def summarize_by_icd9_code(klass, icd9_code):
""" For a given icd9 code, return the number of admissions and patients
diagnosed with this code """
num_patients = session.query(klass.subject_id).filter(
klass.icd9_code == icd9_code).distinct().count()
num_admissions = session.query(klass.hadm_id).filter(
klass.icd9_code == icd9_code).distinct().count()
return num_patients, num_admissions
def get_from_subject_ids(klass, subject_ids):
if isinstance(subject_ids, int):
subject_ids = [subject_ids]
return session.query(klass).filter(klass.subject_id.in_(subject_ids)).all()
import pandas as pd
from mimicry_db.base import session
from mimicry_db.patient import Patient
from mimicry_db.file_path_utils import matched_waveform_summary_filepath
patient = Patient()
assert hasattr(patient, 'has_matched_waveform')
matched_records_df = pd.read_csv(matched_waveform_summary_filepath)
patients = matched_records_df.Patient.values
def process_patient_list(patient_list):
removed_p = [s.strip("p") for s in patient_list]
removed_leading_zeros = [s.lstrip("0") for s in removed_p]
return [int(patient_id) for patient_id in removed_leading_zeros]
subject_ids_with_waveform = process_patient_list(patients)
all_subject_ids_query = session.query(Patient.subject_id).all()
all_subject_ids = []
for result in all_subject_ids_query:
all_subject_ids.append(result.subject_id)
# Update each patient that has an associated waveform
patients = Patient.get_from_subject_ids(subject_ids_with_waveform)
for patient in patients:
patient.has_matched_waveform = True
session.commit()
def find_by_long_title(klass, pattern):
return session.query(klass).filter(klass.long_title.like(pattern)).all()
def find_by_icd9(klass, icd9_code_pattern):
return session.query(klass).filter(klass.icd9_code.like(icd9_code_pattern)).all()
def get_by_icd9(klass, icd9_code):
return session.query(klass).filter(klass.icd9_code==icd9_code).first()
def find_diagnosed_diagnosis_descriptions(klass, limit=None):
return session.query(klass).filter(klass.diagnoses != None).limit(limit).all()