from pyhealth.data import Patient, Visit
[docs]def drug_recommendation_mimic3_fn(patient: Patient):
"""Processes a single patient for the drug recommendation task.
Drug recommendation aims at recommending a set of drugs given the patient health
history (e.g., conditions and procedures).
Args:
patient: a Patient object
Returns:
samples: a list of samples, each sample is a dict with patient_id, visit_id,
and other task-specific attributes as key, like this
{
"patient_id": xxx,
"visit_id": xxx,
"conditions": [list of diag in visit 1, list of diag in visit 2, ..., list of diag in visit N],
"procedures": [list of prod in visit 1, list of prod in visit 2, ..., list of prod in visit N],
"drugs_hist": [list of drug in visit 1, list of drug in visit 2, ..., list of drug in visit (N-1)],
"drugs": list of drug in visit N, # this is the predicted target
}
Examples:
>>> from pyhealth.datasets import MIMIC3Dataset
>>> mimic3_base = MIMIC3Dataset(
... root="/srv/local/data/physionet.org/files/mimiciii/1.4",
... tables=["DIAGNOSES_ICD", "PROCEDURES_ICD", "PRESCRIPTIONS"],
... code_mapping={"ICD9CM": "CCSCM"},
... )
>>> from pyhealth.tasks import drug_recommendation_mimic3_fn
>>> mimic3_sample = mimic3_base.set_task(drug_recommendation_mimic3_fn)
>>> mimic3_sample.samples[0]
{
'visit_id': '174162',
'patient_id': '107',
'conditions': [['139', '158', '237', '99', '60', '101', '51', '54', '53', '133', '143', '140', '117', '138', '55']],
'procedures': [['4443', '4513', '3995']],
'drugs_hist': [[]],
'drugs': ['0000', '0033', '5817', '0057', '0090', '0053', '0', '0012', '6332', '1001', '6155', '1001', '6332', '0033', '5539', '6332', '5967', '0033', '0040', '5967', '5846', '0016', '5846', '5107', '5551', '6808', '5107', '0090', '5107', '5416', '0033', '1150', '0005', '6365', '0090', '6155', '0005', '0090', '0000', '6373'],
}
"""
samples = []
for i in range(len(patient)):
visit: Visit = patient[i]
conditions = visit.get_code_list(table="DIAGNOSES_ICD")
procedures = visit.get_code_list(table="PROCEDURES_ICD")
drugs = visit.get_code_list(table="PRESCRIPTIONS")
# ATC 3 level
drugs = [drug[:4] for drug in drugs]
# exclude: visits without condition, procedure, or drug code
if len(conditions) * len(procedures) * len(drugs) == 0:
continue
# TODO: should also exclude visit with age < 18
samples.append(
{
"visit_id": visit.visit_id,
"patient_id": patient.patient_id,
"conditions": conditions,
"procedures": procedures,
"drugs": drugs,
"drugs_hist": drugs,
}
)
# exclude: patients with less than 2 visit
if len(samples) < 2:
return []
# add history
samples[0]["conditions"] = [samples[0]["conditions"]]
samples[0]["procedures"] = [samples[0]["procedures"]]
samples[0]["drugs_hist"] = [samples[0]["drugs_hist"]]
for i in range(1, len(samples)):
samples[i]["conditions"] = samples[i - 1]["conditions"] + [
samples[i]["conditions"]
]
samples[i]["procedures"] = samples[i - 1]["procedures"] + [
samples[i]["procedures"]
]
samples[i]["drugs_hist"] = samples[i - 1]["drugs_hist"] + [
samples[i]["drugs_hist"]
]
# remove the target drug from the history
for i in range(len(samples)):
samples[i]["drugs_hist"][i] = []
return samples
[docs]def drug_recommendation_mimic4_fn(patient: Patient):
"""Processes a single patient for the drug recommendation task.
Drug recommendation aims at recommending a set of drugs given the patient health
history (e.g., conditions and procedures).
