Epidemiology/Biostat at Analytics & Data Mining Conference in San Diego, California, May 23-25
Lisa Solomon <
lisas@...>
2012-05-02 20:23:41 GMT
Don't miss it, it's this month!
The 2012 Salford Analytics & Data Mining Conference aims to bring together
researchers, practitioners, and data enthusiasts to exchange ideas and
experiences.
Attendees will have the chance to have one-on-one meetings with the creators of the CART and RandomForests algorithms
(Dr. Adele Cutler, Dr. Jerome Friedman and Dr. Richard Olshen).
Attendees will have the chance to learn from practical and theoretical experts including:
* Dr. Richard Olshen (Chief, Division of Biostatistics at Stanford University School of Medicine and
co-creator of CART)
* Dr. Adele Cutler (co-developer of RandomForests with the late Leo Breiman)
* Dr. Marsha Wilcox, Director of Epidemiology, Johnson & Johnson
* Ben Trzaskoma, Senior Statistical Scientist, Genentech, Inc.
* Dr. Richard Straub, Investigator at Lieber Institute for Brain Development
* Onchee Yu, Biostatistician at Group Health Research Institute
* Dr. Robert Aronoff, Physician/Researcher and Assistant Professor of Medicine, Milton S. Hershey
Medical Center
* Dr. Jason Haukoos, Director of Research and Clinical Research Fellowship, Department of Emergency Medicine,
Denver Health Medical Center; Associate Professor, University of Colorado, School of Medicine;
Associate Professor, Department of Epidemiology
at the Colorado School of Public Health.
And many more!
Presentations include:
--"Applying CART to cluster analyses in asthma databases to reduce the dimensionality of testing drug interactions"
--"Algorithms to identify second breast cancer events using administrative healthcare data"
--"Genes, Cognition, and Psychosis: Insights from TreeNet Gradient Boosting and RandomForests"
--"Predicting Hospital Readmission Using TreeNet Gradient Boosting"
(Continue reading)
1. Gwet KL. Computing inter-rater reliability and its variance in the presence of high agreement. Br J Math Stat Psychol. 2008 ;61(Pt 1):2948. # AC1 statistic for 2 raters special case # table = k x k table which represents table(rater1,rater2), must have equal number of rows and columns # N = population size which will be stick in standard error correction, N=Inf is no correction. # conflev = Confidence Level associated with the confidence interval (0.95 is the default value) AC1 <- function(table,conflev=0.95,N=Inf,print=TRUE){ if(dim(table)[1] != dim(table)[2]){ stop('The table should have the same number of rows and columns!') } n <- sum(table) f <- n/N pa <- sum(diag(table))/n # formula 18 q <- ncol(table) # number of categories pkk <- diag(table)/n pak <- sapply(1:q,function(i)sum(table[i,]))/n pbk <- sapply(1:q,function(i)sum(table[,i]))/n pik <- (pak + pbk)/2
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