Abstract
Recent digitization technologies empower mHealth users to conveniently record their Ecological Momentary Assessments (EMA) through web applications, smartphones, and wearable devices. These recordings can help clinicians understand how the users’ condition changes, but appropriate learning and visualization mechanisms are required for this purpose. We propose a web-based visual analytics tool, which processes clinical data as well as EMAs that were recorded through a mHealth application. The goals we pursue are (1) to predict the condition of the user in the near and the far future, while also identifying the clinical data that mostly contribute to EMA predictions, (2) to identify users with outlier EMA, and (3) to show to what extent the EMAs of a user are in line with or diverge from those users similar to him/her. We report our findings based on a pilot study on patient empowerment, involving tinnitus patients who recorded EMAs with the mHealth app TinnitusTips. To validate our method, we also derived synthetic data from the same pilot study. Based on this setting, results for different use cases are reported.
Highlights
Ecological Momentary Assessments (EMAs) are utilized to capture the immediate behavioral experience for a medical phenomenon
We demonstrate the users neighborhood comparisons over data and utilize them for predicting user’s EMA recordings and show that users neighborhood are useful in making the ahead predictions
The registration data are used for the neighborhood creation, and the obtained nearest neighbor time series from the EMA data is used for the ahead predictions of the test users
Summary
Ecological Momentary Assessments (EMAs) are utilized to capture the immediate behavioral experience for a medical phenomenon. EMAs are mainly recorded with help of mobile technology, namely, by digital devices that notify the users multiple times for a period of days or weeks, so that they record current or recent medical states, behaviors, or environmental conditions [1]. By using this approach, the period of recall can be reduced to hours or minutes. Comprehensible Artificial Intelligence (cAI) [10] is a transition framework that encompasses multiple disciplines such as AI, Human-Computer Interaction (HCI), and
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