Predicting pneumonia outcomes: EDA part 2
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This post is a supplementary material for an assignment. The assignment is part of the Augmented Machine Learning unit for a Specialised Diploma in Data Science for Business. The aim of the assignment is to use DataRobot for predictive modelling. Exploratory data analysis and feature engineering will be done here in R before the data is imported into DataRobot.
Intro
The aim of this project is to classify if patients with Community Acquired Pneumonia (CAP) became better after seeing a doctor or became worse despite seeing a doctor. The variables of the dataset can be classified into 13 categories. The first 8 categories have been explored in the previous post. The remaining categories will be explored in this post.
library(tidyverse)
theme_set(theme_light())
# previously and partial EDA dataset
load("CAP_EDA1.RData")
# 13 categories
categories13<- readxl::read_excel("Incidence rate of community-acquired pneumonia in adults a population-based prospective active surveillance study in three cities in South America.xls", sheet=3)
categories13 %>% DT::datatable(rownames = F, options = list(searchHighlight = TRUE, paging= T))
Customized EDA functions from the previous post will be used here.
dtype<- function(datafr, x){
datafr%>% select(starts_with(x, ignore.case = F)) %>% str()
}
eda_c<- function(datafr,x){
datafr %>% select(starts_with(x, ignore.case = F)) %>% map(~ table(.x, useNA = "always"))
}
eda_n_NAplt<- function (datafr, x){
datafr %>% select(starts_with(x, ignore.case = F)) %>% summarise(across(starts_with(x), ~mean(is.na(.)))) %>% pivot_longer(cols = everything(), names_to= "Variables" , values_to="pct_na") %>% mutate(Variables= fct_reorder(Variables, pct_na)) %>% ggplot(aes(x=Variables, y=pct_na, fill= pct_na))+ geom_col() + coord_flip() + scale_y_continuous(labels=scales::percent_format()) + scale_fill_viridis_c(option = "plasma")}
eda_n_NAcutoff<- function(datafr, x, low, high){
datafr%>% select(starts_with(x, ignore.case = F)) %>% summarise(across(starts_with(x), ~mean(is.na(.)))) %>% pivot_longer(cols = everything(), names_to="Variables", values_to="pct_na") %>% filter((pct_na>low & pct_na<high)) %>% pull(Variables)}
eda_n_outlier<-function(datafr, x_selected){
# nested df with plots
plt<-datafr %>% select(all_of(x_selected)) %>% pivot_longer(cols=everything(),names_to="Variables", values_to="values") %>% nest(-Variables) %>% mutate(plot= map2(.x= data, .y= Variables,
~ggplot(data=.x, aes(x= values)) + geom_boxplot() + labs(title = .y)
))
# print the plots
for (i in 1:length(x_selected)){
p<-plt[[3]][[i]]
print(p)}
}
9 Lab_ related category
Sodium levels Lab_Na should be in numeric form but it is registered as a string. Upon closer inspection, there are no characters found in the variable. The variable can be converted into a numeric variable.
