Get targets

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

Snakefile

@@ -14,7 +14,9 @@ rule all:
                             days_before_surgery = config["METRICS_FOR_ANALYSIS"]["DAYS_BEFORE_SURGERY"],
                             days_after_discharge= config["METRICS_FOR_ANALYSIS"]["DAYS_AFTER_DISCHARGE"],
                             days_in_hospital= config["METRICS_FOR_ANALYSIS"]["DAYS_IN_HOSPITAL"]),
-
+        expand("data/processed/{pid}/targets_{summarised}.csv", 
+                            pid = config["PIDS"],
+                            summarised = config["METRICS_FOR_ANALYSIS"]["SUMMARISED"]),
         # Feature extraction
         expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["SENSORS"]),
         expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["FITBIT_TABLE"]),

rules/mystudy.snakefile

@@ -9,3 +9,13 @@ rule days_to_analyse:
         "data/interim/{pid}/days_to_analyse_{days_before_surgery}_{days_in_hospital}_{days_after_discharge}.csv"
     script:
         "../src/models/select_days_to_analyse.py"
+
+rule get_targets:
+    input:
+        participant_info = "data/raw/{pid}/" + config["METRICS_FOR_ANALYSIS"]["GROUNDTRUTH_TABLE"] + "_raw.csv"
+    params:
+        summarised = "{summarised}"
+    output:
+        "data/processed/{pid}/targets_{summarised}.csv"
+    script:
+        "../src/models/get_targets.py"

src/models/get_targets.py

@@ -0,0 +1,16 @@
+import pandas as pd
+
+participant_info = pd.read_csv(snakemake.input["participant_info"])
+summarised = snakemake.params["summarised"]
+pid = snakemake.input["participant_info"].split("/")[2]
+
+targets = pd.DataFrame({"pid": [pid], "target": [None]})
+if summarised == "summarised":
+    if not participant_info.empty:
+        cesds = participant_info.loc[0, ["preop_cesd_total", "inpatient_cesd_total", "postop_cesd_total", "3month_cesd_total"]]
+        # targets: 1 => 50% (ceiling) or more of available CESD scores were 16 or higher; 0 => otherwise
+        threshold_num = (cesds.count() + 1) // 2
+        threshold_cesd = 16
+        target = 1 if cesds.apply(lambda x : 1 if x >= threshold_cesd else 0).sum() >= threshold_num else 0
+        targets.loc[0, "target"] = target
+targets.to_csv(snakemake.output[0], index=False)