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Updated: Jan 18, 2026
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Published on: May 2, 2018
Using Machine Learning Algorithms to Identify Key Predictors of Invasive Mold Infection Surveillance
Samantha L Williams1, Zainab Salah2, Brendan R Jackson1
1Mycotic Diseases Branch, Centers for Disease Control and Prevention, Atlanta, Georgia, USA.
Background:
Invasive mold infections (IMI) can lead to severe morbidity and mortality, but routine public health surveillance is lacking. Although extensive evaluation is needed for clinical diagnosis, case classification prediction models may inform surveillance efforts, which are essential to better characterize epidemiologic trends and assess the value of a more inclusive IMI case definition.
Methods:
We modeled medical record data of potential IMI cases from 4 medical centers in Houston, Texas, during September 2016 to August 2018. We used least absolute shrinkage and selection operator and random forest machine learning methods to identify key host and clinical factors, mycological evidence, diagnostics, and health care exposures predictive of IMI case versus noncase status using both conventional and novel definitions. We assessed feature importance by measuring each variable's impact on prediction error using leave-one-covariate-out and permutation feature importance approaches.
Results:
Receipt of systemic antifungal medication, hospital billing codes related to IMI, and positive pulmonary histopathology results were identified as the most important predictors of IMI case status across all measures. Removal of these features from the models resulted in reductions to prediction accuracy ranging from 3.6% (95% confidence interval [CI], 3.2%-3.2%) to 7.6% (95% CI, 7.2%-8.0%). Some IMI risk factors, including cancer diagnosis and prolonged receipt of corticosteroid medications, worsened prediction in several assessments of feature importance.
Conclusions:
Features identified as important predictors of IMI case status using machine learning methods deviated from classic IMI risk factors. Our results will inform robust and feasible IMI case prediction models for public health surveillance.
Insights
Public health surveillance for invasive mold infections (IMI) is lacking. Machine learning models identified key predictors of IMI cases, including antifungal medication and specific diagnostic results, to improve surveillance efforts.
Area of Science:
- Medical informatics
- Epidemiology
- Machine learning in healthcare
Background:
- Invasive mold infections (IMI) cause significant morbidity and mortality.
- Current public health surveillance for IMI is insufficient.
- Predictive models can aid surveillance and inform case definitions.
Purpose of the Study:
- To develop and validate machine learning models for predicting Invasive Mold Infections (IMI) case status.
- To identify key factors predictive of IMI using medical record data.
- To inform the development of robust public health surveillance for IMI.
Main Methods:
- Utilized least absolute shrinkage and selection operator (LASSO) and random forest machine learning algorithms.
- Modeled medical record data from four Houston medical centers (September 2016 - August 2018).
- Assessed feature importance using leave-one-covariate-out and permutation methods.
Main Results:
- Systemic antifungal medication, IMI-related billing codes, and positive pulmonary histopathology were top predictors.
- Removing these key features reduced model prediction accuracy by 3.6% to 7.6%.
- Certain traditional IMI risk factors, like cancer and corticosteroid use, paradoxically worsened prediction accuracy.
Conclusions:
- Machine learning identified key predictors of IMI that differ from traditional risk factors.
- These findings support the development of feasible prediction models for IMI public health surveillance.
- The study highlights the utility of machine learning in enhancing epidemiological surveillance for infectious diseases.
