dataset <- read.table("stdin", header=TRUE, sep=",")
#setwd("C:\\Users\\mehrnaz\\Dropbox\\ma+del\\advanced\\set-a")
#dataset <- read.table("142673.txt", header=TRUE, sep=",")
TIME=sapply(strsplit(as.character(dataset[,1]),":"),function(x) {
x <- as.numeric(x)
x[1]
}
)
data=cbind(TIME,dataset[,2:3])
d0=t(subset(data,data[,2]=="RecordID")[,-2])
d01=t(subset(data,data[,2]=="ALP")[,-2])
d02=t(subset(data,data[,2]=="ALT")[,-2])
d03=t(subset(data,data[,2]=="AST")[,-2])
d04=t(subset(data,data[,2]=="Age")[,-2])
d1=t(subset(data,data[,2]=="Albumin")[,-2])
d2=t(subset(data,data[,2]=="BUN")[,-2])
d3=t(subset(data,data[,2]=="Bilirubin")[,-2])
d4=t(subset(data,data[,2]=="Cholesterol")[,-2])
d5=t(subset(data,data[,2]=="Creatinine")[,-2])
d6=t(subset(data,data[,2]=="DiasABP")[,-2])
d7=t(subset(data,data[,2]=="FiO2")[,-2])
d8=t(subset(data,data[,2]=="GCS")[,-2])
d9=t(subset(data,data[,2]=="Gender")[,-2])
d10=t(subset(data,data[,2]=="Glucose")[,-2])
d11=t(subset(data,data[,2]=="HCO3")[,-2])
d12=t(subset(data,data[,2]=="HCT")[,-2])
d13=t(subset(data,data[,2]=="HR")[,-2])
d14=t(subset(data,data[,2]=="Height")[,-2])
d15=t(subset(data,data[,2]=="ICUType")[,-2])
d16=t(subset(data,data[,2]=="K")[,-2])
d17=t(subset(data,data[,2]=="Lactate")[,-2])
d18=t(subset(data,data[,2]=="MAP")[,-2])
d19=t(subset(data,data[,2]=="MechVent")[,-2])
d20=t(subset(data,data[,2]=="Mg")[,-2])
d21=t(subset(data,data[,2]=="NIDiasABP")[,-2])
d22=t(subset(data,data[,2]=="NIMAP")[,-2])
d23=t(subset(data,data[,2]=="NISysABP")[,-2])
d24=t(subset(data,data[,2]=="Na")[,-2])
d25=t(subset(data,data[,2]=="PaCO2")[,-2])
d26=t(subset(data,data[,2]=="PaO2")[,-2])
d27=t(subset(data,data[,2]=="Platelets")[,-2])
d28=t(subset(data,data[,2]=="RespRate")[,-2])
d29=t(subset(data,data[,2]=="SaO2")[,-2])
d30=t(subset(data,data[,2]=="SysABP")[,-2])
d31=t(subset(data,data[,2]=="Temp")[,-2])
d32=t(subset(data,data[,2]=="TroponinI")[,-2])
d33=t(subset(data,data[,2]=="Urine")[,-2])
d34=t(subset(data,data[,2]=="WBC")[,-2])
d35=t(subset(data,data[,2]=="Weight")[,-2])
d36=t(subset(data,data[,2]=="pH")[,-2])
d37=t(subset(data,data[,2]=="TroponinT")[,-2])
b=list(d0,d01,d02,d03,d04,d1,d2,d3,d4,d5,d6,d7,d8,d9,d10,d11,d12,d13,d14,d15,d16,d17,d18,d19,d20,
d21,d22,d23,d24,d25,d26,d27,d28,d29,d30,d31,d32,d33,d34,d35,d36,d37)
t24=t(as.matrix(sapply(b,function(d) mean(d[2,][d[1,]<=24]))))
colnames(t24)=c("X.RecordID.","X.ALP.","X.ALT.","X.AST.", "X.Age.", "X.Albumin.", "X.BUN.", "X.Bilirubin.", "X.Cholesterol.", "X.Creatinine.", "X.DiasABP.", "X.FiO2.", "X.GCS.", "X.Gender.", "X.Glucose.", "X.HCO3.", "X.HCT.", "X.HR.", "X.Height.", "X.ICUType.", "X.K.", "X.Lactate.", "X.MAP.", "X.MechVent.", "X.Mg.", "X.NIDiasABP.", "X.NIMAP.", "X.NISysABP.", "X.Na.", "X.PaCO2.", "X.PaO2.", "X.Platelets.", "X.RespRate.", "X.SaO2.", "X.SysABP.", "X.Temp.", "X.TroponinI.", "X.Urine.", "X.WBC.", "X.Weight.", "X.pH.", "X.TroponinT.")
t48=t(as.matrix(sapply(b,function(d) mean(d[2,][d[1,]<=48 && d[1,]>24]))))
colnames(t48)=c("X.RecordID.","X.ALP.","X.ALT.","X.AST.", "X.Age.", "X.Albumin.", "X.BUN.", "X.Bilirubin.", "X.Cholesterol.", "X.Creatinine.", "X.DiasABP.", "X.FiO2.", "X.GCS.", "X.Gender.", "X.Glucose.", "X.HCO3.", "X.HCT.", "X.HR.", "X.Height.", "X.ICUType.", "X.K.", "X.Lactate.", "X.MAP.", "X.MechVent.", "X.Mg.", "X.NIDiasABP.", "X.NIMAP.", "X.NISysABP.", "X.Na.", "X.PaCO2.", "X.PaO2.", "X.Platelets.", "X.RespRate.", "X.SaO2.", "X.SysABP.", "X.Temp.", "X.TroponinI.", "X.Urine.", "X.WBC.", "X.Weight.", "X.pH.", "X.TroponinT.")
t24=data.frame(t24)
t48=data.frame(t48)
if (!is.na(t24$X.Weight.) & t24$X.Weight.==-1) {t24$X.Weight.=NA}
if (!is.na(t48$X.Weight.) & t48$X.Weight.==-1) {t48$X.Weight.=NA}
for (i in 1:ncol(t24))
{
t24[[i]]=mapply(function(x,y) { if (is.na(x)) {x=y}
else{x=x}}, t24[[i]], t48[[i]])
}
for (i in 1:ncol(t48))
{
t48[[i]]=mapply(function(x,y) { if (is.na(x)) {x=y}
else{x=x}}, t48[[i]], t24[[i]])
}
#if (!is.na(a1$X.TroponinI.) && !is.na(a1$X.TroponinT.)) {}
a=t24
b=t48
#remove troponint and troponin i
a=a[c(-37,-42)]
b=b[c(-37,-42)]
for (i in 1:nrow(a))
{
if (is.na(a$X.MechVent.[i]))
{a$X.MechVent.[i]=0}
else
{a$X.MechVent.[i]=1
}
}
#mehrnaz model for troponin patients
#add variable BMI
BMI=a$X.Weight./(a$X.Height.^2)
a=data.frame(a,BMI)
BMI=b$X.Weight./(b$X.Height.^2)
b=data.frame(b,BMI)
delta=b-a
#getting rid off delta of (record id,age,heigth,gender,icutype,mechanic ventilator)
delta=delta[c(-1,-5,-14,-20,-19,-24)]
#str(delta)
#if delta=0 put it NaN
delta3=delta
for (i in 1:ncol(delta))
{
delta3[i]=sapply(delta[[i]],function(x){if ( !is.na(x) & x==0) {x=NA}
else {x=x}})
}
d=data.frame(a,delta3)
patientRecordID=d$X.RecordID.
