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Predictive Analytics for Retention in Care in an Urban HIV Clinic
Arthi Ramachandran1,2, Avishek Kumar1, Hannes Koenig1
1Center for Data Science and Public Policy, Department of Computer Science, University of Chicago, Chicago, United States.
Scientific Reports
|April 15, 2020
Summary
A new machine learning model can identify people living with HIV who are at high risk of not returning to medical care. This tool helps clinics proactively offer support, improving retention in HIV care and public health outcomes.
Area of Science:
- Public Health
- Infectious Diseases
- Machine Learning
Background:
- Consistent medical care is crucial for individuals with HIV and public health.
- Retention in care leads to antiretroviral medication adherence and viral suppression, preventing HIV transmission.
- Less than half of HIV-positive individuals in the US are retained in care, and identifying at-risk patients is challenging.
Purpose of the Study:
- To develop and evaluate a machine learning model for predicting patients at risk of dropping out of HIV care.
- To improve proactive identification of patients needing interventions to enhance retention in care.
Main Methods:
- Utilized electronic medical records and geospatial data from an urban HIV clinic.
- Developed a machine learning model to predict patients at risk for disengagement from care.
- Compared the model's performance against a logistic regression model and baseline rates.
Main Results:
- The machine learning model achieved a 34.6% positive predictive value (PPV) for identifying the top 10% highest-risk patients.
- This performance surpasses the logistic regression model (17% PPV) and the baseline rate (11.1% PPV).
Conclusions:
- Machine learning models demonstrate superior predictive ability for identifying patients at risk of falling out of HIV care.
- This approach enables proactive interventions to improve patient retention and health outcomes.
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