 9.9 (standard deviation ± 0.99). The GWRF outperformed the conventional random forest. We found an indication, for example, that temperature was inversely associated with asthma prevalence, while poverty showed positive associations. The partial dependence plots showed that these associations had a non-linear shape. Ranking the socio-physical environmental factors concerning their global importance showed that smoking prevalence and depression prevalence were most relevant, while green space and limited language were of minor relevance. The local variable importance measures showed striking geographical differences. CONCLUSION Our findings strengthen the evidence that socio-physical environments play a role in explaining asthma prevalence, but their relevance seems to vary geographically. The results are vital for implementing future asthma prevention programs that should be tailor-made for specific areas.
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Affiliation(s)
- Aynaz Lotfata
- Department of Pathology, Microbiology, and Immunology, School of Veterinary Medicine, University of California, Davis, CA, USA
| | - Mohammad Moosazadeh
- Integrated Engineering, Department of Environmental Science and Engineering, College of Engineering, KyungHee University, Yongin, 446-701, Republic of Korea
| | - Marco Helbich
- Department of Human Geography and Spatial Planning, Faculty of Geosciences, University Utrecht, Utrecht, The Netherlands
| | - Benyamin Hoseini
- Pharmaceutical Research Center, Pharmaceutical Technology Institute, Mashhad University of Medical Sciences, Mashhad, Iran.
- Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
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Shamji MH, Ollert M, Adcock IM, Bennett O, Favaro A, Sarama R, Riggioni C, Annesi-Maesano I, Custovic A, Fontanella S, Traidl-Hoffmann C, Nadeau K, Cecchi L, Zemelka-Wiacek M, Akdis CA, Jutel M, Agache I. EAACI guidelines on environmental science in allergic diseases and asthma - Leveraging artificial intelligence and machine learning to develop a causality model in exposomics. Allergy 2023; 78:1742-1757. [PMID: 36740916 DOI: 10.1111/all.15667] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 01/17/2023] [Accepted: 02/01/2023] [Indexed: 02/07/2023]
Abstract
Allergic diseases and asthma are intrinsically linked to the environment we live in and to patterns of exposure. The integrated approach to understanding the effects of exposures on the immune system includes the ongoing collection of large-scale and complex data. This requires sophisticated methods to take full advantage of what this data can offer. Here we discuss the progress and further promise of applying artificial intelligence and machine-learning approaches to help unlock the power of complex environmental data sets toward providing causality models of exposure and intervention. We discuss a range of relevant machine-learning paradigms and models including the way such models are trained and validated together with examples of machine learning applied to allergic disease in the context of specific environmental exposures as well as attempts to tie these environmental data streams to the full representative exposome. We also discuss the promise of artificial intelligence in personalized medicine and the methodological approaches to healthcare with the final AI to improve public health.
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Affiliation(s)
- Mohamed H Shamji
- National Heart and Lung Institute, Imperial College London, London, UK
- NIHR Imperial Biomedical Research Centre, London, UK
| | - Markus Ollert
- Department of Infection and Immunity, Luxembourg Institute of Health (LIH), Esch-sur-Alzette, Luxembourg
- Department of Dermatology and Allergy Center, Odense Research Center for Anaphylaxis (ORCA), University of Southern Denmark, Odense, Denmark
| | - Ian M Adcock
- National Heart and Lung Institute, Imperial College London, London, UK
- NIHR Imperial Biomedical Research Centre, London, UK
| | | | | | - Roudin Sarama
- National Heart and Lung Institute, Imperial College London, London, UK
- NIHR Imperial Biomedical Research Centre, London, UK
| | - Carmen Riggioni
- Pediatric Allergy and Clinical Immunology Service, Institut de Reserca Sant Joan de Deú, Barcelona, Spain
| | - Isabella Annesi-Maesano
- Research Director and Deputy DIrector of Institut Desbrest of Epidemiology and Public Health (IDESP) French NIH (INSERM) and University of Montpellier, Montpellier, France
| | - Adnan Custovic
- National Heart and Lung Institute, Imperial College London, London, UK
- NIHR Imperial Biomedical Research Centre, London, UK
| | - Sara Fontanella
- National Heart and Lung Institute, Imperial College London, London, UK
- NIHR Imperial Biomedical Research Centre, London, UK
| | - Claudia Traidl-Hoffmann
- Environmental Medicine Faculty of Medicine University of Augsburg, Augsburg, Germany
- CK-CARE, Christine Kühne Center for Allergy Research and Education, Davos, Switzerland
| | - Kari Nadeau
- Sean N. Parker Center for Allergy and Asthma Research, Stanford University School of Medicine, Stanford, California, USA
| | - Lorenzo Cecchi
- SOS Allergology and Clinical Immunology, USL Toscana Centro, Prato, Italy
| | | | - Cezmi A Akdis
- Swiss Institute of Allergy and Asthma Research (SIAF), University Zurich, Davos, Switzerland
| | - Marek Jutel
- Department of Clinical Immunology, Wroclaw Medical University, Wroclaw, Poland
- ALL-MED Medical Research Institute, Wroclaw, Poland
| | - Ioana Agache
- Faculty of Medicine, Transylvania University, Brasov, Romania
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González-Iglesias V, Martínez-Pérez I, Rodríguez Suárez V, Fernández-Somoano A. Spatial distribution of hospital admissions for asthma in the central area of Asturias, Northern Spain. BMC Public Health 2023; 23:787. [PMID: 37118792 PMCID: PMC10141842 DOI: 10.1186/s12889-023-15731-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Accepted: 04/22/2023] [Indexed: 04/30/2023] Open
Abstract
BACKGROUND Asturias is one of the communities with the highest rates of hospital admission for asthma in Spain. The environmental pollution or people lifestyle are some of the factors that contribute to the appearance or aggravation of this illness. The aim of this study was to show the spatial distribution of asthma admissions risks in the central municipalities of Asturias and to analyze the observed spatial patterns. METHODS Urgent hospital admissions for asthma and status asthmaticus occurred between 2016 to 2018 on the public hospitals of the central area of Asturias were used. Population data were assigned in 5 age groups. Standardised admission ratio (SAR), smoothed relative risk (SRR) and posterior risk probability (PP) were calculated for each census tract (CT). A spatial trend analysis was run, a spatial autocorrelation index (Morans I) was calculated and a cluster and outlier analysis (Anselin Local Morans I) was finally performed in order to analyze spatial clusters. RESULTS The total number of hospital urgent asthma admissions during the study period was 2324, 1475 (63.46%) men and 849 (36.56%) women. The municipalities with the highest values of SRR and PP were located on the northwest area: Avilés, Gozón, Carreño, Corvera de Asturias, Castrillón and Illas. A high risk cluster was found for the municipalities of Avilés, Gozón y Corvera de Asturias. CONCLUSIONS The spatial analysis showed high risk of hospitalization for asthma on the municipalities of the northwest area of the study, which highlight the existence of spatial inequalities on the distribution of urgent hospital admissions.
