pyhealth.datasets.SampleEHRDataset#

This class the takes a list of samples as input (either from BaseEHRDataset.set_task() or user-provided json input), and provides a uniform interface for accessing the samples.

class pyhealth.datasets.SampleEHRDataset(samples, code_vocs=None, dataset_name='', task_name='')[source]#

Bases: SampleBaseDataset

Sample EHR dataset class.

This class inherits from SampleBaseDataset and is specifically designed: for EHR datasets.

Parameters:

samples (List[Dict]) – a list of samples, each sample is a dict with patient_id, visit_id, and other task-specific attributes as key.
dataset_name – the name of the dataset. Default is None.
task_name – the name of the task. Default is None.

Currently, the following types of attributes are supported:

a single value. Type: int/float/str. Dim: 0.
a single vector. Type: int/float. Dim: 1.
a list of codes. Type: str. Dim: 2.
a list of vectors. Type: int/float. Dim: 2.
a list of list of codes. Type: str. Dim: 3.
a list of list of vectors. Type: int/float. Dim: 3.

input_info#: Dict, a dict whose keys are the same as the keys in the samples, and values are the corresponding input information: - “type”: the element type of each key attribute, one of float, int, str. - “dim”: the list dimension of each key attribute, one of 0, 1, 2, 3. - “len”: the length of the vector, only valid for vector-based attributes.

patient_to_index#: Dict[str, List[int]], a dict mapping patient_id to a list of sample indices.

visit_to_index#: Dict[str, List[int]], a dict mapping visit_id to a list of sample indices.

Examples

>>> from pyhealth.datasets import SampleEHRDataset
>>> samples = [
...         {
...             "patient_id": "patient-0",
...             "visit_id": "visit-0",
...             "single_vector": [1, 2, 3],
...             "list_codes": ["505800458", "50580045810", "50580045811"],  # NDC
...             "list_vectors": [[1.0, 2.55, 3.4], [4.1, 5.5, 6.0]],
...             "list_list_codes": [["A05B", "A05C", "A06A"], ["A11D", "A11E"]],  # ATC-4
...             "list_list_vectors": [
...                 [[1.8, 2.25, 3.41], [4.50, 5.9, 6.0]],
...                 [[7.7, 8.5, 9.4]],
...             ],
...             "label": 1,
...         },
...         {
...             "patient_id": "patient-0",
...             "visit_id": "visit-1",
...             "single_vector": [1, 5, 8],
...             "list_codes": [
...                 "55154191800",
...                 "551541928",
...                 "55154192800",
...                 "705182798",
...                 "70518279800",
...             ],
...             "list_vectors": [[1.4, 3.2, 3.5], [4.1, 5.9, 1.7]],
...             "list_list_codes": [["A04A", "B035", "C129"], ["A07B", "A07C"]],
...             "list_list_vectors": [
...                 [[1.0, 2.8, 3.3], [4.9, 5.0, 6.6]],
...                 [[7.7, 8.4, 1.3]],
...             ],
...             "label": 0,
...         },
...     ]
>>> dataset = SampleEHRDataset(samples=samples)
>>> dataset.input_info
{'patient_id': {'type': <class 'str'>, 'dim': 0}, 'visit_id': {'type': <class 'str'>, 'dim': 0}, 'single_vector': {'type': <class 'int'>, 'dim': 1, 'len': 3}, 'list_codes': {'type': <class 'str'>, 'dim': 2}, 'list_vectors': {'type': <class 'float'>, 'dim': 2, 'len': 3}, 'list_list_codes': {'type': <class 'str'>, 'dim': 3}, 'list_list_vectors': {'type': <class 'float'>, 'dim': 3, 'len': 3}, 'label': {'type': <class 'int'>, 'dim': 0}}
>>> dataset.patient_to_index
{'patient-0': [0, 1]}
>>> dataset.visit_to_index
{'visit-0': [0], 'visit-1': [1]}

property available_keys: List[str]#

Returns a list of available keys for the dataset.

Return type:: List[str]
Returns:: List of available keys.

get_distribution_tokens(key)[source]#

Gets the distribution of tokens with a specific key in the samples.

Parameters:: key (str) – the key of the tokens in the samples.
Returns:: a dict mapping token to count.
Return type:: distribution

stat()[source]#

Returns some statistics of the task-specific dataset.

Return type:: str