A Combined Simulation and Machine Learning Approach for Real-Time Delay Prediction for Waitlisted Neurosurgery Candidates

In this study, we present a method to predict whether a patient seeking admission to the neurosurgery ward of a large public tertiary care hospital in north India receives admission within a prespecified duration. The prediction needs to be made at the time the patient is seeking admission at the ward, so that they can then decide whether to wait for admission into the neurosurgery ward or seek care elsewhere. We accomplish this by simulating the admission and patient stay processes at the neurosurgery ward, and use the simulation to generate data to train machine learning algorithms to predict whether the patient is admitted as a function of the state of the simulation at the time the patient is seeking admission at the ward. With ensemble tree classifiers, we achieve generalization area under the curve scores of 95% for all patients taken together and between 80-95% depending upon patient subtype.