Year: 2025
Type: book-chapter
Abstract: This study presents a methodology to acquire, integrate, and analyze clinical data based on an innovative application of the Optimal Survival Tree (OST) algorithm. It has been tested on a clinical dataset of 4114 patients with a follow-up of 59.0 ± 19.3 months, collected in 10 years at the University of Catanzaro Medical Hospital. Tests confirmed the capacity of the OST-based methodology to predict four profiles of 10-year atrial fibrillation risk with good results. Results have been compared with those obtained by using (i) Classification and Regression Tree (CART), (ii) Conditional Inference Tree (cTree), and (iii) Random Forest (RF). Performances for OST reported an AUC of 0.794 and a Brier Score of 0.131 whereas CART, C-Tree and RF reported an AUC of 0.764, 0.766 and 0.804, and a Brier Score of 0.137, 0.156 and 0.131, respectively, proving the efficacy of the proposed methodology.
DOI: 10.3233/shti250291
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W4410448053