Abstract
The sampling rate of variables collected in the hospital setting is dependent on several factors. Patients have different lengths of stay in the hospital, during which distinct physiological parameters are measured. The frequency of measurements depends ultimately in the type of variable and in the patient condition. Hence, when performing data based modeling for knowledge discovery in medical databases, one should have in consideration the heterogeneity of variables. This paper proposes an extension of a mixed fuzzy clustering algorithm in order to handle time invariant and time variant features of unequal lengths. Additionally, a novel approach for deriving Takagi-Sugeno fuzzy models, based on feature transformation using fuzzy c-means is implemented and compared with approaches based on mixed fuzzy clustering. The proposed approaches are tested on real data for mortality prediction in intensive care units of patients diagnosed with acute kidney injury and for ICU readmission prediction. Overall, mixed fuzzy clustering yields better results than fuzzy c-means. Moreover, the proposed extension for time series of unequal lengths improves previous results. Mortality is classified with an AUC of 0.73 and readmissions with an AUC of 0.64.
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Acknowledgments
This work was supported by FCT, through IDMEC, under project iDecision4Care (IF/00833/2014/CP1238/CT0002). S. Vieira acknowledges support by Program Investigador FCT (IF/00833/ 2014) from FCT, co-funded by the European Social Fund (ESF) through the Operational Program Human Potential (POPH). This work was supported by FCT, through IDMEC, under LAETA, project UID/EMS/50022/2013.
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Salgado, C.M., Vieira, S.M., Sousa, J.M.C. (2016). Fuzzy Modeling Based on Mixed Fuzzy Clustering for Multivariate Time Series of Unequal Lengths. In: Carvalho, J., Lesot, MJ., Kaymak, U., Vieira, S., Bouchon-Meunier, B., Yager, R. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2016. Communications in Computer and Information Science, vol 611. Springer, Cham. https://doi.org/10.1007/978-3-319-40581-0_60
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