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1. www.nature.com › articles › s41598/021/85223-4Predictive model for the 5-year survival status of ... - Nature

   www.nature.com › articles › s41598/021/85223-4

   Mar 10, 2021 · Therefore, we aimed to construct an artificial intelligence (AI) model for predicting the 5-year survival of osteosarcoma patients by using extreme gradient boosting (XGBoost), a...

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2. pubmed.ncbi.nlm.nih.gov › 33692453Predictive model for the 5-year survival status of ... - PubMed

   pubmed.ncbi.nlm.nih.gov › 33692453

   Mar 10, 2021 · Therefore, we aimed to construct an artificial intelligence (AI) model for predicting the 5-year survival of osteosarcoma patients by using extreme gradient boosting (XGBoost), a large-scale machine-learning algorithm.

   • Author: Jiuzhou Jiang, Hao Pan, Mobai Li, Bao Qian, Xianfeng Lin, Shunwu Fan

   • Publish Year: 2021

3. www.nature.com › articles › s41598/024/54222-6Integrating artificial intelligence in osteosarcoma prognosis ...

   www.nature.com › articles › s41598/024/54222-6

   Feb 21, 2024 · The principal aim of this investigation is to identify pivotal biomarkers linked to the prognosis of osteosarcoma (OS) through the application of artificial intelligence (AI), with an ultimate...

4. www.researchgate.net › publication › 349960301(PDF) Predictive model for the 5-year survival status of ...

   www.researchgate.net › publication › 349960301

   Mar 10, 2021 · Therefore, we aimed to construct an artificial intelligence (AI) model for predicting the 5-year survival of osteosarcoma patients by using extreme gradient boosting (XGBoost), a...

5. www.ncbi.nlm.nih.gov › pmc › articlesPredictive model for the 5-year survival status of ...

   www.ncbi.nlm.nih.gov › pmc › articles

   Mar 10, 2021 · Therefore, we aimed to construct an artificial intelligence (AI) model for predicting the 5-year survival of osteosarcoma patients by using extreme gradient boosting (XGBoost), a large-scale machine-learning algorithm.

   • Author: Jiuzhou Jiang, Hao Pan, Mobai Li, Bao Qian, Xianfeng Lin, Shunwu Fan
   • DOI: 10.1038/s41598-021-85223-4

   • Publish Year: 2021
   • Publication: Sci Rep. 2021; 11: 5542.

6. www.nature.com › articles › s41598/021/85223-4Predictive model for the 5-year survival status of ... - Nature

   www.nature.com › articles › s41598/021/85223-4

   Therefore, we aimed to construct an articial intelligence (AI) model for predicting the {‑year survival of osteosarcoma patients by using extreme gradient boosting (XGBoost), a large‑scale...

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   Can artificial intelligence predict the 5-year survival of osteosarcoma patients?

   • However, there is still no prediction model with a high accuracy rate for osteosarcoma. Therefore, we aimed to construct an artificial intelligence (AI) model for predicting the 5-year survival of osteosarcoma patients by using extreme gradient boosting (XGBoost), a large-scale machine-learning algorithm.

   Predictive model for the 5-year survival status of osteosarcoma patient…

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   Which survival prediction models are used for osteosarcoma patients?

   • Several survival prediction models have been used for osteosarcoma patients, including those based on nomograms (constructed by regression models) 13, tomography images 12, or the ML algorithm 11. A 1-year survival prediction model using the Bayesian network was constructed in 2017 11, with an AUC of 0.767. However, this was a single-center study.

   Predictive model for the 5-year survival status of osteosarcoma patient…

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   Can a 5-year survival curve predict osteosarcoma patients?

   • The overall survival curve for 2694 osteosarcoma patients from the SEER program database declined much rapidly before the 5-year cut-off, compared with a slow downward trend in patient survival after 5 years (Fig. 1 ). Thus, predicting 5-year survival of osteosarcoma patients is of clinical value for treatment planning systems.

   Predictive model for the 5-year survival status of osteosarcoma patient…

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   Does XGBoost predict osteosarcoma survival?

   • Therefore, our AI model based on XGBoost had a higher accuracy in predicting the 5-year survival of osteosarcoma patients (AUC = 0.977 and 0.911 in the training and validation sets, respectively); the accuracy of a prediction model is considered the most important quality 14.

   Predictive model for the 5-year survival status of osteosarcoma patient…

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8. www.semanticscholar.org › paper › Predictive-model[PDF] Predictive model for the 5-year survival status of ...

   www.semanticscholar.org › paper › Predictive-model

   Mar 10, 2021 · An artificial intelligence model for predicting the 5-year survival of osteosarcoma patients by using extreme gradient boosting (XGBoost), a large-scale machine-learning algorithm that had excellent accuracy and is therefore useful in clinical settings.