Prediction of candidemia with machine learning techniques: state of the art-Reference-Cited by-同舟云学术

Prediction of candidemia with machine learning techniques: state of the art

Published:2024-05-20 Issue:10 Volume:19 Page:931-940
ISSN:1746-0913
Container-title:Future Microbiology
language:en
Short-container-title:Future Microbiology

Author:

Giacobbe Daniele Roberto¹²,Marelli Cristina²,Mora Sara³,Cappello Alice²,Signori Alessio⁴,Vena Antonio¹²,Guastavino Sabrina⁵,Rosso Nicola³,Campi Cristina⁵⁶,Giacomini Mauro⁷,Bassetti Matteo¹²

Affiliation:

1. Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy

2. UO Clinica Malattie Infettive, IRCCS Ospedale Policlinico San Martino, Genoa, Italy

3. UO Information & Communication Technologies (ICT), IRCCS Ospedale Policlinico San Martino, Genoa, Italy

4. Section of Biostatistics, Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy

5. Department of Mathematics (DIMA), University of Genoa, Genoa, Italy

6. Life Science Computational Laboratory (LISCOMP), IRCCS Ospedale Policlinico San Martino, Genoa, Italy

7. Department of Informatics, Bioengineering, Robotics & System Engineering (DIBRIS), University of Genoa, Genoa, Italy

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.2217/fmb-2023-0269

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5. Comparison of Non-Culture-Based Methods for Detection of Systemic Fungal Infections, with an Emphasis on Invasive Candida Infections