Clinical Prediction Rules

By A. Jull, Alba DiCenso, G. Guyatt. 2004

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Citations in the corpus

Opportunities and obstacles for deep learning in biology and medicine (2017, methodology): “In 1996, Tu [263] compared the effectiveness of artificial neural networks and logistic regression, questioning whether these techniques would replace traditional statistical methods for predicting medical outcomes such as myocardial infarction [264] or mortality [265].”
External validation is necessary in prediction research: a clinical example. (2003, methodology): “It is generally recommended to compare the overall characteristics of the derivation and validation set before applying an internally validated prediction model to a new patient population [6,8].”
Prediction of In-hospital Mortality in Emergency Department Patients With Sepsis: A Local Big Data-Driven, Machine Learning Approach. (2016, background): “5 Furthermore, CDRs frequently suffer from questions of generalizability and performance variance when applied to the populations different from the derivation cohort, 6,7 often take years to develop and validate, 8 and are unlikely to be updated as new information becomes available.”
Predicting mortality among patients hospitalized for heart failure: derivation and validation of a clinical model. (2003, methodology): “Score-based prediction rules for mortality at 30 days and 1 year were developed from logistic regression models by using a regression coefficient-based scoring method.(33,34) Integer scores were assigned by dividing risk-factor coeffiPREDICTORS OF HEART FAILURE MORTALITY”