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Machine learning to predict 5-year survival among pediatric Acute Myeloid Leukemia patients and development of OSPAM-C online survival prediction tool
Ashis Das
Shiba Mishra
Devi Kalyan Mishra
Saji Saraswathy Gopalan
Acceso Abierto
Background: Acute myeloid leukemia (AML) accounts for a fifth of childhood leukemia. Although survival rates for AML have greatly improved over the past few decades, they vary depending on demographic and AML type factors. Objectives: To predict the five-year survival among pediatric AML patients using machine learning algorithms and deploy the best performing algorithm as an online survival prediction tool. Materials and methods: Pediatric patients (0 to 14 years) with a microscopically confirmed AML were extracted from the Surveillance Epidemiology and End Results (SEER) database (2000-2011) and randomly split into training and test datasets (80/20 ratio). Four machine learning algorithms (logistic regression, support vector machine, gradient boosting, and K nearest neighbor) were trained on features to predict five-year survival. Performances of the algorithms were compared, and the best performing algorithm was deployed as an online prediction tool. Results: A total of 1,477 patients met our inclusion criteria. The gradient boosting algorithm was the best performer in terms of discrimination and predictive ability. It was deployed as the online survival prediction tool named OSPAM-C ( Conclusions: Our study provides a framework for the development and deployment of an online survival prediction tool for pediatric patients with AML. While external validation is needed, our survival prediction tool presents an opportunity to reach informed clinical decision-making for AML patients.
Epidemia COVID-19
Público en general
Versión publicada
publishedVersion - Versión publicada
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