Schmalzle SA, Maroosis D, Masur H, Kottilil S, Mathur P. Use of a machine learning model to predict retention in care in an urban HIV clinic.
AIDS 2024;
38:125-127. [PMID:
38061023 PMCID:
PMC10783757 DOI:
10.1097/qad.0000000000003735]
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Abstract
Identifying barriers to retention in care (RIC) is critical to ending the HIV epidemic in the United States. Therefore, we developed a machine learning model (MLM) to identify predictive factors for RIC in an urban HIV clinic. Our MLM yielded a positive predictive value of 84%, higher than previously reported MLMs. We found that MLM can be used to develop interventional strategies to enhance RIC in HIV care.
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