WITHDRAWN: A neural network based machine learning model in digital health care for wait-time prediction

Abstract

It is an inevitable part of life to wait in queues, and not knowing how long the wait would last can be a major concern. In order to minimise this, businesses also try to forecast waiting times in order to be able to handle their sleeves. In medical care, this is extremely important because the patients are possibly already in some pain. This study compares the performance of two separate ML methods and a simulation approach to the issue of wait-time prediction in digital healthcare environments. In order to match the best ML model with the simulation approach, a hybrid method was also introduced. The ML approaches used historical data of the patient queue to build a model for new patients who enter the queue to estimate the waiting time. The simulation algorithm imitates the queue in a simulated world and simulates time going forward to allocate a clinician to the new patient who enters the queue and thus creates a wait time assessment. As an additional feature for the best ML model, the simulation approach used the wait-time estimates provided by the simulation algorithm. The Machine Learning sequence modelling approach was implemented and defined by a Time Convolutional Network (TCN) model and a Long Short Term Memory Network (LSTM) model. The conventional approach to ML was implemented as a Random Forest Regressor (RF) model and a Support Vector Regressor (SVR) model. The exponential smoothing pre-processing technique was used to incorporate the temporal dimension into the conventional ML approach. The findings showed that all models vary statistically significantly. The TCN model and simulation algorithm had all individual models with the lowest mean square error (MSE). Compared to both conventional ML models, both sequence models had lower MSE. The MSE model had the lowest MSE of the whole and had both the ML and the simulation method the highest output characteristics. The hybrid model is however the most complex and thus needs the most maintenance.