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Forecasting Future Monthly Patient Volume using Deep Learning and Statistical Models.

Zeydin Pala¹, Ramazan Atıcı², Erkan Yaldız³

¹Department of Software Engineering, Engineering Faculty, Mus Alparslan University, Mus, Turkey.

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|May 11, 2023

Summary

This summary is machine-generated.

Accurate forecasting of monthly radiology images using deep learning and statistical models enhances hospital preparedness. The Long Short-Term Memory (LSTM) network and AUTO.ARIMA model demonstrated superior performance in predicting imaging demand.

Keywords:

Deep learning models Monthly radiology patient volume Radiological data Radiology unit Statistics-based models Time series

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Area of Science:

Health Informatics
Artificial Intelligence in Healthcare
Radiology Department Management

Background:

Increasing disease prevalence necessitates enhanced hospital capacity, particularly in emergency and radiology departments.
Efficient patient flow and resource allocation in radiology are crucial due to radiation exposure and the need for timely imaging.
Accurate demand forecasting is vital for future planning and operational efficiency in radiology units.

Purpose of the Study:

To estimate the monthly number of radiological images using advanced predictive modeling techniques.
To compare the performance of deep learning and statistical models for radiology image demand forecasting.
To inform future planning and improve patient management in hospital radiology departments.

Main Methods:

Implementation of deep learning models: Long Short-Term Memory (LSTM), Multilayer Perceptron (MLP), NNAR, and Extreme Learning Machine (ELM).
Application of statistical time-series models: Autoregressive Integrated Moving Average (ARIMA), Simple Exponential Smoothing (SES), TBATS, Holt, and Theta.
Performance evaluation using symmetric mean absolute percentage error (sMAPE) and mean absolute scaled error (MASE) metrics.

Main Results:

The LSTM model achieved the highest accuracy among the deep learning models for forecasting monthly radiological images.
The AUTO.ARIMA model demonstrated superior performance compared to other statistical-based prediction models.
Both LSTM and AUTO.ARIMA provide reliable estimations for radiology image demand.

Conclusions:

Predictive modeling, particularly using LSTM and AUTO.ARIMA, can significantly improve radiology department planning and patient flow management.
Enhanced forecasting leads to increased operational efficiency, better service quality, and higher patient satisfaction.
The study findings support the integration of AI and statistical methods for strategic decision-making in healthcare settings.