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Collection and Extraction of Occupational Air Samples for Analysis of Fungal DNA

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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.

The Journal of Infectious Diseases

|June 3, 2025

Summary

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.

Keywords:

aspergillosis fungal infections invasive mold infections machine learning surveillance

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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.