Symptom Tracking and Experimentation Platform for Covid-19 or Similar Infections
Remote symptom tracking is critical for the prevention of Covid-19 spread. The qualified medical staff working in the call centers of primary health care units have to take critical decisions often based on vague information about the patient condition. The congestion and the medical protocols that...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-02-01
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| Series: | Computers |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-431X/10/2/22 |
| _version_ | 1827602309583994880 |
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| author | Nikos Petrellis George K. Adam |
| author_facet | Nikos Petrellis George K. Adam |
| author_sort | Nikos Petrellis |
| collection | DOAJ |
| description | Remote symptom tracking is critical for the prevention of Covid-19 spread. The qualified medical staff working in the call centers of primary health care units have to take critical decisions often based on vague information about the patient condition. The congestion and the medical protocols that are constantly changing often lead to incorrect decisions. The proposed platform allows the remote assessment of symptoms and can be useful for patients, health institutes and researchers. It consists of mobile desktop applications and medical sensors connected to cloud infrastructure. The unique features offered by the proposed solution are: (a) dynamic adaptation of Medical Protocols (MP) is supported (for the definition of alert rules, sensor sampling strategy and questionnaire structure) covering different medical cases (pre- or post-hospitalization, vulnerable population, etc.), (b) anonymous medical data can be statistically processed in the context of the research about an infection such as Covid-19, (c) reliable diagnosis is supported since several factors are taken into consideration, (d) the platform can be used to drastically reduce the congestion in various healthcare units. For the demonstration of (b), new classification methods based on similarity metrics have been tested for cough sound classification with an accuracy in the order of 90%. |
| first_indexed | 2024-03-09T05:14:27Z |
| format | Article |
| id | doaj.art-e2a438036a7b44358d4a5340bc203982 |
| institution | Directory Open Access Journal |
| issn | 2073-431X |
| language | English |
| last_indexed | 2024-03-09T05:14:27Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computers |
| spelling | doaj.art-e2a438036a7b44358d4a5340bc2039822023-12-03T12:46:53ZengMDPI AGComputers2073-431X2021-02-011022210.3390/computers10020022Symptom Tracking and Experimentation Platform for Covid-19 or Similar InfectionsNikos Petrellis0George K. Adam1Department of Electrical and Computer Engineering, University of Peloponnese, 26334 Patra, GreeceDepartment of Digital Systems, University of Thessaly, 41500 Larisa, GreeceRemote symptom tracking is critical for the prevention of Covid-19 spread. The qualified medical staff working in the call centers of primary health care units have to take critical decisions often based on vague information about the patient condition. The congestion and the medical protocols that are constantly changing often lead to incorrect decisions. The proposed platform allows the remote assessment of symptoms and can be useful for patients, health institutes and researchers. It consists of mobile desktop applications and medical sensors connected to cloud infrastructure. The unique features offered by the proposed solution are: (a) dynamic adaptation of Medical Protocols (MP) is supported (for the definition of alert rules, sensor sampling strategy and questionnaire structure) covering different medical cases (pre- or post-hospitalization, vulnerable population, etc.), (b) anonymous medical data can be statistically processed in the context of the research about an infection such as Covid-19, (c) reliable diagnosis is supported since several factors are taken into consideration, (d) the platform can be used to drastically reduce the congestion in various healthcare units. For the demonstration of (b), new classification methods based on similarity metrics have been tested for cough sound classification with an accuracy in the order of 90%.https://www.mdpi.com/2073-431X/10/2/22symptom trackingmobile appcloudclassificationsimilaritysound processing |
| spellingShingle | Nikos Petrellis George K. Adam Symptom Tracking and Experimentation Platform for Covid-19 or Similar Infections Computers symptom tracking mobile app cloud classification similarity sound processing |
| title | Symptom Tracking and Experimentation Platform for Covid-19 or Similar Infections |
| title_full | Symptom Tracking and Experimentation Platform for Covid-19 or Similar Infections |
| title_fullStr | Symptom Tracking and Experimentation Platform for Covid-19 or Similar Infections |
| title_full_unstemmed | Symptom Tracking and Experimentation Platform for Covid-19 or Similar Infections |
| title_short | Symptom Tracking and Experimentation Platform for Covid-19 or Similar Infections |
| title_sort | symptom tracking and experimentation platform for covid 19 or similar infections |
| topic | symptom tracking mobile app cloud classification similarity sound processing |
| url | https://www.mdpi.com/2073-431X/10/2/22 |
| work_keys_str_mv | AT nikospetrellis symptomtrackingandexperimentationplatformforcovid19orsimilarinfections AT georgekadam symptomtrackingandexperimentationplatformforcovid19orsimilarinfections |