Simultaneous forecasting of vital sign trajectories in the ICU
Abstract Individual health trajectory forecasting is a major opportunity for computational methods to integrate with precision healthcare. Recently developed generative AI models have demonstrated promising results in capturing short and long range dependencies in time series data. While these model...
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Nature Portfolio
2025-04-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-99719-w |
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| author | Rosemary He Jeffrey N. Chiang |
| author_facet | Rosemary He Jeffrey N. Chiang |
| author_sort | Rosemary He |
| collection | DOAJ |
| description | Abstract Individual health trajectory forecasting is a major opportunity for computational methods to integrate with precision healthcare. Recently developed generative AI models have demonstrated promising results in capturing short and long range dependencies in time series data. While these models have also been applied in healthcare, most state-of-the-art are local models, i.e. one model per feature, which is unrealistic in a clinical setting where multiple measures are taken at once. In this work, we extend the framework temporal fusion transformer (TFT), a multi-horizon time series prediction tool, and propose TFT-multi, a global model that can predict multiple vital trajectories simultaneously. We apply TFT-multi to forecast 5 vital signs recorded in the intensive care unit: blood pressure, pulse, SpO2, temperature and respiratory rate. We hypothesize that by jointly predicting these measures, which are often correlated with one another, we can make more accurate predictions, especially in variables with large missingness. We validate our model on the public MIMIC dataset and an independent institutional dataset, and demonstrate our model’s competitive performance and computational efficiency compared to state-of-the-art prediction tools. Furthermore, we perform a study case analysis by applying our pipeline to forecast blood pressure changes in response to actual and hypothetical pressor administration. |
| format | Article |
| id | doaj-art-a529bc45808d4d42845ca18d76740416 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-a529bc45808d4d42845ca18d767404162025-08-20T03:52:23ZengNature PortfolioScientific Reports2045-23222025-04-0115111010.1038/s41598-025-99719-wSimultaneous forecasting of vital sign trajectories in the ICURosemary He0Jeffrey N. Chiang1Department of Computer Science, University of California Los AngelesDepartment of Computational Medicine, University of California Los AngelesAbstract Individual health trajectory forecasting is a major opportunity for computational methods to integrate with precision healthcare. Recently developed generative AI models have demonstrated promising results in capturing short and long range dependencies in time series data. While these models have also been applied in healthcare, most state-of-the-art are local models, i.e. one model per feature, which is unrealistic in a clinical setting where multiple measures are taken at once. In this work, we extend the framework temporal fusion transformer (TFT), a multi-horizon time series prediction tool, and propose TFT-multi, a global model that can predict multiple vital trajectories simultaneously. We apply TFT-multi to forecast 5 vital signs recorded in the intensive care unit: blood pressure, pulse, SpO2, temperature and respiratory rate. We hypothesize that by jointly predicting these measures, which are often correlated with one another, we can make more accurate predictions, especially in variables with large missingness. We validate our model on the public MIMIC dataset and an independent institutional dataset, and demonstrate our model’s competitive performance and computational efficiency compared to state-of-the-art prediction tools. Furthermore, we perform a study case analysis by applying our pipeline to forecast blood pressure changes in response to actual and hypothetical pressor administration.https://doi.org/10.1038/s41598-025-99719-wGlobal trajectory predictionsGenerative AITime seriesClinical support |
| spellingShingle | Rosemary He Jeffrey N. Chiang Simultaneous forecasting of vital sign trajectories in the ICU Scientific Reports Global trajectory predictions Generative AI Time series Clinical support |
| title | Simultaneous forecasting of vital sign trajectories in the ICU |
| title_full | Simultaneous forecasting of vital sign trajectories in the ICU |
| title_fullStr | Simultaneous forecasting of vital sign trajectories in the ICU |
| title_full_unstemmed | Simultaneous forecasting of vital sign trajectories in the ICU |
| title_short | Simultaneous forecasting of vital sign trajectories in the ICU |
| title_sort | simultaneous forecasting of vital sign trajectories in the icu |
| topic | Global trajectory predictions Generative AI Time series Clinical support |
| url | https://doi.org/10.1038/s41598-025-99719-w |
| work_keys_str_mv | AT rosemaryhe simultaneousforecastingofvitalsigntrajectoriesintheicu AT jeffreynchiang simultaneousforecastingofvitalsigntrajectoriesintheicu |