Publications

@inproceedings{nokey,

title = {How does a GPT perform in Forecasting Severe Respiratory Disease Hospitalizations?},

author = {Steffen Albrecht and Alex Kim and Jo\~{a}o Madelino and Katharina Dost and Johnny Zhu and David Broderick and Nooriyan Poonawala-Lohani and Sarah Jamison and Damayanthi Rasanathan and Alicia Stanley and Shirley Lawrence and Samantha Marsh and Lorraine Castelino and Adrian Trenholme and Nikki Turner and Peter McIntyre and Janine Paynter and Pat Riddle and Cameron Grant and J\"{o}rg Wicker and Gillian Dobbie},

doi = { 10.24135/iconip1},

year  = {2025},

date = {2025-03-17},

booktitle = {31th International Conference on Neural Information Processing (ICONIP) },

volume = {1},

publisher = {Auckland University of Technology (AUT) Library},

abstract = {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 na\"{i}ve forecasts. DOI: https://doi.org/10.24135/ICONIP1

},

keywords = {influenza, machine learning, time series, time series forecasting},

pubstate = {published},

tppubtype = {inproceedings}

}