A Survey on Patient Flow Prediction via mutually-Correcting process

摘要

Over the past decade due to increasing the population Overcrowding in emergency care unit (CU) departments is a problem in many countries around the world, including the United States and Chile. This Emergency department (ED) overcrowding causes problems for indispensible for shortening the length of hospital stays, improving patient outcomes, allocating critical care resources, including increased waiting times. As crowding increases, the quality of care is reduced due to lack of adequate resources. Generally predicting the transition processes of patients (PFP) IS more difficult and crucial process, because predicting patient flow includes patients' duration time within each care unit and transition probability among different units, and including patient's underlying condition and clinical state, disease progression, and availability of care team and staff resources. So Data mining is an effective way to solve such problems in the medical service. This paper surveys various techniques and methods used to Patient Flow Prediction in the Medical care units.