An evaluation framework for the comparison of fine-grained predictive models in health care
van Breda WR, Hoogendoorn M, Eiben AE, Berking M (2015)
Publication Type: Conference contribution
Publication year: 2015
Journal
Publisher: Springer Verlag
Book Volume: 9105
Pages Range: 148-152
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Pavia
ISBN: 9783319195506
DOI: 10.1007/978-3-319-19551-3_18
Abstract
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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Netherlands (NL)How to cite
APA:
van Breda, W.R., Hoogendoorn, M., Eiben, A.E., & Berking, M. (2015). An evaluation framework for the comparison of fine-grained predictive models in health care. In Riccardo Bellazzi, Lucia Sacchi, John H. Holmes, Niels Peek (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 148-152). Pavia, IT: Springer Verlag.
MLA:
van Breda, Ward R.J., et al. "An evaluation framework for the comparison of fine-grained predictive models in health care." Proceedings of the 15th Conference on Artificial Intelligence in Medicine, AIME 2015, Pavia Ed. Riccardo Bellazzi, Lucia Sacchi, John H. Holmes, Niels Peek, Springer Verlag, 2015. 148-152.
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