| Abstract |
Within the domain of health care, more and more fine-grained models are observed that predict the development of specific health (or disease-related) states over time. This is due to the increased use of sensors, allowing for continuous assessment, leading to a sharp increase of data. These specific models are often much more complex than high-level predictive models that e.g. give a general risk score for a disease. This makes the evaluation of these models far from trivial. In this paper, we present an evaluation framework which is able to score fine-grained temporal models that aim at predicting multiple health states. Hereby, models are evaluated on their capability to describe data, their capability to predict, the quality of the models parameters, and the model complexity. The framework is applied in the domain of mental health, specifically depression, by comparing two predictive models, thereby showing how the framework can be applied. |