gabrielaltay
commited on
Commit
•
7b2f556
1
Parent(s):
be18ceb
upload hubscripts/ehr_rel_hub.py to hub from bigbio repo
Browse files- ehr_rel.py +222 -0
ehr_rel.py
ADDED
@@ -0,0 +1,222 @@
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+
# coding=utf-8
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# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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+
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"""
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+
EHR-Rel is a novel open-source1 biomedical concept relatedness dataset consisting of 3630 concept pairs, six times more
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+
than the largest existing dataset. Instead of manually selecting and pairing concepts as done in previous work,
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+
the dataset is sampled from EHRs to ensure concepts are relevant for the EHR concept retrieval task.
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+
A detailed analysis of the concepts in the dataset reveals a far larger coverage compared to existing datasets.
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"""
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import csv
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from pathlib import Path
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from typing import Dict, Iterator, List, Tuple
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+
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import datasets
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from .bigbiohub import pairs_features
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from .bigbiohub import BigBioConfig
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from .bigbiohub import Tasks
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+
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_LANGUAGES = ['English']
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_PUBMED = False
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_LOCAL = False
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+
_CITATION = """\
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@inproceedings{schulz-etal-2020-biomedical,
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title = {Biomedical Concept Relatedness {--} A large {EHR}-based benchmark},
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author = {Schulz, Claudia and
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Levy-Kramer, Josh and
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Van Assel, Camille and
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+
Kepes, Miklos and
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Hammerla, Nils},
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booktitle = {Proceedings of the 28th International Conference on Computational Linguistics},
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month = {dec},
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year = {2020},
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address = {Barcelona, Spain (Online)},
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publisher = {International Committee on Computational Linguistics},
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url = {https://aclanthology.org/2020.coling-main.577},
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doi = {10.18653/v1/2020.coling-main.577},
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pages = {6565--6575},
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}
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"""
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+
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_DATASETNAME = "ehr_rel"
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_DISPLAYNAME = "EHR-Rel"
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+
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_DESCRIPTION = """\
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+
EHR-Rel is a novel open-source1 biomedical concept relatedness dataset consisting of 3630 concept pairs, six times more
|
60 |
+
than the largest existing dataset. Instead of manually selecting and pairing concepts as done in previous work,
|
61 |
+
the dataset is sampled from EHRs to ensure concepts are relevant for the EHR concept retrieval task.
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62 |
+
A detailed analysis of the concepts in the dataset reveals a far larger coverage compared to existing datasets.
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63 |
+
"""
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+
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_HOMEPAGE = "https://github.com/babylonhealth/EHR-Rel"
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+
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_LICENSE = 'Apache License 2.0'
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+
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_URLS = {
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_DATASETNAME: "https://github.com/babylonhealth/EHR-Rel/archive/refs/heads/master.zip",
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}
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+
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_SUPPORTED_TASKS = [Tasks.SEMANTIC_SIMILARITY]
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+
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_SOURCE_VERSION = "1.0.0"
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_BIGBIO_VERSION = "1.0.0"
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class EHRRelDataset(datasets.GeneratorBasedBuilder):
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"""Dataset for EHR-Rel Corpus"""
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SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
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BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
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+
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BUILDER_CONFIGS = [
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BigBioConfig(
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name="ehr_rel_source",
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version=SOURCE_VERSION,
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description="EHR-Rel combined source schema",
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schema="source",
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subset_id="ehr_rel",
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),
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BigBioConfig(
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name="ehr_rel_a_source",
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version=SOURCE_VERSION,
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description="EHR-Rel-A source schema",
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schema="source",
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subset_id="ehr_rel_a",
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),
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BigBioConfig(
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name="ehr_rel_b_source",
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version=SOURCE_VERSION,
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description="EHR-Rel-B source schema",
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schema="source",
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subset_id="ehr_rel_b",
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),
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BigBioConfig(
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name="ehr_rel_bigbio_pairs",
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version=BIGBIO_VERSION,
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description="EHR-Rel BigBio schema",
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schema="bigbio_pairs",
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subset_id="ehr_rel",
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),
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]
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DEFAULT_CONFIG_NAME = "ehr_rel_bigbio_pairs"
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def _info(self) -> datasets.DatasetInfo:
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if self.config.schema == "source":
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features = datasets.Features(
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{
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"document_id": datasets.Value("string"),
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"snomed_id_1": datasets.Value("string"),
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"snomed_label_1": datasets.Value("string"),
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"snomed_id_2": datasets.Value("string"),
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"snomed_label_2": datasets.Value("string"),
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"rater_A": datasets.Value("string"),
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"rater_B": datasets.Value("string"),
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"rater_C": datasets.Value("string"),
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"rater_D": datasets.Value("string"),
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"rater_E": datasets.Value("string"),
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"mean_rating": datasets.Value("string"),
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"CUI_1": datasets.Value("string"),
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"CUI_2": datasets.Value("string"),
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}
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)
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elif self.config.schema == "bigbio_pairs":
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features = pairs_features
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+
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=features,
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+
homepage=_HOMEPAGE,
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license=str(_LICENSE),
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citation=_CITATION,
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)
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+
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def _split_generators(self, dl_manager) -> List[datasets.SplitGenerator]:
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"""Returns SplitGenerators."""
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urls = _URLS[_DATASETNAME]
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data_dir = Path(dl_manager.download_and_extract(urls))
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data_dir = data_dir.joinpath("EHR-Rel-master")
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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+
gen_kwargs={"data_dir": data_dir},
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),
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]
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+
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def _generate_examples(self, data_dir: Path) -> Iterator[Tuple[str, Dict]]:
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+
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if self.config.subset_id == "ehr_rel_a":
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files = ["EHR-RelA.tsv"]
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elif self.config.subset_id == "ehr_rel_b":
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files = ["EHR-RelB.tsv"]
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else:
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files = ["EHR-RelA.tsv", "EHR-RelB.tsv"]
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+
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uid = -1 # want first instance to be 0
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+
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for filename in files:
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file = data_dir.joinpath(filename)
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document_id = str(file.stem)
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with open(file, encoding="utf-8") as csv_file:
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csv_reader = csv.reader(csv_file, quotechar='"', delimiter="\t")
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+
next(csv_reader, None) # remove column headers
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for id_, row in enumerate(csv_reader):
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uid += 1
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+
(
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+
snomed_id_1,
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+
snomed_label_1,
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+
snomed_id_2,
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+
snomed_label_2,
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+
rater_A,
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+
rater_B,
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+
rater_C,
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+
rater_D,
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+
rater_E,
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+
mean_rating,
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+
CUI_1,
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+
CUI_2,
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) = row
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+
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if self.config.schema == "source":
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yield uid, {
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"document_id": document_id,
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"snomed_id_1": snomed_id_1,
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+
"snomed_label_1": snomed_label_1,
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"snomed_id_2": snomed_id_1,
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"snomed_label_2": snomed_label_2,
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"rater_A": rater_A,
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"rater_B": rater_B,
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+
"rater_C": rater_C,
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"rater_D": rater_D,
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+
"rater_E": rater_E,
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+
"mean_rating": mean_rating,
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"CUI_1": CUI_1,
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"CUI_2": CUI_2,
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+
}
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+
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+
elif self.config.schema == "bigbio_pairs":
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yield uid, {
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"id": uid,
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"document_id": document_id,
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"text_1": snomed_label_1,
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"text_2": snomed_label_2,
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"label": mean_rating,
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+
}
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