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:
BaseDatasetDataset 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:
Create a PhysioNet account at https://physionet.org
Complete the required CITI training
Sign the data use agreement
Download from https://physionet.org/content/deidentifiedmedicaltext/1.0/
- Example::
>>> dataset = PhysioNetDeIDDataset(root="./data/physionet_deid")
- create_tmpdir()#
Creates and returns a new temporary directory within the cache.
- Returns:
The path to the new temporary directory.
- Return type:
- 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:
- 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:
- iter_patients(df=None)#
Yields Patient objects for each unique patient in the dataset.
- 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:
ValueError – If the table is not found in the config.
FileNotFoundError – If the CSV file for the table or join is not found.
- 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:
- Raises:
AssertionError – If no default task is found and task is None.