#######################################################
#mehrnaz
#define the input for logistic regression
GCS=d$X.GCS./15
agee=sapply(d$X.Age., function(age) {
if (age<20)
{age2=1}
else if (age<30)
{age2=2}
else if (age<40)
{age2=3}
else if (age<50)
{age2=4}
else if (age<60)
{age2=5}
else if (age<70)
{age2=6}
else {age2=7}})
agee=agee/7
alp=log(d$X.ALP.+1)
pao2=log(1+d$X.PaO2.)
urine=log(1+d$X.Urine.)
wbc=log(1+d$X.WBC.)
createnin=log(0.01+ d$X.Creatinine.)
lactate=log(0.1+d$X.Lactate.)
bun=log(1+d$X.BUN.)
diasabp.d=log(d$X.DiasABP..1+ 70.35185+1)
wbc.d=log(d$X.WBC..1+22.83333+1)
alp.d=log(d$X.ALP..1+90+1)
ast.d=log(d$X.AST..1+9006+1)
#
d1=data.frame(
alp,
pao2,
urine,
wbc,
createnin,
lactate,
bun,
diasabp.d,
wbc.d,alp.d,ast.d,d$X.Albumin..1,d$X.PaCO2..1,d$X.HCO3.,d$X.HR.,d$X.Height.,d$X.FiO2..1,d$X.GCS..1,
d$X.MAP..1,d$X.NIMAP..1,d$BMI.1, d$X.PaCO2.,d$X.Albumin.
)
#ad<-scale(d1, center = TRUE, scale = TRUE)
mean1=c(4.50005094, 4.953607683, 4.32827816, 2.568075717, 0.901376019, 0.849259199, 3.381712382, 4.248351574, 3.131786867, 4.403365137, 9.094818797, -0.072301697, -0.139183126, 22.554101898, 87.415143119,
166.003319344, -0.071291865, 0.343415562, 0.911073234, -0.013663701, -0.003188958, 39.122005999, 2.924540544
)
sd1=c(0.43876487, 0.38881934, 0.97433465, 0.44944589, 0.04251337, 0.51645715, 0.61646042, 0.15514354, 0.22495596, 0.29835991, 0.25928707, 0.20067406, 5.10357788, 4.98520008, 16.5684575,
11.18376336, 0.11542437, 2.38174523, 8.56982557, 9.54947489, 0.0130926, 7.71283842, 0.50386189
)
d2=mapply(function(x,y,z){z=(x-z)/y},mean1,sd1,d1)
d2=t(data.frame(d2))
colnames(d2)<-c("alp",
"pao2",
"urine",
"wbc",
"createnin",
"lactate",
"bun",
"diasabp.d",
"wbc.d","alp.d","ast.d","d$X.Albumin..1","d$X.PaCO2..1","d$X.HCO3.","d$X.HR.","d$X.Height.","d$X.FiO2..1","d$X.GCS..1",
"d$X.MAP..1","d$X.NIMAP..1","d$BMI.1", "d$X.PaCO2.","d$X.Albumin."
)
d1=data.frame(GCS,agee,d2)
GCS=sapply(GCS,function(x){
if (is.na(x))
{
x=6.750000e-01
}
else
{x=x}
})
agee=sapply(agee,function(x){
if (is.na(x))
{
x2=1
}
else
{x2=x}})
BMI.1=sapply(d1$d.BMI.1,function(x){
if (is.na(x)|| x>=2)
{
x2=0.24710462
}
else{x2=x}
})
alp=sapply(d1$alp,function(x){
if (is.na(x) || x>=4)
{
x2=-0.02602
}
else{x2=x}
})
pao2=sapply(d1$pao2,function(x){
if (is.na(x) || x<=-3.5)
{
x2=0.066563
}else{x2=x}
})
urine=sapply(d1$urine,function(x){
if (is.na(x))
{
x2=0.14553
}else{x2=x}
})
wbc=sapply(d1$wbc,function(x){
if (is.na(x))
{
x2=-0.006956
}else{x2=x}
})
createnin=sapply(d1$createnin,function(x){
if (is.na(x) || x>=3)
{
x2=-0.0815
}else{x2=x}
})
lactate=sapply(d1$lactate,function(x){
if (is.na(x))
{
x2=-0.1152
}else{x2=x}
})
bun=sapply(d1$bun,function(x){
if (is.na(x)|| x<=-3)
{
x2=-0.0611
}else{x2=x}
})
diasabp.d=sapply(d1$diasabp.d,function(x){
if (is.na(x) || x<=-5)
{
x2=0.00009
}else{x2=x}
})
wbc.d=sapply(d1$wbc.d,function(x){
if (is.na(x) || x<=-10)
{
x2=0.0899
}else{x2=x}
})
alp.d.1=sapply(d1$alp.d, function(x) {
if (!is.na(x) & x>=1)
{x2=1}
else
{x2=0}
})
ast.d.1=sapply(d1$ast.d, function(x) {
if (!is.na(x) & x>=1)
{x2=1}
else
{x2=0}
})
Albumin..11=sapply(d1$d.X.Albumin..1, function(x) {
if (!is.na(x) & (x>=4 ||x<=-4) )
{x2=1}
else
{x2=0}
})
PaCO2..11=sapply(d1$d.X.PaCO2..1, function(x) {
if (!is.na(x) & x>=1)
{x2=1}
else
{x2=0}
})
HCO3.=sapply(d1$d.X.HCO3. ,function(x){
if (is.na(x))
{
x2=-0.04428
}else {x2=x}
})
HR.=sapply(d1$d.X.HR.,function(x){
if (is.na(x))
{
x2=-0.05188
}else{x2=x}
})
Height.=sapply(d1$d.X.Height.,function(x){
if (is.na(x))
{
x2=0.021035
}else{x2=x}
})
FiO2..1 =sapply(d1$d.X.FiO2..1 ,function(x){
if (is.na(x))
{
x2=-0.1393
}else{x2=x}
})
BMI.11 =sapply(d1$d.BMI.1 ,function(x){
if (is.na(x))
{
x2=2.471046e-01
}else{x2=x}
})
MAP..11 =sapply(d1$d.X.MAP..1 ,function(x){
if (is.na(x))
{
x2=1.504427e-02
}else{x2=x}
})
NIMAP..11 =sapply(d1$d.X.NIMAP..1 ,function(x){
if (is.na(x))
{
x2=9.298194e-02
}else{x2=x}
})
PaCO2.=sapply(d1$d.X.PaCO2. ,function(x){
if (is.na(x))
{
x2=-6.768014e-02
}else{x2=x}
})
Albumin.=sapply(d1$d.X.Albumin. ,function(x){
if (is.na(x))
{
x2=-4.870490e-02
}else{x2=x}
})
GCS..1=sapply(d1$d.X.GCS..1 ,function(x){
if (is.na(x))
{
x2=-7.289626e-02
}else{x2=x}
})
b24=data.frame(alp, GCS, GCS..1 , FiO2..1, agee,urine,HR.,
lactate,Height., wbc.d ,diasabp.d,bun,createnin ,pao2 ,ast.d.1
, wbc ,alp.d.1 ,HCO3.,BMI.11,MAP..11,NIMAP..11,PaCO2.,Albumin.
