Background: Hepatic Encephalopathy (HE) is a serious complication with a wide spectrum of clinical symptoms, from minimal changes to profound coma. HE is hard to diagnose without advanced laboratory parameters such as ammoni. This study aims to develop a scoring model to diagnose HE using clinical and laboratory parameters. Methods: An analytical cross-sectional study collected data from 96 hospitalized patients with liver cirrhosis from November 2021 to January 2022. Employing multivariate logistic regression analysis, the study aimed to identify autonomous factors associated with HE. Each significant variable was used to calculate patient probabilities. The score for each variable was computed utilizing the (B/SE)/lowest(B/SE) formula, demonstrating robust discriminatory capability. The scoring model was formulated and evaluated based on its sensitivity and specificity.Results: Nineteen point eight percent, equivalent to ninenteen patients, were admitted with HE. The scoring model was crafted based on nineteen variables. There were four significant variables in this model: Aspartate Aminotransferase (AST) (p=0.01), Total Bilirubin (p=0.007), Fibrosis-4-Index (FIB-4) (p=0.014), and Ascites (p=0.016). Each variable was scored as 1 for AST, -1 for total bilirubin, 1 for FIB-4-index, and 1 for Ascites. The probability was 2%, 14.2%, 57%, 91.4%, and 50%, following the total score of -1, 0, 1, 2, and 3, respectively. The sensitivity and specificity of the scoring model were 68.4% and 85.3%, respectively (AUC=84.7%).Conclusion: Daily laboratory and clinical manifestations related to hepatic cirrhosis could give a clue to diagnosing hepatic encephalopathy.