Predictive models for personalized asthma attacks based on patient’s biosignals and environmental factors: a systematic review
Abstract 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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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BMC
2021-12-01
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| Series: | BMC Medical Informatics and Decision Making |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12911-021-01704-6 |
| _version_ | 1818388649209757696 |
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| author | Eman T. Alharbi Farrukh Nadeem Asma Cherif |
| author_facet | Eman T. Alharbi Farrukh Nadeem Asma Cherif |
| author_sort | Eman T. Alharbi |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-12-14T04:29:11Z |
| format | Article |
| id | doaj.art-bb001d92287948eab8f474a9d02951ce |
| institution | Directory Open Access Journal |
| issn | 1472-6947 |
| language | English |
| last_indexed | 2024-12-14T04:29:11Z |
| publishDate | 2021-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj.art-bb001d92287948eab8f474a9d02951ce2022-12-21T23:17:08ZengBMCBMC Medical Informatics and Decision Making1472-69472021-12-0121111310.1186/s12911-021-01704-6Predictive models for personalized asthma attacks based on patient’s biosignals and environmental factors: a systematic reviewEman T. Alharbi0Farrukh Nadeem1Asma Cherif2Department of Information Systems, King Abdulaziz UniversityDepartment of Information Systems, King Abdulaziz UniversityDepartment of Information Technology, King Abdulaziz UniversityAbstract 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.https://doi.org/10.1186/s12911-021-01704-6PredictionMachine learningAsthma attackBiosignalsEnvironmental factor |
| spellingShingle | Eman T. Alharbi Farrukh Nadeem Asma Cherif Predictive models for personalized asthma attacks based on patient’s biosignals and environmental factors: a systematic review BMC Medical Informatics and Decision Making Prediction Machine learning Asthma attack Biosignals Environmental factor |
| title | Predictive models for personalized asthma attacks based on patient’s biosignals and environmental factors: a systematic review |
| title_full | Predictive models for personalized asthma attacks based on patient’s biosignals and environmental factors: a systematic review |
| title_fullStr | Predictive models for personalized asthma attacks based on patient’s biosignals and environmental factors: a systematic review |
| title_full_unstemmed | Predictive models for personalized asthma attacks based on patient’s biosignals and environmental factors: a systematic review |
| title_short | Predictive models for personalized asthma attacks based on patient’s biosignals and environmental factors: a systematic review |
| title_sort | predictive models for personalized asthma attacks based on patient s biosignals and environmental factors a systematic review |
| topic | Prediction Machine learning Asthma attack Biosignals Environmental factor |
| url | https://doi.org/10.1186/s12911-021-01704-6 |
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