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Affiliation(s)
- Verónica González-Iglesias
- Departamento de Medicina, IUOPA-Área de Medicina Preventiva Y Salud Pública, Universidad de Oviedo. C/Julián Clavería S/N, 33006, Oviedo (Asturias), Spain
| | - Isabel Martínez-Pérez
- Departamento de Medicina, IUOPA-Área de Medicina Preventiva Y Salud Pública, Universidad de Oviedo. C/Julián Clavería S/N, 33006, Oviedo (Asturias), Spain.
| | - Valentín Rodríguez Suárez
- Dirección General de Salud Pública, Consejería de Salud, Principado de Asturias, C/ Ciriaco Miguel Vigil, 9, 33006, Oviedo, Spain
| | - Ana Fernández-Somoano
- Departamento de Medicina, IUOPA-Área de Medicina Preventiva Y Salud Pública, Universidad de Oviedo. C/Julián Clavería S/N, 33006, Oviedo (Asturias), Spain
- CIBER Epidemiología Y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Avenida Monforte de Lemos, 3-5, 28029, Madrid, Spain
- Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), Avenida Roma, S/N, 33001, Oviedo, Spain
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Bedolla-Pulido TR, Morales-Romero J, Bedolla-Pulido A, Meza-López C, Valdez-Soto JA, Bedolla-Barajas M. [Geographical variation of asthma prevalence among Mexican children during the COVID-19 pandemic]. REVISTA ALERGIA MÉXICO 2023; 69:164-170. [PMID: 37218044 DOI: 10.29262/ram.v69i4.1116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 01/26/2023] [Indexed: 05/24/2023] Open
Abstract
OBJECTIVE The purpose of this study was to analyze the geographic variation in the prevalence of asthma in children, according to their place of residence in Mexico. METHODS A cross-sectional analysis of the epidemiological surveillance system dataset for respiratory diseases in Mexico carried on. From 27 February to 5 November 2020, a total of 1,048,576 subjects were screened for SARS-CoV2 infection, of which 35,899 were children under 18 years of age. The strength of the association was estimated by odds ratio (OR). RESULTS Of 1,048,576 patients who attended for SARS-CoV2 infection detection, 35,899 corresponded to pediatric patients who met the study criteria. The estimated national prevalence of asthma was 3.9% (95% CI: 3.7-4.1%). The nationwide prevalence of asthma was 3.9% (95% CI: 3.7% - 4.1%); the minimum was 2.8% (Southeast region) and the maximum 6.8% (Southeast region). Compared to the South-West Region that presented the minimum prevalence at the national level, the Northwest (OR = 2.41) and Southeast (OR = 1.33) regions showed the highest risk of asthma in pediatric population. CONCLUSIONS The prevalence of asthma in children differed markedly among the different regions of Mexico; two regions, Northwest and Southeast, stood out. This study puts into context the role of the environment on the prevalence of asthma in children.
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Affiliation(s)
| | | | - Angie Bedolla-Pulido
- Universidad de Guadalajara, Centro Universitario en Ciencias de la Salud, Licenciatura en Medicina
| | - Carlos Meza-López
- Servicio de Pediatría, Nuevo Hospital Civil de Guadalajara Dr. Juan I. Menchaca, Jalisco
| | - Jorge Alejandro Valdez-Soto
- Servicio de Alergia e Inmunología Clínica, Nuevo Hospital Civil de Guadalajara Dr. Juan I. Menchaca, Jalisco
| | - Martín Bedolla-Barajas
- Servicio de Alergia e Inmunología Clínica, Nuevo Hospital Civil de Guadalajara Dr. Juan I. Menchaca, Jalisco.
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van der Burg N, Tufvesson E. Is asthma's heterogeneity too vast to use traditional phenotyping for modern biologic therapies? Respir Med 2023; 212:107211. [PMID: 36924848 DOI: 10.1016/j.rmed.2023.107211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 03/07/2023] [Accepted: 03/11/2023] [Indexed: 03/18/2023]
Affiliation(s)
- Nicole van der Burg
- Department of Clinical Sciences Lund, Respiratory Medicine and Allergology, Lund University, Lund, Sweden.
