Toshihiko Takada
0000-0002-8032-6224
9 papers found
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Systematic review finds “spin” practices and poor reporting standards in studies on machine learning-based prediction models
Systematic review identifies the design and methodological conduct of studies on machine learning-based prediction models
Accuracy of approximations to recover incompletely reported logistic regression models depended on other available information
Completeness of reporting of clinical prediction models developed using supervised machine learning: a systematic review
Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review
Internal-external cross-validation helped to evaluate the generalizability of prediction models in large clustered datasets
Protocol for a systematic review on the methodological and reporting quality of prediction model studies using machine learning techniques
Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal
Added value of inflammatory markers to vital signs to predict mortality in patients suspected of severe infection
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