TY - GEN
T1 - Learning a Causal Model for Intracranial Pressure in Patients with Traumatic Brain Injury
AU - Zanga, Alessio
AU - Graziano, Francesca
AU - Citerio, Giuseppe
AU - Rebora, Paola
AU - Galiberti, Stefania
AU - Bhattacharyay, Shubhayu
AU - Menon, David K.
AU - Steyerberg, Ewout W.
AU - Stella, Fabio
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
PY - 2026/4
Y1 - 2026/4
N2 - 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.
AB - 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.
KW - Continuous time Bayesian network
KW - Intensive care unit
KW - Intracranial pressure
KW - Traumatic brain injury
UR - https://www.scopus.com/pages/publications/105038414999
U2 - 10.1007/978-3-032-16708-8_22
DO - 10.1007/978-3-032-16708-8_22
M3 - Conference contribution
AN - SCOPUS:105038414999
SN - 9783032167071
T3 - Communications in Computer and Information Science
SP - 270
EP - 282
BT - Artificial Intelligence for Healthcare, and Hybrid Models for Coupling Deductive and Inductive Reasoning - 1st International Joint Conference, HC@AIxIA+HYDRA 2025, Proceedings
A2 - Bruno, Pierangela
A2 - Calimeri, Francesco
A2 - Terracina, Giorgio
A2 - Cauteruccio, Francesco
A2 - Dragoni, Mauro
A2 - Stella, Fabio
PB - Springer Science and Business Media Deutschland GmbH
T2 - 1st International Joint Workshop on Artificial Intelligence for Healthcare, and Hybrid Models for Coupling Deductive and Inductive Reasoning, HC@AIxIA+HYDRA 2025
Y2 - 25 October 2025 through 26 October 2025
ER -