codiesp.py

# 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 loading script for the CODIESP corpus.

The CODIESP dataset is a collection of 1,000 manually selected clinical
case studies in Spanish that was designed for the Clinical Case Coding
in Spanish Shared Task, as part of the CLEF 2020 conference. This community
task was divided into 3 sub-tasks: diagnosis coding (CodiEsp-D), procedure
coding (CodiEsp-P) and Explainable AI (CodiEsp-X). The script can also load
an additional dataset of abstracts with ICD10 codes.
"""

import json
import os
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple

import datasets
import pandas as pd

from .bigbiohub import kb_features
from .bigbiohub import text_features
from .bigbiohub import BigBioConfig
from .bigbiohub import Tasks

_LANGUAGES = ['Spanish']
_PUBMED = False
_LOCAL = False
_CITATION = """\
@article{miranda2020overview,
  title={Overview of Automatic Clinical Coding: Annotations, Guidelines, and Solutions for non-English Clinical Cases at CodiEsp Track of CLEF eHealth 2020.},
  author={Miranda-Escalada, Antonio and Gonzalez-Agirre, Aitor and Armengol-Estap{\'e}, Jordi and Krallinger, Martin},
  journal={CLEF (Working Notes)},
  volume={2020},
  year={2020}
}
"""

_DATASETNAME = "codiesp"
_DISPLAYNAME = "CodiEsp"

_DESCRIPTION = """\
Synthetic corpus of 1,000 manually selected clinical case studies in Spanish
that was designed for the Clinical Case Coding in Spanish Shared Task, as part
of the CLEF 2020 conference.

The goal of the task was to automatically assign ICD10 codes (CIE-10, in
Spanish) to clinical case documents, being evaluated against manually generated
ICD10 codifications. The CodiEsp corpus was selected manually by practicing
physicians and clinical documentalists and annotated by clinical coding
professionals meeting strict quality criteria. They reached an inter-annotator
agreement of 88.6% for diagnosis coding, 88.9% for procedure coding and 80.5%
for the textual reference annotation.

The final collection of 1,000 clinical cases that make up the corpus had a total
of 16,504 sentences and 396,988 words. All documents are in Spanish language and
CIE10 is the coding terminology (the Spanish version of ICD10-CM and ICD10-PCS).
The CodiEsp corpus has been randomly sampled into three subsets. The train set
contains 500 clinical cases, while the development and test sets have 250
clinical cases each. In addition to these, a collection of 176,294 abstracts
from Lilacs and Ibecs with the corresponding ICD10 codes (ICD10-CM and
ICD10-PCS) was provided by the task organizers. Every abstract has at least one
associated code, with an average of 2.5 ICD10 codes per abstract.

The CodiEsp track was divided into three sub-tracks (2 main and 1 exploratory):

- CodiEsp-D: The Diagnosis Coding sub-task, which requires automatic ICD10-CM
  [CIE10-Diagnóstico] code assignment.
- CodiEsp-P: The Procedure Coding sub-task, which requires automatic ICD10-PCS
  [CIE10-Procedimiento] code assignment.
- CodiEsp-X: The Explainable AI exploratory sub-task, which requires to submit
  the reference to the predicted codes (both ICD10-CM and ICD10-PCS). The goal 
  of this novel task was not only to predict the correct codes but also to 
  present the reference in the text that supports the code predictions.

For further information, please visit https://temu.bsc.es/codiesp or send an
email to [email protected]
"""

_HOMEPAGE = "https://temu.bsc.es/codiesp/"

_LICENSE = 'Creative Commons Attribution 4.0 International'

_URLS = {
    "codiesp": "https://zenodo.org/record/3837305/files/codiesp.zip?download=1",
    "extra": "https://zenodo.org/record/3606662/files/abstractsWithCIE10_v2.zip?download=1",
}

_SUPPORTED_TASKS = [
    Tasks.TEXT_CLASSIFICATION,
    Tasks.NAMED_ENTITY_RECOGNITION,
    Tasks.NAMED_ENTITY_DISAMBIGUATION,
]

_SOURCE_VERSION = "1.4.0"

_BIGBIO_VERSION = "1.0.0"


class CodiespDataset(datasets.GeneratorBasedBuilder):
    """Collection of 1,000 manually selected clinical case studies in Spanish."""

