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Pattern recognition in data as a diagnosis tool



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Carpio, Ana and Simón, Alejandro and Torres, Alicia and Villa, Luis F. (2022) Pattern recognition in data as a diagnosis tool. Journal of mathematics in industry, 12 . ISSN 2190-5983

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Official URL: https://doi.org/10.1186/s13362-022-00119-w


Medical data often appear in the form of numerical matrices or sequences. We develop mathematical tools for automatic screening of such data in two medical contexts: diagnosis of systemic lupus erythematosus (SLE) patients and identification of cardiac abnormalities. The idea is first to implement adequate data normalizations and then identify suitable hyperparameters and distances to classify relevant patterns. To this purpose, we discuss the applicability of Plackett-Luce models for rankings to hyperparameter and distance selection. Our tests suggest that, while Hamming distances seem to be well adapted to the study of patterns in matrices representing data from laboratory tests, dynamic time warping distances provide robust tools for the study of cardiac signals. The techniques developed here may set a basis for automatic screening of medical information based on pattern comparison.

Item Type:Article
Uncontrolled Keywords:Pattern classification; Hyperparameter selection; Plackett-Luce models; Hamming distance; Dynamic time warping distance; Wasserstein distance; Medical diagnosis; Systemic lupus erythematosus; Electrocardiogram
Subjects:Sciences > Mathematics > Mathematical analysis
Medical sciences > Medicine
ID Code:74955
Deposited On:05 Oct 2022 11:10
Last Modified:05 Oct 2022 11:56

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