Args:
patient: a Patient object
Returns:
samples: a list of samples, each sample is a dict with patient_id, visit_id,
and other task-specific attributes as key
{
"patient_id": xxx,
"visit_id": xxx,
"conditions": [list of diag in visit 1, list of diag in visit 2, ..., list of diag in visit N],
"procedures": [list of prod in visit 1, list of prod in visit 2, ..., list of prod in visit N],
"drugs_hist": [list of drug in visit 1, list of drug in visit 2, ..., list of drug in visit (N-1)],
"drugs": list of drug in visit N, # this is the predicted target
}
Examples:
>>> from pyhealth.datasets import MIMIC4Dataset
>>> mimic4_base = MIMIC4Dataset(
... root="/srv/local/data/physionet.org/files/mimiciv/2.0/hosp",
... tables=["diagnoses_icd", "procedures_icd"],
... code_mapping={"ICD10PROC": "CCSPROC"},
... )
>>> from pyhealth.tasks import drug_recommendation_mimic4_fn
>>> mimic4_sample = mimic4_base.set_task(drug_recommendation_mimic4_fn)
>>> mimic4_sample.samples[0]
[{'visit_id': '130744', 'patient_id': '103', 'conditions': [['42', '109', '19', '122', '98', '663', '58', '51']], 'procedures': [['1']], 'label': [['2', '3', '4']]}]
"""
samples = []
for i in range(len(patient)):
visit: Visit = patient[i]
conditions = visit.get_code_list(table="diagnoses_icd")
procedures = visit.get_code_list(table="procedures_icd")
drugs = visit.get_code_list(table="prescriptions")
# ATC 3 level
drugs = [drug[:4] for drug in drugs]
# exclude: visits without condition, procedure, or drug code
if len(conditions) * len(procedures) * len(drugs) == 0:
continue
# TODO: should also exclude visit with age < 18
samples.append(
{
"visit_id": visit.visit_id,
"patient_id": patient.patient_id,
"conditions": conditions,
"procedures": procedures,
"drugs": drugs,
"drugs_hist": drugs,
}
)
# exclude: patients with less than 2 visit
if len(samples) < 2:
return []
# add history
samples[0]["conditions"] = [samples[0]["conditions"]]
samples[0]["procedures"] = [samples[0]["procedures"]]
samples[0]["drugs_hist"] = [samples[0]["drugs_hist"]]
for i in range(1, len(samples)):
samples[i]["conditions"] = samples[i - 1]["conditions"] + [
samples[i]["conditions"]
]
samples[i]["procedures"] = samples[i - 1]["procedures"] + [
samples[i]["procedures"]
]
samples[i]["drugs_hist"] = samples[i - 1]["drugs_hist"] + [
samples[i]["drugs_hist"]
]
# remove the target drug from the history
for i in range(len(samples)):
samples[i]["drugs_hist"][i] = []
return samples
[docs]def drug_recommendation_eicu_fn(patient: Patient):
"""Processes a single patient for the drug recommendation task.
Drug recommendation aims at recommending a set of drugs given the patient health
history (e.g., conditions and procedures).
Args:
patient: a Patient object
Returns:
samples: a list of samples, each sample is a dict with patient_id, visit_id,
and other task-specific attributes as key
Examples:
>>> from pyhealth.datasets import eICUDataset
>>> eicu_base = eICUDataset(
... root="/srv/local/data/physionet.org/files/eicu-crd/2.0",
... tables=["diagnosis", "medication"],
... code_mapping={},
... dev=True
... )
>>> from pyhealth.tasks import drug_recommendation_eicu_fn
>>> eicu_sample = eicu_base.set_task(drug_recommendation_eicu_fn)
>>> eicu_sample.samples[0]
[{'visit_id': '130744', 'patient_id': '103', 'conditions': [['42', '109', '98', '663', '58', '51']], 'procedures': [['1']], 'label': [['2', '3', '4']]}]
"""
samples = []
for i in range(len(patient)):
visit: Visit = patient[i]
conditions = visit.get_code_list(table="diagnosis")
procedures = visit.get_code_list(table="physicalExam")
drugs = visit.get_code_list(table="medication")
# exclude: visits without condition, procedure, or drug code
if len(conditions) * len(procedures) * len(drugs) == 0:
continue
# TODO: should also exclude visit with age < 18
samples.append(
{
"visit_id": visit.visit_id,
"patient_id": patient.patient_id,
"conditions": conditions,
"procedures": procedures,
"drugs": drugs,
"drugs_all": drugs,
}
)
# exclude: patients with less than 2 visit
if len(samples) < 2:
return []
# add history
samples[0]["conditions"] = [samples[0]["conditions"]]
samples[0]["procedures"] = [samples[0]["procedures"]]
samples[0]["drugs_all"] = [samples[0]["drugs_all"]]
for i in range(1, len(samples)):
samples[i]["conditions"] = samples[i - 1]["conditions"] + [
samples[i]["conditions"]
]
samples[i]["procedures"] = samples[i - 1]["procedures"] + [
samples[i]["procedures"]
]
samples[i]["drugs_all"] = samples[i - 1]["drugs_all"] + [
samples[i]["drugs_all"]
]
return samples
[docs]def drug_recommendation_omop_fn(patient: Patient):
"""Processes a single patient for the drug recommendation task.
