Machine learning models predicting multidrug resistant urinary tract infections using “DsaaS”

The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation

(2020) Davide Chicco et al.   BMC GENOMICS

CiliateGEM: an open-project and a tool for predictions of ciliate metabolic variations and experimental condition design

(2018) Alessio Mancini et al.   BMC BIOINFORMATICS

A Critical Review for Developing Accurate and Dynamic Predictive Models Using Machine Learning Methods in Medicine and Health Care

(2017) Hamdan O. Alanazi et al.   JOURNAL OF MEDICAL SYSTEMS

Implementing an Antibiotic Stewardship Program: Guidelines by the Infectious Diseases Society of America and the Society for Healthcare Epidemiology of America

(2016) Tamar F. Barlam et al.   CLINICAL INFECTIOUS DISEASES

Urinary tract infections: epidemiology, mechanisms of infection and treatment options

(2015) Ana L. Flores-Mireles et al.   NATURE REVIEWS MICROBIOLOGY

Building Predictive Models inRUsing thecaretPackage

(2015) Max Kuhn   Journal of Statistical Software

A Graphical User Interface forRin a Rich Client Platform for Ecological Modeling

(2015) Marcel Austenfeld et al.   Journal of Statistical Software

The epidemiology of urinary tract infection

(2010) Betsy Foxman   Nature Reviews Urology