Spinal Cord Injured (SCI) patients' length of stay (LOS) prediction based on hospital admission data

Abstract

In order to take better care and to ensure better facilities to the inpatients, predicting length of stay serves a great importance. Since, the resources and the doctors are limited in the hospital, especially in a developing country like Bangladesh, it is quite difficult to provide proper healthcare to the inpatients. Not only the hospital resources are limited but also, it is difficult for the inpatients to bear the expense for a long period of time. In addition to that, if doctors can predict length of stay at the early stage of pre-admission, they can map a well instructed way for example, which treatment, which instrument will treat patients best. As a result, the patient can start his treatment with a slight assumption of the expenses. If we can predict accurate length of stay, patients do not have to leave in between the treatment without medical advice. Keeping all these points in mind, we develop a system using machine learning algorithms to predict Spinal Cord Injured (SCI) patient's length of stay. For this purpose, we choose Centre for the Rehabilitation of the Paralyzed (CRP) to collect data. They provide us around 600 inpatients' admission data who have been admitted to the hospital. After that we successfully build a model for predicting length of stay (LOS) with 81.62% accuracy.