parent
ac758e3776
commit
ea46df63d5
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@ -14,7 +14,9 @@ rule all:
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days_before_surgery = config["METRICS_FOR_ANALYSIS"]["DAYS_BEFORE_SURGERY"],
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days_after_discharge= config["METRICS_FOR_ANALYSIS"]["DAYS_AFTER_DISCHARGE"],
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days_in_hospital= config["METRICS_FOR_ANALYSIS"]["DAYS_IN_HOSPITAL"]),
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expand("data/processed/{pid}/targets_{summarised}.csv",
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pid = config["PIDS"],
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summarised = config["METRICS_FOR_ANALYSIS"]["SUMMARISED"]),
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# Feature extraction
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expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["SENSORS"]),
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expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["FITBIT_TABLE"]),
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@ -9,3 +9,13 @@ rule days_to_analyse:
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"data/interim/{pid}/days_to_analyse_{days_before_surgery}_{days_in_hospital}_{days_after_discharge}.csv"
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script:
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"../src/models/select_days_to_analyse.py"
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rule get_targets:
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input:
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participant_info = "data/raw/{pid}/" + config["METRICS_FOR_ANALYSIS"]["GROUNDTRUTH_TABLE"] + "_raw.csv"
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params:
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summarised = "{summarised}"
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output:
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"data/processed/{pid}/targets_{summarised}.csv"
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script:
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"../src/models/get_targets.py"
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@ -0,0 +1,16 @@
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import pandas as pd
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participant_info = pd.read_csv(snakemake.input["participant_info"])
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summarised = snakemake.params["summarised"]
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pid = snakemake.input["participant_info"].split("/")[2]
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targets = pd.DataFrame({"pid": [pid], "target": [None]})
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if summarised == "summarised":
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if not participant_info.empty:
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cesds = participant_info.loc[0, ["preop_cesd_total", "inpatient_cesd_total", "postop_cesd_total", "3month_cesd_total"]]
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# targets: 1 => 50% (ceiling) or more of available CESD scores were 16 or higher; 0 => otherwise
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threshold_num = (cesds.count() + 1) // 2
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threshold_cesd = 16
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target = 1 if cesds.apply(lambda x : 1 if x >= threshold_cesd else 0).sum() >= threshold_num else 0
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targets.loc[0, "target"] = target
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targets.to_csv(snakemake.output[0], index=False)
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