(dtype(df,"Lab")) ## tibble [2,112 x 20] (S3: tbl_df/tbl/data.frame) ## $ Lab_RBC : num [1:2112] 32 43 27 33 10.4 33 45 35 35.5 21.3 ... ## $ Lab_Hb : num [1:2112] 10.7 14.2 9.6 11.4 4.8 11 13.3 11.2 11 6.8 ... ## $ Lab_WBC : num [1:2112] 15.7 12.5 6.6 9.5 75.3 18.9 8.3 13.1 8.5 13.9 ... ## $ Lab_NeuImu : num [1:2112] NA NA NA NA NA NA NA NA NA NA ... ## $ Lab_Neu : num [1:2112] 89 80 86 76 NA 89 88 82 81 92 ... ## $ Lab_plt : num [1:2112] 175 170 120 274 27 128 333 621 496 180 ... ## $ Lab_Na : chr [1:2112] NA NA NA NA ... ## $ Lab_urea : num [1:2112] 60 NA 99 56 143 56.3 49 19 25 214 ... ## $ Lab_Cr : num [1:2112] 1.61 NA 0.77 0.84 2.94 0.88 0.95 0.83 0.73 8.11 ... ## $ Lab_Bicarb : num [1:2112] NA NA NA NA NA NA NA NA NA NA ... ## $ Lab_Sugar : num [1:2112] 76 NA 83 111 88 70 93 78 100 75 ... ## $ Lab_Alb : num [1:2112] NA NA NA NA NA NA NA NA NA NA ... ## $ Lab_lactate : num [1:2112] NA NA NA NA NA NA NA NA NA NA ... ## $ Lab_lactateHigh: chr [1:2112] "Unavailable" "Unavailable" "Unavailable" "Unavailable" ... ## $ Lab_CRP : num [1:2112] 48 96 NA 92 192 48 96 48 48 192 ... ## $ Lab_CRPHigh : chr [1:2112] "Yes" "Yes" "Unavailable" "Yes" ... ## $ Lab_pH : num [1:2112] NA NA NA NA NA NA NA NA NA NA ... ## $ Lab_CO2 : num [1:2112] NA NA NA NA NA NA NA NA NA NA ... ## $ Lab_O2 : num [1:2112] NA NA NA NA NA NA NA NA NA NA ... ## $ Lab_FiO2 : num [1:2112] NA NA NA NA NA NA NA NA NA NA ... ## NULL # check for characters (df %>% mutate(char= str_detect(Lab_Na, pattern = "[A-z0-9]")) %>% filter(Lab_Na==T)) ## # A tibble: 0 x 131 ## # ... with 131 variables: Pt_Site <chr>, Pt_CaseNumber <int>, Pt_Age <dbl>, ## # R_CXR_infiltrate <chr>, R_CXR_cavitation <chr>, R_CXR_effusionSite <chr>, ## # R_CT_inflitrate <chr>, R_CT_cavitation <chr>, R_CT_effusionSite <chr>, ## # SS_cough <chr>, SS_phlegm <chr>, SS_lungSounds <chr>, SS_temp <chr>, ## # SS_breathing <chr>, SS_daysOfRespSymp <dbl>, Hx_mass <chr>, ## # Hx_stroke <chr>, Hx_kidney <chr>, Hx_liver <chr>, Hx_brainMental <chr>, ## # Hx_diabetes <chr>, Hx_pastCAP <chr>, Hx_asp <chr>, Hx_alcohol <chr>, ## # Hx_immune <chr>, Hx_COPD <chr>, Social_drugs <chr>, ## # Social_overcrowded <chr>, Hx_heart_type <chr>, Social_smoke <chr>, ## # Hx_HIV <chr>, HCAP_hospStay <chr>, HCAP_IVAbx <chr>, HCAP_Chemo <chr>, ## # HCAP_diaylsis <chr>, HCAP_injury <chr>, PE_AMS <chr>, PE_HR <dbl>, ## # PE_RR <dbl>, PE_BP_S <dbl>, PE_BP_D <dbl>, PE_temp <dbl>, Lab_RBC <dbl>, ## # Lab_Hb <dbl>, Lab_WBC <dbl>, Lab_NeuImu <dbl>, Lab_Neu <dbl>, ## # Lab_plt <dbl>, Lab_Na <chr>, Lab_urea <dbl>, Lab_Cr <dbl>, ## # Lab_Bicarb <dbl>, Lab_Sugar <dbl>, Lab_Alb <dbl>, Lab_lactate <dbl>, ## # Lab_lactateHigh <chr>, Lab_CRP <dbl>, Lab_CRPHigh <chr>, Lab_pH <dbl>, ## # Lab_CO2 <dbl>, Lab_O2 <dbl>, Lab_FiO2 <dbl>, CS_Resp <chr>, CS_Blood <chr>, ## # CS_Urine <chr>, CS_screen <chr>, CS_agent <chr>, CS_Organism1 <chr>, ## # CS_Organism1Blood <chr>, CS_Organism1Sputum <chr>, ## # CS_Organism1Tracheal <chr>, CS_Organism1BAL <chr>, CS_Organism1Urine <chr>, ## # CS_Organism1Sero <chr>, CS_Organism1Other <chr>, ## # CS_Organism1Comments <chr>, CS_Organism2 <chr>, CS_Orgainsim2Blood <chr>, ## # CS_Organism2Sputum <chr>, CS_Organism2Tracheal <chr>, ## # CS_Organism2BAL <chr>, CS_Organism2Urine <chr>, CS_OrganismSero <chr>, ## # CS_OrganismOther <chr>, CS_OrganismComments <chr>, ## # Abx_AmoxicillinSulbactam <chr>, Abx_AmoxicillinSulbactamOral <chr>, ## # Abx_AmoxicillinSulbactamNonoral <chr>, ## # Abx_AmoxicillinSulbactamNonoralStart <chr>, ## # Abx_AmoxicillinSulbactamNonoralEnd <chr>, Abx_Ampicillin <chr>, ## # Abx_AmpicillinStart <chr>, Abx_AmpicillinEnd <chr>, ## # Abx_AmpicillinSulbactam <chr>, Abx_Azithromycin <chr>, ## # Abx_Ceftriaxone <chr>, Abx_Cefotaxime <chr>, Abx_ClarithromycinOral <chr>, ## # Abx_Cefepime <chr>, Abx_CefepimeStart <lgl>, ... # convert Lab_na to num df<-df %>% mutate(Lab_Na=as.numeric(Lab_Na))
Missing Lab values
More than half of the Lab_ variables have >40% missing values. These variables will be removed. Lab_CRPHigh and Lab_lactateHigh are binary variables indicating if CRP Lab_CRP and lactate levels Lab_lactate are above normal limits. As Lab_CRP and Lab_lactate will be dropped due to too many missing values, Lab_CRPHigh and Lab_lactateHigh will also be dropped .
(eda_n_NAplt(df,"Lab"))
# remove unwanted Lab_ col
lab_selected<-eda_n_NAcutoff(df, "Lab", 0, .40)
lab_all<-df %>% select(starts_with("Lab")) %>% colnames()
lab_removed<-setdiff(lab_all, lab_selected)
df<- df %>% select(-any_of(lab_removed))
Outlier Lab values
The following Lab values have numerous outliers warranting further investigation:
- Haemoglobin levels ,
Lab_Hb>100 - Neutrophil levels ,
Lab_Neu, <35 - Sugar levels,
Lab_Sugar> 450
eda_n_outlier(df, lab_selected) ## Warning: All elements of `...` must be named. ## Did you want `data = c(values)`? ## Warning: Removed 443 rows containing non-finite values (stat_boxplot).
## Warning: Removed 466 rows containing non-finite values (stat_boxplot).
## Warning: Removed 424 rows containing non-finite values (stat_boxplot).
## Warning: Removed 557 rows containing non-finite values (stat_boxplot).
## Warning: Removed 808 rows containing non-finite values (stat_boxplot).
## Warning: Removed 660 rows containing non-finite values (stat_boxplot).
## Warning: Removed 744 rows containing non-finite values (stat_boxplot).
## Warning: Removed 813 rows containing non-finite values (stat_boxplot).
High Lab_Hb levels
In conventional units, a healthy female has 12-16 g/DL of haemoglobin. In SI units, a healthy female has 120-160 g/L of haemoglobin. Based on the distribution of values Lab_hb, the outliers are likely measured in g/L (e.g. 111-149) will need to be converted to g/DL (e.g. 11.1-14.9).
(df %>% filter(Lab_Hb>100) %>% select(Lab_Hb)) ## # A tibble: 6 x 1 ## Lab_Hb ## <dbl> ## 1 114 ## 2 111 ## 3 132 ## 4 112 ## 5 135 ## 6 149 # insert decimal point df<-df %>% mutate(Lab_Hb= if_else(Lab_Hb>100, Lab_Hb/10, Lab_Hb))
Low Lab_Neu
Neutrophil <40 are considered below normal limits. The initially hypothesis is that these patients with low neutrophil have either HIV or on immunosuppression drugs. However, majority of these patients have neither. Although, the initial hypothesis was incorrect, there are other differentials for low neutrophil levels. Considering, the number of these outliers is small (n=22), these observations shall be kept.
