def _get_average_orders_per_patient(self):
# Initialize DB cursor.
cursor = self._connection.cursor()
# Get average number of results for this lab test per patient.
query = SQLQuery()
if LocalEnv.DATASET_SOURCE_NAME == 'STRIDE': #TODO: add STRIDE component routine
query.addSelect('CAST(pat_id AS BIGINT) AS pat_id')
query.addSelect('COUNT(sop.order_proc_id) AS num_orders')
query.addFrom('stride_order_proc AS sop')
query.addFrom('stride_order_results AS sor')
query.addWhere('sop.order_proc_id = sor.order_proc_id')
query.addWhereIn("proc_code", [self._lab_panel])
components = self._get_components_in_lab_panel()
query.addWhereIn("base_name", components)
query.addGroupBy('pat_id')
elif LocalEnv.DATASET_SOURCE_NAME == 'UMich':
query.addSelect('CAST(pat_id AS BIGINT) AS pat_id')
query.addSelect('COUNT(order_proc_id) AS num_orders')
query.addFrom('labs')
query.addWhereIn(self._varTypeInTable, [self._lab_var])
components = self._get_components_in_lab_panel()
query.addWhereIn("base_name", components)
query.addGroupBy('pat_id')
log.debug('Querying median orders per patient...')
results = DBUtil.execute(query)
order_counts = [row[1] for row in results]
if len(order_counts) == 0:
error_msg = '0 orders for lab "%s."' % self._lab_var
log.critical(error_msg)
raise Exception(error_msg)
# sys.exit('[ERROR] %s' % error_msg) # sxu: sys.exit cannot be caught by Exception
else:
return numpy.median(order_counts)
def _get_random_patient_list(self):
#sx: this function is for avoid RANDOM() on the database
cursor = self._connection.cursor()
# Get average number of results for this lab test per patient.
query = SQLQuery()
query.addSelect('pat_id')
query.addSelect('COUNT(sop.order_proc_id) AS num_orders')
query.addFrom('stride_order_proc AS sop')
query.addFrom('stride_order_results AS sor')
query.addWhere('sop.order_proc_id = sor.order_proc_id')
##
query.addWhereIn("base_name", [self._component])
query.addGroupBy('pat_id')
log.debug('Querying median orders per patient...')
results = DBUtil.execute(query)
order_counts = [ row[1] for row in results ]
if len(results) == 0:
error_msg = '0 orders for component "%s."' % self._component #sx
log.critical(error_msg)
sys.exit('[ERROR] %s' % error_msg)
else:
avg_orders_per_patient = numpy.median(order_counts)
log.info('avg_orders_per_patient: %s' % avg_orders_per_patient)
# Based on average # of results, figure out how many patients we'd
# need to get for a feature matrix of requested size.
self._num_patients = int(numpy.max([self._num_requested_episodes / \
avg_orders_per_patient, 1]))
# Some components may have fewer associated patients than the required sample size
patient_number_chosen = min([len(results),self._num_patients]) #
inds_random_patients = numpy.random.choice(len(results), size=patient_number_chosen, replace=False)
# print 'inds_random_patients:', inds_random_patients
pat_IDs_random_patients = []
for ind in inds_random_patients:
pat_IDs_random_patients.append(results[ind][0])
# print pat_IDs_random_patients
return pat_IDs_random_patients
def _get_average_orders_per_patient(self):
# Initialize DB cursor.
cursor = self._connection.cursor()
# Get average number of results for this lab test per patient.
query = SQLQuery()
query.addSelect('pat_id')
query.addSelect('COUNT(sop.order_proc_id) AS num_orders')
query.addFrom('stride_order_proc AS sop')
query.addFrom('stride_order_results AS sor')
query.addWhere('sop.order_proc_id = sor.order_proc_id')
query.addWhereIn("proc_code", [self._lab_panel])
components = self._get_components_in_lab_panel()
query.addWhereIn("base_name", components)
query.addGroupBy('pat_id')
log.debug('Querying median orders per patient...')
