pyhealth.tasks.mortality_prediction

pyhealth.tasks.mortality_prediction.mortality_prediction_mimic3_fn(patient)

Processes a single patient for the mortality prediction task.

Mortality prediction aims at predicting whether the patient will decease in the next hospital visit based on the clinical information from current visit (e.g., conditions and procedures).

Parameters:

patient (Patient) – a Patient object

Returns:

a list of samples, each sample is a dict with patient_id,

visit_id, and other task-specific attributes as key

Return type:

samples

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 mortality_prediction_mimic3_fn
>>> mimic3_sample = mimic3_base.set_task(mortality_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': 0}]

pyhealth.tasks.mortality_prediction.mortality_prediction_mimic4_fn(patient)

Processes a single patient for the mortality prediction task.

Mortality prediction aims at predicting whether the patient will decease in the next hospital visit based on the clinical information from current visit (e.g., conditions and procedures).

Parameters:

patient (Patient) – a Patient object

Returns:

a list of samples, each sample is a dict with patient_id,

visit_id, and other task-specific attributes as key

Return type:

samples

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 mortality_prediction_mimic4_fn
>>> mimic4_sample = mimic4_base.set_task(mortality_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': 1}]

pyhealth.tasks.mortality_prediction.mortality_prediction_eicu_fn(patient)

Processes a single patient for the mortality prediction task.

Mortality prediction aims at predicting whether the patient will decease in the next hospital visit 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

Parameters:

patient (Patient) – a Patient object

Returns:

a list of samples, each sample is a dict with patient_id,

visit_id, and other task-specific attributes as key

Return type:

samples

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 mortality_prediction_eicu_fn
>>> eicu_sample = eicu_base.set_task(mortality_prediction_eicu_fn)
>>> eicu_sample.samples[0]
[{'visit_id': '130744', 'patient_id': '103', 'conditions': [['42', '109', '98', '663', '58', '51']], 'procedures': [['1']], 'label': 0}]

pyhealth.tasks.mortality_prediction.mortality_prediction_eicu_fn2(patient)

Processes a single patient for the mortality prediction task.

Mortality prediction aims at predicting whether the patient will decease in the next hospital visit based on the clinical information from current visit (e.g., conditions and procedures).

Similar to mortality_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

Parameters:

patient (Patient) – a Patient object

Returns:

a list of samples, each sample is a dict with patient_id,

visit_id, and other task-specific attributes as key

Return type:

samples

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", "admissionDx", "treatment"],
...     code_mapping={},
...     dev=True
... )
>>> from pyhealth.tasks import mortality_prediction_eicu_fn2
>>> eicu_sample = eicu_base.set_task(mortality_prediction_eicu_fn2)
>>> eicu_sample.samples[0]
{'visit_id': '130744', 'patient_id': '103', 'conditions': [['42', '109', '98', '663', '58', '51']], 'procedures': [['1']], 'label': 0}

pyhealth.tasks.mortality_prediction.mortality_prediction_omop_fn(patient)

Processes a single patient for the mortality prediction task.

Mortality prediction aims at predicting whether the patient will decease in the next hospital visit based on the clinical information from current visit (e.g., conditions and procedures).

Parameters:

patient (Patient) – a Patient object

Returns:

a list of samples, each sample is a dict with patient_id,

visit_id, and other task-specific attributes as key

Return type:

samples

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 mortality_prediction_omop_fn
>>> omop_sample = omop_base.set_task(mortality_prediction_eicu_fn)
>>> omop_sample.samples[0]
[{'visit_id': '130744', 'patient_id': '103', 'conditions': [['42', '109', '98', '663', '58', '51']], 'procedures': [['1']], 'label': 1}]