df %>% select (Lab_Neu, Hx_HIV, Hx_immune) %>% filter(Lab_Neu<35) ## # A tibble: 22 x 3 ## Lab_Neu Hx_HIV Hx_immune ## <dbl> <chr> <chr> ## 1 11 No No ## 2 12 No Yes ## 3 0 Unavailable No ## 4 24 No No ## 5 6 No No ## 6 34 No No ## 7 0 No <NA> ## 8 2 No Yes ## 9 5 Unavailable No ## 10 1 No No ## # ... with 12 more rows
High Lab_Sugar
It is plausible to have very high sugar levels if the patient has diabetes. All of these outliers have diabetes thus the outlier values are plausible.
df %>% filter(Lab_Sugar>450) %>% count(Hx_diabetes) ## # A tibble: 1 x 2 ## Hx_diabetes n ## <chr> <int> ## 1 Yes 7
10 CS_ cultures related category
There are 23 variables under CS and the most important variables are CS_Organism1 and CS_Organism2 as they indicate which organism is causing the CAP. The majority of the other CS variables are methods to identify the organism. However, there are >90% missing values for CS_Organism1 and CS_Organism2 thus the methods of identifying the organisms though may not be missing are useless. All variables under CS will be removed.
(dtype(df, "CS")) ## tibble [2,112 x 23] (S3: tbl_df/tbl/data.frame) ## $ CS_Resp : chr [1:2112] "No" "No" "No" "No" ... ## $ CS_Blood : chr [1:2112] "No" "No" "No" "No" ... ## $ CS_Urine : chr [1:2112] "Yes" "Yes" "Yes" "Yes" ... ## $ CS_screen : chr [1:2112] "No" "No" "No" "No" ... ## $ CS_agent : chr [1:2112] "No" "No" "No" "Yes" ... ## $ CS_Organism1 : chr [1:2112] NA NA NA "Streptococcus pneumoniae" ... ## $ CS_Organism1Blood : chr [1:2112] "No" "No" "No" "No" ... ## $ CS_Organism1Sputum : chr [1:2112] "No" "No" "No" "No" ... ## $ CS_Organism1Tracheal: chr [1:2112] "No" "No" "No" "No" ... ## $ CS_Organism1BAL : chr [1:2112] "No" "No" "No" "No" ... ## $ CS_Organism1Urine : chr [1:2112] "No" "No" "No" "Yes" ... ## $ CS_Organism1Sero : chr [1:2112] "No" "No" "No" "No" ... ## $ CS_Organism1Other : chr [1:2112] NA NA NA NA ... ## $ CS_Organism1Comments: chr [1:2112] NA NA NA NA ... ## $ CS_Organism2 : chr [1:2112] NA NA NA NA ... ## $ CS_Orgainsim2Blood : chr [1:2112] "No" "No" "No" "No" ... ## $ CS_Organism2Sputum : chr [1:2112] "No" "No" "No" "No" ... ## $ CS_Organism2Tracheal: chr [1:2112] "No" "No" "No" "No" ... ## $ CS_Organism2BAL : chr [1:2112] "No" "No" "No" "No" ... ## $ CS_Organism2Urine : chr [1:2112] "No" "No" "No" "No" ... ## $ CS_OrganismSero : chr [1:2112] "No" "No" "No" "No" ... ## $ CS_OrganismOther : chr [1:2112] NA NA NA NA ... ## $ CS_OrganismComments : chr [1:2112] NA NA NA NA ... ## NULL (eda_n_NAplt(df, "CS"))
df<-select(df, -starts_with("CS"))
11 Abx_ antibiotics related category
