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, making 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, considering their capability to describe data, their capability to predict, the quality of the models parameters, and the model complexity.
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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: Holmes, J., Bellazzi, R., Sacchi, L., Peek, N. (eds) Artificial Intelligence in Medicine. AIME 2015. Lecture Notes in Computer Science(), vol 9105. Springer, Cham. https://doi.org/10.1007/978-3-319-19551-3_18
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DOI: https://doi.org/10.1007/978-3-319-19551-3_18
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-19550-6
Online ISBN: 978-3-319-19551-3
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