Predicting Patient Length Of Stay at Time of Admission Using Machine Learning
Olle Andersson · KTH Publication Database DiVA (KTH Royal Institute of Technology) · 2019
This master thesis investigates the possibility of using machine learning methods to predict patient length of stay at the time of admission to a clinical ward from the emergency department. The main aim of this thesis is to provide a comparative analysis of different algorithms and to suggest a suitable model that can be used in a hospital prediction software. The results show that it is possible to achieve a balanced accuracy of 0.72 at the time of admission and of 0.75 at a later stage in the process. The suggested algorithm was Random Forest which combines good accuracy with effective training time, making it suitable for on-line use in a hospital. The study shows that there is a clear potential for the use of machine learning methods for predicting length of stay, but that further improvements have to be made before adaption into the healthcare.