upload hubscripts/n2c2_2018_track1_hub.py to hub from bigbio repo

n2c2_2018_track1.py

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+# 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 2018 cohort selection dataset.
+
+The dataset consists of three archive files,
+├── train.zip - 202 records
+└── n2c2-t1_gold_standard_test_data.zip - 86 records
+
+The individual data files (inside the zip and tar archives) come in
+xml files that contains text as well as labels.
+
+
+The files comprising this dataset must be on the users local machine
+in a single directory that is passed to `datasets.load_dataset` 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).
+
+Data Access from https://portal.dbmi.hms.harvard.edu/projects/n2c2-nlp/
+"""
+
+import os
+import zipfile
+from collections import defaultdict
+from typing import List
+
+import datasets
+from lxml import etree
+
+from .bigbiohub import text.features
+from .bigbiohub import BigBioConfig
+from .bigbiohub import Tasks
+
+_LANGUAGES = ['English']
+_PUBMED = False
+_LOCAL = True
+_CITATION = """\
+@article{DBLP:journals/jamia/StubbsFSHU19,
+  author    = {
+                Amber Stubbs and
+                Michele Filannino and
+                Ergin Soysal and
+                Samuel Henry and
+                Ozlem Uzuner
+               },
+  title     = {Cohort selection for clinical trials: n2c2 2018 shared task track 1},
+  journal   = {J. Am. Medical Informatics Assoc.},
+  volume    = {26},
+  number    = {11},
+  pages     = {1163--1171},
+  year      = {2019},
+  url       = {https://doi.org/10.1093/jamia/ocz163},
+  doi       = {10.1093/jamia/ocz163},
+  timestamp = {Mon, 15 Jun 2020 16:56:11 +0200},
+  biburl    = {https://dblp.org/rec/journals/jamia/StubbsFSHU19.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+"""
+
+_DATASETNAME = "n2c2_2018_track1"
+_DISPLAYNAME = "n2c2 2018 Selection Criteria"
+
+_DESCRIPTION = """\
+Track 1 of the 2018 National NLP Clinical Challenges shared tasks focused
+on identifying which patients in a corpus of longitudinal medical records
+meet and do not meet identified selection criteria.
+
+This shared task aimed to determine whether NLP systems could be trained to identify if patients met or did not meet
+a set of selection criteria taken from real clinical trials. The selected criteria required measurement detection (
+“Any HbA1c value between 6.5 and 9.5%”), inference (“Use of aspirin to prevent myocardial infarction”),
+temporal reasoning (“Diagnosis of ketoacidosis in the past year”), and expert judgment to assess (“Major
+diabetes-related complication”). For the corpus, we used the dataset of American English, longitudinal clinical
+narratives from the 2014 i2b2/UTHealth shared task 4.
+
+The final selected 13 selection criteria are as follows:
+1. DRUG-ABUSE: Drug abuse, current or past
+2. ALCOHOL-ABUSE: Current alcohol use over weekly recommended limits
+3. ENGLISH: Patient must speak English
+4. MAKES-DECISIONS: Patient must make their own medical decisions
+5. ABDOMINAL: History of intra-abdominal surgery, small or large intestine
+resection, or small bowel obstruction.
+6. MAJOR-DIABETES: Major diabetes-related complication. For the purposes of
+this annotation, we define “major complication” (as opposed to “minor complication”)
+as any of the following that are a result of (or strongly correlated with) uncontrolled diabetes:
+    a. Amputation
+    b. Kidney damage
+    c. Skin conditions
+    d. Retinopathy
+    e. nephropathy
+    f. neuropathy
+7. ADVANCED-CAD: Advanced cardiovascular disease (CAD).
+For the purposes of this annotation, we define “advanced” as having 2 or more of the following:
+    a. Taking 2 or more medications to treat CAD
+    b. History of myocardial infarction (MI)
+    c. Currently experiencing angina
+    d. Ischemia, past or present
+8. MI-6MOS: MI in the past 6 months
+9. KETO-1YR: Diagnosis of ketoacidosis in the past year
+10. DIETSUPP-2MOS: Taken a dietary supplement (excluding vitamin D) in the past 2 months
+11. ASP-FOR-MI: Use of aspirin to prevent MI
+12. HBA1C: Any hemoglobin A1c (HbA1c) value between 6.5% and 9.5%
+13. CREATININE: Serum creatinine > upper limit of normal
+
+The training consists of 202 patient records with document-level annotations, 10 records
+with textual spans indicating annotator’s evidence for their annotations while test set contains 86.
+
+Note:
+* The inter-annotator average agreement is 84.9%
+* Whereabouts of 10 records with textual spans indicating annotator’s evidence are unknown.
