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Closing the gap in surveillance and audit of invasive mold diseases for antifungal stewardship using machine learning

Baggio, Diva, Peel, Trisha, Peleg, Anton Y., Avery, Sharon, Prayaga, Madhurima, Foo, Michelle, Haffari, Gholamreza, Liu, Ming, Bergmeir, Christoph and Ananda-Rajah, Michelle 2019, Closing the gap in surveillance and audit of invasive mold diseases for antifungal stewardship using machine learning, Journal of Clinical Medicine, vol. 8, no. 9, pp. 1-13, doi: 10.3390/jcm8091390.

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Title Closing the gap in surveillance and audit of invasive mold diseases for antifungal stewardship using machine learning
Author(s) Baggio, Diva
Peel, Trisha
Peleg, Anton Y.
Avery, Sharon
Prayaga, Madhurima
Foo, Michelle
Haffari, Gholamreza
Liu, Ming
Bergmeir, Christoph
Ananda-Rajah, Michelle
Journal name Journal of Clinical Medicine
Volume number 8
Issue number 9
Article ID 8091390
Start page 1
End page 13
Total pages 13
Publisher MDPI
Place of publication Basel, Switzerland
Publication date 2019
ISSN 2077-0383
Keyword(s) Invasive fungal diseases
Antifungal stewardship
Machine learning
Natural language processing
Aspergillosis
Mold infections
Language eng
DOI 10.3390/jcm8091390
Indigenous content off
HERDC Research category C1.1 Refereed article in a scholarly journal
Free to Read? Yes
Use Rights Creative Commons Attribution licence
Persistent URL http://hdl.handle.net/10536/DRO/DU:30139209

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.