Abstract
Seasonality plays a significant role in the prevalence of infectious diseases. We evaluate the performance of different approaches used to deal with seasonality in clinical prediction models, including a new proposal based on sliding windows. Class imbalance, high dimensionality and interpretable models are also considered since they are common traits of clinical datasets.
We tested these approaches with four datasets: two created synthetically and two extracted from the MIMIC-III database. Our results corroborate that clinical prediction models for infections can be improved by considering the effect of seasonality. However, the techniques employed to obtain the best results are highly dependent on the dataset.
This work was partially funded by the SITSUS project (Ref: RTI2018-094832-B-I00), given by MCIU/AEI/FEDER, UE.
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Cánovas-Segura, B., Morales, A., Juárez, J.M., Campos, M. (2021). Seasonality in Infection Predictions Using Interpretable Models for High Dimensional Imbalanced Datasets. In: Tucker, A., Henriques Abreu, P., Cardoso, J., Pereira Rodrigues, P., Riaño, D. (eds) Artificial Intelligence in Medicine. AIME 2021. Lecture Notes in Computer Science(), vol 12721. Springer, Cham. https://doi.org/10.1007/978-3-030-77211-6_16
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