NAFLD Thesis: Early Prediction of Liver Injury
Master’s thesis project: early prediction of liver injury signals in NAFLD using longitudinal clinical data + ML. Work in progress;
Tech Stack
Overview
A thesis-driven ML project focused on predicting early liver injury patterns in NAFLD using longitudinal patient data with clinically meaningful evaluation and interpretability.
The Problem
NAFLD progression and liver injury risk emerge gradually and unevenly across time; early signals are subtle, data is messy, and outputs must be defensible for clinical use.
The Solution
Building a data-to-model pipeline for longitudinal labs and patient context, engineering clinically grounded features, training baseline + sequence models, and validating with interpretable outputs suitable for a thesis setting.
Results & Impact
In progress , thesis work focused on robust data pipelines, model baselines, and clinically interpretable evaluation.