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Using Machine Learning to Identify Patients at Risk of Acquiring HIV in an Urban Health System.
Nethi, Arun Kumar; Karam, Albert George; Alvarez, Kristin S; Luque, Amneris Esther; Nijhawan, Ank E; Adhikari, Emily; King, Helen Lynne.
Afiliación
  • Nethi AK; PCCI, Dallas, TX.
  • Karam AG; PCCI, Dallas, TX.
  • Alvarez KS; Center of Innovation and Value at Parkland Health, Dallas, TX.
  • Luque AE; Department of Internal Medicine, Division of Infectious Diseases and Geographic Medicine, University of Texas Southwestern Medical Center, Dallas, TX; and.
  • Nijhawan AE; Department of Internal Medicine, Division of Infectious Diseases and Geographic Medicine, University of Texas Southwestern Medical Center, Dallas, TX; and.
  • Adhikari E; Department of Obstetrics & Gynecology, Division of Maternal Fetal Medicine, University of Texas Southwestern Medical Center, Dallas, TX.
  • King HL; Department of Internal Medicine, Division of Infectious Diseases and Geographic Medicine, University of Texas Southwestern Medical Center, Dallas, TX; and.
J Acquir Immune Defic Syndr ; 97(1): 40-47, 2024 Sep 01.
Article en En | MEDLINE | ID: mdl-39116330
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 7030 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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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Infecciones por VIH / Aprendizaje Automático Límite: Adolescent / Adult / Female / Humans / Male / Middle aged Idioma: En Revista: J Acquir Immune Defic Syndr Asunto de la revista: SINDROME DA IMUNODEFICIENCIA ADQUIRIDA (AIDS) Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
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XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Infecciones por VIH / Aprendizaje Automático Límite: Adolescent / Adult / Female / Humans / Male / Middle aged Idioma: En Revista: J Acquir Immune Defic Syndr Asunto de la revista: SINDROME DA IMUNODEFICIENCIA ADQUIRIDA (AIDS) Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos