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 information on age, gender, geography, total cost etc may be added, furthermore and more importantly a clinical specialist may request addition or exclusion of operations, procedures and have other demands for quantitative measures mirroring the clinical developmental program of a specialization. Prevalence models offer very flexible modelling frameworks for quality analysis and decision support tools in the clinic.
In the result below I define a population of patients visiting a ward within a particular month. Then I add information on their activity patterns from the LPR (Danish National Health Register) in 2 years retroperspective and information on whether they are hospitalized (acute) within the next month. Indicators are defined using a short dummy-variable coding function and aggregated with ML techniques. The R-function use a key-variable V_CPR, and needs to be adapted before it is applicable in other settings...
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 information on age, gender, geography, total cost etc may be added, furthermore and more importantly a clinical specialist may request addition or exclusion of operations, procedures and have other demands for quantitative measures mirroring the clinical developmental program of a specialization. Prevalence models offer very flexible modelling frameworks for quality analysis and decision support tools in the clinic.
In the result below I define a population of patients visiting a ward within a particular month. Then I add information on their activity patterns from the LPR (Danish National Health Register) in 2 years retroperspective and information on whether they are hospitalized (acute) within the next month. Indicators are defined using a short dummy-variable coding function and aggregated with ML techniques. The R-function use a key-variable V_CPR, and needs to be adapted before it is applicable in other settings...
dummyl <- function(data, varname, vallevels,datevar,evaldate){
data<-data[trimws(data[[which(names(data)==varname)]]) %in% vallevels,]
df<-as.data.frame(matrix(0,nrow(data),2*length(vallevels))
for(i in 1:length(vallevels)){
df[,c(i,i+length(vallevels))]<-c(1.0*(trimws(data[[which(names(data)==varname)]])==trimws(vallevels[i])),log(as.integer(as.Date(evaldate)as.Date(data[[which(names(data)==datevar)]]))))
df[1.0*(trimws(data[[which(names(data)==varname)]])==trimws(vallevels[i]))<1,i+length(vallevels)]<-rep(NA,sum(1.0*(trimws(data[[which(names(data)==varname)]])==trimws(vallevels[i]))<1))
names(df)[c(i,i+length(vallevels))]<-c(vallevels[i],paste0(c(vallevels[i],"_dto"),sep="",collapse=""))
}
dt<-setorderv(cbind.data.frame(data,df),c("V_CPR",varname,datevar),c(1,1,-1))
return(dt)
}
A 200 line code script generates a fairy good raw prevalence model for prediction of acute hospitalization with a AUC above 0.92, the probability of aligning a pair of patients correct based on estimated risk for acute hospitalization is very high. Least squares and subsequently logistic regression makes a solid foundation for a stable and adjustable prediction model.
#Example of usage, generating indicators for 5% prevalence model used for accumalating measures in regression analysis
temp<-unique(Dat[,c("V_CPR","val")])
tbl<-table(temp$val)
lvs_5pct<-names(table(temp$val)[tbl>5*length(cprnr)/100])
lvs_5pct
dt<-dummyl(Dat,"val",lvs_5pct,"date","2017-12-01")
The data extraction and manipulation uses SQL and ML R-packages RODBC, tidyr, stringr and dplyr. Estimation requires basic R algorithms and GLM modeling.
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