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Erschienen in:
2015 | OriginalPaper | Buchkapitel
An Evaluation Framework for the Comparison of Fine-Grained Predictive Models in Health Care
verfasst von : Ward R. J. van Breda, Mark Hoogendoorn, A. E. Eiben, Matthias Berking
Erschienen in: Artificial Intelligence in Medicine
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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.