How does a GPT perform in Forecasting Severe Respiratory Disease Hospitalizations?

• DOI: 10.24135/iconip1
• Publication Date: 2025-03-16T23:44:41Z
• Authors: Steffen Albrecht; Alex Kim; João Afonso Madelino; Katharina Dost; Johnny Zhu; David Broderick; Nooriyan Poonawal...; Sarah Jamison; Damayanthi Rasana...; Alicia Stanley St...; Shirley Lawrence; Samantha Marsh; Lorraine Castelino; Adrian Trenholme; Nikki Turner; Peter McIntyre; Janine Paynter; Patricia Riddle; Cameron Grant; Jörg Simon Wicker; Gillian Dobbie

Forecasting surges in hospital admissions caused by severe respiratory infections is of crucial importance during the winter season to enable proactive hospital management and timely decision-making to prevent healthcare system overload. As time series derived from hospital surveillance systems for these severe cases are sparse and encode weak seasonality patterns, machine learning is key to computing accurate forecasts. The most recent algorithmic advance in time series forecasting is the adaptation of generative pre-trained transformers (GPTs). Those models, pre-trained on large datasets, have the potential to transfer knowledge to smaller datasets, such as hospital surveillance data, for very specific and well-defined case definitions. We demonstrate that despite this great potential for such practical applications, one of the largest first-generation GPTs is not able to provide accurate forecasts and is outperformed by simple linear models and even naïve forecasts.