209 lines
8.0 KiB
R
209 lines
8.0 KiB
R
library(shiny)
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library(magrittr)
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library(ggplot2)
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library(plotly)
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library(DT)
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calculatorUI <- function(id){
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ns <- NS(id)
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uiOutput("calculatorPage")
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}
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calculatorServer <- function(input, output, session) {
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calculatorRV <-reactiveValues(value=NULL)
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calculatorTV <-reactiveValues(value=NULL)
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output$calculatorPage<-renderUI({
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if(get_page()=="calculator"){
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fluidPage(
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fluidRow(
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column(3,
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tags$div("Panel sterowania") %>% tagAppendAttributes(class="panel-title"),
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wellPanel(
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sliderInput("slider1", strong("Wiek pacjenta:"),min = 14, max = 100, value = 40),
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selectInput("select1",strong("Obecność wodobrzusza:"),choices = list("Nie"=0,"Tak"=1),selected=0),
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selectInput("select2",strong("Obecność przepływu krwi w projekcji brodawkowatej:"),choices = list("Nie"=0,"Tak"=1),selected=0),
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sliderInput("slider2", strong("Największa średnica elementu stałego (w mm):"),min = 0, max = 200, value = 0),
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selectInput("select3",strong("Nieregularna wewnętrzna ściana torbieli:"),choices = list("Nie"=0,"Tak"=1),selected=0),
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selectInput("select4",strong("Obecność cieni akustycznych:"),choices = list("Nie"=0,"Tak"=1),selected=0),
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actionButton("update" ,"Oblicz"),
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downloadButton("report", "Generuj raport"))
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)%>% tagAppendAttributes(id = 'column-panel'),
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column(9,
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tags$div("Kalkulator wskaźnika ryzyka nowotworu jajnika (IOTA LR2)") %>% tagAppendAttributes(class="panel-title"),
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wellPanel(
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p("Aplikacja przeznaczona jest dla lekarzy ginekologów i wdraża wskaźnik złośliwości nowotworu jajnika w oparciu o algorytm IOTA LR2. Wizualizuje również wynik regresji logistycznej."),
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p("Szczegółowy opis algorytmu znajduje się w artykule: Timmerman D, Testa AC, Bourne T, [i in.]. Model regresji logistycznej do rozróżniania łagodnych i złośliwych guzów przydatków przed operacją: wieloośrodkowe badanie przeprowadzone przez International Ovarian Tumor Analysis Group. J Clin Oncol. 2005, 23, 8794-8801."),
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p("Ogólnie algorytm LR2 przewiduje, że nowotwór jest łagodny, gdy pacjent jest młody, lity składnik zmiany jest mały i występują cienie akustyczne. Możesz to sprawdzić empirycznie za pomocą różnych kombinacji wartości wejściowych."),
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p("Wypełnij formularz i kliknij",strong("Oblicz")," "),
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htmlOutput("selected_var"),
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htmlOutput("var"),
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br(),
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plotlyOutput("wykres"),
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uiOutput("calculatorSave")
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)
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)%>% tagAppendAttributes(id = 'column-content')
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) %>% tagAppendAttributes(id = 'row-content'),
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fluidRow(
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column(12,
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tags$span("© Copyright Wszystkie prawa zastrzeżone."))%>% tagAppendAttributes(id = 'column-copyright'),
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)%>% tagAppendAttributes(id = 'row-footer')
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)
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}
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})
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output$report <- downloadHandler(
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filename = "raport.pdf",
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content = function(file) {
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tempReport <- file.path(tempdir(), "report.Rmd")
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file.copy("report.Rmd", tempReport, overwrite = TRUE)
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p=0
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if(as.numeric(input$slider2)>=50){
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p=50
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}
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z=-5.3718+0.0354*as.numeric(input$slider1)+1.6159*as.numeric(input$select1)+1.1768*as.numeric(input$select2)+0.0697*p+0.9586*as.numeric(input$select3)-2.9486*as.numeric(input$select4)
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x=round(1/(1+exp(-z)),3)
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params <- list(n = input$slider1,k=input$slider2,l=input$select1,m=input$select2,p=input$select3,r=input$select4,z=x)
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rmarkdown::render(tempReport, output_file = file,
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params = params,
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envir = new.env(parent = globalenv())
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)
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}
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)
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output$selected_var <- renderText({
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input$update
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p=0
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if(as.numeric(isolate(input$slider2))>=50){
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p=50
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}
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z=-5.3718+0.0354*as.numeric(isolate(input$slider1))+1.6159*as.numeric(isolate(input$select1))+1.1768*as.numeric(isolate(input$select2))+0.0697*p+0.9586*as.numeric(isolate(input$select3))-2.9486*as.numeric(isolate(input$select4))
