JMIR Publications

ABSTRACT

Background:

Screening for influenza in primary care is challenging due to the low sensitivity of rapid antigen tests and the lack of proper screening tests.

Objective:

We developed a machine learning-based screening tool using patient-generated health data (PGHD) obtained from a mobile health application (mHealth app).

Methods:

: We trained a deep learning model based on a gated recurrent unit to screen influenza using the PGHD, including each patient’s fever pattern and drug administration records. We also used meteorological data and app-based surveillance of the number of weekly influenza patients reported through the app. We defined a single episode as the set of consecutive days containing 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 the user’s age, gender, weight, and at least one body temperature record. The total number of episodes was 6,657. Of these, there were 3,326 episodes within which influenza was diagnosed. We divided these episodes into 80% training sets and 20% test sets. Five-fold cross-validation was used on the training set.

Results:

We 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, the 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 > 0.05).

Conclusions:

These 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.