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Showing posts from 2018

Prevalence models in health science

I chose to divide generic prediction models applied in health science and administration into two main groups: Models based on general activity measures such as number of hospitalizations, LOS, number of visits, cost, diagnose groups, age, geography and other background information. A second and neglected group of models is based on prevalence of specific activity measures common for a substantial part of the population in question. Prediction models in health take advantage of RFM-I methodology from market analysis, which have previously been mentioned in posts on SAS macros on this blog, below I discuss the simplicity of prevalence models. Prevalence models have my special attention as pivot for machine learning and deep learning models. Prevalence models include indicators on activity common among 1%, 5% or 10% of a population, e.g. diagnoses, operations and procedures common to 1% of the patients from a ward with a retroperspective ranging from months to years. Background informa

Crowd monitoring and performance analysis

A job for a statistician in the era of data science driven progress within businesses reflecting all aspects of life: Crowd and performance analysis. Using the Microsoft Azure API for analysis of crowd monitoring input pictures. Measuring levels of general emotions like happiness, sadness, surprise, anger, disgust, fear, contempt and neutral as well as gender, age and measures like baldness. Combining from several cameras to measure levels over time comparing with set list, place in crowd, mood on stage and background variables like size of venue, city and socio-demographic info. You can name a countless number of applications in a very dynamic and business oriented context. Crowd displaying surprise, happiness and neutral. Ages 34, 50, 51, 28, 29 and 22. Andy Clayton on stage displaying 71% neutral and 28.9% sadness while Bono is 98% happy .

Illustrative plots and tables (SAS macro)

Basic SAS macros for basic summary statistics and illustrative plots. Features ttests, Van der Waerden and Wilcoxon tests for continuous variables and both chisq and Fisher tests for categorical variables. Overall tests may be based on accumulated measures such as average integrated values, which are interpretable on original scale: %macro averageIntegral(values,timepoints,intlength,retval); &retval=(0 %local count;  %let count=0;  %let time_old=0;  %let val_old=0;  %do %while(%qscan(&values,&count+1,%str( )) ne %str());  %let time=%scan(&timepoints,&count+1,%str( ));   %let val=%scan(&values,&count+1,%str( ));   %if &count GT 0 %then +(&time-&time_old)*(&val_old+&val)/2;  %let time_old=&time;  %let val_old=&val;  %let count=%eval(&count+1);  %end;  )/(&intlength*1.0); %mend; Table output for continuous data (contrasts between groups are evaluated using t-tests, van der waerden and Wilcoxon) c

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