Abstract

ZAMBRANO RAMIREZ, Oscar Daniel  and  FONTBONNE, Jean-Marc. Development of clinically based prediction models using machine learning and Bayesian statistics. Nucleus [online]. 2019, n.65, pp. 6-10.  Epub July 27, 2019. ISSN 2075-5635.

In this work, the framework for developing generic clinically based models is emphasized and illustrated with Bayesian statistics neurologic grade prediction models in order to exemplify the type of models that can be developed from a mathematical point of view. The models are based on clinical records of patients who underwent radiotherapy treatment due to glioblastoma which is an aggressive brain cancer. A first model requires as a parameter the neurologic grade of the patient before the treatment then predicts the grade after the treatment. A second, enhanced, model was developed with the aim of making the prediction more realistic and it uses the neurologic grade before the treatment as well, but it additionally depends on the Clinical Target Volume (CTV). Furthermore, with the aid of Bayesian statistic we were able to estimate the uncertainty of the predictions.

Keywords : learning; adaptive systems; statistics; clinical trials; prediction equations.

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