Hospital length of stay and discharge type prediction using deep learning

Abstract

AbstractThe length of hospital stay (LOS) and the type of discharge are important indicators of how well care is provided at a hospital. The purpose of this study is to leverage in-patient data collected at the hospital to help determine the factors that influence the length of hospital stay and type of discharge. Our research focuses on estimating if the person survived or not after they were admitted to the hospital, as well as the type of discharge. The study uses a retrospective design and examines information from hospital discharged patients’ medical records. Demographic information, diagnosis, treatment, and discharge status were included in the data. We have used the PEDALFAST dataset which stands for PEDiatric Validation of Variables in Trauma. A survey of patients to find out how they feel about the quality of care they received while they were in the hospital was also a part of the study dataset. The findings of this study will shed light on the ways in which various factors influence the LOS in the hospital and the type of discharge, assisting in the formulation of strategies to enhance the quality and effectiveness of health care delivery.