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).

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"],
...     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_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}]