Personalized machine learning approach to predict candidemia in medical wards
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ORIGINAL PAPER
Personalized machine learning approach to predict candidemia in medical wards Andrea Ripoli1 · Emanuela Sozio2 · Francesco Sbrana3 · Giacomo Bertolino4,5 · Carlo Pallotto6,7 · Gianluigi Cardinali8,9 · Simone Meini10 · Filippo Pieralli11 · Anna Maria Azzini12 · Ercole Concia12 · Bruno Viaggi13 · Carlo Tascini14 Received: 30 December 2019 / Accepted: 21 July 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Purpose Candidemia is a highly lethal infection; several scores have been developed to assist the diagnosis process and recently different models have been proposed. Aim of this work was to assess predictive performance of a Random Forest (RF) algorithm for early detection of candidemia in the internal medical wards (IMWs). Methods A set of 42 potential predictors was acquired in a sample of 295 patients (male: 142, age: 72 ± 15 years; candidemia: 157/295; bacteremia: 138/295). Using tenfold cross-validation, a RF algorithm was compared with a classic stepwise multivariable logistic regression model; discriminative performance was assessed by C-statistics, sensitivity and specificity, while calibration was evaluated by Hosmer–Lemeshow test. Results The best tuned RF algorithm demonstrated excellent discrimination (C-statistics = 0.874 ± 0.003, sensitivity = 84.24% ± 0.67%, specificity = 91% ± 2.63%) and calibration (Hosmer–Lemeshow statistics = 12.779 ± 1.369, p = 0.120), markedly greater than the ones guaranteed by the classic stepwise logistic regression (C-statistics = 0.829 ± 0.011, sensitivity = 80.21% ± 1.67%, specificity = 84.81% ± 2.68%; Hosmer–Lemeshow statistics = 38.182 ± 15.983, p
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