| | - Ellen Tufvesson
- Department of Clinical Sciences Lund, Respiratory Medicine and Allergology, Lund University, Lund, Sweden
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Iqbal MA, Devarajan K, Ahmed SM. Optimal convolutional neural network classifier for asthma disease detection using speech signals. INTERNATIONAL JOURNAL OF HEALTHCARE MANAGEMENT 2023. [DOI: 10.1080/20479700.2023.2173774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
Affiliation(s)
- Md. Asim Iqbal
- Department of E.C.E, Annamalai University, Tamil Nadu, India
| | - K. Devarajan
- Department of E.C.E, Annamalai University, Tamil Nadu, India
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Geographic Medical Overview of Noncommunicable Diseases (Cardiovascular Diseases and Diabetes) in the Territory of the AP Vojvodina (Northern Serbia). Healthcare (Basel) 2022; 11:healthcare11010048. [PMID: 36611507 PMCID: PMC9819310 DOI: 10.3390/healthcare11010048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 12/06/2022] [Accepted: 12/20/2022] [Indexed: 12/28/2022] Open
Abstract
The objective of this study was a geographic medical analysis of noncommunicable diseases (cardiovascular diseases from 2010 to 2020 and diabetes from 2010 to 2019) in the AP Vojvodina (northern Serbia) in order to identify the most and least burdened counties as well as to present trends in the mentioned diseases. The Mann-Kendall trend test, a cluster analysis, and Getis-Ord Gi* method for hot spot analysis were applied in this analysis. Regarding acute coronary syndrome and myocardial infarction, the North Backa County had a lower mortality rate although the number of newly reported cases was above average. The largest number of new cases of unstable angina pectoris was in the North Backa, North Banat, and Middle Banat Counties, while the West Backa County was identified as a county with a higher mortality rate. The cluster analysis showed that the number of death cases from diabetes in the Srem County is significantly higher than that in the other counties. Likewise, the West Backa County had a high number of new diabetes patients, but also a much lower mortality rate. Chronic noncommunicable diseases are predominant in newly diagnosed incidences and death cases in the AP Vojvodina. Studies of this kind promote public health and healthcare systems in the researched area and in the Republic of Serbia, as well as in other countries.
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Abstract
OBJECTIVES To highlight novelty studies and current trends in Public Health and Epidemiology Informatics (PHEI). METHODS Similar to last year's edition, a PubMed search of 2021 scientific publications on PHEI has been conducted. The resulting references were reviewed by the two section editors. Then, 11 candidate best papers were selected from the initial 782 references. These papers were then peer-reviewed by selected external reviewers. They included at least two senior researchers, to allow the Editorial Committee of the 2022 IMIA Yearbook edition to make an informed decision for selecting the best papers of the PHEI section. RESULTS Among the 782 references retrieved from PubMed, two were selected as the best papers. The first best paper reports a study which performed a comprehensive comparison of traditional statistical approaches (e.g., Cox Proportional Hazards models) vs. machine learning techniques in a large, real-world dataset for predicting breast cancer survival, with a focus on explainability. The second paper describes the engineering of deep learning models to establish associations between ocular features and major hepatobiliary diseases and to advance automated screening and identification of hepatobiliary diseases from ocular images. CONCLUSION Overall, from this year edition, we observed that the number of studies related to PHEI has decreased. The findings of the two studies selected as best papers on the topic suggest that a significant effort is still being made by the community to compare traditional learning methods with deep learning methods. Using multimodality datasets (images, texts) could improve approaches for tackling public health issues.
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Affiliation(s)
- Gayo Diallo
- Inria SISTM, Team AHeaD - INSERM Bordeaux Population Health Research Center, Univ. Bordeaux, Bordeaux, France,Correspondence to: Gayo Diallo Inria SISTM, Team AHeaD, INSERM Bordeaux Population Health Research CenterUniv. Bordeaux 146, rue Léo Saignat F-33000 BordeauxFrance
| | - Georgeta Bordea
- Team AHeaD - Inserm BPH Research Center & LaBRI UMR 5800, Univ. Bordeaux, Bordeaux, France
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Park Y, Kim SH, Kim SP, Ryu J, Yi J, Kim JY, Yoon HJ. Spatial autocorrelation may bias the risk estimation: An application of eigenvector spatial filtering on the risk of air pollutant on asthma. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 843:157053. [PMID: 35780885 DOI: 10.1016/j.scitotenv.2022.157053] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 06/14/2022] [Accepted: 06/25/2022] [Indexed: 06/15/2023]
Abstract
Air pollutants are major risk factors for respiratory diseases, particularly asthma, socially and spatially correlated. Many existing environment-asthma-related studies, however, have evaluated the impact of crude trends at the largest district level, which accounts only for temporal effects and may produce biased results with spatial autocorrelation. This study aimed to investigate how the spatial autocorrelation affects the air pollution effect estimations (sulfur dioxide [SO2], nitrogen dioxide [NO2], carbon monoxide [CO], and particulate matter [PM10]) on daily asthma emergency department (ED) visits in two metropolitan areas in Korea (Seoul Metropolitan Area [SMA] and Busan Metropolitan City, Ulsan Metropolitan City, Gyeongsangnamdo [BUG]). We applied eigenvector spatial filter (ESF) to the spatio-temporal model to remove spatial autocorrelation and distributed lag nonlinear model (DLNM) to explore nonlinear patterns between air pollutant concentration and lagged days on the three models including aggregated model (a temporal model), spatial model without ESF, and spatial model with ESF (both are spatio-temporal models). The effect of SO2 was not statistically significant for asthma ED visits in the aggregated model for SMA (cumulative relative risks [CRR] = 0.99, confidence intervals [CI]: 0.93-1.05), while the effect was statistically significant in the spatial model with ESF (CRR = 1.10, CI: 1.08-1.12). NO2 and CO were positively correlated to asthma ED visits in the spatial model without ESF (CRR = 0.84, CI: 0.81-0.86; 0.91, 0.89-0.94, respectively), but the spatial model with ESF showed significant risks (CRR = 1.21, CI: 1.18-1.24; 1.13, 1.11-1.16). Moreover, the spatial model with ESF successfully removed spatial autocorrelation (P-values for Moran's I 0.83-0.98) and demonstrated the highest model fit (McFadden's pseudo R2 0.42-0.43 for SMA and 0.26-0.27 for BUG) among the three models. Our findings demonstrate how ESF can be introduced into spatial correlation to remove bias and construct more reliable models.
