File size: 5,393 Bytes
c66e292 edb582c c66e292 edb582c c66e292 edb582c c66e292 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 | def date_dict(df_list, pif_key):
encounter_dates = []
for df in df_list:
df['encounter_date'].fillna('', inplace=True)
df_date = df.loc[df['pif_key'].astype(str) == str(pif_key), 'encounter_date'].values
if len(df_date) > 0:
encounter_dates.extend(df_date)
if encounter_dates:
ingested_date = max(encounter_dates)
else:
ingested_date = ''
date_insert_dict = {
'attribute_name': 'report_date',
'attribute_method': 'cv',
'attribute_normalized_prediction': '',
'attribute_prediction': str(ingested_date),
'attribute_version': 'v2_090523',
'attribute_vocab': '',
'attribute_code': '',
'date_of_service': ''
}
return date_insert_dict, encounter_dates, ingested_date
def report_date_insertion(dict_list, df_list, logging_df):
col_names = {col['attribute_name'] for col in dict_list}
if 'report_date' not in col_names:
pif_key = next((col['attribute_prediction'] for col in dict_list if col['attribute_name'] == 'pif_key'), None)
if pif_key is not None:
date_insert_dict, encounter_dates, ingested_date = date_dict(df_list, pif_key)
dict_list.insert(1, date_insert_dict)
if ingested_date:
if len(encounter_dates)>1:
logging_df = logging_df.append({'pif_key': pif_key,
'json_report_date_exists': False,
'encounter_dates': encounter_dates,
'ingested_date': ingested_date,
'multiple_date': True, 'old_date': 'missing'}, ignore_index=True)
else:
logging_df = logging_df.append({'pif_key': pif_key,
'json_report_date_exists': False,
'encounter_dates': encounter_dates,
'ingested_date': ingested_date,
'multiple_date': False, 'old_date': 'missing'}, ignore_index=True)
else:
logging_df = logging_df.append({'pif_key': pif_key,
'json_report_date_exists': False,
'encounter_dates': None,
'ingested_date': '',
'multiple_date': False, 'old_date': 'missing'}, ignore_index=True)
elif 'report_date' in col_names:
pif_key = next((col['attribute_prediction'] for col in dict_list if col['attribute_name'] == 'pif_key'), None)
if pif_key is not None:
date_insert_dict, encounter_dates, ingested_date = date_dict(df_list, pif_key)
if ingested_date:
for report_date_idx, tm in enumerate(dict_list):
if tm['attribute_name']=='report_date':
old_date = tm['attribute_prediction']
break
dict_list.pop(report_date_idx)
dict_list.insert(1, date_insert_dict)
if len(encounter_dates)>1:
logging_df = logging_df.append({'pif_key': pif_key,
'json_report_date_exists': True,
'encounter_dates': encounter_dates,
'ingested_date': ingested_date,
'multiple_date': True, 'old_date': old_date}, ignore_index=True)
else:
logging_df = logging_df.append({'pif_key': pif_key,
'json_report_date_exists': True,
'encounter_dates': encounter_dates,
'ingested_date': ingested_date,
'multiple_date': False, 'old_date': old_date}, ignore_index=True)
else:
for report_date_idx, tm in enumerate(dict_list):
if tm['attribute_name']=='report_date':
old_date = tm['attribute_prediction']
break
logging_df = logging_df.append({'pif_key': pif_key,
'json_report_date_exists': True,
'encounter_dates': None,
'ingested_date': '',
'multiple_date': False, 'old_date': old_date}, ignore_index=True)
return dict_list, logging_df
def json_report_date_insertion(json_data, df_list, logging_df):
for biomarker_detail in json_data['patient_level']['biomarkers']['details']:
for attribute in biomarker_detail['attribute']:
attribute_details = attribute['attribute_details']
dict_list, logging_df = report_date_insertion(attribute_details, df_list, logging_df)
attribute['attribute_details'] = dict_list
return json_data, logging_df |