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.
Zanga, A., Graziano, F., Citerio, G., Rebora, P., Galimberti, S., Bhattacharyay, S., et al. (2026). Learning a Causal Model for Intracranial Pressure in Patients with Traumatic Brain Injury. In Artificial Intelligence for Healthcare, and Hybrid Models for Coupling Deductive and Inductive Reasoning First International Joint Conference, HC@AIxIA+HYDRA 2025, Bologna, Italy, October 25–26, 2025, Proceedings (pp.270-282) [10.1007/978-3-032-16708-8_22].
Learning a Causal Model for Intracranial Pressure in Patients with Traumatic Brain Injury
Zanga, Alessio
Primo
;Graziano, Francesca;Citerio, Giuseppe;Rebora, Paola;Galimberti, Stefania;Stella, FabioUltimo
2026
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.| File | Dimensione | Formato | |
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