)
#build model
attach(b24, warn.conflicts = FALSE)
y=0.83104+alp*0.33684+GCS*-3.35244+GCS..1*-0.77539+FiO2..1*0.42292+agee*1.5975+
urine*-0.31299+PaCO2..11*0.66486+HR.*0.23167+NIMAP..11*-0.27412+Albumin.*-0.16712+
lactate*0.19954+ BMI.11*0.32796+Height.*-0.32128+ wbc.d*0.27321+
diasabp.d*-0.50324+ bun*0.3836+ createnin*-0.28206+ pao2*-0.19495+
ast.d.1*2.30468+ wbc*0.1808+ MAP..11*0.19799+
alp.d.1*-0.79075+ PaCO2.*-0.20022+ HCO3.*0.16702
#threshold=0.9479000 for all the data
#threshold=0.6714000 for troponin data
threshold=0.6714000
#####################jennifer
Subset24<-subset(t24, select=-X.RecordID.)
Subset48<-subset(t48, select=-X.RecordID.)
Delta<-Subset48-Subset24
ValidationSet<- data.frame(cbind(t24,Delta))
#Extracting the variables for the logistic model
Var<-subset(ValidationSet, select=c(X.RecordID.,X.ALT.,X.BUN., X.Bilirubin., X.DiasABP.,X.GCS., X.Gender., X.ICUType.,X.K..1,X.Lactate..1, X.NIMAP..1, X.Na..1, X.PaCO2., X.PaO2..1,X.SaO2., X.SysABP., X.WBC..1))
#Log transformation
Var_0.1<-replace(Var[, c("X.ALT.","X.BUN.", "X.Bilirubin.","X.PaO2..1")],
Var[,c("X.ALT.","X.BUN.", "X.Bilirubin.","X.PaO2..1")]==0, 0.1)
Log_0.1<-log(Var_0.1)
VarNor<-subset(ValidationSet, select=c("X.RecordID.","X.DiasABP.", "X.GCS.", "X.Gender.", "X.ICUType.","X.K..1","X.Lactate..1", "X.NIMAP..1", "X.Na..1", "X.PaCO2.", "X.SaO2.", "X.SysABP.", "X.WBC..1"))
VarLog<-data.frame(cbind(Log_0.1,VarNor))
#Standardization of the data except the categorical variables Gender + ICUType
Ranges<-matrix(
c(-2.634051958,-2.991844064,-2.388897278, -4.46881697, -2.243334467, -7.197909051, -7.203228589, -10.04151072, -4.929786649, -3.064924714, -2.835090768, -3.238755862, -3.820912872, -5.065010701,
4.326471689, 2.675307479, 3.60430965, 2.781303605, 1.444441927, 4.574522477, 7.192420026, 5.739081632, 4.981612334, 9.66684033, 3.83444211, 0.898110143, 3.004101623, 5.027706308,
0.054334973, -0.094674663, -0.125351218, -0.050924165, -0.060772928, 0.114348676, 0.244893801, -0.030057347, -0.020115326, -0.061831101, 0.063285247, 0.499198065, 0.115539681, 0.041245234,
4.049993063, 3.202056119, 0.016317935, 59.7688116, 9.516428108, -0.112812408, -0.317241581, 0.246182273, 0.065553312, 39.26702457, 1.321781066, 81.76367039, 105.4289793, -0.283626057,
1.120471737, 0.703059312, 0.970700184, 13.37463852, 3.796325619, 0.549305948, 1.295425121, 8.190419206, 3.258873955, 11.50665281, 1.098215461, 20.3052262, 27.59261539, 4.452055209), nrow=5, ncol=14, byrow=TRUE)
M=c("X.ALT.", "X.BUN.", "X.Bilirubin.", "X.DiasABP.", "X.GCS.", "X.K..1", "X.Lactate..1", "X.NIMAP..1", "X.Na..1", "X.PaCO2.", "X.PaO2..1","X.SaO2.", "X.SysABP.", "X.WBC..1")
colnames(Ranges)<-paste(M , sep="")
VarCont<-subset(VarLog, select=-c(X.RecordID., X.Gender.,X.ICUType.))
DataOut=VarCont
for(j in 1:ncol(VarCont)){
for (i in 1:nrow(VarCont)){
DataOut[i,j]<-(VarCont[i,j]-Ranges[4,j])/Ranges[5,j]}}
#Missing values
DataOut2=DataOut
for(j in 1:ncol(DataOut)){
for (i in 1:nrow(DataOut)){
if(is.na(DataOut[i,j])==TRUE){
DataOut2[i,j]<-Ranges[3,j]}
else
{DataOut2[i,j]<-DataOut2[i,j]}}}
#Outliers
DataOut3=DataOut2
for(j in 1:ncol(DataOut2)){
for (i in 1:nrow(DataOut2)){
if((DataOut2[i, j]<=Ranges[1,j])== TRUE || (DataOut2[i, j]>=Ranges[2,j])==TRUE)
{DataOut3[i, j]<-NA}
else
{DataOut3[i, j]<-DataOut3[i, j]}
}}
Ranges2<-matrix(
c(0.054334973, -0.094674663, -0.125351218, -0.050924165, -0.060772928, 0.114348676, 0.244893801, -0.030057347, -0.020115326, -0.061831101, 0.063285247, 0.499198065, 0.115539681, 0.041245234, 1, 3), nrow=1, ncol=16, byrow=TRUE)
M=c("X.ALT.", "X.BUN.", "X.Bilirubin.", "X.DiasABP.", "X.GCS.", "X.K..1", "X.Lactate..1", "X.NIMAP..1", "X.Na..1", "X.PaCO2.", "X.PaO2..1","X.SaO2.", "X.SysABP.", "X.WBC..1", "X.Gender.", "X.ICUType.")
colnames(Ranges2)<-paste(M , sep="")
SubsetB<-subset(VarLog, select=c(X.Gender., X.ICUType.))
