RT Journal Article T1 Pattern recognition in data as a diagnosis tool A1 Carpio Rodríguez, Ana María A1 Simón, Alejandro A1 Torres, Alicia A1 Villa, Luis F. AB 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. PB Springer Nature SN 2190-5983 YR 2022 FD 2022-01-13 LK https://hdl.handle.net/20.500.14352/72126 UL https://hdl.handle.net/20.500.14352/72126 LA eng NO Carpio Rodríguez, A. M., Simón, A., Torres, A. y Villa, L. F. «Pattern Recognition in Data as a Diagnosis Tool». Journal of Mathematics in Industry, vol. 12, n.o 1, diciembre de 2022, p. 3. DOI.org (Crossref), https://doi.org/10.1186/s13362-022-00119-w. NO Ministerio de Ciencia, Innovación y Universidades (España) NO Fondo Europeo de Desarrollo Regional DS Docta Complutense RD 6 abr 2025