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Affiliation(s)
- Yujin Park
- Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, South Korea
| | - Su Hwan Kim
- Biomedical Research Institute, Seoul National University Hospital, Seoul, Republic of Korea
| | - Seong Pyo Kim
- Interdisciplinary Program of Medical Informatics, Seoul National University College of Medicine, Seoul, South Korea
| | - Jiwon Ryu
- Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, South Korea; Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Gyeonggi, Republic of Korea
| | - Jinyeong Yi
- Department of Health Science and Technology, Seoul National University, Seoul, South Korea
| | - Jin Youp Kim
- Interdisciplinary Program of Medical Informatics, Seoul National University College of Medicine, Seoul, South Korea; Department of Otorhinolaryngology-Head and Neck Surgery, Ilsan Hospital, Dongguk University, Goyang, Gyeonggi, South Korea
| | - Hyung-Jin Yoon
- Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, South Korea; Interdisciplinary Program of Medical Informatics, Seoul National University College of Medicine, Seoul, South Korea; Medical Big Data Research Center, Seoul National University Medical Research Center, Seoul National University College of Medicine, Seoul, Republic of Korea.
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Mueller W, Milner J, Loh M, Vardoulakis S, Wilkinson P. Exposure to urban greenspace and pathways to respiratory health: An exploratory systematic review. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 829:154447. [PMID: 35283125 DOI: 10.1016/j.scitotenv.2022.154447] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 03/04/2022] [Accepted: 03/06/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND/OBJECTIVE Urban greenspace may have a beneficial or adverse effect on respiratory health. Our objective was to perform an exploratory systematic review to synthesise the evidence and identify the potential causal pathways relating urban greenspace and respiratory health. METHODS We followed PRISMA guidelines on systematic reviews and searched five databases for eligible studies during 2000-2021. We incorporated a broad range of urban greenspace and respiratory health search terms, including both observational and experimental studies. Screening, data extraction, and risk of bias, assessed using the Navigation Guide criteria, were performed independently by two authors. We performed a narrative synthesis and discuss suggested pathways to respiratory health. RESULTS We identified 108 eligible papers (n = 104 observational, n = 4 experimental). The most common greenspace indicators were the overall greenery or vegetation (also known as greenness), green land use/land cover of physical area classes (e.g., parks, forests), and tree canopy cover. A wide range of respiratory health indicators were studied, with asthma prevalence being the most common. Two thirds (n = 195) of the associations in these studies were positive (i.e., beneficial) with health, with 31% (n = 91) statistically significant; only 9% (n = 25) of reported associations were negative (i.e., adverse) with health and statistically significant. The most consistent positive evidence was apparent for respiratory mortality. There were n = 35 (32%) 'probably low' and n = 73 (68%) 'probably high' overall ratings of bias. Hypothesised causal pathways for health benefits included lower air pollution, more physically active populations, and exposure to microbial diversity; suggested mechanisms with poorer health included exposure to pollen and other aeroallergens. CONCLUSION Many studies showed positive association between urban greenspace and respiratory health, especially lower respiratory mortality; this is suggestive, but not conclusive, of causal effects. Results underscore the importance of contextual factors, greenspace metric employed, and the potential bias of subtle selection factors, which should be explored further.
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Affiliation(s)
- William Mueller
- Institute of Occupational Medicine, Edinburgh, UK; London School of Hygiene & Tropical Medicine, UK.
| | - James Milner
- London School of Hygiene & Tropical Medicine, UK
| | - Miranda Loh
- Institute of Occupational Medicine, Edinburgh, UK
| | - Sotiris Vardoulakis
- National Centre for Epidemiology and Population Health, Australian National University, Australia
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Environmental Pollution Analysis and Impact Study-A Case Study for the Salton Sea in California. ATMOSPHERE 2022. [DOI: 10.3390/atmos13060914] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
A natural experiment conducted on the shrinking Salton Sea, a saline lake in California, showed that each one foot drop in lake elevation resulted in a 2.6% average increase in PM2.5 concentrations. The shrinking has caused the asthma rate continues to increase among children, with one in five children being sent to the emergency department, which is related to asthma. In this paper, several data-driven machine learning (ML) models are developed for forecasting air quality and dust emission to study, evaluate and predict the impacts on human health due to the shrinkage of the sea, such as the Salton Sea. The paper presents an improved long short-term memory (LSTM) model to predict the hourly air quality (O3 and CO) based on air pollutants and weather data in the previous 5 h. According to our experiment results, the model generates a very good R2 score of 0.924 and 0.835 for O3 and CO, respectively. In addition, the paper proposes an ensemble model based on random forest (RF) and gradient boosting (GBoost) algorithms for forecasting hourly PM2.5 and PM10 using the air quality and weather data in the previous 5 h. Furthermore, the paper shares our research results for PM2.5 and PM10 prediction based on the proposed ensemble ML models using satellite remote sensing data. Daily PM2.5 and PM10 concentration maps in 2018 are created to display the regional air pollution density and severity. Finally, the paper reports Artificial Intelligence (AI) based research findings of measuring air pollution impact on asthma prevalence rate of local residents in the Salton Sea region. A stacked ensemble model based on support vector regression (SVR), elastic net regression (ENR), RF and GBoost is developed for asthma prediction with a good R2 score of 0.978.
