# coding=utf-8 # Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ A dataset loader for the n2c2 2006 smoking status dataset. https://portal.dbmi.hms.harvard.edu/projects/n2c2-nlp/ The dataset consists of two archive files, * smokers_surrogate_train_all_version2.zip * smokers_surrogate_test_all_groundtruth_version2.zip The individual data files (inside the zip archives) come in just 1 type: * xml (*.xml files): contains the id and text of the patient records, and corresponding smoking status labels The files comprising this dataset must be on the users local machine in a single directory that is passed to `datasets.load_datset` via the `data_dir` kwarg. This loader script will read the archive files directly (i.e. the user should not uncompress, untar or unzip any of the files). For example, if the following directory structure exists on the users local machine, n2c2_2006 ├── smokers_surrogate_train_all_version2.zip ├── smokers_surrogate_test_all_groundtruth_version2.zip Data Access from https://www.i2b2.org/NLP/DataSets/Main.php "As always, you must register AND submit a DUA for access. If you previously accessed the data sets here on i2b2.org, you will need to set a new password for your account on the Data Portal, but your original DUA will be retained." """ import os import xml.etree.ElementTree as et import zipfile from typing import Dict, List, Tuple import datasets from .bigbiohub import text_features from .bigbiohub import BigBioConfig from .bigbiohub import Tasks _DATASETNAME = "n2c2_2006" _DISPLAYNAME = "n2c2 2006 Smoking Status" # https://academic.oup.com/jamia/article/15/1/14/779738 _LANGUAGES = ['English'] _PUBMED = False _LOCAL = True _CITATION = """\ @article{uzuner2008identifying, author = { Uzuner, Ozlem and Goldstein, Ira and Luo, Yuan and Kohane, Isaac }, title = {Identifying Patient Smoking Status from Medical Discharge Records}, journal = {Journal of the American Medical Informatics Association}, volume = {15}, number = {1}, pages = {14-24}, year = {2008}, month = {01}, url = {https://doi.org/10.1197/jamia.M2408}, doi = {10.1136/amiajnl-2011-000784}, eprint = {https://academic.oup.com/jamia/article-pdf/15/1/14/2339646/15-1-14.pdf} } """ _DESCRIPTION = """\ The data for the n2c2 2006 smoking challenge consisted of discharge summaries from Partners HealthCare, which were then de-identified, tokenized, broken into sentences, converted into XML format, and separated into training and test sets. Two pulmonologists annotated each record with the smoking status of patients based strictly on the explicitly stated smoking-related facts in the records. These annotations constitute the textual judgments of the annotators. The annotators were asked to classify patient records into five possible smoking status categories: a past smoker, a current smoker, a smoker, a non-smoker and an unknown. A total of 502 de-identified medical discharge records were used for the smoking challenge. """ _HOMEPAGE = "https://portal.dbmi.hms.harvard.edu/projects/n2c2-nlp/" _LICENSE = 'Data User Agreement' _SUPPORTED_TASKS = [Tasks.TEXT_CLASSIFICATION] _SOURCE_VERSION = "1.0.0" _BIGBIO_VERSION = "1.0.0" _CLASS_NAMES = ["current smoker", "non-smoker", "past smoker", "smoker", "unknown"] def _read_zip(file_path): _, filename = os.path.split(file_path) zipped = zipfile.ZipFile(file_path, "r") file = zipped.read(filename.split(".")[0] + ".xml") root = et.fromstring(file) ids = [] notes = [] labels = [] documents = root.findall("./RECORD") for document in documents: ids.append(document.attrib["ID"]) notes.append(document.findall("./TEXT")[0].text) labels.append(document.findall("./SMOKING")[0].attrib["STATUS"].lower()) return [(id, note, label) for id, note, label in zip(ids, notes, labels)] class N2C22006SmokingDataset(datasets.GeneratorBasedBuilder): """n2c2 2006 smoking status identification task""" SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION) BUILDER_CONFIGS = [ BigBioConfig( name="n2c2_2006_smokers_source", version=SOURCE_VERSION, description="n2c2_2006_smokers source schema", schema="source", subset_id="n2c2_2006_smokers", ), BigBioConfig( name="n2c2_2006_smokers_bigbio_text", version=BIGBIO_VERSION, description="n2c2_2006_smokers BigBio schema", schema="bigbio_text", subset_id="n2c2_2006_smokers", ), ] DEFAULT_CONFIG_NAME = "n2c2_2006_smokers_source" def _info(self) -> datasets.DatasetInfo: if self.config.schema == "source": features = datasets.Features( { "document_id": datasets.Value("string"), "text": datasets.Value("string"), "label": datasets.ClassLabel(names=_CLASS_NAMES), } ) elif self.config.schema == "bigbio_text": features = text_features return datasets.DatasetInfo( description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, license=str(_LICENSE), citation=_CITATION, ) def _split_generators( self, dl_manager: datasets.DownloadManager ) -> List[datasets.SplitGenerator]: """Returns SplitGenerators.""" if self.config.data_dir is None: raise ValueError( "This is a local dataset. Please pass the data_dir kwarg to load_dataset." ) else: data_dir = self.config.data_dir return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "data_dir": data_dir, "split": "train", }, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ "data_dir": data_dir, "split": "test", }, ), ] def _generate_examples(self, data_dir, split: str) -> Tuple[int, Dict]: """Yields examples as (key, example) tuples.""" if split == "train": _id = 0 path = os.path.join(data_dir, "smokers_surrogate_train_all_version2.zip") samples = _read_zip(path) for sample in samples: if self.config.schema == "source": yield _id, { "document_id": sample[0], "text": sample[1], "label": sample[-1], } elif self.config.schema == "bigbio_text": yield _id, { "id": sample[0], "document_id": sample[0], "text": sample[1], "labels": [sample[-1]], } _id += 1 elif split == "test": _id = 0 path = os.path.join( data_dir, "smokers_surrogate_test_all_groundtruth_version2.zip" ) samples = _read_zip(path) for sample in samples: if self.config.schema == "source": yield _id, { "document_id": sample[0], "text": sample[1], "label": sample[-1], } elif self.config.schema == "bigbio_text": yield _id, { "id": sample[0], "document_id": sample[0], "text": sample[1], "labels": [sample[-1]], } _id += 1