Advanced Patient Risk Stratification: Integrating Machine Learning and Deep Learning for ART Continuity
In the realm of HIV care, the uninterrupted continuation of Antiretroviral Therapy (ART) plays a pivotal role in ensuring positive health outcomes for patients. Discontinuation of ART poses significant risks, including the development of drug resistance and compromised immune function. Despite the critical importance of maintaining patients on ART, healthcare providers face the ongoing challenge of predicting and preventing discontinuation.
To address this challenge, our objective is to develop an advanced predictive model utilizing both machine learning and deep learning techniques. The primary goal of this model is to stratify patients into distinct risk categories—high, medium, and low—based on their likelihood of discontinuing ART. This stratification will be achieved by meticulously analyzing a comprehensive set of patient data, including viral load (VL), CD4 count, relative CD4 levels, gender, ethnicity, baseline drug combinations, and other pertinent features.
The implementation of this predictive model holds significant promise for healthcare providers. By proactively identifying patients at an elevated risk of discontinuation, the model empowers healthcare professionals to tailor interventions and allocate resources more effectively. This strategic approach not only contributes to improved patient outcomes but also enhances the overall efficiency of HIV care programs.