cs229-project/label_data.py at master · bryankim96/cs229-project · GitHub

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import pandas as pd


# Conditions for a report indicating a positive response
def good_results(results):
    if " yp" in results and "T0" in results and "N0" in results:
        return 1
    elif " yp" in results:
        return -1
    else:
        return 0


# Conditions for a report corresponding to a patient with a future positive response
def good_results_exist(target_id, target_time, row_id, row_time, response):
    if not (len(str(target_time).split('-')) == 3 and len(str(row_time).split('-')) == 3):
        return False
    target_day, target_month, target_year = target_time.split('-')
    row_day, row_month, row_year = row_time.split('-')
    later_date = int(row_year) > int(target_year) or (int(row_year) == int(target_year) and (
            int(row_month) > int(target_month) or (
            int(row_month) == int(target_month) and int(row_day) > int(target_day))))
    return target_id == row_id and later_date and response == 1


# Conditions for a report corresponding to a patient with a future negative response
def bad_results_exist(target_id, target_time, row_id, row_time, response):
    if not (len(str(target_time).split('-')) == 3 and len(str(row_time).split('-')) == 3):
        return False
    target_day, target_month, target_year = target_time.split('-')
    row_day, row_month, row_year = row_time.split('-')
    later_date = int(row_year) > int(target_year) or (int(row_year) == int(target_year) and (
            int(row_month) > int(target_month) or (
            int(row_month) == int(target_month) and int(row_day) > int(target_day))))
    return target_id == row_id and later_date and response == -1


# Labels whether patient had positive response at some point in the future
def label(row_id, row_time, df):
    print(row_id)
    if True in df.apply(
            lambda row: good_results_exist(row_id, row_time, row['ANON_ID'], row['RESULT_TIME'], row['GOOD_RESPONSE']),
            axis=1).values:
        return 1
    elif True in df.apply(
            lambda row: bad_results_exist(row_id, row_time, row['ANON_ID'], row['RESULT_TIME'], row['GOOD_RESPONSE']),
            axis=1).values:
        return -1
    else:
        return 0


def label_data(read_path_1, read_path_2, write_path):
    df1 = pd.read_csv(read_path_1, sep='|')
    df2 = pd.read_csv(read_path_2, sep='|')

    response_df = df1.copy()

    response_df['GOOD_RESPONSE'] = response_df.apply(lambda row: good_results(row['REPORT']), axis=1)

    label_df = df2.copy()

    keep_response = response_df['GOOD_RESPONSE'] != 0
    response_df = response_df[keep_response]

    print(response_df)

    label_df['LABEL'] = label_df.apply(lambda row: label(row['ANON_ID'], row['RESULT_TIME'], response_df), axis=1)

    keep_label = label_df['LABEL'] != 0
    label_df = label_df[keep_label]

    print(label_df)

    label_df.to_csv(write_path, sep='|')


if __name__ == "__main__":
    label_data('../haruka_pathology_reports_111618.csv', '../haruka_radiology_reports_111618.csv',
               '../labeled_radiology_reports.csv')