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

Abstract

Opportunistic infections (OIs) are the leading cause of hospitalisation and mortality among Human Immunodeficiency Virus-infected (HIV-infected) patients. The diverse pathogen types and intricate clinical manifestations associated present a formidable challenge to the timely diagnosis of these infections. This study aims to use machine learning techniques to develop a diagnostic model that quickly identifies whether HIV-infected patients have any type of OIs, without being limited to specific infections, thus adapting to various clinical scenarios. This study is a retrospective cohort study that collected clinical data from HIV-infected patients at four healthcare organisations in China. A total of twelve machine learning classification algorithms were employed for the purposes of model training and evaluation. Additionally, feature reduction was conducted through the implementation of an importance ranking, with the objective of eliminating any redundant features. In conclusion, both the five features based on Shapley additive explanations (procalcitonin, haemoglobin, lymphocyte, creatinine, platelet) and the five features based on Permutation Importance explanations (procalcitonin, lymphocyte, haemoglobin, creatinine, indirect bilirubin) achieved the highest F1 score when evaluated using the adaptive boosting classifier model. The scores on the test set were 0.9016 and 0.9063, respectively, which significantly outperformed the best 32-feature model, gradient boosting classifier, which had a test set F1 score of 0.8991.