DataOut4<-data.frame(cbind(DataOut3, SubsetB))
#Missing Values 2
Val=DataOut4
for(j in 1:ncol(DataOut4)){
for (i in 1:nrow(DataOut4)){
if(is.na(DataOut4[i,j])==TRUE){
Val[i,j]<-Ranges2[1,j]}
else
{Val[i,j]<-Val[i,j]}}}
SubsetC<-subset(VarLog, select=c(X.RecordID.))
Val2<-data.frame(cbind(Val, SubsetC))
#Logistic Model
attach(Val2, warn.conflicts=FALSE)
LogModel<-0.18693*X.ALT.+ 0.72686*X.BUN. + 0.10174 *X.Bilirubin. + 0.08186*X.DiasABP.+ -0.35686 *X.GCS. -0.23083*X.Gender. +0.11584* X.ICUType.+ 0.07489*X.K..1+ 0.08698 *X.Lactate..1 + -0.16908* X.NIMAP..1 + 0.12889 *X.Na..1 -0.19166 *X.PaCO2. + 0.19979*X.PaO2..1+ 0.12604 *X.SaO2. -0.12882*X.SysABP.+0.23154*X.WBC..1
Y=LogModel
Yhat<-exp(Y)/(1+exp(Y))
Threshold=0.6582000
Fitted<- ifelse(Yhat>Threshold,1,0)
########################alireza
a=t24
b=t48
#*******************************************************************************Alireza***********************************
#Mech vent variable
for (i in 1:nrow(a))
{
if (is.na(a$X.MechVent.[i]))
{a$X.MechVent.[i]=0}
else
{a$X.MechVent.[i]=1
}
}
##adding BMI for 24
BMI=a$X.Weight./((a$X.Height./100)^2)
a=data.frame(a,BMI)
##adding BMI for 48
BMI=b$X.Weight./((b$X.Height./100)^2)
b=data.frame(b,BMI)
#delta (48-24)
delta=b-a
#getting rid off delta of (record id,age,heigth,gender,icutype,mechanic ventilator)
delta=delta[c(-1,-5,-14,-20,-19,-24)]
#str(delta)
#if delta=0 put it NaN
delta3=delta
for (i in 1:ncol(delta))
{
delta3[i]=sapply(delta[[i]],function(x){if ( !is.na(x) & x==0) {x=NA}
else {x=x}})
}
d=data.frame(a,delta3)
patientRecordID=d$X.RecordID.
###define the input for logistic regression
#GCS
GCS=d$X.GCS./15
#AGE grouping
agee=sapply(d$X.Age., function(age) {
if (age<20)
{age2=1}
else if (age<30)
{age2=2}
else if (age<40)
{age2=3}
else if (age<50)
{age2=4}
else if (age<60)
{age2=5}
else if (age<70)
{age2=6}
else {age2=7}})
agee=agee/7
##define final threshhold:
thresh=0.3362
if (is.na(d$X.ALP.))
{thresh=0.28}
##log transformation:
bilbi=log(d$X.Bilirubin.+0.01)
alp=log(d$X.ALP.+0.01)
alt=log(d$X.ALT.+0.01)
ast=log(d$X.AST.+10)
pao2=log(1+d$X.PaO2.)
paco2=log(1+d$X.PaCO2.)
fio2=log(0.01+d$X.FiO2.)
K=log(0.01+d$X.K.)
Mg=log(0.01+d$X.Mg.)
lactate=log(0.1+d$X.Lactate.)
bun=log(1+d$X.BUN.)
createnin=log(0.01+d$X.Creatinine.)
plat=log(d$X.Platelets.+1)
urine=log(1+d$X.Urine.)
wbc=log(1+d$X.WBC.)
Weight=log(0.1+d$X.Weight.)
lactate.d=log(d$X.Lactate..1+9.648484847+1)
FiO2.d=log(d$X.FiO2..1+0.65+1)
Mg.d=log(d$X.Mg..1+2.806666667+1)
Temp.d=log(d$X.Temp..1+7.47142857+1)
###STANDARDIZATION
d1=data.frame(alp, alt, ast, d$X.Albumin., bun, bilbi, createnin, d$X.DiasABP., fio2, d$X.Glucose., d$X.HCO3., d$X.HCT.,
d$X.HR., d$X.Height., K, lactate, d$X.MAP., d$X.MechVent., Mg, d$X.NIDiasABP., d$X.NISysABP., d$X.Na., paco2,
pao2, plat, d$X.SysABP., d$X.Temp., urine, wbc, Weight, d$X.pH., d$BMI, d$X.BUN..1, d$X.Creatinine..1, d$X.DiasABP..1,
d$X.FiO2..1, d$X.GCS..1, d$X.Glucose..1, d$X.HCO3..1, d$X.HCT..1, d$X.HR..1, d$X.K..1, lactate.d, d$X.MAP..1, Mg.d, d$X.NIDiasABP..1,
d$X.NISysABP..1, d$X.Na..1, d$X.PaCO2..1, d$X.PaO2..1, d$X.Platelets..1, d$X.SysABP..1, Temp.d, d$X.Urine..1, d$X.WBC..1,
d$X.pH..1, d$BMI.1)
#ad<-scale(d1, center = TRUE, scale = TRUE)
mean1=c(4.428792, 3.751404, 4.377866, 2.950229, 3.185411, -0.1377478, 0.8950096, 59.65327, -0.5278706, 144.1531, 22.7077, 32.11874,
88.77805, 170.0834, 1.41549, 0.8592167, 81.20807, 0.6358339, 0.6892202, 57.56831, 114.3365, 139.0517, 3.683789, 4.978323,
5.194582, 114.7876, 36.89164, 4.546272, 2.508671, 4.387889, 7.490792, 210.2489, -0.7093838, 0.01846575, 0.602998, -0.08731749,
0.769466, -17.21709, 0.8136559, -1.072103, -1.081304, -0.09029135, 2.282917, 0.8655896, 1.348159, 0.06620109, 1.163557, 0.1317891,
-0.4752844, -39.57504, -20.90938, 2.952707, 2.149836, -7.146416, -0.609545, -0.0006686606, -33.55544)
