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

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.

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Published
2025-03-16
DOI
https://doi.org/10.24135/iconip1
Primary Topic
Emergency and Acute Care Studies
Type
article
Field-Weighted Citation Impact
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article

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

Samantha Marsh, Peter McIntyre, Cameron Charles Grant, Gillian Dobbie et al.
Emergency and Acute Care Studies
article

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

Samantha Marsh, Peter McIntyre, Cameron Charles Grant, Gillian Dobbie, Patricia Riddle, Nikki Turner, Sarah Jamison, Adrian Trenholme, Katharina Dost, Janine Paynter, David Broderick, Jörg Wicker, Shirley Lawrence, Alicia Stanley, Alex C. Kim, Lorraine Castelino, Steffen Albrecht, Damayanthi Rasanathan, Nooriyan Poonawala-Lohani, Johnny Zhu, João Afonso Madelino
article en

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 naïve forecasts.

University of Auckland (NZ), Jožef Stefan Institute (SI), Counties Manukau District Health Board (NZ), Primary HealthCare (MT), Starship Children's Health (NZ), University of Otago (NZ)
Openalex Percentile: Top 91%
Emergency and Acute Care Studies
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