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Razavi-Termeh SV, Sadeghi-Niaraki A, Choi SM. Coronavirus disease vulnerability map using a geographic information system (GIS) from 16 April to 16 May 2020. PHYSICS AND CHEMISTRY OF THE EARTH (2002) 2022; 126:103043. [PMID: 35637755 PMCID: PMC9133353 DOI: 10.1016/j.pce.2021.103043] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Revised: 04/05/2021] [Accepted: 05/29/2021] [Indexed: 06/15/2023]
Abstract
In recent months, the world has been affected by the infectious coronavirus disease and Iran is one of the most affected countries. The Iranian government's health facilities for an urgent investigation of all provinces do not exist simultaneously. There is no management tool to identify the vulnerabilities of Iranian provinces in prioritizing health services. The aim of this study was to prepare a coronavirus vulnerability map of Iranian provinces using geographic information system (GIS) to monitor the disease. For this purpose, four criteria affecting coronavirus, including population density, percentage of older people, temperature, and humidity, were prepared in the GIS. A multiscale geographically weighted regression (MGWR) model was used to determine the vulnerability of coronavirus in Iran. An adaptive neuro-fuzzy inference system (ANFIS) model was used to predict vulnerability in the next two months. Results indicated that, population density and older people have a more significant impact on coronavirus in Iran. Based on MGWR models, Tehran, Mazandaran, Gilan, and Alborz provinces were more vulnerable to coronavirus in February and March. The ANFIS model findings showed that West Azerbaijan, Zanjan, Fars, Yazd, Semnan, Sistan and Baluchistan, and Tehran provinces were more vulnerable in April and May.
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Affiliation(s)
- Seyed Vahid Razavi-Termeh
- Geoinformation Tech. Center of Excellence, Factulty of Geomatics, K.N. Toosi University of Technology, Tehran, Iran
| | - Abolghasem Sadeghi-Niaraki
- Geoinformation Tech. Center of Excellence, Factulty of Geomatics, K.N. Toosi University of Technology, Tehran, Iran
- Dept. of Computer Science and Engineering, and Convergence Engineering for Intelligent Drone, Sejong University, Seoul, Republic of Korea
| | - Soo-Mi Choi
- Dept. of Computer Science and Engineering, and Convergence Engineering for Intelligent Drone, Sejong University, Seoul, Republic of Korea
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Sousa AC, Pastorinho MR, Masjedi MR, Urrutia-Pereira M, Arrais M, Nunes E, To T, Ferreira AJ, Robalo-Cordeiro C, Borrego C, Teixeira JP, Taborda-Barata L. Issue 1 - "Update on adverse respiratory effects of outdoor air pollution" Part 2): Outdoor air pollution and respiratory diseases: Perspectives from Angola, Brazil, Canada, Iran, Mozambique and Portugal. Pulmonology 2022; 28:376-395. [PMID: 35568650 DOI: 10.1016/j.pulmoe.2021.12.007] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 12/12/2021] [Indexed: 12/01/2022] Open
Abstract
OBJECTIVE To analyse the GARD perspective on the health effects of outdoor air pollution, and to synthesise the Portuguese epidemiological contribution to knowledge on its respiratory impact. RESULTS Ambient air pollution has deleterious respiratory effects which are more apparent in larger, densely populated and industrialised countries, such as Canada, Iran, Brazil and Portugal, but it also affects people living in low-level exposure areas. While low- and middle-income countries (LMICs), are particularly affected, evidence based on epidemiological studies from LMICs is both limited and heterogeneous. While nationally, Portugal has a relatively low level of air pollution, many major cities face with substantial air pollution problems. Time series and cross-sectional epidemiological studies have suggested increased respiratory hospital admissions, and increased risk of respiratory diseases in people who live in urban areas and are exposed to even a relatively low level of air pollution. CONCLUSIONS Adverse respiratory effects due to air pollution, even at low levels, have been confirmed by epidemiological studies. However, evidence from LMICs is heterogeneous and relatively limited. Furthermore, longitudinal cohort studies designed to study and quantify the link between exposure to air pollutants and respiratory diseases are needed. Worldwide, an integrated approach must involve multi-level stakeholders including governments (in Portugal, the Portuguese Ministry of Health, which hosts GARD-Portugal), academia, health professionals, scientific societies, patient associations and the community at large. Such an approach not only will garner a robust commitment, establish strong advocacy and clear objectives, and raise greater awareness, it will also support a strategy with adequate measures to be implemented to achieve better air quality and reduce the burden of chronic respiratory diseases (CRDs).
