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An In Vitro Model for Measuring Immune Responses to Malaria in the Context of HIV Co-infection

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Published on: October 6, 2015

A Machine Learning Model for Diagnosing Opportunistic Infections in HIV Patients: Broad Applicability Across

Hao Chen^1,2, Fanxuan Chen³, Yijun Wang⁴

¹Department of Infectious Diseases, Wenzhou Central Hospital, Wenzhou, China.

Journal of Cellular and Molecular Medicine

|March 23, 2025

Summary

This summary is machine-generated.

Machine learning models can quickly diagnose opportunistic infections (OIs) in Human Immunodeficiency Virus (HIV)-infected patients. Key biomarkers like procalcitonin and haemoglobin improve diagnostic accuracy, aiding timely treatment.

Keywords:

AIDS HIV diagnostic model machine learning opportunistic infections

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Area of Science:

Infectious Diseases
Medical Informatics
Machine Learning in Healthcare

Background:

Opportunistic infections (OIs) are a major cause of mortality and hospitalization in Human Immunodeficiency Virus (HIV)-infected individuals.
Diagnosing OIs is challenging due to diverse pathogens and complex clinical presentations.

Purpose of the Study:

To develop a machine learning diagnostic model for rapid, generalized identification of OIs in HIV-infected patients.
To create a model adaptable to various clinical scenarios, not limited to specific infections.

Main Methods:

A retrospective cohort study involving HIV-infected patients from four Chinese healthcare organizations.
Utilized twelve machine learning classification algorithms for model training and evaluation.
Implemented feature reduction techniques, including Shapley Additive Explanations and Permutation Importance, to identify key diagnostic indicators.

Main Results:

The top-performing models, using five key features (procalcitonin, haemoglobin, lymphocyte, creatinine, platelet/indirect bilirubin), achieved high F1 scores (0.9016-0.9063) with the adaptive boosting classifier.
These models significantly outperformed a 32-feature gradient boosting model (F1 score 0.8991).

Conclusions:

Machine learning models can effectively and efficiently diagnose opportunistic infections in HIV-infected patients.
A reduced set of biomarkers can achieve high diagnostic accuracy, simplifying clinical application.