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Open AccessArticle

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

1
Department of Infectious Diseases, The Alfred Hospital and Central Clinical School, Monash University, Melbourne 3004, VIC, Australia
2
General Medical Unit, Alfred Health, Melbourne 3004, VIC, Australia
3
Infection and Immunity Program, Monash Biomedicine Discovery Institute, Department of Microbiology, Monash University, Clayton 3800, VIC, Australia
4
Haematology and Bone Marrow Transplant Service, Alfred Health, Melbourne 3004, VIC, Australia
5
Faculty of Information Technology, Monash University, Clayton 3800, VIC, Australia
*
Author to whom correspondence should be addressed.
J. Clin. Med. 2019, 8(9), 1390; https://doi.org/10.3390/jcm8091390
Received: 20 July 2019 / Revised: 30 August 2019 / Accepted: 2 September 2019 / Published: 5 September 2019
(This article belongs to the Special Issue The Future of Artificial Intelligence in Clinical Medicine)
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 machine learning-based natural language processing (NLP) to non-selectively screen chest tomography (CT) reports for pulmonary IMD, verified by clinical review against international definitions and benchmarked against key AFS measures. NLP screened 3014 reports from 1 September 2008 to 31 December 2017, generating 784 positives that after review, identified 205 IMD episodes (44% probable-proven) in 185 patients from 50,303 admissions. Breakthrough-probable/proven-IMD on antifungal prophylaxis accounted for 60% of episodes with serum monitoring of voriconazole or posaconazole in the 2 weeks prior performed in only 53% and 69% of episodes, respectively. Fiberoptic bronchoscopy within 2 days of CT scan occurred in only 54% of episodes. The average turnaround of send-away bronchoalveolar galactomannan of 12 days (range 7–22) was associated with high empiric liposomal amphotericin consumption. A random audit of 10% negative reports revealed two clinically significant misses (0.9%, 2/223). This is the first successful use of applied machine learning for institutional IMD surveillance across an entire hematology population describing process and outcome measures relevant to AFS. Compared to current methods of clinical audit, semi-automated surveillance using NLP is more efficient and inclusive by avoiding restrictions based on any underlying hematologic condition, and has the added advantage of being potentially scalable. View Full-Text
Keywords: invasive fungal diseases; antifungal stewardship; machine learning; natural language processing; aspergillosis; mold infections invasive fungal diseases; antifungal stewardship; machine learning; natural language processing; aspergillosis; mold infections
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MDPI and ACS Style

Baggio, D.; Peel, T.; Peleg, A.Y.; Avery, S.; Prayaga, M.; Foo, M.; Haffari, G.; Liu, M.; Bergmeir, C.; Ananda-Rajah, M. Closing the Gap in Surveillance and Audit of Invasive Mold Diseases for Antifungal Stewardship Using Machine Learning. J. Clin. Med. 2019, 8, 1390.

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