dtype(df, "Abx") ## tibble [2,112 x 35] (S3: tbl_df/tbl/data.frame) ## $ Abx_AmoxicillinSulbactam : chr [1:2112] "No" "Yes" "Unavailable" "No" ... ## $ Abx_AmoxicillinSulbactamOral : chr [1:2112] "No" "No" "Unavailable" "No" ... ## $ Abx_AmoxicillinSulbactamNonoral : chr [1:2112] "No" "No" "Unavailable" "No" ... ## $ Abx_AmoxicillinSulbactamNonoralStart: chr [1:2112] NA NA NA NA ... ## $ Abx_AmoxicillinSulbactamNonoralEnd : chr [1:2112] NA NA NA NA ... ## $ Abx_Ampicillin : chr [1:2112] "No" "No" "Unavailable" "No" ... ## $ Abx_AmpicillinStart : chr [1:2112] NA NA NA NA ... ## $ Abx_AmpicillinEnd : chr [1:2112] NA NA NA NA ... ## $ Abx_AmpicillinSulbactam : chr [1:2112] "No" "No" "Unavailable" "No" ... ## $ Abx_Azithromycin : chr [1:2112] "No" "Yes" "Yes" "Yes" ... ## $ Abx_Ceftriaxone : chr [1:2112] "Yes" "No" "Yes" "No" ... ## $ Abx_Cefotaxime : chr [1:2112] "No" "No" "Yes" "Yes" ... ## $ Abx_ClarithromycinOral : chr [1:2112] "No" "No" "Unavailable" "No" ... ## $ Abx_Cefepime : chr [1:2112] "No" "No" "Unavailable" "No" ... ## $ Abx_CefepimeStart : logi [1:2112] NA NA NA NA NA NA ... ## $ Abx_CefepimeEnd : logi [1:2112] NA NA NA NA NA NA ... ## $ Abx_ClarithromycinIV : chr [1:2112] "No" "No" "Unavailable" "No" ... ## $ Abx_ClarithromycinIVStart : chr [1:2112] NA NA NA NA ... ## $ Abx_ClarithromycinIVEnd : chr [1:2112] NA NA NA NA ... ## $ Abx_Doxycycline : chr [1:2112] "No" "No" "Unavailable" "No" ... ## $ Abx_DoxycyclineStart : chr [1:2112] NA NA NA NA ... ## $ Abx_DoxycyclineEnd : chr [1:2112] NA NA NA NA ... ## $ Abx_Levofloxacin : chr [1:2112] "No" "No" "Yes" "Yes" ... ## $ Abx_Moxifloxacin : chr [1:2112] "Yes" "No" "Unavailable" "No" ... ## $ Abx_Piperacillin : chr [1:2112] "No" "No" "Unavailable" "No" ... ## $ Abx_PiperacillinStart : chr [1:2112] NA NA NA NA ... ## $ Abx_PiperacillinEnd : chr [1:2112] NA NA NA NA ... ## $ Abx_Trimethoprim : chr [1:2112] "No" "No" "Unavailable" "No" ... ## $ Abx_TrimethoprimStart : chr [1:2112] NA NA NA NA ... ## $ Abx_TrimethoprimEnd : chr [1:2112] NA NA NA NA ... ## $ Abx_OtherYN : chr [1:2112] "No" "No" "No" "No" ... ## $ Abx_OtherDetail : chr [1:2112] NA NA NA NA ... ## $ Abx_Class : chr [1:2112] "Beta-lactams + Quinolones" "Beta-lactams + Macrolides" "Beta-lactams + Quinolones" "Macrolides" ... ## $ Abx_ClassOther : chr [1:2112] NA NA NA NA ... ## $ Abx_Duration : num [1:2112] 14 10 5 16 8 13 10 10 10 10 ...
The antibiotics category can be divided into 3 sub-categories:
- Class of empirical antibiotics given
- Antibiotics given
- Duration of antibiotics
11i Class of empirical antibiotics given
Majority of the Abx_ClassOther are NA because they have values in Abx_Class.
eda_n_NAplt(df, "Abx_Class")
Majority of the NA Abx_ClassOther are NA because they have values in Abx_Class.
