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Predicthor: AI-Powered Predictive Risk Model for 30-Day Mortality and 30-Day Complications in Patients Undergoing Thoracic Surgery for Lung Cancer.

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Bibliographic Details
Title:	Predicthor: AI-Powered Predictive Risk Model for 30-Day Mortality and 30-Day Complications in Patients Undergoing Thoracic Surgery for Lung Cancer.
Authors:	Durand X; From the SurgeCare, SAS, Department of Data Science, Paris, France., Hédou J; From the SurgeCare, SAS, Department of Data Science, Paris, France.; Sorbonne Université, Inserm, UMRS_938, Centre de Recherche Saint-Antoine (CRSA), Paris, France., Bellan G; From the SurgeCare, SAS, Department of Data Science, Paris, France., Thomas PA; Department of Thoracic Surgery and Diseases of the Esophagus, Aix-Marseille University, Marseille, France., Pages PB; Department of Thoracic Surgery, CHU Dijon, France., D'Journo XB; Department of Thoracic Surgery and Diseases of the Esophagus, Aix-Marseille University, Marseille, France., Brouchet L; Department of Thoracic Surgery, CHU Toulouse, Toulouse, France., Rivera C; Department of Thoracic Surgery, Centre Hospitalier de la Côte Basque, Bayonne, France., Falcoz PE; Department of Thoracic Surgery, CHU Strasbourg, Strasbourg, France., Gillibert A; Department of Cardio-Thoracic Surgery, CHU Rouen, Inserm, UNIVROUEN, France., Baste JM; Department of Cardio-Thoracic Surgery, CHU Rouen, Inserm, UNIVROUEN, France.
Source:	Annals of surgery open : perspectives of surgical history, education, and clinical approaches [Ann Surg Open] 2025 May 27; Vol. 6 (2), pp. e578. Date of Electronic Publication: 2025 May 27 (Print Publication: 2025).
Publication Type:	Journal Article
Journal Info:	Publisher: Wolters Kluwer Country of Publication: United States NLM ID: 101769928 Publication Model: eCollection Cited Medium: Internet ISSN: 2691-3593 (Electronic) Linking ISSN: 26913593 NLM ISO Abbreviation: Ann Surg Open Subsets: PubMed not MEDLINE
Database:	MEDLINE Ultimate

Description
ISSN:	2691-3593
DOI:	10.1097/AS9.0000000000000578