sd1=c(0.6066783, 1.288708, 1.146172, 0.595363, 0.6869114, 1.043874, 0.04468801, 10.33693, 0.2128702, 54.1664, 4.719987, 5.050711,
16.4147, 16.75468, 0.1387224, 0.4910585, 12.99533, 0.4813709, 0.1716615, 12.22031, 19.80241, 4.647058, 0.1865474, 0.3911182,
0.5808523, 18.40125, 0.9844662, 0.9129201, 0.5381392, 0.2729483, 2.683078, 5723.599, 8.796758, 0.5259562, 7.190248, 0.1080009,
2.662966, 60.13094, 2.942004, 3.375774, 11.18836, 0.5724884, 0.1473419, 9.631489, 0.0857281, 7.683106, 15.97342, 3.160388,
4.981642, 50.95536, 45.65448, 13.30604, 0.1170007, 93.00365, 4.318247, 0.5854828, 107.8054)
d2=mapply(function(x,y,z){z=(z-x)/y},mean1,sd1,d1)
d2=t(data.frame(d2))
colnames(d2)<-c("alp", "alt", "ast", "d.X.Albumin.", "bun", "bilbi", "createnin", "d.X.DiasABP.", "fio2", "d.X.Glucose.",
"d.X.HCO3.", "d.X.HCT.", "d.X.HR.", "d.X.Height.", "K", "lactate", "d.X.MAP.", "d.X.MechVent.", "Mg", "d.X.NIDiasABP.", "d.X.NISysABP.",
"d.X.Na.", "paco2", "pao2", "plat", "d.X.SysABP.", "d.X.Temp.", "urine", "wbc", "Weight", "d.X.pH.", "d.BMI", "d.X.BUN..1",
"d.X.Creatinine..1", "d.X.DiasABP..1", "d.X.FiO2..1", "d.X.GCS..1", "d.X.Glucose..1", "d.X.HCO3..1", "d.X.HCT..1", "d.X.HR..1",
"d.X.K..1", "lactate.d", "d.X.MAP..1", "Mg.d", "d.X.NIDiasABP..1", "d.X.NISysABP..1", "d.X.Na..1", "d.X.PaCO2..1",
"d.X.PaO2..1", "d.X.Platelets..1", "d.X.SysABP..1", "Temp.d", "d.X.Urine..1", "d.X.WBC..1", "d.X.pH..1", "d.BMI.1")
d1=data.frame(GCS,agee,d2)
d3=data.frame( d1$alp, d1$urine, d1$d.X.GCS..1, d1$GCS, d1$bun, d1$d.X.BUN..1, d1$lactate, d1$createnin,
d1$pao2, d1$paco2, d1$d.X.Na..1, d1$lactate.d, d1$d.X.HCO3., d1$d.X.Temp., d1$agee,
d1$d.X.Albumin., d1$d.X.Glucose., d1$d.X.Creatinine..1, d1$ast)
#d3=data.frame(d3,death)
###### make a subset of ALP
#b=subset(d3,!is.na(d$X.ALP.))
#d1=subset(d1,!is.na(d$X.ALP.))
## for all the validation set:
b=d3
###------------------------------------------
# Outliers and Missing values imputation:
###------------------------------------------
##b$d1.alp
b$d1.alp=sapply(b$d1.alp,function(x){
if (is.na(x) || x<=-3)
{
x2=-0.139870244
}
else{x2=x}
})
##b$d1.urine
b$d1.urine=sapply(b$d1.urine,function(x){
if (is.na(x)|| x>=3)
{
x2=0.10219898
}else{x2=x}
})
##b$d1.d.X.GCS..1
b$d1.d.X.GCS..1=sapply(b$d1.d.X.GCS..1,function(x){
if (is.na(x)|| x>=3)
{
x2=-0.063638063
}else{x2=x}
})
##b$d1.GCS
b$d1.GCS=sapply(b$d1.GCS,function(x){
if (is.na(x))
{
x=0.688888889
}
else
{x=x}
})
##b$d1.bun
b$d1.bun=sapply(b$d1.bun,function(x){
if (is.na(x)|| x>=3 || x<=-3)
{
x=-0.104664478
}
else
{x=x}
})
##b$d1.d.X.BUN..1
b$d1.d.X.BUN..1=sapply(b$d1.d.X.BUN..1,function(x){
if (is.na(x) || x<=-3)
{
x2=-0.033036739
}
else{x2=x}
})
##b$d1.lactate
b$d1.lactate=sapply(b$d1.lactate,function(x){
if (is.na(x) || x<=-3)
{
x2=-0.048071416
}
else{x2=x}
})
##b$d1.createnin
b$d1.createnin=sapply(b$d1.createnin,function(x){
if (is.na(x) || x<=-3)
{
x2=-0.363778551
}
else{x2=x}
})
##b$d1.pao2
b$d1.pao2=sapply(b$d1.pao2,function(x){
if (is.na(x)|| x>=3 || x<=-3)
{
x=0.039642804
}
else
{x=x}
})
##b$d1.paco2
b$d1.paco2=sapply(b$d1.paco2,function(x){
if (is.na(x)|| x>=3)
{
x2=0.021621913
}else{x2=x}
})
##b$d1.d.X.Na..1
b$d1.d.X.Na..1=sapply(b$d1.d.X.Na..1,function(x){
if (is.na(x))
{
x2=0.069289425
}else{x2=x}
})
##b$d1.lactate.d
b$d1.lactate.d=sapply(b$d1.lactate.d,function(x){
if (is.na(x))
{
x2=0.115348192
}else{x2=x}
})
##b$d1.d.X.HCO3.
b$d1.d.X.HCO3.=sapply(b$d1.d.X.HCO3.,function(x){
if (is.na(x) || x<=-3)
{
x2=-0.0086942
}
else{x2=x}
})
##b$d1.d.X.Temp.
b$d1.d.X.Temp.=sapply(b$d1.d.X.Temp.,function(x){
if (is.na(x)|| x>=3 || x<=-3)
{
x=0.038366966
}
else
{x=x}
})
##b$d1.agee
b$d1.agee=sapply(b$d1.agee,function(x){
if (is.na(x))
{
x2=0.857142857
}
else
{x2=x}})
##b$d1.d.X.Albumin.
b$d1.d.X.Albumin.=sapply(b$d1.d.X.Albumin.,function(x){
if (is.na(x)|| x>=3 || x<=-3)
{
x=-0.031444932
}
else
{x=x}
})
##b$d1.d.X.Glucose.
