from pyhealth.data import Patient, Visit
# TODO: time_window cannot be passed in to base_dataset
[docs]def readmission_prediction_mimic3_fn(patient: Patient, time_window=15):
"""Processes a single patient for the readmission prediction task.
Readmission prediction aims at predicting whether the patient will be readmitted
into hospital within time_window days based on the clinical information from
current visit (e.g., conditions and procedures).
Args:
patient: a Patient object
time_window: the time window threshold (gap < time_window means label=1 for
the task)
Returns:
samples: a list of samples, each sample is a dict with patient_id, visit_id,
and other task-specific attributes as key
Note that we define the task as a binary classification task.
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 readmission_prediction_mimic3_fn
>>> mimic3_sample = mimic3_base.set_task(readmission_prediction_mimic3_fn)
>>> mimic3_sample.samples[0]
[{'visit_id': '130744', 'patient_id': '103', 'conditions': [['42', '109', '19', '122', '98', '663', '58', '51']], 'procedures': [['1']], 'label': 1}]
"""
samples = []
# we will drop the last visit
for i in range(len(patient) - 1):
visit: Visit = patient[i]
next_visit: Visit = patient[i + 1]
# get time difference between current visit and next visit
time_diff = (next_visit.encounter_time - visit.encounter_time).days
readmission_label = 1 if time_diff < time_window else 0
conditions = visit.get_code_list(table="DIAGNOSES_ICD")
procedures = visit.get_code_list(table="PROCEDURES_ICD")
drugs = visit.get_code_list(table="PRESCRIPTIONS")
# 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],
"label": readmission_label,
}
)
# no cohort selection
return samples
[docs]def readmission_prediction_mimic4_fn(patient: Patient, time_window=15):
"""Processes a single patient for the readmission prediction task.
Readmission prediction aims at predicting whether the patient will be readmitted
into hospital within time_window days based on the clinical information from
current visit (e.g., conditions and procedures).
Args:
patient: a Patient object
time_window: the time window threshold (gap < time_window means label=1 for
the task)
Returns:
samples: a list of samples, each sample is a dict with patient_id, visit_id,
and other task-specific attributes as key
Note that we define the task as a binary classification task.
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 readmission_prediction_mimic4_fn
>>> mimic4_sample = mimic4_base.set_task(readmission_prediction_mimic4_fn)
>>> mimic4_sample.samples[0]
[{'visit_id': '130744', 'patient_id': '103', 'conditions': [['42', '109', '19', '122', '98', '663', '58', '51']], 'procedures': [['1']], 'label': 0}]
"""
samples = []
# we will drop the last visit
for i in range(len(patient) - 1):
visit: Visit = patient[i]
next_visit: Visit = patient[i + 1]
# get time difference between current visit and next visit
time_diff = (next_visit.encounter_time - visit.encounter_time).days
readmission_label = 1 if time_diff < time_window else 0
conditions = visit.get_code_list(table="diagnoses_icd")
procedures = visit.get_code_list(table="procedures_icd")
drugs = visit.get_code_list(table="prescriptions")
# 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],
"label": readmission_label,
}
)
# no cohort selection
return samples
[docs]def readmission_prediction_eicu_fn(patient: Patient, time_window=5):
"""Processes a single patient for the readmission prediction task.
Readmission prediction aims at predicting whether the patient will be readmitted
into hospital within time_window days based on the clinical information from
current visit (e.g., conditions and procedures).
Features key-value pairs:
- using diagnosis table (ICD9CM and ICD10CM) as condition codes
- using physicalExam table as procedure codes
- using medication table as drugs codes
Args:
patient: a Patient object
time_window: the time window threshold (gap < time_window means label=1 for
the task)
Returns:
samples: a list of samples, each sample is a dict with patient_id, visit_id,
and other task-specific attributes as key
Note that we define the task as a binary classification task.
Examples:
>>> from pyhealth.datasets import eICUDataset
>>> eicu_base = eICUDataset(
... root="/srv/local/data/physionet.org/files/eicu-crd/2.0",
... tables=["diagnosis", "medication", "physicalExam"],
... code_mapping={},
... dev=True
... )
>>> from pyhealth.tasks import readmission_prediction_eicu_fn
>>> eicu_sample = eicu_base.set_task(readmission_prediction_eicu_fn)
>>> eicu_sample.samples[0]
[{'visit_id': '130744', 'patient_id': '103', 'conditions': [['42', '109', '98', '663', '58', '51']], 'procedures': [['1']], 'label': 1}]
"""
samples = []
# we will drop the last visit
for i in range(len(patient) - 1):
visit: Visit = patient[i]
next_visit: Visit = patient[i + 1]
# get time difference between current visit and next visit
time_diff = (next_visit.encounter_time - visit.encounter_time).days
readmission_label = 1 if time_diff < time_window else 0
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],
"label": readmission_label,
}
)
# no cohort selection
return samples
[docs]def readmission_prediction_eicu_fn2(patient: Patient, time_window=5):
"""Processes a single patient for the readmission prediction task.
