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. 2024 Dec;36(12):1745-1753.
doi: 10.1080/09540121.2024.2361245. Epub 2024 Jun 4.

Use of machine learning approaches to predict transition of retention in care among people living with HIV in South Carolina: a real-world data study

Ruilie Cai  1   2 , Xueying Yang  2   3 , Yunqing Ma  1   2 , Hao H Zhang  4 , Bankole Olatosi  2   5 , Sharon Weissman  2   6 , Xiaoming Li  2   3 , Jiajia Zhang  1   2
Affiliations

Use of machine learning approaches to predict transition of retention in care among people living with HIV in South Carolina: a real-world data study

Ruilie Cai et al. AIDS Care. 2024 Dec.

Abstract

Maintaining retention in care (RIC) for people living with HIV (PLWH) helps achieve viral suppression and reduce onward transmission. This study aims to identify the best machine learning model that predicts the RIC transition over time. Extracting from the enhanced HIV/AIDS reporting system, this study included 9765 PLWH from 2005 to 2020 in South Carolina. Transition of RIC was defined as the change of RIC status in each two-year time window. We applied seven classifiers, such as Random Forest, Support Vector Machine, eXtreme Gradient Boosting and Long-short-term memory, for each lagged response to predict the subsequent year's RIC transition. Classification performance was assessed using balanced prediction accuracy, the area under the curve (AUC), recall, precision and F1 scores. The proportion of the four categories of RIC transition was 13.59%, 29.78%, 9.06% and 47.57%, respectively. Support Vector Machine was the best approach for every lag model based on both the F1 score (0.713, 0.717 and 0.719) and AUC (0.920, 0.925 and 0.928). The findings could facilitate the risk augment of PLWH who are prone to follow-up so that clinicians and policymakers could come up with more specific strategies and relocate resources for intervention to keep them sustained in HIV care.

Keywords: HIV; Retention in care; SDG 3: Good health and well-being; big data; machine learning.

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Conflict of interest statement

Conflict of Interest Disclosures: There are no conflicts of interest.

Figures

Figure 1.
Figure 1.
Machine Learning Pipeline and Relative Data Flow
Figure 2.
Figure 2.
One-verse-Other ROC curves for lag-3 model
See this image and copyright information in PMC

References

    1. Benjamini Y, & Hochberg Y (1995). Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society. Series B (Methodological), 57(1), 289–300.
    1. Centers for Disease Control and Prevention. (2019). Understanding the HIV Care Continuum. https://www.cdc.gov/hiv/pdf/library/factsheets/cdc-hiv-care-continuum.pdf
    1. Cohen MS, Chen YQ, McCauley M, Gamble T, Hosseinipour MC, Kumarasamy N, Hakim JG, Kumwenda J, Grinsztejn B, Pilotto JHS, Godbole SV, Mehendale S, Chariyalertsak S, Santos BR, Mayer KH, Hoffman IF, Eshleman SH, Piwowar-Manning E, Wang L, … Fleming TR (2011). Prevention of HIV-1 Infection with Early Antiretroviral Therapy. New England Journal of Medicine, 365(6), 493–505. 10.1056/NEJMoa1105243 - DOI - PMC - PubMed
    1. Cook JA, Grey DD, Burke-Miller JK, Cohen MH, Vlahov D, Kapadia F, Wilson TE, Cook R, Schwartz RM, Golub ET, Anastos K, Ponath C, Goparaju L, & Levine AM (2007). Illicit drug use, depression and their association with highly active antiretroviral therapy in HIV-positive women. Drug and Alcohol Dependence, 89(1), 74–81. 10.1016/j.drugalcdep.2006.12.002 - DOI - PMC - PubMed
    1. Coyle RP, Schneck CD, Morrow M, Coleman SS, Gardner EM, Zheng J-H, Ellison L, Bushman LR, Kiser JJ, Mawhinney S, Anderson PL, & Castillo-Mancilla JR (2019). Engagement in Mental Health Care is Associated with Higher Cumulative Drug Exposure and Adherence to Antiretroviral Therapy. AIDS and Behavior, 23(12), 3493–3502. 10.1007/s10461-019-02441-8 - DOI - PMC - PubMed

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