让ChatGPT编写交互式网页应用的临床预测模型

tabBox(title = "Data Result",height = "500px", width = NULL,    
                    tabPanel("Dataview",h4("we can see the inut data"),
                             textOutput("text1"),
                             dataTableOutput("data1"), # 展示录入数据
                             dataTableOutput("data2"),
                             textOutput("prediction_text")
                             ), # 展示模型输出
                    tabPanel("Plotfigure", h4("Here output the AUC"),
                             textOutput("text2"),
                             verbatimTextOutput("Data_Management_Normal"),
                             plotOutput("plot1"))
                    )

library(shiny)
library(ggplot2)
library(pROC)  
library(DT)
library(tidyverse)
library(shinydashboard)
library(shinydashboardPlus)
library(shinyWidgets)

# 产生fake数据
set.seed(123) # for reproducibility

# create a data frame with patient data
patients <- data.frame(
  age = round(rnorm(500, 50, 10)), # age (normally distributed, mean=50, sd=10)
  sex = sample(c("M", "F"), 500, replace = TRUE), # sex
  blood_pressure = round(rnorm(500, 120, 10)), # blood pressure (normally distributed, mean=120, sd=10)
  cholesterol = round(rnorm(500, 200, 30)), # cholesterol (normally distributed, mean=200, sd=30)
  blood_sugar = round(rnorm(500, 5, 1)), # blood sugar (normally distributed, mean=5, sd=1)
  heart_rate = round(rnorm(500, 70, 10)), # heart rate (normally distributed, mean=70, sd=10)
  disease = sample(c(0, 1), 500, replace = TRUE, prob = c(0.8, 0.2)) # heart disease (20% of patients have the disease)
)


test_data<- data.frame(
  age = round(rnorm(200, 50, 10)), # age (normally distributed, mean=50, sd=10)
  sex = sample(c("M", "F"), 200, replace = TRUE), # sex
  blood_pressure = round(rnorm(200, 120, 10)), # blood pressure (normally distributed, mean=120, sd=10)
  cholesterol = round(rnorm(200, 200, 30)), # cholesterol (normally distributed, mean=200, sd=30)
  blood_sugar = round(rnorm(200, 5, 1)), # blood sugar (normally distributed, mean=5, sd=1)
  heart_rate = round(rnorm(200, 70, 10)), # heart rate (normally distributed, mean=70, sd=10)
  disease = sample(c(0, 1), 200, replace = TRUE, prob = c(0.8, 0.2)) # heart disease (20% of patients have the disease)
)


# create a factor variable for sex
patients$sex <- factor(patients$sex)

# show the first few rows of the dataset
head(patients)

# fit a logistic regression model
modelx <- glm(disease ~ age + sex + blood_pressure + cholesterol + blood_sugar + heart_rate, 
             data = patients, family = "binomial")

# show the model summary
summary(modelx)

library(broom)
# convert model output to a tidy data frame
model_summary <- tidy(modelx)




## Shiny web
ui <- fluidPage(
  titlePanel("临床预测模型"),
  sidebarLayout(
    sidebarPanel(
      numericInput("age", "年龄（岁）：", min = 0, max = 150, value = 50),
      selectInput("sex", "性别：", choices = c("F", "M")),
      numericInput("blood_pressure", "血压（mmHg）：", min = 0, max = 300, value = 120),
      numericInput("cholesterol", "胆固醇（mg/dL）：", min = 0, max = 500, value = 200),
      numericInput("blood_sugar", "血糖（mmol/L）：", min = 0, max = 50, value = 5),
      numericInput("heart_rate", "心率（次/分）：", min = 0, max = 300, value = 70),
      actionButton("calculate", "计算患病概率")
    ),
    
    mainPanel(
      
      

             tabBox(title = "Data management",height = "500px", width = NULL,   
                    tabPanel("Dataview",h4("we can see the inut data"),
                             textOutput("text1"),
                             dataTableOutput("data1"), # 展示录入数据
                             dataTableOutput("data2"),
                             textOutput("prediction_text")
                             ), # 展示模型输出
                    tabPanel("Plotfigure", h4("Here output the AUC"),
                             textOutput("text2"),
                             verbatimTextOutput("Data_Management_Normal"),
                             plotOutput("plot1"))
                    ),

    )
  )
)

server <- function(input, output) {
  # 定义数据框
  datax <- reactive({
    data.frame(
      age = input$age,
      sex = factor(input$sex),
      blood_pressure =input$blood_pressure,
      cholesterol = input$cholesterol,
      blood_sugar = input$blood_sugar,
      heart_rate = input$heart_rate) 

  })
  
  # 输出录入数据
  output$data1 <- renderDataTable({
    datax()
  })
  
  # 输出LR模型结果
  output$data2 <- renderDataTable({
    model_summary
  })

  # 计算患病概率
  prediction <- eventReactive(input$calculate, {
    prob <- predict(modelx,newdata =  datax(),type = "response")
    prob 
  })
  
  # 输出患病概率
  output$text1 <- renderText({
    paste0("该病人的患病概率为：", round(prediction() * 100, 2), "%")
    #print(str(datax()))
  })
  
  # 绘制AUC图表
  output$plot1 <- renderPlot({
    ggplot(mtcars)+ geom_point(aes(mpg,hp))
    #验证集
    test_data$P1<-predict(modelx,newdata = test_data , type = "response")
    roc1 <- plot.roc(test_data$disease,test_data$P1,ci=TRUE,print.auc=TRUE,levels = c(0,1),
                     direction='<')
    plot(roc1, print.auc=TRUE,auc.polygon=TRUE, grid=c(0.2, 0.2),
         grid.col=c("green", "red"), max.auc.polygon=TRUE,legacy.axes=T,
         auc.polygon.col="skyblue", print.thres=F,main="mtDNA-CN")
    
  })
  
  
}

shinyApp(ui = ui, server = server)