b$d1.d.X.Glucose.=sapply(b$d1.d.X.Glucose.,function(x){
if (is.na(x) || x<=-3)
{
x2=-0.242828212
}
else{x2=x}
})
##b$d1.d.X.Creatinine..1
b$d1.d.X.Creatinine..1=sapply(b$d1.d.X.Creatinine..1,function(x){
if (is.na(x))
{
x2=-0.022762693
}
else{x2=x}
})
##b$d1.ast
b$d1.ast=sapply(b$d1.ast,function(x){
if (is.na(x) || x<=-3)
{
x2=-0.276942249
}
else{x2=x}
})
##---------------------------------------------
# (The end of Outliers and Missing values imputation)
##--------------------------------------------
##making 4 variables: (d.X.DiasABP..11, d.X.HCT..11, d.X.HCT.1, d.X.Na.1)
##First, cleaning parent data columns:
##d1$d.X.DiasABP..1
d1$d.X.DiasABP..1=sapply(d1$d.X.DiasABP..1,function(x){
if (is.na(x)|| x>=5 || x<=-5)
{
x2=0
}else{x2=x}
})
##d1$d.X.HCT..1
d1$d.X.HCT..1=sapply(d1$d.X.HCT..1,function(x){
if (is.na(x)|| x>=5 || x<=-5)
{
x2=0
}else{x2=x}
})
##d1$d.X.HCT.
d1$d.X.HCT.=sapply(d1$d.X.HCT.,function(x){
if (is.na(x)|| x>=5 || x<=-5)
{
x2=0
}else{x2=x}
})
##d1$d.X.Na.
d1$d.X.Na.=sapply(d1$d.X.Na.,function(x){
if (is.na(x)|| x>=5 || x<=-5)
{
x2=0
}else{x2=x}
})
## Second: ( from alp v4 ) : study the ones that are less associated. if their outliers are associated with death, then we made it a binary variable.
#d.X.DiasABP..11
#d1$d.X.DiasABP..1<= -2.5 or >= 2.2 can be associated with death therefore we will make this variable a binary:
d.X.DiasABP..11=sapply(d1$d.X.DiasABP..1, function(x) {
if (x>=2.2 ||x<=-2.5 )
{x2=1}
else
{x2=0}
})
#b[60]=d.X.DiasABP..11
#d.X.HCT..11
#d1$d$X.HCT..1<= -2.4 or >= 2.5 can be associated with death therefore we will make this variable a binary:
d.X.HCT..11=sapply(d1$d.X.HCT..1, function(x) {
if (x>=2.5 ||x<=-2.4 )
{x2=1}
else
{x2=0}
})
#b[61]=d.X.HCT..11
#d.X.HCT.1
#d1$d.X.HCT. <= -2.486213828 or >= 2.6 can be associated with death therefore we will make this variable a binary:
d.X.HCT.1=sapply(d1$d.X.HCT., function(x) {
if (x>= 2.6 ||x<= -2.486213828 )
{x2=1}
else
{x2=0}
})
#b[62]=d.X.HCT.1
#d.X.Na.1
#d1$d.X.Na. <= -2 or >= 2 can be associated with death therefore we will make this variable a binary:
d.X.Na.1=sapply(d1$d.X.Na., function(x) {
if (x>= 2 ||x<= -2 )
{x2=1}
else
{x2=0}
})
b=data.frame(b,d.X.DiasABP..11, d.X.HCT..11, d.X.HCT.1, d.X.Na.1)
##-------------------
## LogReg Model:
##-------------------
b24=data.frame(b$d1.alp, b$d1.urine, b$d1.d.X.GCS..1, b$d1.GCS, b$d1.bun, b$d1.d.X.BUN..1, b$d1.lactate, b$d1.createnin,
b$d1.pao2, b$d1.paco2, b$d1.d.X.Na..1, b$d1.lactate.d, b$d1.d.X.HCO3., b$d1.d.X.Temp., b$d1.agee, b$d1.d.X.Albumin.,
b$d1.d.X.Glucose., b$d1.d.X.Creatinine..1, b$d1.ast, b$d.X.DiasABP..11, b$d.X.HCT..11, b$d.X.HCT.1, b$d.X.Na.1)
#build model
attach(b24, warn.conflicts = FALSE)
y1= (-1.01648 +(b$d1.alp)*0.32492 +b$d1.urine*-0.383 +b$d1.d.X.GCS..1*-0.63786 +b$d1.GCS*-2.71744 +b$d1.bun*0.52039 +b$d1.d.X.BUN..1*0.45308
+b$d1.lactate*0.29553 +b$d1.createnin*-0.2694 +b$d.X.HCT..11*1.67211 +b$d1.pao2*-0.28836 +b$d1.paco2*-0.41355 +b$d1.d.X.Na..1*0.23602
+b$d1.lactate.d*0.25241 +b$d1.d.X.HCO3.*0.34784 +b$d1.d.X.Temp.*-0.24007 +b$d.X.Na.1*0.7556 +b$d1.agee*0.99375 +b$d1.d.X.Albumin.*-0.21477
+b$d1.d.X.Glucose.*0.14364 +b$d1.d.X.Creatinine..1*-0.17765 +b$d1.ast*0.14487 +b$d.X.DiasABP..11*0.71979 +b$d.X.HCT.1*1.55271)
####################################jining
#jining
#Lactate.1
#Lactate.1
#Extraction the data
Subset24<-subset(t24, select=-X.RecordID.)
Subset48<-subset(t48, select=-X.RecordID.)
Delta<-Subset24-Subset48
ValidationSet<- data.frame(cbind(t24,Delta))
#Extracting the variables for the logistic model
ValSubset<-subset(ValidationSet, select=c(X.RecordID.,X.Age., X.DiasABP., X.GCS. , X.HCO3. , X.HCT. , X.HR. , X.K. , X.NIDiasABP. , X.Na. , X.Temp. , X.BUN..1 , X.Creatinine..1 , X.DiasABP..1 ,
X.HR..1, X.NIDiasABP..1 , X.Na..1 , X.PaCO2..1 , X.Temp..1 , X.WBC..1 , X.BUN. , X.Creatinine. ,
X.Glucose. , X.Lactate. , X.PaO2. , X.Platelets. , X.Urine. , X.Weight. , X.Mg..1 , X.ICUType. , X.MechVent.))
#Log transformation
ValSubset_2<-replace(ValSubset[, c( "X.BUN.", "X.Creatinine.", "X.Glucose." , "X.Lactate." , "X.PaO2." , "X.Platelets." , "X.Urine." ,"X.Weight." , "X.Mg..1")], , 1)
ValSubset_3<-subset(ValSubset,select=c ("X.BUN.", "X.Creatinine.", "X.Glucose." , "X.Lactate." , "X.PaO2." , "X.Platelets." , "X.Urine." ,"X.Weight." , "X.Mg..1"))
ValSubset_1<- ValSubset_2+ ValSubset_3
Var_1<-replace(ValSubset_1[, c ("X.BUN.", "X.Creatinine.", "X.Glucose." , "X.Lactate." , "X.PaO2." , "X.Platelets." , "X.Urine." ,"X.Weight." , "X.Mg..1")],
ValSubset_1[,c ("X.BUN.", "X.Creatinine.", "X.Glucose." , "X.Lactate." , "X.PaO2." , "X.Platelets." , "X.Urine." ,"X.Weight." , "X.Mg..1")]==0, 1)
Log_1<-log10(Var_1)
VarNor<-subset(ValidationSet, select=c(X.RecordID.,X.Age., X.DiasABP., X.GCS. , X.HCO3. , X.HCT. , X.HR. , X.K. , X.NIDiasABP. , X.Na. , X.Temp. , X.BUN..1 , X.Creatinine..1 , X.DiasABP..1 ,X.GCS..1,X.Glucose..1 ,X.HCO3..1, X.HCT..1,
X.HR..1, X.NIDiasABP..1 , X.Na..1 , X.PaCO2..1 , X.Temp..1 , X.WBC..1 , X.ICUType. , X.MechVent.))
