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.
CITATION STYLE
van Breda, W. R. J., Hoogendoorn, M., Eiben, A. E., & Berking, M. (2015). An evaluation framework for the comparison of fine-grained predictive models in health care. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9105, pp. 148–152). Springer Verlag. https://doi.org/10.1007/978-3-319-19551-3_18
Mendeley helps you to discover research relevant for your work.