+However, author did a simple script based validation to check if any of the tags contained any text
+in any of the training set and they do not, which confirms that atleast train and test do not
+ have any evidence tagged alongside corresponding tags.
+"""
+
+_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"
+
+# Constants
+SOURCE = "source"
+BIGBIO_TEXT = "bigbio_text"
+
+
+def _read_zip(file_path):
+    samples = defaultdict(dict)
+    with zipfile.ZipFile(file_path) as zf:
+        for info in zf.infolist():
+
+            base, filename = os.path.split(info.filename)
+            _, ext = os.path.splitext(filename)
+            ext = ext[1:]  # get rid of dot
+            sample_id = filename.split(".")[0]
+
+            if ext == "xml" and not filename.startswith("."):
+                content = zf.read(info).decode("utf-8").encode()
+                root = etree.XML(content)
+                text, tags = root.getchildren()
+                samples[sample_id]["txt"] = text.text
+                samples[sample_id]["tags"] = {}
+                for child in tags:
+                    samples[sample_id]["tags"][child.tag] = child.get("met")
+
+    return samples
+
+
+class N2C22018CohortSelectionDataset(datasets.GeneratorBasedBuilder):
+    """i2b2 2018 track 1 cohort selection task"""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
+
+    _SOURCE_CONFIG_NAME = _DATASETNAME + "_" + SOURCE
+    _BIGBIO_CONFIG_NAME = _DATASETNAME + "_" + BIGBIO_TEXT
+
+    BUILDER_CONFIGS = [
+        BigBioConfig(
+            name=_SOURCE_CONFIG_NAME,
+            version=SOURCE_VERSION,
+            description=_DATASETNAME + " source schema",
+            schema=SOURCE,
+            subset_id=_DATASETNAME,
+        ),
+        BigBioConfig(
+            name=_BIGBIO_CONFIG_NAME,
+            version=BIGBIO_VERSION,
+            description=_DATASETNAME + " BigBio schema",
+            schema=BIGBIO_TEXT,
+            subset_id=_DATASETNAME,
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = _SOURCE_CONFIG_NAME
+    LABEL_CLASS_NAMES = [
+        "ABDOMINAL",
+        "ADVANCED-CAD",
+        "ALCOHOL-ABUSE",
+        "ASP-FOR-MI",
+        "CREATININE",
+        "DIETSUPP-2MOS",
+        "DRUG-ABUSE",
+        "ENGLISH",
+        "HBA1C",
+        "KETO-1YR",
+        "MAJOR-DIABETES",
+        "MAKES-DECISIONS",
+        "MI-6MOS",
+    ]
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == SOURCE:
+            labels = {
+                key: datasets.ClassLabel(names=["met", "not met"])
+                for key in self.LABEL_CLASS_NAMES
+            }
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "document_id": datasets.Value("string"),
+                    "text": datasets.Value("string"),
+                    "tags": labels,
+                }
+            )
+
+        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) -> List[datasets.SplitGenerator]:
+
+        if self.config.data_dir is None or self.config.name is None:
+            raise ValueError(
+                "This is a local dataset. Please pass the data_dir and name kwarg to load_dataset."
+            )
+        else:
+            data_dir = self.config.data_dir
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "file_path": os.path.join(data_dir, "train.zip"),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "file_path": os.path.join(
+                        data_dir, "n2c2-t1_gold_standard_test_data.zip"
+                    ),
+                },
+            ),
+        ]
+
+    @staticmethod
+    def _get_source_sample(sample_id, sample):
+        return {
+            "id": sample_id,
+            "document_id": sample_id,
+            "text": sample.get("txt", ""),
+            "tags": sample.get("tags", {}),
+        }
+
+    @staticmethod
+    def _get_bigbio_sample(sample_id, sample) -> dict:
+
+        tags = sample.get("tags", None)
+        if tags:
+            labels = [name for name, met_status in tags.items() if met_status == "met"]
+        else:
+            labels = []
+
+        return {
+            "id": sample_id,
+            "document_id": sample_id,
+            "text": sample.get("txt", ""),
+            "labels": labels,
+        }
+
+    def _generate_examples(self, file_path):
+        samples = _read_zip(file_path)
+
+        _id = 0
+        for sample_id, sample in samples.items():
+
+            if self.config.name == self._SOURCE_CONFIG_NAME:
+                yield _id, self._get_source_sample(sample_id, sample)
+            elif self.config.name == self._BIGBIO_CONFIG_NAME:
+                yield _id, self._get_bigbio_sample(sample_id, sample)
+
+            _id += 1