VarLog<-data.frame(cbind(VarNor,Log_1))
#Upload matrix (Min,Max,Median,Mean,Std)
Ranges<-matrix(
c(-2.931788088,-4.991871097, -1.955340967, -3.19349387, -2.906231889, -3.025206339, -3.005646747, -4.697963119, -6.316690383, -17.81527166, -5.24659202, -11.15473814, -8.060183788, -4.274982279, -6.958523009, -7.63721675, -4.683086753, -4.976089775, -6.206248907, -7.107897327, -9.915323476, -10.56276104, -5.156197132, -2.935062376, -1.600483426, -4.020017278, -2.017178013, -3.875726147, -4.381801349, -4.669653686, -6.91063195, -8.098375159,
1.334002657, 4.130394335, 1.155747808, 4.739979569, 4.103153531, 3.373568144, 6.713015548, 4.758235901, 6.844329613, 3.174707678, 7.497471628, 8.65640186, 9.492857124, 7.089512945, 8.341200705, 3.8302355, 4.61220678, 4.258111093, 18.22195187, 21.63985191, 3.955648922, 10.51648159, 7.38487439, 2.954185239, 5.062382639, 4.550957393, 4.904824302, 2.839772341, 2.904145163, 3.670282227, 4.626332871, 10.8729273,
0.181086239, -0.001455303, 0.015015257, 0.01588858, -0.144958845, -0.063182502, -0.144741829, -0.084022953, 0.026809717, 0.037154474, 0.008802496, 0.079843935, 0.037366579, 0.157547302, -0.116460715, 0.011667898, -0.016704511, 0.011776403, -0.039472028, 0.014802642, -0.047619061, 0.028371817, -0.035986679, -0.117036108, -0.287589122, -0.094240987, -0.195529099, 0.003631955, 0.124739263, 0.125581162, 0.008208008, 0.156530218,
66.85864064, 58.36995804, 10.54208359, 22.92432554, 31.95070862, 88.11734369, 4.18600814, 56.43796299, 138.8718014, 36.84828908, -0.184334197, -0.011042211, -0.426992716, -0.530200233, 13.70851559, -0.533102, 0.972391541, 0.36812208, 0.442787385, -0.136364185, 0.471207074, -0.122888071, 0.356732414, 1.395686256, 0.362698826, 2.144113788, 0.51849072, 2.150082926, 2.265356275, 1.940280011, 1.896594561, -0.049863287,
17.3473113, 11.69300186, 3.857170551, 4.762821049, 5.178770723, 16.5538732, 0.594217581, 12.01328354, 4.78179756, 0.92370084, 8.160662319, 0.55577852, 7.456010729, 2.683005219, 61.89654256, 2.837014938, 3.335917604, 10.88280936, 9.951031255, 3.357341098, 5.695346926, 1.058477534, 4.35528973, 0.290153361, 0.177144965, 0.146221085, 0.184595847, 0.185441162, 0.242191786, 0.415508331, 0.125795517, 0.176240041
), nrow=5, ncol=32, byrow=TRUE)
M=c( "X.Age.", "X.DiasABP.", "X.GCS." , "X.HCO3." , "X.HCT." , "X.HR." , "X.K." , "X.NIDiasABP." , "X.Na." , "X.Temp." , "X.BUN..1" , "X.Creatinine..1" , "X.DiasABP..1" ,
"X.GCS..1","X.Glucose..1" ,"X.HCO3..1", "X.HCT..1",
"X.HR..1", "X.NIDiasABP..1", "X.Na..1" , "X.PaCO2..1" , "X.Temp..1" , "X.WBC..1" , "X.BUN." , "X.Creatinine." ,
"X.Glucose." , "X.Lactate." , "X.PaO2." , "X.Platelets." , "X.Urine." , "X.Weight." , "X.Mg..1")
colnames(Ranges)<-paste(M , sep="")
#Standarization
VarCont<-subset(VarLog, select=-c(X.RecordID., X.ICUType. , X.MechVent.))
DataOut=VarCont
for(j in 1:ncol(VarCont)){
for (i in 1:nrow(VarCont)){
DataOut[i,j]<-(VarCont[i,j]-Ranges[4,j])/Ranges[5,j]}}
#Missing values
DataOut2=DataOut
for(j in 1:ncol(DataOut)){
for (i in 1:nrow(DataOut)){
if(is.na(DataOut[i,j])==TRUE){
DataOut2[i,j]<-Ranges[3,j]}
else
{DataOut2[i,j]<-DataOut2[i,j]}}}
#Outliers
DataOut3=DataOut2
for(j in 1:ncol(DataOut2)){
for (i in 1:nrow(DataOut2)){
if((DataOut2[i, j]<=Ranges[1,j])== TRUE || (DataOut2[i, j]>=Ranges[2,j])==TRUE)
{DataOut3[i, j]<-NA}
else
{DataOut3[i, j]<-DataOut3[i, j]}
}}
Ranges2<-matrix(
c( 0.1810862, -0.001455303, 0.01501526, 0.01588858, -0.1449588, -0.0631825, -0.1447418, -0.08402295, 0.02680972 , 0.03715447, 0.008802496 , 0.07984394, 0.03736658,
0.1575473, -0.1164607, 0.0116679, -0.01670451 , 0.0117764 , -0.03947203 , 0.01480264, -0.04761906 , 0.02837182, -0.03598668, -0.1170361 , -0.2875891, -0.09424099,
-0.1955291, 0.003631955, 0.1247393, 0.1255812, 0.008208008 , 0.15653022, 3,1), nrow=1, ncol=34, byrow=TRUE)
M=c( "X.Age.", "X.DiasABP.", "X.GCS." , "X.HCO3." , "X.HCT." , "X.HR." , "X.K." , "X.NIDiasABP." , "X.Na." , "X.Temp." , "X.BUN..1", "X.Creatinine..1", "X.DiasABP..1","X.GCS..1","X.Glucose..1","X.HCO3..1", "X.HCT..1", "X.HR..1", "X.NIDiasABP..1", "X.Na..1" , "X.PaCO2..1" , "X.Temp..1" , "X.WBC..1" , "X.BUN." , "X.Creatinine.","X.Glucose." , "X.Lactate." , "X.PaO2." , "X.Platelets." , "X.Urine." , "X.Weight." , "X.Mg..1", " X.ICUType.", " X.MechVent.")
colnames(Ranges2)<-paste(M , sep="")
SubsetB<-subset(VarLog, select=c( X.ICUType., X.MechVent.))