Readmission prediction aims at predicting whether the patient will be readmitted
into hospital within time_window days based on the clinical information from
current visit (e.g., conditions and procedures).
Similar to readmission_prediction_eicu_fn, but with different code mapping:
- using admissionDx table and diagnosisString under diagnosis table as condition codes
- using treatment table as procedure codes
Args:
patient: a Patient object
time_window: the time window threshold (gap < time_window means label=1 for
the task)
Returns:
samples: a list of samples, each sample is a dict with patient_id, visit_id,
and other task-specific attributes as key
Note that we define the task as a binary classification task.
Examples:
>>> from pyhealth.datasets import eICUDataset
>>> eicu_base = eICUDataset(
... root="/srv/local/data/physionet.org/files/eicu-crd/2.0",
... tables=["diagnosis", "treatment", "admissionDx"],
... code_mapping={},
... dev=True
... )
>>> from pyhealth.tasks import readmission_prediction_eicu_fn2
>>> eicu_sample = eicu_base.set_task(readmission_prediction_eicu_fn2)
>>> eicu_sample.samples[0]
[{'visit_id': '130744', 'patient_id': '103', 'conditions': [['42', '109', '98', '663', '58', '51']], 'procedures': [['1']], 'label': 1}]
"""
samples = []
# we will drop the last visit
for i in range(len(patient) - 1):
visit: Visit = patient[i]
next_visit: Visit = patient[i + 1]
# get time difference between current visit and next visit
time_diff = (next_visit.encounter_time - visit.encounter_time).days
readmission_label = 1 if time_diff < time_window else 0
admissionDx = visit.get_code_list(table="admissionDx")
diagnosisString = list(
set(
[
dx.attr_dict["diagnosisString"]
for dx in visit.get_event_list("diagnosis")
]
)
)
treatment = visit.get_code_list(table="treatment")
# exclude: visits without treatment, admissionDx, diagnosisString
if len(admissionDx) * len(diagnosisString) * len(treatment) == 0:
continue
# TODO: should also exclude visit with age < 18
samples.append(
{
"visit_id": visit.visit_id,
"patient_id": patient.patient_id,
"conditions": admissionDx + diagnosisString,
"procedures": treatment,
"label": readmission_label,
}
)
# no cohort selection
return samples
[docs]def readmission_prediction_omop_fn(patient: Patient, time_window=15):
"""Processes a single patient for the readmission prediction task.
Readmission prediction aims at predicting whether the patient will be readmitted
into hospital within time_window days based on the clinical information from
current visit (e.g., conditions and procedures).
Args:
patient: a Patient object
time_window: the time window threshold (gap < time_window means label=1 for
the task)
Returns:
samples: a list of samples, each sample is a dict with patient_id, visit_id,
and other task-specific attributes as key
Note that we define the task as a binary classification task.
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 readmission_prediction_omop_fn
>>> omop_sample = omop_base.set_task(readmission_prediction_eicu_fn)
>>> omop_sample.samples[0]
[{'visit_id': '130744', 'patient_id': '103', 'conditions': [['42', '109', '98', '663', '58', '51']], 'procedures': [['1']], 'label': 1}]
"""
samples = []
# we will drop the last visit
for i in range(len(patient) - 1):
visit: Visit = patient[i]
next_visit: Visit = patient[i + 1]
time_diff = (next_visit.encounter_time - visit.encounter_time).days
readmission_label = 1 if time_diff < time_window else 0
conditions = visit.get_code_list(table="condition_occurrence")
procedures = visit.get_code_list(table="procedure_occurrence")
drugs = visit.get_code_list(table="drug_exposure")
# labs = get_code_from_list_of_event(
# visit.get_event_list(table="measurement")
# )
# 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],
"label": readmission_label,
}
)
# no cohort selection
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", "NDC": "ATC"},
refresh_cache=False,
)
sample_dataset = base_dataset.set_task(task_fn=readmission_prediction_mimic3_fn)
sample_dataset.stat()
print(sample_dataset.available_keys)
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=readmission_prediction_mimic4_fn)
sample_dataset.stat()
print(sample_dataset.available_keys)
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=readmission_prediction_eicu_fn)
sample_dataset.stat()
print(sample_dataset.available_keys)
base_dataset = eICUDataset(
root="/srv/local/data/physionet.org/files/eicu-crd/2.0",
tables=["diagnosis", "admissionDx", "treatment"],
dev=True,
refresh_cache=False,
)
sample_dataset = base_dataset.set_task(task_fn=readmission_prediction_eicu_fn2)
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=readmission_prediction_omop_fn)
sample_dataset.stat()
print(sample_dataset.available_keys)