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Classifying patients with invasive fungal disease: towards a unified case definition?
Mehmet Ergün1,2, Roger J M Brüggemann1,3, Alexandre Alanio4,5
1Radboudumc-CWZ Center of Expertise for Mycology, Radboud University Medical Center, Nijmegen, The Netherlands.
Abstract:
Management of invasive fungal disease (IFD) is increasingly challenging due to recognition of novel at-risk groups, emergence of new fungal pathogens and antifungal drug resistance. Together with the availability of new diagnostic tests and treatment modalities, robust and broadly applicable IFD case definitions are critical to support research. However, the ability to classify IFDs with the current definitions has decreased, prompting the development of new case definitions. Furthermore, current case definitions rely on a single positive test as mycological evidence, while not considering discordant evidence. We propose to explore the development of a machine learning (ML)-based IFD classification model, which uses algorithms to automatically 'learn' from observed data to consistently and accurately classify IFDs. Although developing and validating an ML-based IFD classification model is a significant undertaking, such an endeavour should be considered a worthwhile investment by the mycology community to standardize and reduce the ambiguity in the diagnosis of non-proven IFD.
Insights
Developing a machine learning (ML) model can improve the classification of invasive fungal diseases (IFDs). This approach aims to standardize diagnosis by learning from data, addressing limitations in current IFD case definitions.
Area of Science:
- Mycology
- Infectious Diseases
- Computational Biology
Background:
- Invasive fungal diseases (IFDs) present growing management challenges due to new risk groups, emerging pathogens, and antifungal resistance.
- Current IFD case definitions are becoming less effective for research and clinical classification.
- Existing definitions often rely on single positive tests and do not account for discordant evidence.
Purpose of the Study:
- To explore the development of a machine learning (ML)-based model for IFD classification.
- To create a standardized and less ambiguous method for diagnosing IFDs, particularly non-proven cases.
Main Methods:
- Proposing the use of machine learning algorithms to automatically learn from observed data.
- Developing a classification model that can consistently and accurately classify IFDs.
Main Results:
- The study proposes a novel ML-based approach for IFD classification.
- This method aims to overcome the limitations of current case definitions.
Conclusions:
- Developing and validating an ML-based IFD classification model is a significant but worthwhile endeavor.
- Such a model could standardize IFD diagnosis and reduce ambiguity in the mycology community.
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