DataOut4<-data.frame(cbind(DataOut3, SubsetB))
#Missing Values 2
Val=DataOut4
for(j in 1:ncol(DataOut4)){
for (i in 1:nrow(DataOut4)){
if(is.na(DataOut4[i,j])==TRUE){
Val[i,j]<-Ranges2[1,j]}
else
{Val[i,j]<-Val[i,j]}}
}
SubsetC<-subset(VarLog, select=c( X.RecordID.))
Val2<-data.frame(cbind(Val, SubsetC))
#Logistic Model
attach(Val2, warn.conflicts=FALSE)
LogModel1<- 0.24700*X.Age. -0.10596*X.DiasABP. -1.14655* X.GCS. + 0.06563* X.HCO3. + 0.10478*X.HCT. +0.31262* X.HR. -0.07651*X.K. +
-0.13791* X.NIDiasABP. -0.13281* X.Na. -0.35529*X.Temp. -0.33219* X.BUN..1 + 0.19644* X.Creatinine..1 + 0.08843*X.DiasABP..1 +
0.83896*X.GCS..1 -0.26208*X.Glucose..1 + 0.21503*X.HCO3..1 -0.13543* X.HCT..1 -0.16196*X.HR..1 + 0.20817*X.NIDiasABP..1 -0.21593*X.Na..1 -0.19061*X.PaCO2..1 +0.26140*X.Temp..1 -0.06916*X.WBC..1 + 0.64054*X.BUN. -0.13759*X.Creatinine. +
0.20441*X.Glucose. + 0.24079*X.Lactate. -0.15721*X.PaO2. -0.09627*X.Platelets. -0.26113*X.Urine. -0.23382*X.Weight. -0.06223*X.Mg..1 +
0.21225*X.ICUType. -0.69089*X.MechVent.
Y=LogModel1
Yhat<-exp(Y)/(1+exp(Y))
Threshold1=0.6236000
Fitted<- ifelse(Yhat>Threshold,1,0)
################################
a=t24
tropmodel=0
alpmodel=0
lacmodel=0
generalmodel=0
lacm=0
alpm=0
tropm=0
tropalpm=0
troplacm=0
alplacm=0
allm=0
y2=exp(y)/(1+exp(y))
LogModel2=exp(LogModel)/(1+exp(LogModel))
LogModel12=exp(LogModel1)/(1+exp(LogModel1))
y12=exp(y1)/(1+exp(y1))
if(!is.na(a$X.TroponinI.) || !is.na(a$X.TroponinT.))
{ tropmodel=1 }
if (!is.na(a$X.ALP.))
{ alpmodel=1}
if (!is.na(a$X.Lactate. ))
{ lacmodel=1}
if (tropmodel==0 & alpmodel==0 & lacmodel==0)
{
thresh1=0.28
w=c(0, 0, 0.54, 0.46)
ab1=w[1]*y2+w[2]*LogModel2+w[3]*y12+w[4]*LogModel12
ab=ab1
thresh=w[1]*threshold+w[2]*Threshold+w[3]*thresh1+w[4]*Threshold1
predict=0
if (ab >= thresh)
{predict=1}
}
if (tropmodel==0 & alpmodel==0 & lacmodel==1)
{
thresh1=0.28
w=c(0, 0.5, 0, 0.5)
ab1=w[1]*y2+w[2]*LogModel2+w[3]*y12+w[4]*LogModel12
ab=ab1
thresh=w[1]*threshold+w[2]*Threshold+w[3]*thresh1+w[4]*Threshold1
predict=0
if (ab >= thresh)
{predict=1}
}
if (tropmodel==1 & alpmodel==0 & lacmodel==0)
{
thresh1=0.28
w=c(0.34, 0, 0, 0.66)
ab1=w[1]*y2+w[2]*LogModel2+w[3]*y12+w[4]*LogModel12
ab=ab1
thresh=w[1]*threshold+w[2]*Threshold+w[3]*thresh1+w[4]*Threshold1
predict=0
if (ab >= thresh)
{predict=1}
}
if (tropmodel==0 & alpmodel==1 & lacmodel==0)
{
thresh1=0.3362
w=c(0,0,0.42,0.58)
ab1=w[1]*y2+w[2]*LogModel2+w[3]*y12+w[4]*LogModel12
ab=ab1
thresh=w[1]*threshold+w[2]*Threshold+w[3]*thresh1+w[4]*Threshold1
predict=0
if (ab >= thresh)
{predict=1}
}
if (tropmodel==1 & alpmodel==1 & lacmodel==0)
{ w=c(0, 0, 0.82, 0.18)
thresh1=0.3362
ab1=w[1]*y2+w[2]*LogModel2+w[3]*y12+w[4]*LogModel12
ab=ab1
thresh=w[1]*threshold+w[2]*Threshold+w[3]*thresh1+w[4]*Threshold1
predict=0
if (ab >= thresh)
{predict=1}
}
if (tropmodel==1 & alpmodel==0 & lacmodel==1)
{
thresh1=0.28
w=c(0, 0, 0.54, 0.46)
ab1=w[1]*y2+w[2]*LogModel2+w[3]*y12+w[4]*LogModel12
ab=ab1
thresh=w[1]*threshold+w[2]*Threshold+w[3]*thresh1+w[4]*Threshold1
predict=0
if (ab >= thresh)
{predict=1}
}
if (tropmodel==0 & alpmodel==1 & lacmodel==1)
{ w=c(0, 0.58, 0, 0.42)
thresh1=0.3362
ab1=w[1]*y2+w[2]*LogModel2+w[3]*y12+w[4]*LogModel12
ab=ab1
thresh=w[1]*threshold+w[2]*Threshold+w[3]*thresh1+w[4]*Threshold1
predict=0
if (ab >= thresh)
{predict=1}
}
if (tropmodel==1 & alpmodel==1 & lacmodel==1)
{ w=c(0, 0, 0.84, 0.16)
thresh1=0.3362
ab1=w[1]*y2+w[2]*LogModel2+w[3]*y12+w[4]*LogModel12
ab=ab1
thresh=w[1]*threshold+w[2]*Threshold+w[3]*thresh1+w[4]*Threshold1
predict=0
if (ab >= thresh)
{predict=1}
}
#############################
#*******************************************************************************Alireza***********************************
cat(sprintf("%i,%i,%f\n", patientRecordID, predict, ab));