Predicting Patient-Based Time-Dependent Mobile Health Data

Smartphones and other mobile devices offer a valuable opportunity to gather patient-specific health data during everyday life. However, the increasing popularity of mobile health apps demands specialized data analysis methods that can handle the unique, patient-based, time-dependent, and often multivariate data collected by these apps. This work explores the analysis of patient-based mHealth data to develop personalized prediction models. The models can incorporate data not only from the individual patient, but also from other similar patients using patient-specific neighborhoods. Our approach entails selecting the appropriate data for a particular prediction task and dataset. We also discuss when to utilize data from other patients and offer guidance on selecting similarity functions, models, and model combinations. The approach is illustrated on the case study of tinnitus, a perception of sound without an external source, which can be highly distressing. Its presentation and treatment success are patient-specific. As part of the UNITI project, daily diary data of the patients is collected. Evaluation favored the use of personalized models using patient-specific neighborhoods over a global model using all data, or only using a patient's own data for tinnitus distress prediction.