Filippos Filias
University of Patras
Machine learningWeighted votingArtificial intelligenceDysuriaProstate cancerClassifier (linguistics)PopulationComputer scienceEnsemble learningVotingGenetic algorithm
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#1Filippos Filias (University of Patras)
#2Eugenia Mylona (University of Rennes)H-Index: 4
Last. Oscar Acosta (University of Rennes)H-Index: 21
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Prediction of urinary toxicity after prostate cancer radiotherapy (RT) is remarkably challenging. Not only it is a multifaceted phenomenon, encompassing different symptoms (retention, dysuria, haematuria, etc.), but also a multifactorial problem, as it depends on both patient-specific clinical factors, individual biological parameters, and dosimetric patterns. Thus, there are a plethora of potential predictors compared to the paucity of available symptom-specific toxicity data. On top of that, i...
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