def medical_D(path_to_alarms_time):
alarms = ['Yellow Alarm', 'Red Alarm']
total_alarms = []
sheets = []
yellow_dataframe = pd.read_excel(path_to_alarms_time + 'Alarm_Time.xlsx',
sheet_name=alarms[0])
red_dataframe = pd.read_excel(path_to_alarms_time + 'Alarm_Time.xlsx',
sheet_name=alarms[1])
dataframe = pd.concat([yellow_dataframe, red_dataframe],
axis=0).sort_values(by='Row')
dataframe = dataframe.reset_index(drop=True)
unique_beds = dataframe['Bed Label'].unique().tolist()
med_dataframe = md.AlarmDataFrame()
med_dataframe.set_dataframe(dataframe)
med_dataframe.analize(action_type=unique_beds, by_label=['Bed Label'])
beds_dataframe = med_dataframe.get_dataframe()[1]
unique_alarm_trigger = md.get_unique_alarms(dataframe, flag='P')
for j in range(len(unique_alarm_trigger)):
alarm_trigger_dataframe = []
for k in range(0, len(beds_dataframe)):
alarm_trigger_dataframe.append(
md.alarm_deep_analysis(beds_dataframe[k],
unique_alarm_trigger[j],
bed_info=True,
unique=True))
if alarm_trigger_dataframe[len(alarm_trigger_dataframe) -
1] is None:
alarm_trigger_dataframe.pop()
if alarm_trigger_dataframe:
total_alarms.append(
pd.concat(alarm_trigger_dataframe, axis=0, sort=True))
colon = re.search(r':', unique_alarm_trigger[j])
slash = re.search(r'/', unique_alarm_trigger[j])
if not colon is None:
sheets.append(unique_alarm_trigger[j].replace(':', '|'))
elif not slash is None:
sheets.append(unique_alarm_trigger[j].replace('/', '|'))
else:
sheets.append(unique_alarm_trigger[j])
total_alarms[len(total_alarms) -
1] = total_alarms[len(total_alarms) -
1].fillna(value=0)
total_alarms = pd.concat(total_alarms, axis=0)
total_alarms = total_alarms.fillna(value=0)
md.save_excel(total_alarms, name='AlarmTriggers', sheet_name='Sheet1')
def medical_C(path_to_alarms_time):
alarms = ['Yellow Alarm', 'Red Alarm']
physiological_dataframe = []
sheets = []
for alarm in alarms:
dataframe = pd.read_excel(path_to_alarms_time + 'Alarm_Time.xlsx',
sheet_name=alarm)
sheets.append(alarm)
physiological_dataframe.append(md.threshold_feature_include(dataframe))
if alarm == 'Yellow Alarm':
symbol = '✱'
single = []
double = []
for j in range(len(dataframe)):
f = re.search(r'^' + symbol * 2, dataframe['Action'][j])
if not f is None:
double.append(j)
else:
single.append(j)
if len(single) == 0 or len(double) == 0:
continue
else:
if len(single) > 0:
physiological_dataframe.append(
physiological_dataframe[len(physiological_dataframe) -
1].iloc[single])
sheets.append('Single ' + symbol)
if len(double) > 0:
physiological_dataframe.append(physiological_dataframe[
len(physiological_dataframe) -
2].iloc[double]) #changed 2 to 1
sheets.append('Double ' + symbol)
unique_alarms = md.get_unique_alarms(dataframe, flag='P')
physiological_dataframe.append(
md.get_threshold_dataframe(dataframe, unique_alarms))
sheets.append(alarm + ' Activity')
else:
unique_alarms = md.get_unique_alarms(dataframe, flag='P')
physiological_dataframe.append(
md.get_threshold_dataframe(dataframe, unique_alarms))
sheets.append(alarm + ' Activity')
md.save_excel(physiological_dataframe,
name='PhyAlarms_Threshold',
sheet_name=sheets)
def medical_E(name, sheet_name='Sheet1'):
med_dataframe = md.AlarmDataFrame(name + '.xlsx', sheet_name)
unique_actions = med_dataframe.get_dataframe()[0]['Action_Type'].unique(
).tolist()