Drug recommendation aims at recommending a set of drugs given the patient health
history (e.g., conditions and procedures).
Args:
patient: a Patient object
Returns:
samples: a list of samples, each sample is a dict with patient_id, visit_id,
and other task-specific attributes as key
Examples:
>>> from pyhealth.datasets import OMOPDataset
>>> omop_base = OMOPDataset(
... root="https://storage.googleapis.com/pyhealth/synpuf1k_omop_cdm_5.2.2",
... tables=["condition_occurrence", "procedure_occurrence"],
... code_mapping={},
... )
>>> from pyhealth.tasks import drug_recommendation_omop_fn
>>> omop_sample = omop_base.set_task(drug_recommendation_eicu_fn)
>>> omop_sample.samples[0]
[{'visit_id': '130744', 'patient_id': '103', 'conditions': [['42', '109', '98', '663', '58', '51'], ['98', '663', '58', '51']], 'procedures': [['1'], ['2', '3']], 'label': [['2', '3', '4'], ['0', '1', '4', '5']]}]
"""
samples = []
for i in range(len(patient)):
visit: Visit = patient[i]
conditions = visit.get_code_list(table="condition_occurrence")
procedures = visit.get_code_list(table="procedure_occurrence")
drugs = visit.get_code_list(table="drug_exposure")
# exclude: visits without condition, procedure, or drug code
if len(conditions) * len(procedures) * len(drugs) == 0:
continue
# TODO: should also exclude visit with age < 18
samples.append(
{
"visit_id": visit.visit_id,
"patient_id": patient.patient_id,
"conditions": conditions,
"procedures": procedures,
"drugs": drugs,
"drugs_all": drugs,
}
)
# exclude: patients with less than 2 visit
if len(samples) < 2:
return []
# add history
samples[0]["conditions"] = [samples[0]["conditions"]]
samples[0]["procedures"] = [samples[0]["procedures"]]
samples[0]["drugs_all"] = [samples[0]["drugs_all"]]
for i in range(1, len(samples)):
samples[i]["conditions"] = samples[i - 1]["conditions"] + [
samples[i]["conditions"]
]
samples[i]["procedures"] = samples[i - 1]["procedures"] + [
samples[i]["procedures"]
]
samples[i]["drugs_all"] = samples[i - 1]["drugs_all"] + [
samples[i]["drugs_all"]
]
return samples
if __name__ == "__main__":
# from pyhealth.datasets import MIMIC3Dataset
# base_dataset = MIMIC3Dataset(
# root="/srv/local/data/physionet.org/files/mimiciii/1.4",
# tables=["DIAGNOSES_ICD", "PROCEDURES_ICD", "PRESCRIPTIONS"],
# dev=True,
# code_mapping={"ICD9CM": "CCSCM"},
# refresh_cache=False,
# )
# sample_dataset = base_dataset.set_task(task_fn=drug_recommendation_mimic3_fn)
# sample_dataset.stat()
# print(sample_dataset.available_keys)
# print(sample_dataset.samples[0])
from pyhealth.datasets import MIMIC4Dataset
base_dataset = MIMIC4Dataset(
root="/srv/local/data/physionet.org/files/mimiciv/2.0/hosp",
tables=["diagnoses_icd", "procedures_icd", "prescriptions"],
dev=True,
code_mapping={"NDC": "ATC"},
refresh_cache=False,
)
sample_dataset = base_dataset.set_task(task_fn=drug_recommendation_mimic4_fn)
sample_dataset.stat()
print(sample_dataset.available_keys)
print(sample_dataset.samples[0])
# from pyhealth.datasets import eICUDataset
# base_dataset = eICUDataset(
# root="/srv/local/data/physionet.org/files/eicu-crd/2.0",
# tables=["diagnosis", "medication", "physicalExam"],
# dev=True,
# refresh_cache=False,
# )
# sample_dataset = base_dataset.set_task(task_fn=drug_recommendation_eicu_fn)
# sample_dataset.stat()
# print(sample_dataset.available_keys)
# from pyhealth.datasets import OMOPDataset
# base_dataset = OMOPDataset(
# root="/srv/local/data/zw12/pyhealth/raw_data/synpuf1k_omop_cdm_5.2.2",
# tables=["condition_occurrence", "procedure_occurrence", "drug_exposure"],
# dev=True,
# refresh_cache=False,
# )
# sample_dataset = base_dataset.set_task(task_fn=drug_recommendation_omop_fn)
# sample_dataset.stat()
# print(sample_dataset.available_keys)