pyhealth.tasks.readmission_prediction

pyhealth.tasks.readmission_prediction.readmission_prediction_mimic3_fn(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).

Parameters:

• patient (Patient) – a Patient object

• time_window – the time window threshold (gap < time_window means label=1 for the task)

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 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}]

pyhealth.tasks.readmission_prediction.readmission_prediction_mimic4_fn(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).

Parameters:

• patient (Patient) – a Patient object

• time_window – the time window threshold (gap < time_window means label=1 for the task)

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 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}]

pyhealth.tasks.readmission_prediction.readmission_prediction_eicu_fn(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

Parameters:

• patient (Patient) – a Patient object

• time_window – the time window threshold (gap < time_window means label=1 for the task)

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 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}]

pyhealth.tasks.readmission_prediction.readmission_prediction_eicu_fn2(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

Parameters:

• patient (Patient) – a Patient object

• time_window – the time window threshold (gap < time_window means label=1 for the task)

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", "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}]

pyhealth.tasks.readmission_prediction.readmission_prediction_omop_fn(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).

Parameters:

• patient (Patient) – a Patient object

• time_window – the time window threshold (gap < time_window means label=1 for the task)

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 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}]