Closing the Gap in Surveillance and Audit of Invasive Mold Diseases for Antifungal Stewardship Using Machine Learning

Closing the Gap in Surveillance and Audit of Invasive Mold Diseases for Antifungal Stewardship Using Machine Learning

Clinical audit of invasive mold disease (IMD) in hematology patients is inefficient due to the difficulties of case finding. This results in antifungal stewardship (AFS) programs preferentially reporting drug cost and consumption rather than measures that actually reflect quality of care. We used ma...

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Bibliographic Details
Main Authors: Diva Baggio, Trisha Peel, Anton Y. Peleg, Sharon Avery, Madhurima Prayaga, Michelle Foo, Gholamreza Haffari, Ming Liu, Christoph Bergmeir, Michelle Ananda-Rajah
Format: Article
Language:English
Published: MDPI AG 2019-09-01
Series:Journal of Clinical Medicine
Subjects:
invasive fungal diseases
antifungal stewardship
machine learning
natural language processing
aspergillosis
mold infections
Online Access:https://www.mdpi.com/2077-0383/8/9/1390

Internet

https://www.mdpi.com/2077-0383/8/9/1390

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