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. 2024 Sep 1;97(1):40-47.
doi: 10.1097/QAI.0000000000003464.

Using Machine Learning to Identify Patients at Risk of Acquiring HIV in an Urban Health System

Arun Kumar Nethi  1 , Albert George Karam  1 , Kristin S Alvarez  2 , Amneris Esther Luque  3 , Ank E Nijhawan  3 , Emily Adhikari  4 , Helen Lynne King  3
Affiliations

Using Machine Learning to Identify Patients at Risk of Acquiring HIV in an Urban Health System

Arun Kumar Nethi et al. J Acquir Immune Defic Syndr. .

Abstract

Background: Effective measures exist to prevent the spread of HIV. However, the identification of patients who are candidates for these measures can be a challenge. A machine learning model to predict risk for HIV may enhance patient selection for proactive outreach.

Setting: Using data from the electronic health record at Parkland Health, 1 of the largest public healthcare systems in the country, a machine learning model is created to predict incident HIV cases. The study cohort includes any patient aged 16 or older from 2015 to 2019 (n = 458,893).

Methods: Implementing a 70:30 ratio random split of the data into training and validation sets with an incident rate <0.08% and stratified by incidence of HIV, the model is evaluated using a k-fold cross-validated (k = 5) area under the receiver operating characteristic curve leveraging Light Gradient Boosting Machine Algorithm, an ensemble classifier.

Results: The light gradient boosting machine produces the strongest predictive power to identify good candidates for HIV PrEP. A gradient boosting classifier produced the best result with an AUC of 0.88 (95% confidence interval: 0.86 to 0.89) on the training set and 0.85 (95% confidence interval: 0.81 to 0.89) on the validation set for a sensitivity of 77.8% and specificity of 75.1%.

Conclusions: A gradient boosting model using electronic health record data can be used to identify patients at risk of acquiring HIV and implemented in the clinical setting to build outreach for preventative interventions.

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Conflict of interest statement

A.N. and H.L.K. receive research funding from Gilead Sciences. Authors have no conflict of interest to declare. In addition, the abovementioned contributors in the acknowledgment received no compensation for their work other than their usual salary and have no conflicts of interest relevant to this article.

Figures

FIGURE 1.
FIGURE 1.
ROC curves for the candidate models (legend displays model name—corresponding AUC with CIs).
FIGURE 2.
FIGURE 2.
Relative variable importance.
See this image and copyright information in PMC

References

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