The Identification of Prolonged Length of Stay for Surgery Patients

When the hospitalization periods of an unexpectedly high number of patients are extended, the income of a hospital is substantially affected and the rate of hospital bed occupancy increases. Because none of the currently available score systems can be used to evaluate the possibility that patients who require urgent surgery in a single department prolong their length of stay (LOS), this study attempts to build a prolonged LOS prediction model utilizing a number of supervised learning techniques. This study involved analyzing the complete historical medical records and lab data of 897 clinical cases in which surgeries were performed by general surgery physicians. These clinical cases were divided into an urgent operation (UO) group comprising 462 cases and a non-UO group comprising 434 cases to develop a prolonged LOS prediction model by using several supervised learning techniques. The results indicated that the random forest method constituted the most accurate and stable prediction model. This study demonstrated that supervised learning techniques can be used to analyze patient medical records to accurately predict a prolonged LOS, thus, supervised learning techniques can serve as valuable reference tools for patient prognoses. The developed prediction models can facilitate the decision making of physicians when patients require surgery and increase patient safety.