AIMC Topic: Retention in Care

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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.

AIDS care Jun 4, 2024
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 f...
Humans Machine Learning Female Adult Male Middle Aged HIV Infections Anti-HIV Agents Support Vector Machine South Carolina Retention in Care
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Development of a predictive model for retention in HIV care using natural language processing of clinical notes.

Journal of the American Medical Informatics Association : JAMIA Jan 15, 2021
OBJECTIVE: Adherence to a treatment plan from HIV-positive patients is necessary to decrease their mortality and improve their quality of life, however some patients display poor appointment adherence and become lost to follow-up (LTFU). We applied n...
Humans Electronic Health Records Female Adult Male HIV Infections Models, Theoretical Natural Language Processing Patient Compliance Lost to Follow-Up Retention in Care
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