Novel prognostication of patients with spinal and pelvic chondrosarcoma using deep survival neural networks

Background We used the Surveillance, Epidemiology, and End Results (SEER) database to develop and validate deep survival neural network machine learning (ML) algorithms to predict survival following a spino-pelvic chondrosarcoma diagnosis. Methods The SEER 18 registries were used to apply the Risk E...

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Bibliographic Details
Published in:	BMC medical informatics and decision making Vol. 20; no. 1; pp. 3 - 10
Main Authors:	Ryu, Sung Mo, Seo, Sung Wook, Lee, Sun-Ho
Format:	Journal Article
Language:	English
Published:	London BioMed Central 06.01.2020 BioMed Central Ltd Springer Nature B.V BMC
Subjects:	Age Algorithms Artificial intelligence Artificial neural networks Bone Neoplasms Cancer therapies Chemotherapy Chondrosarcoma Clinical decision-making Confidence intervals Diagnosis Epidemiology Health aspects Health Informatics Humans Indexing Information Systems and Communication Service Intelligence gathering knowledge support systems Learning algorithms Machine Learning Management of Computing and Information Systems Medical prognosis Medicine Medicine & Public Health Neural network Neural networks Neural Networks, Computer Patients Pelvis Prediction Prognosis Radiation Radiation (Physics) Radiation therapy Radiotherapy Research Article Statistical analysis Subgroups Surgery Survival Test sets theory Time Training Tumors Windows (intervals) South Korea United States > US Chondrosarcoma Neural network Artificial intelligence Survival Prediction
ISSN:	1472-6947, 1472-6947
Online Access:	Get full text
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