med_dataframe.analize(action_type=unique_actions, by_label=['Action_Type'])
dataframe = med_dataframe.get_others()[0]
unique_alarms = md.get_unique_alarms(dataframe, flag='N')
alarm_list_counter = [0 for i in unique_alarms]
dummy = ''
for row in dataframe['Action']:
i = 0
found = False
search = re.search(r'^.*?(?=\s*\d{1,4}/)|^.*?(?<=User Request)' \
+ '|^.*?(?<=Limit)|^.*?(?=:)|^.*?(?=\s+set)|^.*?(?=\s+to)|^.*?(?=\s+from)|^.*', row)
if not search is None:
if search.group() == "12 Lead":
dummy = search.group() + " printed"
else:
remove = re.search(
r'^.*?(?=Order)|(?:\s+[A-Z]{1,3}\d*\.{1}[A-Z]*\d+)',
search.group())
if not remove is None:
dummy = search.group().replace(remove.group(), '')
else:
search = re.search(
r'^.*?(?=\s+from)|^.*?(?=\s+for)|^.*?(?=\s+is)|^.*',
search.group())
dummy = search.group()
for alarm in unique_alarms:
if alarm == dummy:
found = True
break
i += 1
if found:
alarm_list_counter[i] += 1
alarm_dataframe = pd.concat([
pd.DataFrame(unique_alarms, columns=['Alarms']),
pd.DataFrame(alarm_list_counter, columns=['# of Ocurrences'])
],
axis=1)
md.save_excel(alarm_dataframe,
name='OtherAlarms',
sheet_name='Other Activity')
def medical_B(name, sheet_name='Sheet1'):
alarm_type = [
'Logged Inop', 'Yellow Inop', 'Red Inop', 'Yellow Alarm', 'Red Alarm'
]
sheets = alarm_type[:]
med_dataframe = md.AlarmDataFrame(data_path=name + '.xlsx',
sheet_name=sheet_name)
med_dataframe.analize(action_type=alarm_type, by_label=['Action_Type'])
dataframes = med_dataframe.get_dataframe()[1]
for i in range(len(dataframes) - 1, -1, -1):
if dataframes[i].empty:
del dataframes[i]
else:
unique_devices = dataframes[i]['Device Name'].unique().tolist()
med_dataframe.set_dataframe(dataframes[i])
med_dataframe.analize(action_type=unique_devices,
by_label=['Device Name'])
dataframes[i] = md.time_feature_include(
med_dataframe.get_dataframe()[1])
med_dataframe.set_dataframe(pd.concat([i for i in dataframes], axis=0))
med_dataframe.analize(action_type=alarm_type, by_label=['Action_Type'])
single, double = med_dataframe.get_alarm_priority()
if len(single) > 1:
sheets.append('Single ✱')
sheets.append('Single !')
else:
symbol = re.search(r'\!', single[0]['Action'][0])
if not symbol is None:
sheets.append('Single !')
else:
sheets.append('Single ✱')
if len(double) > 1:
sheets.append('Double ✱')
sheets.append('Double !')
else:
symbol = re.search(r'\!', double[0]['Action'][0])
if not symbol is None:
sheets.append('Double !')
else:
sheets.append('Double ✱')
i = 0
dataframes = []
for frame in med_dataframe.get_dataframe()[1] + single + double:
if not frame.empty:
dataframes.append(frame)
else:
del sheets[i]
i += 1
md.save_excel(dataframe=dataframes, name='Alarm_Time', sheet_name=sheets)
#-----------------------------------------------------------------------------------
# Part B: InopAlarms_Time.xlsx
inop = []
physiological = []
for frame in med_dataframe.get_dataframe()[1]:
if not frame.empty:
if 'Inop' in frame['Action_Type'][0]:
inop.append(frame)
else:
physiological.append(frame)
dataframes = []
sheets = []
for frame in inop:
unique_devices = frame['Device Name'].unique().tolist()
sheets.append(frame['Action_Type'][0])
if 'Yellow' in frame['Action_Type'][0]:
P = True
else:
P = False
dataframes.append(
md.get_alarms_per_device(frame,
list_of_devices=unique_devices,
priority_dataframe=P,
priority_symbol='!'))