    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)

    BUILDER_CONFIGS = [
        BigBioConfig(
            name="codiesp_D_source",
            version=SOURCE_VERSION,
            description="CodiEsp source schema for the Diagnosis Coding subtask",
            schema="source",
            subset_id="codiesp_d",
        ),
        BigBioConfig(
            name="codiesp_P_source",
            version=SOURCE_VERSION,
            description="CodiEsp source schema for the Procedure Coding sub-task",
            schema="source",
            subset_id="codiesp_p",
        ),
        BigBioConfig(
            name="codiesp_X_source",
            version=SOURCE_VERSION,
            description="CodiEsp source schema for the Explainable AI sub-task",
            schema="source",
            subset_id="codiesp_x",
        ),
        BigBioConfig(
            name="codiesp_extra_mesh_source",
            version=SOURCE_VERSION,
            description="Abstracts from Lilacs and Ibecs with MESH Codes",
            schema="source",
            subset_id="codiesp_extra_mesh",
        ),
        BigBioConfig(
            name="codiesp_extra_cie_source",
            version=SOURCE_VERSION,
            description="Abstracts from Lilacs and Ibecs with CIE10 Codes",
            schema="source",
            subset_id="codiesp_extra_cie",
        ),
        BigBioConfig(
            name="codiesp_D_bigbio_text",
            version=BIGBIO_VERSION,
            description="CodiEsp BigBio schema for the Diagnosis Coding subtask",
            schema="bigbio_text",
            subset_id="codiesp_d",
        ),
        BigBioConfig(
            name="codiesp_P_bigbio_text",
            version=BIGBIO_VERSION,
            description="CodiEsp BigBio schema for the Procedure Coding sub-task",
            schema="bigbio_text",
            subset_id="codiesp_p",
        ),
        BigBioConfig(
            name="codiesp_X_bigbio_kb",
            version=BIGBIO_VERSION,
            description="CodiEsp BigBio schema for the Explainable AI sub-task",
            schema="bigbio_kb",
            subset_id="codiesp_x",
        ),
        BigBioConfig(
            name="codiesp_extra_mesh_bigbio_text",
            version=BIGBIO_VERSION,
            description="Abstracts from Lilacs and Ibecs with MESH Codes",
            schema="bigbio_text",
            subset_id="codiesp_extra_mesh",
        ),
        BigBioConfig(
            name="codiesp_extra_cie_bigbio_text",
            version=BIGBIO_VERSION,
            description="Abstracts from Lilacs and Ibecs with CIE10 Codes",
            schema="bigbio_text",
            subset_id="codiesp_extra_cie",
        ),
    ]

    DEFAULT_CONFIG_NAME = "codiesp_source"

    def _info(self) -> datasets.DatasetInfo:

        if self.config.schema == "source" and self.config.name != "codiesp_X_source":
            features = datasets.Features(
                {
                    "id": datasets.Value("string"),
                    "document_id": datasets.Value("string"),
                    "text": datasets.Value("string"),
                    "labels": datasets.Sequence(datasets.Value("string")),
                },
            )

        elif self.config.schema == "source" and self.config.name == "codiesp_X_source":
            features = datasets.Features(
                {
                    "id": datasets.Value("string"),
                    "document_id": datasets.Value("string"),
                    "text": datasets.Value("string"),
                    "task_x": [
                        {
                            "label": datasets.Value("string"),
                            "code": datasets.Value("string"),
                            "text": datasets.Value("string"),
                            "spans": datasets.Sequence(datasets.Value("int32")),
                        }
                    ],
                },
            )

        elif self.config.schema == "bigbio_kb":
            features = kb_features

        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]:
        """
        Downloads/extracts the data to generate the train, validation and test splits.

