Tag: longitudinal data
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Ghebremichael Lab Unveils Framework for Longitudinal Biomarker Evaluation
New framework advances how we study biomarkers over time Researchers at the Ghebremichael Lab, part of the Ragon Institute, have published a pioneering statistical framework in the Journal of Applied Statistics. The framework addresses a long-standing challenge in clinical research: how to accurately evaluate the diagnostic performance of biomarkers when measurements are taken repeatedly over…
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Ghebremichael Lab Unveils Statistical Framework for Longitudinal Biomarkers in Clinical Studies
Overview: A New Way to Judge Longitudinal Biomarkers Researchers at the Ghebremichael Lab at the Ragon Institute have introduced a powerful statistical framework designed to properly evaluate the diagnostic performance of biomarkers that are measured repeatedly over time in clinical studies. Published in the Journal of Applied Statistics, this work addresses a long-standing challenge: how…
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AI-Driven CVD Prediction from Longitudinal Iran Data: Development and Validation
Overview Cardiovascular disease (CVD) remains a leading cause of mortality worldwide, with Iran experiencing particularly high rates. This article summarizes a study that develops and validates an artificial intelligence (AI) based model to predict CVD events using longitudinal health data collected over time. By leveraging deep learning and mixed-effects logistic modeling, the research aims to…
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Development and Validation of an AI-Based Model for Cardiovascular Disease Prediction in Iran Using Longitudinal Data
Overview Cardiovascular disease (CVD) remains a leading cause of mortality in Iran, underscoring the need for accurate, data-driven risk prediction tools. This study introduces an artificial intelligence (AI) based model designed to predict CVD events by leveraging longitudinal health data. By comparing advanced deep learning techniques with a traditional (yet sophisticated) mixed-effects logistic regression approach,…
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AI-Based Cardiovascular Disease Prediction: Insights from Longitudinal Data in Iran
Overview Cardiovascular disease (CVD) remains a leading cause of mortality worldwide. In Iran, where mortality rates from heart conditions are notably high, researchers are turning to artificial intelligence (AI) and machine learning (ML) to improve risk prediction. This article summarizes the development and validation of an AI-based model designed to predict cardiovascular events using longitudinal…