A ubiquitous asthma monitoring framework based on ambient air pollutants and individuals’ contexts

Charandabi, Neda Kaffash; Alesheikh, Ali Asghar; Sharif, Mohammad

doi:10.1007/s11356-019-04185-3

Cited by 24 publications

(29 citation statements)

References 44 publications

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“…The second research articles group includes studies that use environmental data only to build their prediction models [20,21,22,23,24]. Finally, the third group includes the studies that used both biosignals and environmental data in their models [25,26,27,28,29,30].…”

Section: Search Resultsmentioning

confidence: 99%

“…participants. However, having a limited number of participants in a prediction study, such as [26,28], restricts the circumstances of the study and leads to unreliable results. Some studies used a large population size [21,22,23] and acquired their data from national databases; therefore, some participants were excluded due to uncompleted records.…”

Section: Population Characteristicsmentioning

confidence: 99%

“…Most of the models were built using home-based telemonitoring technology, which was increasingly used recently to monitor and control different diseases, including asthma. This method was used in [16,17,25,28,30], where the patients record their symptoms daily and submit them through the application interface. This acquisition method relies on the patients' ability to use technology and increases the burden on them.…”

Section: Participant's Datasetsmentioning

confidence: 99%

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Predictive Models for Personalized Asthma Attacks Based on Patient’s Biosignals and Environmental Factors: A Systematic Review

Alharbi¹,

Nadeem²,

Cherif³

2021

Preprint

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Background: Asthma is a chronic disease that exacerbates due to various risk factors, including the patient's biosignals and environmental conditions. It is affecting on average 7% of the world population. Preventing an asthma attack is the main challenge for asthma patients, which requires keeping track of any risk factor that can cause a seizure. Many researchers developed asthma attacks prediction models that used various asthma biosignals and environmental factors. These predictive models can help asthmatic patients predict asthma attacks in advance, and thus preventive measures can be taken. This paper introduces a review of these models to evaluate the used methods, model's performance, and determine the need to improve research in this field.Method: A systematic review was conducted for the research articles introducing asthma attack prediction models for children and adults. We searched the PubMed, ScienceDirect, Springer, and IEEE databases from January 2000 to December 2020. The search includes the prediction models that used biosignal, environmental, and both risk factors. The research article's quality was assessed and scored based on two checklists, the Checklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS) and the Critical Appraisal Skills Programme clinical prediction rule checklist (CASP). The highest scored articles were selected to review.Result: From 1068 research articles we reviewed, we found that most of the studies used asthma biosignal factors only for prediction, few of the studies used environmental factors, and limited studies used both of these factors. Fifteen different asthma attack predictive models were selected for this review. we found that most of the studies used traditional prediction methods, like Support Vector Machine and regression. We have identified the pros and cons of the reviewed asthma attack prediction models and propose solutions to advance the studies in this field.Conclusion: Asthma attack predictive models become more significant when using both patient's biosignal and environmental factors. There is a lack of utilizing advanced machine learning methods, like deep learning techniques. Besides, there is a need to build smart healthcare systems that provide patients with decision-making systems to identify risk and visualize high-risk regions.

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Section: Search Resultsmentioning

confidence: 99%

Section: Population Characteristicsmentioning

confidence: 99%

Section: Participant's Datasetsmentioning

confidence: 99%

See 1 more Smart Citation

Predictive Models for Personalized Asthma Attacks Based on Patient’s Biosignals and Environmental Factors: A Systematic Review

Alharbi¹,

Nadeem²,

Cherif³

2021

Preprint

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“…Identifying the spatially and temporally significant high-risk areas of the diseases can be applied as an effective tool for health policymakers to prevent, predict, and control them (10,11). Geospatial Information Systems (GIS) can provide potential solutions to understand better the high aggregated cases (clusters) throughout space and time, which is of requirements of health authorities (12)(13)(14)(15)(16)(17).…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Spatiotemporal Variability of Zoonotic Cutaneous Leishmaniasis Based on Sociodemographic Heterogeneity. The Case of Northeastern Iran, 2011–2016

Tabasi

Alesheikh

2021

Jpn J Infect Dis

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Zoonotic Cutaneous Leishmaniasis (ZCL) is one of the most prevalent zoonoses in Iran, especially in its central and northeast parts. This research aims to examine if there are spatiotemporal clusters of the ZCL cases, and if so, whether there are disparities in clustering according to age, gender, home situation, and occupation. The spatial analysis, including global and local spatial autocorrelations, inverse distance weighting, and space-time scan statistics were applied to determine potential clusters in Golestan villages during 2011-2016. Several spatially significant (p < 0.05) clusters were observed in the north and the northeastern regions, where most of them persisted for the last years of the study period. Children (0-10 years) living in rural settings were more likely to have the infection than those living in other areas. Despite the focus of the disease in the northern regions, housekeepers, females, and patients aged 21-30 and 41-50 years were found to be the high-risk groups in the southern areas. The seasonal pattern indicates that the outbreak mainly begins in late summer, peaks in October, and diminishes in December. By exploring spatiotemporal variations of ZCL by sociodemographic information, this study can identify priority areas for health decision-makers and resource allocation.

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“…Both PSSP and TSSP models assume that the distance between the target trajectory and the trajectory most similar to it remains constant over the prediction duration. However, vessel movement is affected by external contexts, such as wind, waves and sea currents (Kawan et al, 2017;Sharif and Alesheikh, 2018;Kaffash-Charandabi et al, 2019). In addition, the maritime navigation environment is a complex system that varies over time (Tang et al, 2019).…”

Section: Point-based Similarity Search Prediction (Pssp)mentioning

confidence: 99%

Vessel Trajectory Prediction Using Historical Automatic Identification System Data

2020

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For maritime safety and security, vessels should be able to predict the trajectories of nearby vessels to avoid collision. This research proposes three novel models based on similarity search of trajectories that predict vessels' trajectories in the short and long term. The first and second prediction models are, respectively, point-based and trajectory-based models that consider constant distances between target and sample trajectories. The third prediction model is a trajectory-based model that exploits a long short-term memory approach to measure the dynamic distance between target and sample trajectories. To evaluate the performance of the proposed models, they are applied to a real automatic identification system (AIS) vessel dataset in the Strait of Georgia, USA. The models' accuracies in terms of Haversine distance between the predicted and actual positions show relative prediction error reductions of 40·85% for the second model compared with the first model and 23% for the third model compared with the second model.

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A ubiquitous asthma monitoring framework based on ambient air pollutants and individuals’ contexts

Cited by 24 publications

References 44 publications

Predictive Models for Personalized Asthma Attacks Based on Patient’s Biosignals and Environmental Factors: A Systematic Review

Predictive Models for Personalized Asthma Attacks Based on Patient’s Biosignals and Environmental Factors: A Systematic Review

Spatiotemporal Variability of Zoonotic Cutaneous Leishmaniasis Based on Sociodemographic Heterogeneity. The Case of Northeastern Iran, 2011–2016

Vessel Trajectory Prediction Using Historical Automatic Identification System Data

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