A Genetic Neural Architecture Search Framework to Predict Biomarker Status in Cardiovascular Disease Patients during Pandemics

Résumé fourni par la source

Background: The COVID-19 pandemic disrupted healthcare systems, especially impacting high-risk groups such as individuals with cardiovascular diseases (CVDs). CVD biomarkers such as LDL cholesterol and HbA1c are critical indicators of patient health, disease progression and treatment efficacy. Prior predictive models for CVD biomarkers during this pandemic predominantly explored traditional machine learning techniques, which had inadequate predictive capacity. Genetic Neural Architecture Search (GANAS), employs a genetic algorithm to iteratively evolve neural network architectures and search a large hyperparameter space to optimize model performance via simulated natural selection. Our work is the first to explore GANAS for CVD biomarker trajectory prediction.

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