Learning a Causal Model for Intracranial Pressure in Patients with Traumatic Brain Injury

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Access status: Embargo until 2026-10-01 , 978-3-032-16708-8_22.pdf (1.08 MB)

Publication date

2026

Authors

Zanga, Alessio
Graziano, Francesca
Citerio, Giuseppe
Rebora, Paola
Galiberti, Stefania
Bhattacharyay, Shubhayu
Menon, David K.
Steyerberg, Ewout WORCID 0000-0002-7787-0122
Stella, Fabio

Editors

Bruno, Pierangela
Calimeri, Francesco
Terracina, Giorgio
Cauteruccio, Francesco
Dragoni, Mauro
Stella, Fabio

Advisors

Supervisors

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Part of book

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taverne

Abstract

Traumatic brain injury is a sequence of pathophysiological events that originates from an acute biomechanical insult. One of the major challenges physician face routinely in traumatic brain injury patients is the management of intracranial pressure. Elevated intracranial pressure may lead to herniation, causing injury through compression of brain tissue. Studying the underlying mechanism of intracranial pressure is crucial to develop personalized therapy planning. In this paper, we build a causal model from clinical experts knowledge and partially-observed event-based data to represent the trajectory of patients over time. We show how to derive insights on the effectiveness of multiple treatments allocations from the model parameters and evaluate the model against treatment policies reported in clinical guidelines.

Keywords

Continuous time Bayesian network, Intensive care unit, Intracranial pressure, Traumatic brain injury, Taverne, General Computer Science, General Mathematics

Citation

Zanga, A, Graziano, F, Citerio, G, Rebora, P, Galiberti, S, Bhattacharyay, S, Menon, D K, Steyerberg, E W & Stella, F 2026, Learning a Causal Model for Intracranial Pressure in Patients with Traumatic Brain Injury. in P Bruno, F Calimeri, G Terracina, F Cauteruccio, M Dragoni & F Stella (eds), Artificial Intelligence for Healthcare, and Hybrid Models for Coupling Deductive and Inductive Reasoning - 1st International Joint Conference, HC@AIxIA+HYDRA 2025, Proceedings. Communications in Computer and Information Science, vol. 2830 CCIS, Springer Science and Business Media Deutschland GmbH, pp. 270-282, 1st International Joint Workshop on Artificial Intelligence for Healthcare, and Hybrid Models for Coupling Deductive and Inductive Reasoning, HC@AIxIA+HYDRA 2025, Bologna, Italy, 25/10/25. https://doi.org/10.1007/978-3-032-16708-8_22, conference