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x=round(1/(1+exp(-z)),3)
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calculatorRV$value<-x
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if(as.numeric(input$update)>0){
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paste("Surowa wartość predyktora (im niższa, tym lepiej): ", strong(x))
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}
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})
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output$var <- renderText({
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input$update
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p=0
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if(as.numeric(isolate(input$slider2))>=50){
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p=50
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}
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z=-5.3718+0.0354*as.numeric(isolate(input$slider1))+1.6159*as.numeric(isolate(input$select1))+1.1768*as.numeric(isolate(input$select2))+0.0697*p+0.9586*as.numeric(isolate(input$select3))-2.9486*as.numeric(isolate(input$select4))
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x=round(1/(1+exp(-z)),3)
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if(as.numeric(input$update)>0){
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if(x>0.1){
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paste("Klasa guza: ",strong("złośliwy"))
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calculatorTV$value<-paste("Klasa guza: ",strong("złośliwy"))
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} else {
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paste("Klasa guza: ",strong("łagodny"))
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calculatorTV$value <- paste("Klasa guza: ",strong("łagodny"))
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}
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}
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})
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output$wykres <- renderPlotly({
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input$update
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p=0
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if(as.numeric(isolate(input$slider2))>=50){
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p=50
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}
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z=-5.3718+0.0354*as.numeric(isolate(input$slider1))+1.6159*as.numeric(isolate(input$select1))+1.1768*as.numeric(isolate(input$select2))+0.0697*p+0.9586*as.numeric(isolate(input$select3))-2.9486*as.numeric(isolate(input$select4))
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x=seq(by=1,-8,8)
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y=round(1/(1+exp(-x)),3)
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d=data.frame(x,y)
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if(as.numeric(input$update)>0){
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g=ggplot(data=d,aes(x=x,y=y))+
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geom_line()+
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geom_point(aes(x=z,y=round(1/(1+exp(-z)),3)),color="red",size=4)+
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geom_hline(aes(yintercept=0.1),linetype = "dashed")+
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geom_text(aes(x=6,y=0.15),label="próg złośliwości: 0.1")+
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labs(x="Realność",y="Prognoza")+
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theme_light()
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ggplotly(g)
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}
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})
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output$calculatorSave<-renderUI({
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if(as.numeric(input$update)>0){
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actionButton("calculatorSubmit","Zapisz")
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}
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})
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observeEvent(input$calculatorSubmit, {
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calculatorSave<-data.frame(slider1<-input$slider1,
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select1<-input$select1,
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select2<-input$select2,
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slider2<-input$slider2,
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select3<-input$select3,
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select4<-input$select4)
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print(calculatorSave)
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calculatorParameterInts = list(list(name="parameter1",value = calculatorSave$slider1),
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list(name="parameter2",value = calculatorSave$select1),
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list(name="parameter3",value = calculatorSave$select2),
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list(name="parameter4",value = calculatorSave$slider2),
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list(name="parameter5",value = calculatorSave$select3),
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list(name="parameter6",value = calculatorSave$select4))
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prediction = list(
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name = "IOTA",
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parameterInts = calculatorParameterInts,
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resultValue = calculatorRV$value,
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resultText = calculatorTV$value
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)
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r<-httr::POST("https://syi-back.herokuapp.com/api/prediction/save",add_headers(Authorization=paste("Bearer",input$token,sep=" ")),body=prediction,encode = 'json')
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# SPRAWDZENIE POBIERANIA JEDNEGO I WIELU POMIAROW
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# r<-httr::GET("http://localhost:8080/api/prediction/get/7",add_headers(Authorization=paste("Bearer",input$token,sep=" ")),encode = 'json')
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# r<-httr::GET("http://localhost:8080/api/prediction/usersPredictions/ind",add_headers(Authorization=paste("Bearer",input$token,sep=" ")),encode = 'json')
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if (r$status_code==200){
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TRUE
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}else{
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FALSE
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}
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# print(toJSON(content(r,as = "parsed")))
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})
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}
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