results = DBUtil.execute(query)
order_counts = [ row[1] for row in results ]
if len(order_counts) == 0:
error_msg = '0 orders for lab panel "%s."' % self._lab_panel
log.critical(error_msg)
sys.exit('[ERROR] %s' % error_msg)
else:
return numpy.median(order_counts)
def _get_random_patient_list(self):
# Initialize DB cursor.
cursor = self._connection.cursor()
query = SQLQuery()
query.addSelect('CAST(pat_id AS BIGINT) AS pat_id')
if LocalEnv.DATASET_SOURCE_NAME == 'STRIDE':
if self._isLabPanel:
query.addSelect('COUNT(sop.order_proc_id) AS num_orders')
query.addFrom('stride_order_proc AS sop')
query.addFrom('stride_order_results AS sor')
if self._time_limit:
if self._time_limit[0]:
query.addWhere("sop.order_time > '%s'" %
self._time_limit[0])
if self._time_limit[1]:
query.addWhere("sop.order_time < '%s'" %
self._time_limit[1])
query.addWhere('sop.order_proc_id = sor.order_proc_id')
query.addWhereIn('proc_code', [self._lab_var])
'''
sbala: Technically it's possible for someone to get a lab ordered without getting results
'''
query.addWhereIn("base_name", self._lab_components)
else:
query.addSelect('COUNT(sor.order_proc_id) AS num_orders')
query.addFrom('stride_order_proc AS sop')
query.addFrom('stride_order_results AS sor')
query.addWhere('sop.order_proc_id = sor.order_proc_id')
##
query.addWhereIn("base_name", [self._lab_var])
elif LocalEnv.DATASET_SOURCE_NAME == 'UMich':
query_str = "SELECT CAST(pat_id AS BIGINT) AS pat_id , "
query_str += "COUNT(order_proc_id) AS num_orders "
query_str += "FROM labs "
# query_str += " WHERE %s IN (%s) "%(self._varTypeInTable, self._lab_var)
query_str += "WHERE %s = '%s' " % (self._varTypeInTable,
self._lab_var)
if self.notUsePatIds:
query_str += "AND pat_id NOT IN ("
for pat_id in self.notUsePatIds:
query_str += "%s," % pat_id
query_str = query_str[:-1] + ") " # get rid of comma
query_str += "GROUP BY pat_id"
log.debug('Querying median orders per patient...')
# TODO: best way to integrate UMich code
results = DBUtil.execute(query_str)
order_counts = [row[1] for row in results]
if len(results) == 0:
error_msg = '0 orders for order "%s."' % self._lab_var # sx
log.critical(error_msg)
raise Exception(error_msg)
# sys.exit('[ERROR] %s' % error_msg) # sxu: sys.exit cannot be caught by Exception
else:
avg_orders_per_patient = numpy.median(order_counts)
log.info('avg_orders_per_patient: %s' % avg_orders_per_patient)
# Based on average # of results, figure out how many patients we'd
# need to get for a feature matrix of requested size.
self._num_patients = int(numpy.max([self._num_requested_episodes / \
avg_orders_per_patient, 1]))
# Some components may have fewer associated patients than the required sample size
patient_number_chosen = min([len(results),
self._num_patients]) #
inds_random_patients = numpy.random.choice(
len(results), size=patient_number_chosen, replace=False)
# print 'inds_random_patients:', inds_random_patients
pat_IDs_random_patients = []
for ind in inds_random_patients:
pat_IDs_random_patients.append(results[ind][0])
return pat_IDs_random_patients
else:
query.addSelect('COUNT(order_proc_id) AS num_orders')
query.addFrom('labs')
if self._isLabPanel:
query.addWhereIn("proc_code", [self._lab_var])
query.addWhereIn("base_name", self._lab_components)
else:
query.addWhereIn("base_name", [self._lab_var])
'''
Fo hold-out set, do not use the patients already used in training/validation.
'''
if self._notUsePatIds:
query.addWhereNotIn('pat_id', self._notUsePatIds)
query.addGroupBy('pat_id')
log.debug('Querying the number of orders per patient...')
results = DBUtil.execute(query)
order_counts = [row[1] for row in results]
if len(results) == 0:
error_msg = '0 orders for component "%s."' % self._lab_var # sx
log.critical(error_msg)
raise Exception(error_msg)
# sys.exit('[ERROR] %s' % error_msg) # sxu: sys.exit cannot be caught by Exception
else:
avg_orders_per_patient = numpy.median(order_counts)
log.info('avg_orders_per_patient: %s' % avg_orders_per_patient)
# Based on average # of results, figure out how many patients we'd
# need to get for a feature matrix of requested size.
self._num_patients = int(numpy.max([self._num_requested_episodes / \
avg_orders_per_patient, 1]))
# Some components may have fewer associated patients than the required sample size
patient_number_chosen = min([len(results), self._num_patients]) #
'''
Set seed to ensure re-producibility of patient episodes.
