Journal ArticleDOI
Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach.
TLDR
A nonparametric approach to the analysis of areas under correlated ROC curves is presented, by using the theory on generalized U-statistics to generate an estimated covariance matrix.Citations
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Maximum entropy modeling of species geographic distributions
TL;DR: In this paper, the use of the maximum entropy method (Maxent) for modeling species geographic distributions with presence-only data was introduced, which is a general-purpose machine learning method with a simple and precise mathematical formulation.
Journal ArticleDOI
FastTree 2--approximately maximum-likelihood trees for large alignments.
TL;DR: Improvements to FastTree are described that improve its accuracy without sacrificing scalability, and FastTree 2 allows the inference of maximum-likelihood phylogenies for huge alignments.
Journal ArticleDOI
pROC: an open-source package for R and S+ to analyze and compare ROC curves
Xavier Robin,Natacha Turck,Alexandre Hainard,Natalia Tiberti,Frédérique Lisacek,Jean-Charles Sanchez,Markus Müller +6 more
TL;DR: pROC as mentioned in this paper is a package for R and S+ that contains a set of tools displaying, analyzing, smoothing and comparing ROC curves in a user-friendly, object-oriented and flexible interface.
Journal ArticleDOI
Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data.
Hude Quan,Vijaya Sundararajan,Patricia Halfon,Andrew Fong,Bernard Burnand,Jean-Christophe Luthi,L. Duncan Saunders,Cynthia A. Beck,Thomas E Feasby,William A. Ghali +9 more
TL;DR: A multistep process to develop ICD-10 coding algorithms to define Charlson and Elixhauser comorbidities in administrative data and assess the performance of the resulting algorithms found these newly developed algorithms produce similar estimates ofComorbidity prevalence in administrativeData, and may outperform existing I CD-9-CM coding algorithms.
Journal ArticleDOI
Acute respiratory distress syndrome: the Berlin Definition.
Ards Definition Task Force,V. Marco Ranieri,Gordon D. Rubenfeld,B. Taylor Thompson,Niall D. Ferguson,Ellen Caldwell,Eddy Fan,Luigi Camporota,Luigi Camporota,Arthur S. Slutsky +9 more
TL;DR: The updated and revised Berlin Definition for ARDS addresses a number of the limitations of the AECC definition and may serve as a model to create more accurate, evidence-based, critical illness syndrome definitions and to better inform clinical care, research, and health services planning.
References
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Journal ArticleDOI
The meaning and use of the area under a receiver operating characteristic (ROC) curve.
TL;DR: A representation and interpretation of the area under a receiver operating characteristic (ROC) curve obtained by the "rating" method, or by mathematical predictions based on patient characteristics, is presented and it is shown that in such a setting the area represents the probability that a randomly chosen diseased subject is (correctly) rated or ranked with greater suspicion than a random chosen non-diseased subject.
Journal ArticleDOI
Basic principles of ROC analysis
TL;DR: ROC analysis is shown to be related in a direct and natural way to cost/benefit analysis of diagnostic decision making and the concepts of "average diagnostic cost" and "average net benefit" are developed and used to identify the optimal compromise among various kinds of diagnostic error.
Book ChapterDOI
A Class of Statistics with Asymptotically Normal Distribution
TL;DR: In this article, the authors considered the problem of estimating a U-statistic of the population characteristic of a regular functional function, where the sum ∑″ is extended over all permutations (α 1, α m ) of different integers, 1 α≤ (αi≤ n, n).
Journal ArticleDOI
The area above the ordinal dominance graph and the area below the receiver operating characteristic graph
TL;DR: In this article, receiver operating characteristic graphs are shown to be a variant form of ordinal dominance graphs, and several different methods of constructing confidence intervals for the area measure are presented and the strengths and weaknesses of each of these methods are discussed.