Artificial Intelligence In Intensive Care

Sawsen Chakroun, Maha Ben Mansour, Amine Ben Slimene (Autoren)

Buch | Softcover

52 Seiten

2025
Our Knowledge Publishing (Verlag)
978-620-8-69553-8 (ISBN)

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Early detection of septic shock is crucial for improving patient outcomes. This study aims to develop a machine learning model using XGBoost to predict septic shock six hours in advance. The model was trained on a public dataset comprising 40,336patients. It was tested on a portion of this set, achieving an accuracy of 0.97 and an AUC of 0.874. Predictions were also made for 8, 10 and 12 hours ahead, giving accuracies of 0.899, 0.891 and 0.8954, and AUCs of 0.867, 0.8639 and 0.8530, respectively.In addition, the model was tested on a local dataset from Fattouma Bourguiba University Hospital, comprising 30 patients. For prediction at 6 hours on the local dataset, the model achieved an accuracy of 0.89 and an AUC of 0.74. Predictions for 8, 10 and 12 hours ahead showed accuracies of 0.8861, 0.8772 and 0.8718, and AUCs of 0.73, 0.72 and 0.72, respectively. The XGBoost model shows potential for early detection of septic shock, but requires further testing and optimization for clinical application.

Prof. Sawsen Chakroun - Head of the Paediatric Anaesthesia and Intensive Care Department at the University Hospital Centre in Monastir, Tunisia.Associate Professor in Anaesthesia and Intensive Care.

Erscheinungsdatum	05.03.2025
Sprache	englisch
Maße	152 x 229 mm
Gewicht	82 g
Themenwelt	Medizin / Pharmazie ► Allgemeines / Lexika
Themenwelt	Medizin / Pharmazie ► Gesundheitsfachberufe
Schlagworte	Artificial Intelligence • early detection • Health. • machine learning • Septic shock • xgboost
ISBN-10	620-8-69553-8 / 6208695538
ISBN-13	978-620-8-69553-8 / 9786208695538
Zustand	Neuware