df %>% select(starts_with("Abx_Class")) %>% filter(is.na(Abx_ClassOther)) %>% count(Abx_Class)
## # A tibble: 7 x 2
## Abx_Class n
## <chr> <int>
## 1 Beta-lactams 1108
## 2 Beta-lactams + Macrolides 736
## 3 Beta-lactams + Quinolones 59
## 4 Macrolides 79
## 5 Other 2
## 6 Quinolones 89
## 7 <NA> 6
Map values from Abx_Class to replace NA values in Abx_ClassOther. After using Abx_Class to expand Abx_ClassOther, Abx_Class will be dropped. Rename the updated Abx_ClassOther to Abx_ClassUpdated for more intuitive understanding of variable name
df<-df %>% mutate(Abx_ClassOther= case_when( Abx_Class=="Beta-lactams" & is.na(Abx_ClassOther) ~ "Beta-lactams", Abx_Class=="Beta-lactams + Macrolides" & is.na(Abx_ClassOther) ~"Beta-lactams + Macrolides", Abx_Class=="Beta-lactams + Quinolones" & is.na(Abx_ClassOther) ~ "Beta-lactams + Quinolones", Abx_Class=="Macrolides" & is.na(Abx_ClassOther) ~ "Macrolides", Abx_Class== "Other" & is.na(Abx_ClassOther) ~ "Other", Abx_Class== "Quinolones" & is.na(Abx_ClassOther) ~ "Quinolones", T ~ Abx_ClassOther )) %>% # remove Abx_Classother select(-Abx_Class) %>% # rename rename(Abx_ClassUpdated= Abx_ClassOther)
11ii Antibiotics given
There are no missing values for antibiotics given
df %>% select(starts_with("Abx")) %>% select(- c(ends_with("Start") | ends_with("End") | Abx_Duration | starts_with("Abx_Class")|ends_with("Detail"))) %>% eda_n_NAplt("Abx")
Number of antibiotics given
From the antibiotics given, the number of antibiotics given can be calculated. There are 4 observations with NA values being calculated. These observations shall be examined to see if there are missing values or if no antibiotics were given to begin with. (Perhaps, the doctor had high index of suspicion it was a viral CAP. In such situation, antibiotics would be ineffective)
## function to extract abx taken by pt (lond df)
abx_taken_Longdf<- function(dfr){
# select case number and abx col
dfr %>% select(Pt_CaseNumber, starts_with("Abx")) %>%
# remove unrelated abx columns
select(- c(ends_with("Start") | ends_with("End") | Abx_Duration | starts_with("Abx_Class")|ends_with("Detail"))) %>%
# into long df
pivot_longer(-Pt_CaseNumber, names_to="Abx_type", values_to="Used") %>%
# filter abx taken
filter(Used=="Yes")
}
## join no of abx taken w main df
df<-left_join(x= df,
y=df %>% abx_taken_Longdf() %>% group_by(Pt_CaseNumber) %>% count(Used, name= "New_Abx_no") %>% ungroup(),
by= "Pt_CaseNumber")
df %>% count(New_Abx_no)
## # A tibble: 8 x 2
## New_Abx_no n
## <int> <int>
## 1 1 778
## 2 2 854
## 3 3 347
## 4 4 121
## 5 5 6
## 6 6 1
## 7 7 1
## 8 NA 4
# convert abx as integer to numeric as Error: Problem with `mutate()` input `Abx_no`. x must be a double vector, not an integer vector. i Input `Abx_no` is `case_when(...)`.
df<- df %>% mutate(New_Abx_no= as.numeric(New_Abx_no))
Patient 254, 916, 964 did not receive any antibiotic. The number of antibiotics taken will be 0 and the antibiotics duration will also be 0. Patient 1864 received Macrolides class antibiotics as an empirical treatment. Fill the number of antibiotics taken as 1 and fill up other antibiotics taken Abx_OtherYN as Yes and fill up details of other antibiotics taken Abx_OtherDetail as Macrolides.
df%>% filter(is.na(New_Abx_no)) %>% select(Pt_CaseNumber, starts_with("Abx")) %>% select(- c(ends_with("Start") | ends_with("End") )) %>% DT::datatable(rownames = F, options = list(searchHighlight = TRUE, paging= T))
# clean up pt 254, 916, 964, 1864
df<-df %>% mutate(Abx_Duration=case_when(Pt_CaseNumber==254 ~0, Pt_CaseNumber== 964 ~0, T~Abx_Duration),
New_Abx_no=case_when(Pt_CaseNumber== 254 ~0, Pt_CaseNumber== 916 ~0, Pt_CaseNumber==964 ~0, Pt_CaseNumber==1864 ~1, T~New_Abx_no),
Abx_OtherYN=if_else(Pt_CaseNumber== 1864, "Yes", Abx_OtherYN),
Abx_OtherDetail=if_else(Pt_CaseNumber==1864, "Macrolides", Abx_OtherDetail)
)
11iii Duration of antibiotics
12 observations had NA antibiotic duration. Mostly like due to some data calculation or data entry error as only 3 observations in the entire dataset did not receive antibiotics.