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Affiliation(s)
- A C Sousa
- Comprehensive Health Research Centre (CHRC) and Department of Biology, University of Évora, Pólo da Mitra, Apartado 94, Évora 7002-554, Portugal; NuESA-Health and Environment Study Unit, Faculty of Health Sciences, University of Beira Interior, Avenida Infante D. Henrique, Covilhã 6200-506, Portugal
| | - M R Pastorinho
- NuESA-Health and Environment Study Unit, Faculty of Health Sciences, University of Beira Interior, Avenida Infante D. Henrique, Covilhã 6200-506, Portugal; Comprehensive Health Research Centre (CHRC), Department of Medical and Health Sciences, University of Évora, Colégio Luís António Verney, Rua Romão Ramalho, 59, Évora 7000-671, Portugal
| | - M R Masjedi
- Department of Pulmonary Medicine, Shahid Beheshti University of Medical Sciences, 7th Floor, Bldg n 2, SBUMS, Arabi Avenue, Daneshjoo Boulevard, Velenjak, Tehran 19839-63113, Iran
| | - M Urrutia-Pereira
- Universidade Federal do Pampa, BR 472 - Km 585, Caixa Postal 118, Uruguaiana (RS) CEP 97501-970, Brazil
| | - M Arrais
- Department of Pulmonology, Military Hospital, Rua 17 de Setembro, 27/29, Cidade Alta, Luanda, Angola; Centro de Investigação em Saúde de Angola - CISA, Caxito, Bengo, Angola
| | - E Nunes
- Department of Pulmonology, Central Hospital of Maputo, Agostinho Neto, 64, Maputo 1100, Mozambique; Faculty of Medicine, Eduardo Mondlane University, Avenida Dr. Salvador Allende, Caixa Postal 257, Maputo, Mozambique
| | - T To
- The Hospital for Sick Children, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario M5G 1 × 8, Canada
| | - A J Ferreira
- Department of Pulmonology, Centro Hospitalar Universitário de Coimbra, Praceta Prof. Mota Pinto, Coimbra 3004-561, Portugal; Faculty of Medicine, University of Coimbra, Azinhaga de Santa Comba, Celas, Coimbra 3000-548, Portugal
| | - C Robalo-Cordeiro
- Department of Pulmonology, Centro Hospitalar Universitário de Coimbra, Praceta Prof. Mota Pinto, Coimbra 3004-561, Portugal; Faculty of Medicine, University of Coimbra, Azinhaga de Santa Comba, Celas, Coimbra 3000-548, Portugal
| | - C Borrego
- CESAM & Department of Environment and Planning, University of Aveiro, Aveiro 3810-193, Portugal; IDAD - Instituto do Ambiente e Desenvolvimento, Campus Universitário de Santiago, Aveiro 3810-193, Portugal
| | - J P Teixeira
- EPIUnit - Instituto de Saúde Pública, University of Porto, Rua das Taipas, 135, Porto 4050-091, Portugal; Department of Environmental Health, Portuguese National Institute of Health, Rua Alexandre Herculano, 321, Porto 4000-055, Portugal
| | - L Taborda-Barata
- NuESA-Health and Environment Study Unit, Faculty of Health Sciences, University of Beira Interior, Avenida Infante D. Henrique, Covilhã 6200-506, Portugal; UBIAir-Clinical & Experimental Lung Centre, UBIMedical, University of Beira Interior, EM506 Covilhã 6200-000, Portugal; CICS-Health Sciences Research Centre, University of Beira Interior, Avenida Infante D. Henrique, Covilhã 6200-506, Portugal.
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15
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A Practical Model for the Evaluation of High School Student Performance Based on Machine Learning. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app112311534] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The objective of this research is to develop an machine learning (ML) -based system that evaluates the performance of high school students during the semester and identify the most significant factors affecting student performance. It also specifies how the performance of models is affected when models run on data that only include the most important features. Classifiers employed for the system include random forest (RF), support vector machines (SVM), logistic regression (LR) and artificial neural network (ANN) techniques. Moreover, the Boruta algorithm was used to calculate the importance of features. The dataset includes behavioral information, individual information and the scores of students that were collected from teachers and a one-by-one survey through an online questionnaire. As a result, the effective features of the database were identified, and the least important features were eliminated from the dataset. The ANN accuracy, which was the best accuracy in the original dataset, was reduced in the decreased dataset. On the contrary, SVM performance was improved, which had the highest accuracy among other models, with 0.78. Moreover, the LR and RF models could provide the same performance in the decreased dataset. The results showed that ML models are influential for evaluating students, and stakeholders can use the identified effective factors to improve education.
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Shogrkhodaei SZ, Razavi-Termeh SV, Fathnia A. Spatio-temporal modeling of PM 2.5 risk mapping using three machine learning algorithms. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2021; 289:117859. [PMID: 34340183 DOI: 10.1016/j.envpol.2021.117859] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Revised: 06/29/2021] [Accepted: 07/26/2021] [Indexed: 06/13/2023]
Abstract
Urban air pollution is one of the most critical issues that affect the environment, community health, economy, and management of urban areas. From a public health perspective, PM2.5 is one of the primary air pollutants, especially in Tehran's metropolis. Owing to the different patterns of PM2.5 in different seasons, Spatio-temporal modeling and identification of high-risk areas to reduce its effects seems necessary. The purpose of this study was Spatio-temporal modeling and preparation of PM2.5 risk mapping using three machine learning algorithms (random forest (RF), AdaBoost, and stochastic gradient descent (SGD)) in the metropolis of Tehran, Iran. Therefore, in the first step, to prepare the dependent variable data, the PM2.5 average was used for the four seasons of spring, summer, autumn, and winter. Then, using remote sensing (RS) and a geographic information system (GIS), independent data such as temperature, maximum temperature, minimum temperature, wind speed, rainfall, humidity, normalized difference vegetation index (NDVI), population density, street density, and distance to industrial centers were prepared as a seasonal average. To Spatio-temporal modeling using machine learning algorithms, 70% of the data were used for training and 30% for validation. The frequency ratio (FR) model was used as input to machine learning algorithms to calculate the spatial relationship between PM2.5 and the effective parameters. Finally, Spatio-temporal modeling and PM2.5 risk mapping were performed using three machine learning algorithms. The receiver operating characteristic (ROC) area under the curve (AUC) results showed that the RF algorithm had the greatest modeling accuracy, with values of 0.926, 0.94, 0.949, and 0.949 for spring, summer, autumn, and winter, respectively. According to the RF model, the most important variable in spring and autumn was NDVI. Temperature and distance to industrial centers were the most important variables in the summer and winter, respectively. The results showed that autumn, winter, summer, and spring had the highest risk of PM2.5, respectively.
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Affiliation(s)
| | - Seyed Vahid Razavi-Termeh
- Geoinformation Tech. Center of Excellence, Faculty of Geodesy and Geomatics Engineering, K.N. Toosi University of Technology, Tehran, 19697, Iran.
| | - Amanollah Fathnia
- Department of Geography, Faculty of Literature and Humanities, Razi University, Kermanshah, Iran.
