pyhealth.datasets.PhysioNetDeIDDataset#

The PhysioNet De-Identification dataset. For more information see here. Access requires PhysioNet credentialing.

class pyhealth.datasets.PhysioNetDeIDDataset(root='.', config_path='/home/docs/checkouts/readthedocs.org/user_builds/pyhealth/envs/latest/lib/python3.12/site-packages/pyhealth/datasets/configs/physionet_deid.yaml', **kwargs)[source]#

Bases: BaseDataset

Dataset class for the PhysioNet De-Identification dataset.

This dataset contains 2,434 nursing notes from 163 patients. Each note has original text with PHI (protected health information) and a de-identified version with [] tags marking PHI spans.

The dataset parses both files to produce token-level BIO labels for 7 PHI categories: AGE, CONTACT, DATE, ID, LOCATION, NAME, PROFESSION.

Data access requires PhysioNet credentialing:
  1. Create a PhysioNet account at https://physionet.org

  2. Complete the required CITI training

  3. Sign the data use agreement

  4. Download from https://physionet.org/content/deidentifiedmedicaltext/1.0/

root#

Root directory containing id.text and id.res files.

Type:

str

dataset_name#

Name of the dataset.

Type:

str

Example::
>>> dataset = PhysioNetDeIDDataset(root="./data/physionet_deid")
clean_tmpdir()#

Cleans up the temporary directory within the cache.

Return type:

None

create_tmpdir()#

Creates and returns a new temporary directory within the cache.

Returns:

The path to the new temporary directory.

Return type:

Path

property default_task: Optional[BaseTask]#

Returns the default task for the dataset.

Returns:

The default task, if any.

Return type:

Optional[BaseTask]

get_patient(patient_id)#

Retrieves a Patient object for the given patient ID.

Parameters:

patient_id (str) – The ID of the patient to retrieve.

Returns:

The Patient object for the given ID.

Return type:

Patient

Raises:

AssertionError – If the patient ID is not found in the dataset.

property global_event_df: LazyFrame#

Returns the path to the cached event dataframe.

Returns:

The path to the cached event dataframe.

Return type:

Path

iter_patients(df=None)#

Yields Patient objects for each unique patient in the dataset.

Yields:

Iterator[Patient] – An iterator over Patient objects.

Return type:

Iterator[Patient]

load_data()#

Loads data from the specified tables.

Returns:

A concatenated lazy frame of all tables.

Return type:

dd.DataFrame

load_table(table_name)#

Loads a table and processes joins if specified.

Parameters:

table_name (str) – The name of the table to load.

Returns:

The processed Dask dataframe for the table.

Return type:

dd.DataFrame

Raises:
set_task(task=None, num_workers=None, input_processors=None, output_processors=None)#

Processes the base dataset to generate the task-specific sample dataset. The cache structure is as follows:

{task_name}_{task_uuid}/        # Cached data for specific task based on task name, schema, and args
    task_df.ld/                 # Intermediate task dataframe based on schema
    samples_{proc_uuid}.ld/     # Final processed samples after applying processors
        schema.pkl              # Saved SampleBuilder schema
        *.bin                   # Processed sample files
Parameters:
  • task (Optional[BaseTask]) – The task to set. Uses default task if None.

  • num_workers (int) – Number of workers for multi-threading. Default is self.num_workers.

  • input_processors (Optional[Dict[str, FeatureProcessor]]) – Pre-fitted input processors. If provided, these will be used instead of creating new ones from task’s input_schema. Defaults to None.

  • output_processors (Optional[Dict[str, FeatureProcessor]]) – Pre-fitted output processors. If provided, these will be used instead of creating new ones from task’s output_schema. Defaults to None.

Returns:

The generated sample dataset.

Return type:

SampleDataset

Raises:

AssertionError – If no default task is found and task is None.

stats()#

Prints statistics about the dataset.

Return type:

None

property unique_patient_ids: List[str]#

Returns a list of unique patient IDs.

Returns:

List of unique patient IDs.

Return type:

List[str]