Temporal Convolutional Networks for Forecasting Patient Volumes in Digital Healthcare

Sammanfattning: Patient volume forecasting is an important tool for staffing clinicians to meet patient demands. In traditional care, the problem has been studied by multiple authors with inconclusive results. Recent advances in using recurrent and convolutional models in the neighbouring area of sequence modeling have not yet been replicated in the area of patient volume forecasting in traditional healthcare. In the growing area of digital care, only one study has attempted the problem to date.In this study, a Long Short-Term Memory Network (LSTM) and a Temporal Convolutional Network (TCN) were implemented and fit in a one-step forecasting problem using historical hourly patient volumes of a digital caregiver, both with and without explicit weekday annotations. The models were evaluated in one-step and multi-step forecasting with an horizon up to 168 time steps (1 week), and compared to statistical baseline models. In the one-step forecasting evaluation the univariate TCN achieved a Mean Squared Error (MSE) of 93.4 2.4, outperforming the univariate LSTM (122.2 5.9 MSE) and all baseline models (best: 193 MSE). In the 168-step forecasting evaluation, the univariate TCN achieved a mean MSE (MMSE) for each step in the forecasted horizon of 143.2 5.5, outperforming the LSTM (261.5 63.0 MMSE) and baseline models (best: 195.8 MMSE). The performance of the LSTM and TCN models were shown to deteriorate for each step ahead in the multi-step forecasts, the LSTM at a faster rate than the TCN. The results indicated that the models learned to approximate the seasonality of the dataset, but when the data deviated, the accuracy of all models worsened. The use of multivariate data lowered the errors slightly. The computational performance of the TCN, attributed to its parallelizable architecture, was shown to be a major advantage over the LSTM.It was concluded that the TCN is a promising alternative to the LSTM in the context of the specific problem, both in terms of accuracy and usability, but that more studies are needed to say anything about the general problem of patient volume forecasting in digital healthcare.