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Shahsavar A, Jamei M, Karbasi M. Experimental evaluation and development of predictive models for rheological behavior of aqueous Fe3O4 ferrofluid in the presence of an external magnetic field by introducing a novel grid optimization based-Kernel ridge regression supported by sensitivity analysis. POWDER TECHNOL 2021. [DOI: 10.1016/j.powtec.2021.07.037] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
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18
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Razavi-Termeh SV, Sadeghi-Niaraki A, Farhangi F, Choi SM. COVID-19 Risk Mapping with Considering Socio-Economic Criteria Using Machine Learning Algorithms. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:9657. [PMID: 34574582 PMCID: PMC8471719 DOI: 10.3390/ijerph18189657] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 09/02/2021] [Accepted: 09/09/2021] [Indexed: 12/15/2022]
Abstract
The reduction of population concentration in some urban land uses is one way to prevent and reduce the spread of COVID-19 disease. Therefore, the objective of this study is to prepare the risk mapping of COVID-19 in Tehran, Iran, using machine learning algorithms according to socio-economic criteria of land use. Initially, a spatial database was created using 2282 locations of patients with COVID-19 from 2 February 2020 to 21 March 2020 and eight socio-economic land uses affecting the disease-public transport stations, supermarkets, banks, automated teller machines (ATMs), bakeries, pharmacies, fuel stations, and hospitals. The modeling was performed using three machine learning algorithms that included random forest (RF), adaptive neuro-fuzzy inference system (ANFIS), and logistic regression (LR). Feature selection was performed using the OneR method, and the correlation between land uses was obtained using the Pearson coefficient. We deployed 70% and 30% of COVID-19 patient locations for modeling and validation, respectively. The results of the receiver operating characteristic (ROC) curve and the area under the curve (AUC) showed that the RF algorithm, which had a value of 0.803, had the highest modeling accuracy, which was followed by the ANFIS algorithm with a value of 0.758 and the LR algorithm with a value of 0.747. The results showed that the central and the eastern regions of Tehran are more at risk. Public transportation stations and pharmacies were the most correlated with the location of COVID-19 patients in Tehran, according to the results of the OneR technique, RF, and LR algorithms. The results of the Pearson correlation showed that pharmacies and banks are the most incompatible in distribution, and the density of these land uses in Tehran has caused the prevalence of COVID-19.
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Affiliation(s)
- Seyed Vahid Razavi-Termeh
- Geoinformation Technology Center of Excellence, Faculty of Geodesy and Geomatics Engineering, K.N. Toosi University of Technology, Tehran 19697, Iran; (S.V.R.-T.); (F.F.)
| | - Abolghasem Sadeghi-Niaraki
- Geoinformation Technology Center of Excellence, Faculty of Geodesy and Geomatics Engineering, K.N. Toosi University of Technology, Tehran 19697, Iran; (S.V.R.-T.); (F.F.)
- Department of Computer Science and Engineering, and Convergence Engineering for Intelligent Drone, Sejong University, Seoul 143-747, Korea;
| | - Farbod Farhangi
- Geoinformation Technology Center of Excellence, Faculty of Geodesy and Geomatics Engineering, K.N. Toosi University of Technology, Tehran 19697, Iran; (S.V.R.-T.); (F.F.)
| | - Soo-Mi Choi
- Department of Computer Science and Engineering, and Convergence Engineering for Intelligent Drone, Sejong University, Seoul 143-747, Korea;
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Haque R, Ho SB, Chai I, Abdullah A. Optimised deep neural network model to predict asthma exacerbation based on personalised weather triggers. F1000Res 2021; 10:911. [PMID: 34745565 PMCID: PMC8543171 DOI: 10.12688/f1000research.73026.1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/01/2021] [Indexed: 11/20/2022] Open
Abstract
Background - Recently, there have been attempts to develop mHealth applications for asthma self-management. However, there is a lack of applications that can offer accurate predictions of asthma exacerbation using the weather triggers and demographic characteristics to give tailored response to users. This paper proposes an optimised Deep Neural Network Regression (DNNR) model to predict asthma exacerbation based on personalised weather triggers. Methods - With the aim of integrating weather, demography, and asthma tracking, an mHealth application was developed where users conduct the Asthma Control Test (ACT) to identify the chances of their asthma exacerbation. The asthma dataset consists of panel data from 10 users that includes 1010 ACT scores as the target output. Moreover, the dataset contains 10 input features which include five weather features (temperature, humidity, air-pressure, UV-index, wind-speed) and five demography features (age, gender, outdoor-job, outdoor-activities, location). Results - Using the DNNR model on the asthma dataset, a score of 0.83 was achieved with Mean Absolute Error (MAE)=1.44 and Mean Squared Error (MSE)=3.62. It was recognised that, for effective asthma self-management, the prediction errors must be in the acceptable loss range (error<0.5). Therefore, an optimisation process was proposed to reduce the error rates and increase the accuracy by applying standardisation and fragmented-grid-search. Consequently, the optimised-DNNR model (with 2 hidden-layers and 50 hidden-nodes) using the Adam optimiser achieved a 94% accuracy with MAE=0.20 and MSE=0.09. Conclusions - This study is the first of its kind that recognises the potentials of DNNR to identify the correlation patterns among asthma, weather, and demographic variables. The optimised-DNNR model provides predictions with a significantly higher accuracy rate than the existing predictive models and using less computing time. Thus, the optimisation process is useful to build an enhanced model that can be integrated into the asthma self-management for mHealth application.
