Sepsis is a life-threatening condition with complex and dynamic progression, often requiring timely and personalized treatment strategies. In this paper, we propose a multivariate longitudinal clustering, an advanced data analysis technique, as a powerful approach to understanding the diverse trajectories of sepsis by grouping patients based on multiple clinical variables measured over time. Dynamic Time Warping (DTW) is integrated into the longitudinal clustering as a distance measure to identify subgroups of patients with similar temporal patterns in multivariate data. By leveraging sepsis-related electronic health records (EHRs), which provide rich time-series data on laboratory results along with patient demographics and underlying health conditions, the proposed method reveals distinct sepsis phenotypes that reflect variations in disease progression. Our results confirm the critical role of the Thrombin-Antigen complex and the International Normalized Ratio as predictors of poor outcomes for septic patients. Despite challenges like missing data and interpretability, multivariate longitudinal clustering in sepsis offers significant potential to enhance clinical decision-making and improve patient outcomes.

Ribino, P., Mannone, M., Di Napoli, C., Paragliola, G., Chicco, D., Gasparini, F. (2024). Analyzing trajectories of clinical markers in patients with sepsis through multivariate longitudinal clustering. In HC@AIxIA 2024 Artificial Intelligence For Healthcare 2024 Proceedings of the 3rd AIxIA Workshop on Artificial Intelligence For Healthcare (HC@AIxIA 2024) co-located with the 23rd International Conference of the Italian Association for Artificial Intelligence (AIxIA 2024) (pp.247-256). CEUR-WS.

Analyzing trajectories of clinical markers in patients with sepsis through multivariate longitudinal clustering

Chicco D.;Gasparini F.
2024

Abstract

Sepsis is a life-threatening condition with complex and dynamic progression, often requiring timely and personalized treatment strategies. In this paper, we propose a multivariate longitudinal clustering, an advanced data analysis technique, as a powerful approach to understanding the diverse trajectories of sepsis by grouping patients based on multiple clinical variables measured over time. Dynamic Time Warping (DTW) is integrated into the longitudinal clustering as a distance measure to identify subgroups of patients with similar temporal patterns in multivariate data. By leveraging sepsis-related electronic health records (EHRs), which provide rich time-series data on laboratory results along with patient demographics and underlying health conditions, the proposed method reveals distinct sepsis phenotypes that reflect variations in disease progression. Our results confirm the critical role of the Thrombin-Antigen complex and the International Normalized Ratio as predictors of poor outcomes for septic patients. Despite challenges like missing data and interpretability, multivariate longitudinal clustering in sepsis offers significant potential to enhance clinical decision-making and improve patient outcomes.
paper
clustering; electronic health records; intensive care unit; longitudinal clustering; patient trajectories; sepsis; unsupervised machine learning;
English
3rd AIxIA Workshop on Artificial Intelligence For Healthcare (HC@AIxIA 2024) co-located with the 23rd International Conference of the Italian Association for Artificial Intelligence (AIxIA 2024) - 27-28 November 2024
2024
HC@AIxIA 2024 Artificial Intelligence For Healthcare 2024 Proceedings of the 3rd AIxIA Workshop on Artificial Intelligence For Healthcare (HC@AIxIA 2024) co-located with the 23rd International Conference of the Italian Association for Artificial Intelligence (AIxIA 2024)
2024
3880
247
256
https://ceur-ws.org/Vol-3880/
open
Ribino, P., Mannone, M., Di Napoli, C., Paragliola, G., Chicco, D., Gasparini, F. (2024). Analyzing trajectories of clinical markers in patients with sepsis through multivariate longitudinal clustering. In HC@AIxIA 2024 Artificial Intelligence For Healthcare 2024 Proceedings of the 3rd AIxIA Workshop on Artificial Intelligence For Healthcare (HC@AIxIA 2024) co-located with the 23rd International Conference of the Italian Association for Artificial Intelligence (AIxIA 2024) (pp.247-256). CEUR-WS.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/534341
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