def create_datasets(load_d: LoadData):
"""
loads already created important files
:param load_d: query processor in case of a missing file
:return: loaded objects
"""
try:
wf_matching = pd.read_csv('matching.csv')
except FileNotFoundError:
create_matching('admissions.csv', "RECORDS-waveforms.csv")
wf_matching = pd.read_csv('matching.csv')
try:
numeric_matching = pd.read_csv('matching_numerics.csv')
except FileNotFoundError:
create_matching('admissions.csv', "RECORDS-numerics.csv", numerics=True)
numeric_matching = pd.read_csv('matching.csv')
try:
mortality = pd.read_csv("in_hospital_mortality.csv")
except FileNotFoundError:
mortality = load_d.query_db(queries.in_hospital_mortality())
mortality.to_csv("in_hospital_mortality.csv")
try:
clinical_features = pd.read_csv("features_clinical.csv")
except FileNotFoundError as e:
print(str(e))
print("please create clinical features file for all patients")
return wf_matching, numeric_matching, mortality, clinical_features
def cardiac_patients(query_proccesor: LoadData, admissions: str, gender: str, path: str, cardiac=True):
"""
Extract patients using a filtering scheme
:param query_proccesor: sql handler
:param admissions: relevant admissions
:param gender: desired gender for the patients
:param path: path to already created file
:param cardiac: cardiac or non-cardiac patients
:return: patient dataframe with demographics
"""
try:
patients = pd.read_csv(path)
except FileNotFoundError:
patients = query_proccesor.query_db(queries.patient_selection(admissions, gender, cardiac))
patients.to_csv(path)
return patients
# /usr/bin/python
from load_data import LoadData
if __name__ == '__main__':
query_mimic = """
select *
from mimiciii.admissions as a
join mimiciii.patients as p on a.subject_id = p.subject_id
join mimiciii.icustays as i on a.hadm_id = i.hadm_id
"""
load_d = LoadData('config_template.ini', 'mimic')
print(load_d.query_db(query_mimic))
load_d.query_and_save(query_mimic)
query_mimic = """
select c.subject_id,c.hadm_id,c.itemid,c.value,c.charttime,p.label
from mimiciii.chartevents as c
join mimiciii.d_items as p on c.itemid = p.itemid
where c.itemid in (211,615,676,772,773,781,789,811,812,821,769,770,3837,3835,1321,227429,851,834,828,223830,1531,198,227010,227443,226760,229759,226766,226765,189,2981,226998,4948,225312,225309,225310)
"""
load_d = LoadData('config_template.ini', 'mimic')
print(load_d.query_db(query_mimic))
load_d.query_and_save(query_mimic, file_name='tests')
# /usr/bin/python
from load_data import LoadData
if __name__ == '__main__':
query_mimic = """
select distinct i.label
from mimiciii.outputevents as o
join mimiciii.d_items as i on i.itemid = o.itemid
limit 5;
"""
load_d = LoadData('config_template.ini', 'mimic')
print(load_d.query_db(query_mimic))
load_d.query_and_save(query_mimic)
query_eicu = """
SELECT DISTINCT
t.patientunitstayid AS pid
,t.treatmentid AS tid
,t.treatmentoffset AS toffset
FROM
eicu_crd.treatment t
WHERE
t.treatmentstring LIKE %(treat_string)s
ORDER BY t.patientunitstayid limit 5;"""
load_d2 = LoadData('config_template.ini', 'eicu')
params = {
'treat_string': 'pulmonary|ventilation and oxygenation|prone position'
