A Personalized Respiratory Disease Exacerbation Prediction Technique Based on a Novel Spatio-Temporal Machine Learning Architecture and Local Environmental Sensor Networks
Chronic respiratory diseases, such as the Chronic Obstructive Pulmonary Disease (COPD) and asthma, are a serious health crisis, affecting a large number of people globally and inflicting major costs on the economy. Current methods for assessing the progression of respiratory symptoms are either subj...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-08-01
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| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/11/16/2562 |
| _version_ | 1797410594235613184 |
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| author | Rohan T. Bhowmik Sam P. Most |
| author_facet | Rohan T. Bhowmik Sam P. Most |
| author_sort | Rohan T. Bhowmik |
| collection | DOAJ |
| description | Chronic respiratory diseases, such as the Chronic Obstructive Pulmonary Disease (COPD) and asthma, are a serious health crisis, affecting a large number of people globally and inflicting major costs on the economy. Current methods for assessing the progression of respiratory symptoms are either subjective and inaccurate, or complex and cumbersome, and do not incorporate environmental factors to track individualized risks. Lacking predictive assessments and early intervention, unexpected exacerbations often lead to hospitalizations and high medical costs. This work presents a multi-modal solution for predicting the exacerbation risks of respiratory diseases, such as COPD, based on a novel spatio-temporal machine learning architecture for real-time and accurate respiratory events detection, and tracking of local environmental and meteorological data and trends. The proposed new neural network model blends key attributes of both convolutional and recurrent neural architectures, allowing extraction of the salient spatial and temporal features encoded in respiratory sounds, thereby leading to accurate classification and tracking of symptoms. Combined with the data from environmental and meteorological sensors, and a predictive model based on retrospective medical studies, this solution can assess and provide early warnings of respiratory disease exacerbations, thereby potentially reducing hospitalization rates and medical costs. |
| first_indexed | 2024-03-09T04:32:28Z |
| format | Article |
| id | doaj.art-ee31239c44224c91a0a6ebb55255f144 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T04:32:28Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-ee31239c44224c91a0a6ebb55255f1442023-12-03T13:34:26ZengMDPI AGElectronics2079-92922022-08-011116256210.3390/electronics11162562A Personalized Respiratory Disease Exacerbation Prediction Technique Based on a Novel Spatio-Temporal Machine Learning Architecture and Local Environmental Sensor NetworksRohan T. Bhowmik0Sam P. Most1School of Medicine, Stanford University, Stanford, CA 94305, USASchool of Medicine, Stanford University, Stanford, CA 94305, USAChronic respiratory diseases, such as the Chronic Obstructive Pulmonary Disease (COPD) and asthma, are a serious health crisis, affecting a large number of people globally and inflicting major costs on the economy. Current methods for assessing the progression of respiratory symptoms are either subjective and inaccurate, or complex and cumbersome, and do not incorporate environmental factors to track individualized risks. Lacking predictive assessments and early intervention, unexpected exacerbations often lead to hospitalizations and high medical costs. This work presents a multi-modal solution for predicting the exacerbation risks of respiratory diseases, such as COPD, based on a novel spatio-temporal machine learning architecture for real-time and accurate respiratory events detection, and tracking of local environmental and meteorological data and trends. The proposed new neural network model blends key attributes of both convolutional and recurrent neural architectures, allowing extraction of the salient spatial and temporal features encoded in respiratory sounds, thereby leading to accurate classification and tracking of symptoms. Combined with the data from environmental and meteorological sensors, and a predictive model based on retrospective medical studies, this solution can assess and provide early warnings of respiratory disease exacerbations, thereby potentially reducing hospitalization rates and medical costs.https://www.mdpi.com/2079-9292/11/16/2562artificial intelligencerespiratory exacerbationmulti-modalsensor networkpersonalized medicine |
| spellingShingle | Rohan T. Bhowmik Sam P. Most A Personalized Respiratory Disease Exacerbation Prediction Technique Based on a Novel Spatio-Temporal Machine Learning Architecture and Local Environmental Sensor Networks Electronics artificial intelligence respiratory exacerbation multi-modal sensor network personalized medicine |
| title | A Personalized Respiratory Disease Exacerbation Prediction Technique Based on a Novel Spatio-Temporal Machine Learning Architecture and Local Environmental Sensor Networks |
| title_full | A Personalized Respiratory Disease Exacerbation Prediction Technique Based on a Novel Spatio-Temporal Machine Learning Architecture and Local Environmental Sensor Networks |
| title_fullStr | A Personalized Respiratory Disease Exacerbation Prediction Technique Based on a Novel Spatio-Temporal Machine Learning Architecture and Local Environmental Sensor Networks |
| title_full_unstemmed | A Personalized Respiratory Disease Exacerbation Prediction Technique Based on a Novel Spatio-Temporal Machine Learning Architecture and Local Environmental Sensor Networks |
| title_short | A Personalized Respiratory Disease Exacerbation Prediction Technique Based on a Novel Spatio-Temporal Machine Learning Architecture and Local Environmental Sensor Networks |
| title_sort | personalized respiratory disease exacerbation prediction technique based on a novel spatio temporal machine learning architecture and local environmental sensor networks |
| topic | artificial intelligence respiratory exacerbation multi-modal sensor network personalized medicine |
| url | https://www.mdpi.com/2079-9292/11/16/2562 |
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