Influenza Screening via Deep Learning Using a Combination of Epidemiological and Patient-Generated Health Data: Development and Validation Study
BackgroundScreening for influenza in primary care is challenging due to the low sensitivity of rapid antigen tests and the lack of proper screening tests. ObjectiveThe aim of this study was to develop a machine learning–based screening tool using patient-generated health data (PGHD) obta...
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| Format: | Article |
| Language: | English |
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JMIR Publications
2020-10-01
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| Series: | Journal of Medical Internet Research |
| Online Access: | http://www.jmir.org/2020/10/e21369/ |
| _version_ | 1819241970584780800 |
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| author | Choo, Hyunwoo Kim, Myeongchan Choi, Jiyun Shin, Jaewon Shin, Soo-Yong |
| author_facet | Choo, Hyunwoo Kim, Myeongchan Choi, Jiyun Shin, Jaewon Shin, Soo-Yong |
| author_sort | Choo, Hyunwoo |
| collection | DOAJ |
| description | BackgroundScreening for influenza in primary care is challenging due to the low sensitivity of rapid antigen tests and the lack of proper screening tests.
ObjectiveThe aim of this study was to develop a machine learning–based screening tool using patient-generated health data (PGHD) obtained from a mobile health (mHealth) app.
MethodsWe trained a deep learning model based on a gated recurrent unit to screen influenza using PGHD, including each patient’s fever pattern and drug administration records. We used meteorological data and app-based surveillance of the weekly number of patients with influenza. We defined a single episode as the set of consecutive days, including the day the user was diagnosed with influenza or another disease. Any record a user entered 24 hours after his or her last record was considered to be the start of a new episode. Each episode contained data on the user’s age, gender, weight, and at least one body temperature record. The total number of episodes was 6657. Of these, there were 3326 episodes within which influenza was diagnosed. We divided these episodes into 80% training sets (2664/3330) and 20% test sets (666/3330). A 5-fold cross-validation was used on the training set.
ResultsWe achieved reliable performance with an accuracy of 82%, a sensitivity of 84%, and a specificity of 80% in the test set. After the effect of each input variable was evaluated, app-based surveillance was observed to be the most influential variable. The correlation between the duration of input data and performance was not statistically significant (P=.09).
ConclusionsThese findings suggest that PGHD from an mHealth app could be a complementary tool for influenza screening. In addition, PGHD, along with traditional clinical data, could be used to improve health conditions. |
| first_indexed | 2024-12-23T14:32:22Z |
| format | Article |
| id | doaj.art-948db09610a745c2bf219899ca979215 |
| institution | Directory Open Access Journal |
| issn | 1438-8871 |
| language | English |
| last_indexed | 2024-12-23T14:32:22Z |
| publishDate | 2020-10-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | Journal of Medical Internet Research |
| spelling | doaj.art-948db09610a745c2bf219899ca9792152022-12-21T17:43:27ZengJMIR PublicationsJournal of Medical Internet Research1438-88712020-10-012210e2136910.2196/21369Influenza Screening via Deep Learning Using a Combination of Epidemiological and Patient-Generated Health Data: Development and Validation StudyChoo, HyunwooKim, MyeongchanChoi, JiyunShin, JaewonShin, Soo-YongBackgroundScreening for influenza in primary care is challenging due to the low sensitivity of rapid antigen tests and the lack of proper screening tests. ObjectiveThe aim of this study was to develop a machine learning–based screening tool using patient-generated health data (PGHD) obtained from a mobile health (mHealth) app. MethodsWe trained a deep learning model based on a gated recurrent unit to screen influenza using PGHD, including each patient’s fever pattern and drug administration records. We used meteorological data and app-based surveillance of the weekly number of patients with influenza. We defined a single episode as the set of consecutive days, including the day the user was diagnosed with influenza or another disease. Any record a user entered 24 hours after his or her last record was considered to be the start of a new episode. Each episode contained data on the user’s age, gender, weight, and at least one body temperature record. The total number of episodes was 6657. Of these, there were 3326 episodes within which influenza was diagnosed. We divided these episodes into 80% training sets (2664/3330) and 20% test sets (666/3330). A 5-fold cross-validation was used on the training set. ResultsWe achieved reliable performance with an accuracy of 82%, a sensitivity of 84%, and a specificity of 80% in the test set. After the effect of each input variable was evaluated, app-based surveillance was observed to be the most influential variable. The correlation between the duration of input data and performance was not statistically significant (P=.09). ConclusionsThese findings suggest that PGHD from an mHealth app could be a complementary tool for influenza screening. In addition, PGHD, along with traditional clinical data, could be used to improve health conditions.http://www.jmir.org/2020/10/e21369/ |
| spellingShingle | Choo, Hyunwoo Kim, Myeongchan Choi, Jiyun Shin, Jaewon Shin, Soo-Yong Influenza Screening via Deep Learning Using a Combination of Epidemiological and Patient-Generated Health Data: Development and Validation Study Journal of Medical Internet Research |
| title | Influenza Screening via Deep Learning Using a Combination of Epidemiological and Patient-Generated Health Data: Development and Validation Study |
| title_full | Influenza Screening via Deep Learning Using a Combination of Epidemiological and Patient-Generated Health Data: Development and Validation Study |
| title_fullStr | Influenza Screening via Deep Learning Using a Combination of Epidemiological and Patient-Generated Health Data: Development and Validation Study |
| title_full_unstemmed | Influenza Screening via Deep Learning Using a Combination of Epidemiological and Patient-Generated Health Data: Development and Validation Study |
| title_short | Influenza Screening via Deep Learning Using a Combination of Epidemiological and Patient-Generated Health Data: Development and Validation Study |
| title_sort | influenza screening via deep learning using a combination of epidemiological and patient generated health data development and validation study |
| url | http://www.jmir.org/2020/10/e21369/ |
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