Statistical process control for validating a classification tree model for predicting mortality - A novel approach towards temporal validation

TL;DR: The case study shows that a simple annual recalibration of a general online risk tool for prostate cancer can improve its accuracy with respect to the local patient practice at hand.

TL;DR: This study assesses the impact of data outliers on the Irish Water Quality Index (IEWQI) model, using machine learning and statistical techniques, and finds that removing outliers improves model performance, but has no significant effect on model architecture or reliability.

TL;DR: New hybrid machine learning ensembles for improving the performance of a computer aided diagnosis system integrated with multimethod assessment process and statistical process control, used for the spine diagnosis based on noninvasive panoramic radiographs are adduced.

TL;DR: A simple method has been provided to tailor the PCPT Risk Calculator to individual hospitals to optimize its accuracy for the patient population at hand.

TL;DR: Suggested that could be demonstrated a live birth and data and demonstrated that were excluded, and developed and could be appropriate aac evidence.

TL;DR: An examination of the performance of the tests when the correct model has a quadratic term but a model containing only the linear term has been fit shows that the Pearson chi-square, the unweighted sum-of-squares, the Hosmer-Lemeshow decile of risk, the smoothed residual sum- of-Squares and Stukel's score test, have power exceeding 50 per cent to detect moderate departures from linearity.

TL;DR: Douglas Altman and colleagues describe how to validate models and discuss some of the problems that need to be solved to validate prognostic models.

TL;DR: Why research into prognosis is important and how to design such research is explained are explained.