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https://www.selleckchem.com/products/mln2480.html ep in the development process. Heuristic evaluation and usability testing in this study provided valuable knowledge about the prototype from a user's perspective. The findings may lead to the development of similar interventions targeting the high school population. High school students have high expectations of digital products. If an mHealth intervention does not offer optimal functions, they may cease to use it. Optimizing the usability of mHealth interventions is a critical step in the development process. Heuristic evaluation and usability testing in this study provided valuable knowledge about the prototype from a user's perspective. The findings may lead to the development of similar interventions targeting the high school population. Screening for influenza in primary care is challenging due to the low sensitivity of rapid antigen tests and the lack of proper screening tests. The 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. We 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. We

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