Abstract
The analysis of clinical time series is currently a key topic in biostatistics and machine learning applications to medical research. The extraction of relevant features from longitudinal patients data brings several problems for which novel algorithms are warranted. It is usually impossible to measure many data points due to practical and also ethical restrictions, which leads to short time series (STS) data. The sampling might also be at unequally spaced time-points and many of the predicted measurements are often missing. These problems constitute the rationale of the present work, where we present two methods to deal with missing data in STS using fuzzy clustering analysis. The methods are tested and compared using data with equal and varying time sampling interval lengths, with and without missing data. The results illustrate the potential of these methods in clinical studies for patient classification and feature selection using biomarker time series data.
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© 2015 Springer International Publishing Switzerland
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Cruz, L.P., Vieira, S.M., Vinga, S. (2015). Fuzzy Clustering for Incomplete Short Time Series Data. In: Pereira, F., Machado, P., Costa, E., Cardoso, A. (eds) Progress in Artificial Intelligence. EPIA 2015. Lecture Notes in Computer Science(), vol 9273. Springer, Cham. https://doi.org/10.1007/978-3-319-23485-4_36
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DOI: https://doi.org/10.1007/978-3-319-23485-4_36
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