(eda_n_NAplt(df, "Abx_Duration"))
(df %>% filter(is.na(Abx_Duration)) %>% distinct(Pt_CaseNumber) %>% count()) ## # A tibble: 1 x 1 ## n ## <int> ## 1 12
All of the patients with missing antibiotic duration took antibiotics.
df %>% filter(is.na(Abx_Duration)) %>% select(Pt_CaseNumber, New_Abx_no) ## # A tibble: 12 x 2 ## Pt_CaseNumber New_Abx_no ## <int> <dbl> ## 1 289 2 ## 2 378 1 ## 3 453 3 ## 4 703 1 ## 5 720 1 ## 6 832 1 ## 7 855 2 ## 8 1198 4 ## 9 1393 4 ## 10 1523 1 ## 11 1713 3 ## 12 2024 2
An attempt is made to calculate the duration using start and end dates of the antibiotics given. 3/12 patients with missing antibiotics duration had the start dates of their antibiotics captured. However, these patients took other antibiotics which did not have the start dates captured. We are unable to impute any of the missing antibiotic duration by calculating the difference in antibiotic start and end dates. We will impute the missing antibiotic duration by other means later. Antibiotic start and end dates will be removed as there are no longer useful and have no predictive power.
# abx with start dates
abx_date<-df %>% select(ends_with("Start")) %>% colnames() %>% str_replace("Start","")
# type of abx taken for pt w m/s abx duration
abx_ms<-
# filter pt with m/s abx_duration
df %>% filter(is.na(Abx_Duration)) %>%
# used above function to find out abx taken
abx_taken_Longdf() %>%
# distinct abx taken by this group of pts
distinct(Abx_type) %>% pull()
# types of abx taken for pt w m/s abx duration which have date of abx captured
abx_msAndDateStarted<-intersect(abx_date, abx_ms)
# pt w m/s abx duration who took abx with at least one abx start date
(df %>% filter(is.na(Abx_Duration)) %>%
abx_taken_Longdf() %>%
group_by(Pt_CaseNumber) %>% filter(any(Abx_type==abx_msAndDateStarted[[1]]) | any(Abx_type==abx_msAndDateStarted[[2]])) %>% summarise(n=n(), .groups="drop"))
## # A tibble: 3 x 2
## Pt_CaseNumber n
## <int> <int>
## 1 453 3
## 2 1198 4
## 3 1393 4
# remove start and end date
df<-df %>% select(- c(ends_with("Start") | ends_with("End")))
12 Care_ continuum of care status category
(dtype(df, "Care")) ## tibble [2,112 x 7] (S3: tbl_df/tbl/data.frame) ## $ Care_admit : chr [1:2112] "Yes" "No" "Yes" "Yes" ... ## $ Care_ICU : chr [1:2112] "No" "No" "No" "No" ... ## $ Care_breathingAid : chr [1:2112] "No" "No" "No" "No" ... ## $ Care_breathingAidType: chr [1:2112] NA NA NA NA ... ## $ Care_BPSupport : chr [1:2112] "No" "No" "Yes" "No" ... ## $ Care_daysUnfit : num [1:2112] 10 10 10 15 NA 12 15 6 12 NA ... ## $ Care_GP/OutptVisit : num [1:2112] 2 4 1 1 1 2 2 1 3 2 ... ## NULL (eda_c(df, "Care")) ## $Care_admit ## .x ## No Unavailable Yes <NA> ## 631 2 1479 0 ## ## $Care_ICU ## .x ## No Unavailable Yes <NA> ## 1729 53 330 0 ## ## $Care_breathingAid ## .x ## No Unavailable Yes <NA> ## 1928 60 124 0 ## ## $Care_breathingAidType ## .x ## ARM CPAP/Bilevel Other <NA> ## 91 26 5 1990 ## ## $Care_BPSupport ## .x ## No Unavailable Yes <NA> ## 1930 85 97 0 ## ## $Care_daysUnfit ## .x ## 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 ## 9 17 30 45 47 116 37 298 62 25 408 22 76 23 210 186 ## 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 ## 18 11 17 6 141 58 9 4 7 25 2 2 1 1 67 2 ## 33 35 36 37 40 45 47 48 50 53 58 59 60 65 69 75 ## 4 6 1 1 8 5 1 1 3 1 1 1 3 1 1 1 ## 80 99 181 <NA> ## 1 20 1 69 ## ## $`Care_GP/OutptVisit` ## .x ## 0 1 2 3 4 5 6 7 10 15 21 99 <NA> ## 57 1460 307 173 56 14 2 5 1 1 1 14 21
replace 99
Again 99 appears as outliers for Care_daysUnfit and Care_GP/OutptVisit. 99 will be replaced with NA.
