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Logistic regression representing the associations between the characteristics of participants and test for SARS-COV-2 among people without having contact with someone tested positi...
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Overview of the procedure followed to reduce class imbalance (equalization strategy).
Logistic regression representing the associations between the characteristics of participants and test outcome for SARS-COV-2 infection.
Baseline characteristics of participants stratified into SARS-COV-2 positive and negative.
Overall performance of machine learning algorithms by AUCs for the 8 subsets.
Proportion of predicted values averaged for 4 subsets.
Clinical predictors of rectal lymphogranuloma venereum infection: results from a multicentre case-control study in the UK
MILP model used to generate a schedule for the RT treatment scheduling problem.
S1 Data -
Schematic overview of the MILP-based schedule construction and validation phases before implementation.