An evaluation framework for the comparison of fine-grained predictive models in health care - van Breda, Ward R.J.; Hoogendoorn, Mark; Eiben, A. E.; Berking, Matthias (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2015)
Within the domain of health care, more andmore 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 oftenmuch more complex than high-level predictivemodels that e. g. give a general risk score for a disease, making the evaluation of thesemodels 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, considering their capability to describe data, their capability to predict, the quality of the models parameters, and the model complexity.
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