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| Κύριοι συγγραφείς: | , , , |
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| Μορφή: | Recurso digital |
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Zenodo
2014
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| Διαθέσιμο Online: | https://doi.org/10.3233/BME-141212 |
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| _version_ | 1866901448237776896 |
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| author | FERNANDEZ-GRANERO, MIGUEL ANGEL Sanchez-Morillo, Daniel Leon-Jimenez, Antonio Crespo, Felipe |
| author_facet | FERNANDEZ-GRANERO, MIGUEL ANGEL Sanchez-Morillo, Daniel Leon-Jimenez, Antonio Crespo, Felipe |
| contents | <p>Chronic Obstructive Pulmonary Disease (COPD) is a progressive disease of the lung with a great prevalence and a remarkable socio-economic impact on patients and health systems. Early detection of exacerbations could diminish the adverse effects on patients' health and cut down costs burdened on patients with COPD. A group of 16 patients were telemonitored at home using a novel electronic daily symptoms questionnaire during a 6-months field trial. Recorded data were used to train and validate a Probabilistic Neural Network (PNN) classifier in order to enable the automatic prediction of exacerbations. The proposed system was able to predict COPD exacerbations early with a margin of 4.8±1.8 days (average ± SD). Detection accuracy was 80.5% (33 out of 41 exacerbations were early detected); 78.8% (26 out of 33) of theses detected events were reported exacerbation and 87.5% (7 out of 8) were unreported episodes. The proposed questionnaire and the designed automatic classifier could support the early detection of COPD exacerbations of benefit to both physicians and patients.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_3233_BME-141212 |
| institution | Zenodo |
| language | |
| publishDate | 2014 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Automatic prediction of chronic obstructive pulmonary disease exacerbations through home telemonitoring of symptoms FERNANDEZ-GRANERO, MIGUEL ANGEL Sanchez-Morillo, Daniel Leon-Jimenez, Antonio Crespo, Felipe <p>Chronic Obstructive Pulmonary Disease (COPD) is a progressive disease of the lung with a great prevalence and a remarkable socio-economic impact on patients and health systems. Early detection of exacerbations could diminish the adverse effects on patients' health and cut down costs burdened on patients with COPD. A group of 16 patients were telemonitored at home using a novel electronic daily symptoms questionnaire during a 6-months field trial. Recorded data were used to train and validate a Probabilistic Neural Network (PNN) classifier in order to enable the automatic prediction of exacerbations. The proposed system was able to predict COPD exacerbations early with a margin of 4.8±1.8 days (average ± SD). Detection accuracy was 80.5% (33 out of 41 exacerbations were early detected); 78.8% (26 out of 33) of theses detected events were reported exacerbation and 87.5% (7 out of 8) were unreported episodes. The proposed questionnaire and the designed automatic classifier could support the early detection of COPD exacerbations of benefit to both physicians and patients.</p> |
| title | Automatic prediction of chronic obstructive pulmonary disease exacerbations through home telemonitoring of symptoms |
| url | https://doi.org/10.3233/BME-141212 |