Recover int random_state here, since numpy requires int while sql requires [-1,1]
'''
numpy.random.seed(int(self._random_state * float(sys.maxsize)))
inds_random_patients = numpy.random.choice(
len(results), size=patient_number_chosen, replace=False)
pat_IDs_random_patients = [
results[ind][0] for ind in inds_random_patients
]
return pat_IDs_random_patients
def _get_random_patient_list(self):
# Initialize DB cursor.
cursor = self._connection.cursor()
if LocalEnv.DATASET_SOURCE_NAME == 'STRIDE':
# Get average number of results for this lab test per patient.
avg_orders_per_patient = self._get_average_orders_per_patient()
log.info('avg_orders_per_patient: %s' % avg_orders_per_patient)
# Based on average # of results, figure out how many patients we'd
# need to get for a feature matrix of requested size.
self._num_patients = int(numpy.max([self._num_requested_episodes / \
avg_orders_per_patient, 1]))
# Get numPatientsToQuery random patients who have gotten test.
# TODO(sbala): Have option to feed in a seed for the randomness.
query = SQLQuery()
query.addSelect('CAST(pat_id AS BIGINT) AS pat_id')
query.addFrom('stride_order_proc AS sop')
query.addWhereIn('proc_code', [self._lab_var]) # TODO: components
query.addOrderBy('RANDOM()')
query.setLimit(self._num_patients)
log.debug('Querying random patient list...')
results = DBUtil.execute(query)
# Get patient list.
random_patient_list = [row[0] for row in results]
return random_patient_list
elif LocalEnv.DATASET_SOURCE_NAME == 'UMich':
# Get average number of results for this lab test per patient.
# query = SQLQuery()
# query.addSelect('CAST(pat_id AS BIGINT) AS pat_id')
# query.addSelect('COUNT(order_proc_id) AS num_orders')
# query.addFrom('labs')
# query.addWhereIn(self._varTypeInTable, [self._lab_var])
# components = self._get_components_in_lab_panel()
# query.addWhereIn("base_name", components)
#
# if self.notUsePatIds:
# query.addWhereNotIn("pat_id", self.notUsePatIds)
#
# query.addGroupBy('pat_id')
query_str = "SELECT CAST(pat_id AS BIGINT) AS pat_id , "
query_str += "COUNT(order_proc_id) AS num_orders "
query_str += "FROM labs "
#query_str += " WHERE %s IN (%s) "%(self._varTypeInTable, self._lab_var)
query_str += "WHERE %s = '%s' " % (self._varTypeInTable,
self._lab_var)
if self.notUsePatIds:
query_str += "AND pat_id NOT IN ("
for pat_id in self.notUsePatIds:
query_str += "%s," % pat_id
query_str = query_str[:-1] + ") " # get rid of comma
query_str += "GROUP BY pat_id"
log.debug('Querying median orders per patient...')
results = DBUtil.execute(query_str)
order_counts = [row[1] for row in results]
if len(results) == 0:
error_msg = '0 orders for order "%s."' % self._lab_var #sx
log.critical(error_msg)
raise Exception(error_msg)
# sys.exit('[ERROR] %s' % error_msg) # sxu: sys.exit cannot be caught by Exception
else:
avg_orders_per_patient = numpy.median(order_counts)
log.info('avg_orders_per_patient: %s' % avg_orders_per_patient)
# Based on average # of results, figure out how many patients we'd
# need to get for a feature matrix of requested size.
self._num_patients = int(numpy.max([self._num_requested_episodes / \
avg_orders_per_patient, 1]))
# Some components may have fewer associated patients than the required sample size
patient_number_chosen = min([len(results),
self._num_patients]) #
inds_random_patients = numpy.random.choice(
len(results), size=patient_number_chosen, replace=False)
# print 'inds_random_patients:', inds_random_patients
pat_IDs_random_patients = []
for ind in inds_random_patients:
pat_IDs_random_patients.append(results[ind][0])
return pat_IDs_random_patients