Enhancing Healthcare Resource Demand Forecasting Using Machine Learning: An Integrated Approach to Addressing Temporal Dynamics and External Influences

This project aims to enhance predictive models for forecasting healthcare resource demand, particularly focusing on hospital bed occupancy and emergency room visits while considering external factors such as disease outbreaks and weather conditions. Utilizing a range of machine learning techniques, the research seeks to improve the accuracy and reliability of these forecasts, essential for optimizing healthcare resource management. The project involves multiple phases, starting with the collection and preparation of historical data from public health databases and hospital records, enriched with external variables such as weather patterns and epidemiological data. Advanced feature engineering is key, transforming raw data into a machine learning-friendly format, including temporal and lag features to identify patterns and trends. The study explores various machine learning methods, from traditional models like ARIMA to advanced techniques such as LSTM networks and GRU models, incorporating rigorous training and validation protocols to ensure robust performance. Model effectiveness is evaluated using metrics like MAE, RMSE, and MAPE, with a strong focus on model interpretability and explainability through techniques like SHAP and LIME. The project also addresses practical implementation challenges and ethical considerations, aiming to bridge academic research with practical healthcare applications. Findings are intended for dissemination through academic papers and conferences, ensuring that the models developed meet both the ethical standards and practical needs of the healthcare industry.