df<-df %>% mutate(Care_daysUnfit= na_if(Care_daysUnfit, 99),
`Care_GP/OutptVisit`= na_if(`Care_GP/OutptVisit`, 99))
Admission status
Care_admit indicates if the patient was admitted to a hospital and Care_ICU indicates if patient had an ICU stay. 324 patients who were hospitalized also had ICU stay. The labels in Care_admit will include details to reflect patient who were admitted AND had ICU stay (label as Yes (w ICU)). After using information from Care_ICU to expand Care_admit, Care_ICU will be dropped.
(table(df$Care_admit, df$Care_ICU, useNA = "always")) ## ## No Unavailable Yes <NA> ## No 584 47 0 0 ## Unavailable 0 2 0 0 ## Yes 1145 4 330 0 ## <NA> 0 0 0 0 df<-df %>% mutate(Care_admit= case_when( Care_admit=="Yes" & Care_ICU=="Yes" ~ "Yes (w ICU)", T~ Care_admit)) %>% select(-Care_ICU) (df %>% count(Care_admit, name = "new_tally")) ## # A tibble: 4 x 2 ## Care_admit new_tally ## <chr> <int> ## 1 No 631 ## 2 Unavailable 2 ## 3 Yes 1149 ## 4 Yes (w ICU) 330
Breathing aid
Care_breathingAid indicates if patient in ICU used any breathing aids. Care_ breathingAidType details the type of breathing aids used.
Details from Care_breathingAidType will be integrated into Care_breathingAid and the Care_breathingAidType will be dropped.
(table(df$Care_breathingAid, df$Care_breathingAidType, useNA = "always")) ## ## ARM CPAP/Bilevel Other <NA> ## No 0 0 0 1928 ## Unavailable 0 0 0 60 ## Yes 91 26 5 2 ## <NA> 0 0 0 0 df<-df %>% mutate(Care_breathingAid= case_when( Care_breathingAid=="Yes" & Care_breathingAidType=="ARM" ~ "ARM", Care_breathingAid=="Yes" & Care_breathingAidType=="CPAP/Bilevel" ~ "CPAP/Bilevel", Care_breathingAid=="Yes" & Care_breathingAidType=="Other" ~ "Other", Care_breathingAid=="Yes" & is.na(Care_breathingAidType) ~ "Other", T~ Care_breathingAid)) %>% select(-Care_breathingAidType) (count(df, Care_breathingAid, name = "new_tally")) ## # A tibble: 5 x 2 ## Care_breathingAid new_tally ## <chr> <int> ## 1 ARM 91 ## 2 CPAP/Bilevel 26 ## 3 No 1928 ## 4 Other 7 ## 5 Unavailable 60
Wrap up
The original dataset had 2302 rows and 176 columns, after EDA the dataset has 2112 rows and 78 columns. More than half of the columns were removed and compressed via EDA.
# Clean up intermediate columns created during EDA df<-df %>% select(-Used) %>% rename(Abx_no=New_Abx_no) dim(df) ## [1] 2112 78
The cleaned up dataset is ready for some action. In the next post, some feature engineering will be done.
df %>% DT::datatable(rownames = F, options = list(searchHighlight = TRUE, paging= T))
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