Prediction of the number of asthma patients using environmental factors based on deep learning algorithms
Abstract Background Air pollution, weather, pollen, and influenza are typical aggravating factors for asthma. Previous studies have identified risk factors using regression-based and ensemble models. However, studies that consider complex relationships and interactions among these factors have yet t...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-12-01
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| Series: | Respiratory Research |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12931-023-02616-x |
| _version_ | 1827603007727992832 |
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| author | Hyemin Hwang Jae-Hyuk Jang Eunyoung Lee Hae-Sim Park Jae Young Lee |
| author_facet | Hyemin Hwang Jae-Hyuk Jang Eunyoung Lee Hae-Sim Park Jae Young Lee |
| author_sort | Hyemin Hwang |
| collection | DOAJ |
| description | Abstract Background Air pollution, weather, pollen, and influenza are typical aggravating factors for asthma. Previous studies have identified risk factors using regression-based and ensemble models. However, studies that consider complex relationships and interactions among these factors have yet to be conducted. Although deep learning algorithms can address this problem, further research on modeling and interpreting the results is warranted. Methods In this study, from 2015 to 2019, information about air pollutants, weather conditions, pollen, and influenza were utilized to predict the number of emergency room patients and outpatients with asthma using recurrent neural network, long short-term memory (LSTM), and gated recurrent unit models. The relative importance of the environmental factors in asthma exacerbation was quantified through a feature importance analysis. Results We found that LSTM was the best algorithm for modeling patients with asthma. Our results demonstrated that influenza, temperature, PM10, NO2, CO, and pollen had a significant impact on asthma exacerbation. In addition, the week of the year and the number of holidays per week were an important factor to model the seasonality of the number of asthma patients and the effect of holiday clinic closures, respectively. Conclusion LSTM is an excellent algorithm for modeling complex epidemiological relationships, encompassing nonlinearity, lagged responses, and interactions. Our study findings can guide policymakers in their efforts to understand the environmental factors of asthma exacerbation. |
| first_indexed | 2024-03-09T05:29:39Z |
| format | Article |
| id | doaj.art-c531af9fdccd48018a2ce5ec472acd0a |
| institution | Directory Open Access Journal |
| issn | 1465-993X |
| language | English |
| last_indexed | 2024-03-09T05:29:39Z |
| publishDate | 2023-12-01 |
| publisher | BMC |
| record_format | Article |
| series | Respiratory Research |
| spelling | doaj.art-c531af9fdccd48018a2ce5ec472acd0a2023-12-03T12:33:40ZengBMCRespiratory Research1465-993X2023-12-012411910.1186/s12931-023-02616-xPrediction of the number of asthma patients using environmental factors based on deep learning algorithmsHyemin Hwang0Jae-Hyuk Jang1Eunyoung Lee2Hae-Sim Park3Jae Young Lee4Environmental Engineering Department, Ajou UniversityDepartment of Allergy and Clinical Immunology, Ajou University School of MedicineDepartment of Neurology, McGovern Medical School, The University of Texas Health Science Center at HoustonDepartment of Allergy and Clinical Immunology, Ajou University School of MedicineEnvironmental and Safety Engineering Department, Ajou UniversityAbstract Background Air pollution, weather, pollen, and influenza are typical aggravating factors for asthma. Previous studies have identified risk factors using regression-based and ensemble models. However, studies that consider complex relationships and interactions among these factors have yet to be conducted. Although deep learning algorithms can address this problem, further research on modeling and interpreting the results is warranted. Methods In this study, from 2015 to 2019, information about air pollutants, weather conditions, pollen, and influenza were utilized to predict the number of emergency room patients and outpatients with asthma using recurrent neural network, long short-term memory (LSTM), and gated recurrent unit models. The relative importance of the environmental factors in asthma exacerbation was quantified through a feature importance analysis. Results We found that LSTM was the best algorithm for modeling patients with asthma. Our results demonstrated that influenza, temperature, PM10, NO2, CO, and pollen had a significant impact on asthma exacerbation. In addition, the week of the year and the number of holidays per week were an important factor to model the seasonality of the number of asthma patients and the effect of holiday clinic closures, respectively. Conclusion LSTM is an excellent algorithm for modeling complex epidemiological relationships, encompassing nonlinearity, lagged responses, and interactions. Our study findings can guide policymakers in their efforts to understand the environmental factors of asthma exacerbation.https://doi.org/10.1186/s12931-023-02616-xRecurrent neural networkLong short-term memoryGated recurrent unitAir pollutionAsthmaInfluenza |
| spellingShingle | Hyemin Hwang Jae-Hyuk Jang Eunyoung Lee Hae-Sim Park Jae Young Lee Prediction of the number of asthma patients using environmental factors based on deep learning algorithms Respiratory Research Recurrent neural network Long short-term memory Gated recurrent unit Air pollution Asthma Influenza |
| title | Prediction of the number of asthma patients using environmental factors based on deep learning algorithms |
| title_full | Prediction of the number of asthma patients using environmental factors based on deep learning algorithms |
| title_fullStr | Prediction of the number of asthma patients using environmental factors based on deep learning algorithms |
| title_full_unstemmed | Prediction of the number of asthma patients using environmental factors based on deep learning algorithms |
| title_short | Prediction of the number of asthma patients using environmental factors based on deep learning algorithms |
| title_sort | prediction of the number of asthma patients using environmental factors based on deep learning algorithms |
| topic | Recurrent neural network Long short-term memory Gated recurrent unit Air pollution Asthma Influenza |
| url | https://doi.org/10.1186/s12931-023-02616-x |
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