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Affiliation(s)
- Radiah Haque
- Faculty of Computing and Informatics, Multimedia University, Cyberjaya, 63100, Malaysia
| | - Sin-Ban Ho
- Faculty of Computing and Informatics, Multimedia University, Cyberjaya, 63100, Malaysia
| | - Ian Chai
- Faculty of Computing and Informatics, Multimedia University, Cyberjaya, 63100, Malaysia
| | - Adina Abdullah
- Faculty of Medicine, University of Malaya, Kuala Lumpur, 50603, Malaysia
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Razavi-Termeh SV, Sadeghi-Niaraki A, Choi SM. Effects of air pollution in Spatio-temporal modeling of asthma-prone areas using a machine learning model. ENVIRONMENTAL RESEARCH 2021; 200:111344. [PMID: 34015292 DOI: 10.1016/j.envres.2021.111344] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Revised: 04/27/2021] [Accepted: 05/12/2021] [Indexed: 06/12/2023]
Abstract
Industrialization and increasing urbanization have led to increased air pollution, which has a devastating effect on public health and asthma. This study aimed to model the spatial-temporal of asthma in Tehran, Iran using a machine learning model. Initially, a spatial database was created consisting of 872 locations of asthma children and six air pollution parameters, including carbon monoxide (CO), particulate matter (PM10 and PM2.5), nitrogen dioxide (NO2), sulfur dioxide (SO2), and ozone (O3) in four-seasons (spring, summer, autumn, and winter). Spatial-temporal modeling and mapping of asthma-prone areas were performed using a random forest (RF) model. For Spatio-temporal modeling and assessment, 70% and 30% of the dataset were used, respectively. The Spearman correlation and RF model findings showed that during different seasons, the PM2.5 parameter had the most important effect on asthma occurrence in Tehran. The assessment of the Spatio-temporal modeling of asthma using the receiver operating characteristic (ROC)-area under the curve (AUC) showed an accuracy of 0.823, 0.821, 0.83, and 0.827, respectively for spring, summer, autumn, and winter. According to the results, asthma occurs more often in autumn than in other seasons.
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Affiliation(s)
- Seyed Vahid Razavi-Termeh
- Geoinformation Tech. Center of Excellence, Faculty of Geodesy and Geomatics Engineering, K.N. Toosi University of Technology, Tehran, 19697, Iran.
| | - Abolghasem Sadeghi-Niaraki
- Geoinformation Tech. Center of Excellence, Faculty of Geodesy and Geomatics Engineering, K.N. Toosi University of Technology, Tehran, 19697, Iran; Dept. of Computer Science and Engineering, and Convergence Engineering for Intelligent Drone, Sejong University, Seoul, Republic of Korea.
| | - Soo-Mi Choi
- Dept. of Computer Science and Engineering, and Convergence Engineering for Intelligent Drone, Sejong University, Seoul, Republic of Korea.
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Spatial Modeling of Asthma-Prone Areas Using Remote Sensing and Ensemble Machine Learning Algorithms. REMOTE SENSING 2021. [DOI: 10.3390/rs13163222] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
In this study, asthma-prone area modeling of Tehran, Iran was provided by employing three ensemble machine learning algorithms (Bootstrap aggregating (Bagging), Adaptive Boosting (AdaBoost), and Stacking). First, a spatial database was created with 872 locations of asthma patients and affecting factors (particulate matter (PM10 and PM2.5), ozone (O3), sulfur dioxide (SO2), carbon monoxide (CO), nitrogen dioxide (NO2), rainfall, wind speed, humidity, temperature, distance to street, traffic volume, and a normalized difference vegetation index (NDVI)). We created four factors using remote sensing (RS) imagery, including air pollution (O3, SO2, CO, and NO2), altitude, and NDVI. All criteria were prepared using a geographic information system (GIS). For modeling and validation, 70% and 30% of the data were used, respectively. The weight of evidence (WOE) model was used to assess the spatial relationship between the dependent and independent data. Finally, three ensemble algorithms were used to perform asthma-prone areas mapping. According to the Gini index, the most influential factors on asthma occurrence were distance to the street, NDVI, and traffic volume. The area under the curve (AUC) of receiver operating characteristic (ROC) values for the AdaBoost, Bagging, and Stacking algorithms was 0.849, 0.82, and 0.785, respectively. According to the findings, the AdaBoost algorithm outperforms the Bagging and Stacking algorithms in spatial modeling of asthma-prone areas.
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22
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Groundwater-Potential Mapping Using a Self-Learning Bayesian Network Model: A Comparison among Metaheuristic Algorithms. WATER 2021. [DOI: 10.3390/w13050658] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Owing to the reduction of surface-water resources and frequent droughts, the exploitation of groundwater resources has faced critical challenges. For optimal management of these valuable resources, careful studies of groundwater potential status are essential. The main goal of this study was to determine the optimal network structure of a Bayesian network (BayesNet) machine-learning model using three metaheuristic optimization algorithms—a genetic algorithm (GA), a simulated annealing (SA) algorithm, and a Tabu search (TS) algorithm—to prepare groundwater-potential maps. The methodology was applied to the town of Baghmalek in the Khuzestan province of Iran. For modeling, the location of 187 springs in the study area and 13 parameters (altitude, slope angle, slope aspect, plan curvature, profile curvature, topography wetness index (TWI), distance to river, distance to fault, drainage density, rainfall, land use/cover, lithology, and soil) affecting the potential of groundwater were provided. In addition, the statistical method of certainty factor (CF) was utilized to determine the input weight of the hybrid models. The results of the OneR technique showed that the parameters of altitude, lithology, and drainage density were more important for the potential of groundwater compared to the other parameters. The results of groundwater-potential mapping (GPM) employing the receiver operating characteristic (ROC) area under the curve (AUC) showed an estimation accuracy of 0.830, 0.818, 0.810, and 0.792, for the BayesNet-GA, BayesNet-SA, BayesNet-TS, and BayesNet models, respectively. The BayesNet-GA model improved the GPM estimation accuracy of the BayesNet-SA (4.6% and 7.5%) and BayesNet-TS (21.8% and 17.5%) models with respect to the root mean square error (RMSE) and mean absolute error (MAE), respectively. Based on metric indices, the GA provides a higher capability than the SA and TS algorithms for optimizing the BayesNet model in determining the GPM.
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