Non-Alcoholic Fatty Liver Disease Clinical Trial
Official title:
Machine Learning to Identify Metabolic Subtypes of Non-Alcoholic Fatty Liver Disease
The purpose of this study was to use machine learning to explore a more precise classification of NAFLD subgroups towards informing individualized therapy.
Clinical characteristics of NAFLD are heterogenous, but current classification for diagnosis is simply based on pathological examination. The conventional pathological classification is insufficient to reflect the complexity and heterogeneity of NAFLD and can not predict the prognosis. Towards precision treatment, a more refined metabolic classification of NAFLD phenotypes is highly demanded for a personalized diagnosis, aiming to identify patients at elevated risk of cardiovascular disease or cirrhosis. This kind of refined classification can provide a more precise diagnosis and enable more individualized preventive interventions and early treatments. In a cross-sectional cohort, unsupervised machine learning was used to cluster patients with biopsy-proved NAFLD from Drum Tower Hospital Affiliated to Nanjing University Medical School based on clinical variables. Verification of the clustering was performed in a longitudinal cohort. ;
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