Abstract: Disclosures herein are directed to methods and compositions for predicting high- and low-risk for liver fibrosis progression in patients. Based on the results achieved from the methods and compositions disclosed herein, liver disease patients can be classified into a prognostic risk group, which enables early diagnosis and prevention of liver fibrosis and other lethal complications. Methods and compositions disclosed herein substantially improve the poor prognosis of subjects having or at risk for one or more liver fibrosis.

Abstract: A method for identifying patients with a high risk of liver cancer development includes receiving patient data describing a plurality of patients and executing a patient identification module on the patient data to identify at least some of the plurality of patients as having a high risk of developing liver cancer. The patient identification module is generated based on an application of machine learning techniques to a training data set, and the patient identification module is validated based on both the training data set and an external validation data set. Further, the method includes generating a grouping of the plurality of patients based on the identification of the at least some of the plurality of patients.