sheets.append(sheets[len(sheets) - 1] + 'Activity')
unique_alarms = md.get_unique_alarms(frame, flag='I')
dataframes.append(md.get_timing_dataframe(frame, unique_alarms))
md.save_excel(dataframe=dataframes,
name='Inop_Alarms_Time',
sheet_name=sheets)
#------------------------------------------------------------------------------------
# Part B: PhyAlarms_Time.xlsx
dataframes = []
sheets = []
for frame in physiological:
unique_devices = frame['Device Name'].unique().tolist()
sheets.append(frame['Action_Type'][0])
if 'Yellow' in frame['Action_Type'][0]:
P = True
else:
P = False
dataframes.append(
md.get_alarms_per_device(frame,
list_of_devices=unique_devices,
priority_dataframe=P,
priority_symbol='✱'))
sheets.append(sheets[len(sheets) - 1] + 'Activity')
unique_alarms = md.get_unique_alarms(frame, flag='P')
dataframes.append(md.get_timing_dataframe(frame, unique_alarms))
md.save_excel(dataframe=dataframes,
name='Phy_Alarms_Time',
sheet_name=sheets)
def medical_A(name, sheet_name='Sheet1'):
alarm_type = [
'Logged Inop', 'Yellow Inop', 'Red Inop', 'Yellow Alarm', 'Red Alarm'
]
sheets = alarm_type[:]
med_dataframe = md.AlarmDataFrame(data_path=name + '.xlsx',
sheet_name=sheet_name)
med_dataframe.analize(action_type=alarm_type, by_label=['Action_Type'])
single, double = med_dataframe.get_alarm_priority()
if len(single) > 1:
sheets.append('Single ✱')
sheets.append('Single !')
else:
symbol = re.search(r'\!', single[0]['Action'][0])
if not symbol is None:
sheets.append('Single !')
else:
sheets.append('Single ✱')
if len(double) > 1:
sheets.append('Double ✱')
sheets.append('Double !')
else:
symbol = re.search(r'\!', double[0]['Action'][0])
if not symbol is None:
sheets.append('Double !')
else:
sheets.append('Double ✱')
i = 0
dataframes = []
for frame in med_dataframe.get_dataframe()[1] + single + double:
if not frame.empty:
dataframes.append(frame)
else:
del sheets[i]
i += 1
md.save_excel(dataframe=dataframes, name='Alarm_Types', sheet_name=sheets)
#-----------------------------------------------------------------------------------
# Part A: InopAlarms.xlsx
inop = []
physiological = []
for frame in med_dataframe.get_dataframe()[1]:
if not frame.empty:
if 'Inop' in frame['Action_Type'][0]:
inop.append(frame)
else:
physiological.append(frame)
dataframes = []
sheets = []
for frame in inop:
unique_devices = frame['Device Name'].unique().tolist()
sheets.append(frame['Action_Type'][0])
if 'Yellow' in frame['Action_Type'][0]:
P = True
else:
P = False
dataframes.append(
md.get_alarms_per_device(frame,
list_of_devices=unique_devices,
priority_dataframe=P,
priority_symbol='!'))
md.save_excel(dataframe=dataframes, name='Inop_Alarms', sheet_name=sheets)
#------------------------------------------------------------------------------------
# Part A: PhyAlarms.xlsx
dataframes = []
sheets = []
for frame in physiological:
unique_devices = frame['Device Name'].unique().tolist()
sheets.append(frame['Action_Type'][0])
if 'Yellow' in frame['Action_Type'][0]:
P = True
else:
P = False
dataframes.append(
md.get_alarms_per_device(frame,
list_of_devices=unique_devices,
priority_dataframe=P,
priority_symbol='✱'))
md.save_excel(dataframe=dataframes, name='Phy_Alarms', sheet_name=sheets)