Split days threshold of data cleaning into days_before_surgery and days_after_discharge

Co-authored-by: JulioV <juliovhz@gmail.com>

config.yaml

@@ -154,7 +154,8 @@ PARAMS_FOR_ANALYSIS:
   COLS_NAN_THRESHOLD: 0.5
   COLS_VAR_THRESHOLD: True
   ROWS_NAN_THRESHOLD: 0.5
-  PARTICIPANTS_DAY_THRESHOLD: 7
+  PARTICIPANT_DAYS_BEFORE_THRESHOLD: 7
+  PARTICIPANT_DAYS_AFTER_THRESHOLD: 4
 
   SUMMARISED: ["summarised"] # "summarised" or "notsummarised"
 

rules/models.snakefile

@@ -64,7 +64,8 @@ rule clean_features_for_individual_model:
         cols_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_NAN_THRESHOLD"],
         cols_var_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_VAR_THRESHOLD"],
         rows_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["ROWS_NAN_THRESHOLD"],
-        participants_day_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANTS_DAY_THRESHOLD"]
+        days_before_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_BEFORE_THRESHOLD"],
+        days_after_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_AFTER_THRESHOLD"]
     output:
         "data/processed/{pid}/data_for_individual_model/{source}_{day_segment}_clean.csv"
     script:
@@ -77,7 +78,8 @@ rule clean_features_for_population_model:
         cols_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_NAN_THRESHOLD"],
         cols_var_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_VAR_THRESHOLD"],
         rows_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["ROWS_NAN_THRESHOLD"],
-        participants_day_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANTS_DAY_THRESHOLD"]
+        days_before_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_BEFORE_THRESHOLD"],
+        days_after_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_AFTER_THRESHOLD"]
     output:
         "data/processed/data_for_population_model/{source}_{day_segment}_clean.csv"
     script:

src/models/clean_features_for_model.R

@@ -2,14 +2,25 @@ source("packrat/init.R")
 library(tidyr)
 library(dplyr)
 
-filter_participant_without_enough_days <- function(clean_features, participants_day_threshold){
+filter_participant_without_enough_days <- function(clean_features, days_before_threshold, days_after_threshold){
-  if("pid" %in% colnames(clean_features))
+  if("pid" %in% colnames(clean_features)){
-    clean_features <- clean_features %>% group_by(pid)
+    clean_features <- clean_features %>% 
-  
+      group_by(pid) %>% 
+      add_count(pid, day_type) # this adds a new column "n"
+  } else {
+    clean_features <- clean_features %>% add_count(day_type)
+  }
+
+  # Only keep participants with enough days before surgery and after discharge
   clean_features <- clean_features %>% 
-    filter(n() >= participants_day_threshold) %>% 
+    mutate(count_before = ifelse(day_type == -1, n, NA), # before surgery
+          count_after = ifelse(day_type == 1, n, NA)) %>%  # after discharge
+    fill(count_before, .direction = "downup") %>% 
+    fill(count_after, .direction = "downup") %>% 
+    filter(count_before >= days_before_threshold & count_after >= days_after_threshold) %>% 
+    select(-n, -count_before, -count_after) %>% 
     ungroup()
-  
+
   return(clean_features)
 }
 
@@ -17,10 +28,12 @@ clean_features <- read.csv(snakemake@input[[1]])
 cols_nan_threshold <- snakemake@params[["cols_nan_threshold"]]
 drop_zero_variance_columns <- snakemake@params[["cols_var_threshold"]]
 rows_nan_threshold <- snakemake@params[["rows_nan_threshold"]]
-participants_day_threshold <- snakemake@params[["participants_day_threshold"]]
+days_before_threshold <- snakemake@params[["days_before_threshold"]]
+days_after_threshold <- snakemake@params[["days_after_threshold"]]
+
 
 # We have to do this before and after dropping rows, that's why is duplicated
-clean_features <- filter_participant_without_enough_days(clean_features, participants_day_threshold)
+clean_features <- filter_participant_without_enough_days(clean_features, days_before_threshold, days_after_threshold)
 
 # drop columns with a percentage of NA values above cols_nan_threshold
 if(nrow(clean_features))
@@ -35,6 +48,9 @@ clean_features <- clean_features %>%
   filter(percentage_na < rows_nan_threshold) %>% 
   select(-percentage_na)
 
-clean_features <- filter_participant_without_enough_days(clean_features, participants_day_threshold)
+if(nrow(clean_features) != 0){
+  clean_features <- filter_participant_without_enough_days(clean_features, days_before_threshold, days_after_threshold)
+  clean_features <- clean_features %>% select(-day_type)
+}
 
 write.csv(clean_features, snakemake@output[[1]], row.names = FALSE)

src/models/select_days_to_analyse.py

@@ -2,19 +2,19 @@ import numpy as np
 import pandas as pd
 from datetime import timedelta
 
-def appendDaysInRange(days_to_analyse, start_date, end_date):
+def appendDaysInRange(days_to_analyse, start_date, end_date, day_type):
     num_of_days = (end_date - start_date).days
     if np.isnan(num_of_days):
         return days_to_analyse
     for day in range(num_of_days + 1):
-            days_to_analyse = days_to_analyse.append({"local_date": start_date + timedelta(days = day)}, ignore_index=True)
+        days_to_analyse = days_to_analyse.append({"local_date": start_date + timedelta(days = day), "day_type": day_type}, ignore_index=True)
     return days_to_analyse
 
 days_before_surgery = int(snakemake.params["days_before_surgery"])
 days_in_hospital = str(snakemake.params["days_in_hospital"])
 days_after_discharge = int(snakemake.params["days_after_discharge"])
 participant_info = pd.read_csv(snakemake.input["participant_info"], parse_dates=["surgery_date", "discharge_date"])
-days_to_analyse = pd.DataFrame(columns = ["local_date"])
+days_to_analyse = pd.DataFrame(columns = ["local_date", "day_type"])
 
 try:
     surgery_date, discharge_date = participant_info["surgery_date"].iloc[0].date(), participant_info["discharge_date"].iloc[0].date()
@@ -24,9 +24,10 @@ else:
     start_date = surgery_date - timedelta(days = days_before_surgery)
     end_date = discharge_date + timedelta(days = days_after_discharge)
 
-    days_to_analyse = appendDaysInRange(days_to_analyse, start_date, surgery_date - timedelta(days = 1))
+    # days before surgery: -1; in hospital: 0; after discharge: 1
+    days_to_analyse = appendDaysInRange(days_to_analyse, start_date, surgery_date - timedelta(days = 1), -1)
     if days_in_hospital == "T":
-        days_to_analyse = appendDaysInRange(days_to_analyse, surgery_date, discharge_date)
+        days_to_analyse = appendDaysInRange(days_to_analyse, surgery_date, discharge_date, 0)
-    days_to_analyse = appendDaysInRange(days_to_analyse, discharge_date + timedelta(days = 1), end_date)
+    days_to_analyse = appendDaysInRange(days_to_analyse, discharge_date + timedelta(days = 1), end_date, 1)
     
 days_to_analyse.to_csv(snakemake.output[0], index=False)