Sepsis is a condition that progresses quickly and is a major cause of mortality in hospitalized patients. Data-driven diagnostic and therapeutic interventions are essential to ensure early diagnosis and appropriate care. The Sequential Organ Failure Assessment (SOFA) score is widely utilized in clinical practice to assess septic patients for organ dysfunction. The SOFA score uses points between 0 and 4 to quantify the level of dysfunction in six organ systems. These points are determined based on expert opinion and not informed by data, thus their usefulness can vary among different medical institutions depending on the targeted use. In this study, we propose multiple strategies to adjust the SOFA score using mixed-integer programming to improve the in-hospital mortality prediction of septic patients based on Electronic Health Records (EHRs). We use the same variables and threshold values of the original SOFA score in each strategy. Thus, the proposed approach takes advantage of optimization and data analysis while taking into account the medical expertise. Our results demonstrate a statistically significant improvement ( ) in the prediction of in-hospital mortality among patients susceptible to sepsis when implementing our proposed strategies. Area under the receiver operator curve (AUC) and accuracy values of 0.8928 and 0.8904 are achieved by optimizing the point values of the SOFA score.