        Each split is created by instantiating a `datasets.SplitGenerator`, which will
        call `this._generate_examples` with the keyword arguments in `gen_kwargs`.
        """

        data_dir = dl_manager.download_and_extract(_URLS)

        if "extra" in self.config.name:
            return [
                datasets.SplitGenerator(
                    name=datasets.Split.TRAIN,
                    gen_kwargs={
                        "filepath": Path(
                            os.path.join(
                                data_dir["extra"], "abstractsWithCIE10_v2.json"
                            )
                        ),
                        "split": "train",
                    },
                )
            ]
        else:
            return [
                datasets.SplitGenerator(
                    name=datasets.Split.TRAIN,
                    gen_kwargs={
                        "filepath": Path(
                            os.path.join(
                                data_dir["codiesp"], "final_dataset_v4_to_publish/train"
                            )
                        ),
                        "split": "train",
                    },
                ),
                datasets.SplitGenerator(
                    name=datasets.Split.TEST,
                    gen_kwargs={
                        "filepath": Path(
                            os.path.join(
                                data_dir["codiesp"], "final_dataset_v4_to_publish/test"
                            )
                        ),
                        "split": "test",
                    },
                ),
                datasets.SplitGenerator(
                    name=datasets.Split.VALIDATION,
                    gen_kwargs={
                        "filepath": Path(
                            os.path.join(
                                data_dir["codiesp"], "final_dataset_v4_to_publish/dev"
                            )
                        ),
                        "split": "dev",
                    },
                ),
            ]

    def _generate_examples(self, filepath, split: str) -> Tuple[int, Dict]:
        """
        This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
        Method parameters are unpacked from `gen_kwargs` as given in `_split_generators`.
        """

        if "extra" not in self.config.name:
            paths = {"text_files": Path(os.path.join(filepath, "text_files"))}
            for task in ["codiesp_d", "codiesp_p", "codiesp_x"]:
                paths[task] = Path(
                    os.path.join(filepath, f"{split}{task[-1].upper()}.tsv")
                )

        if (
            self.config.name == "codiesp_D_bigbio_text"
            or self.config.name == "codiesp_P_bigbio_text"
        ):
            df = pd.read_csv(paths[self.config.subset_id], sep="\t", header=None)

            file_codes_dict = defaultdict(list)
            for idx, row in df.iterrows():
                file, code = row[0], row[1]
                file_codes_dict[file].append(code)

            for guid, (file, codes) in enumerate(file_codes_dict.items()):
                text_file = Path(os.path.join(paths["text_files"], f"{file}.txt"))
                example = {
                    "id": str(guid),
                    "document_id": file,
                    "text": text_file.read_text(),
                    "labels": codes,
                }
                yield guid, example

        elif self.config.name == "codiesp_X_bigbio_kb":
            df = pd.read_csv(paths[self.config.subset_id], sep="\t", header=None)

            task_x_dict = defaultdict(list)
            for idx, row in df.iterrows():
                file, label, code, text, spans = row[0], row[1], row[2], row[3], row[4]

                appearances = spans.split(";")
                spans = []
                for a in appearances:
                    spans.append((int(a.split()[0]), int(a.split()[1])))

                task_x_dict[file].append(
                    {"label": label, "code": code, "text": text, "spans": spans}
                )

            for guid, (file, data) in enumerate(task_x_dict.items()):
                example = {
                    "id": str(guid),
                    "document_id": file,
                    "passages": [],
                    "entities": [],
                    "events": [],
                    "coreferences": [],
                    "relations": [],
                }

                for idx, d in enumerate(data):
                    example["entities"].append(
                        {
                            "id": str(guid) + str(idx),
                            "type": d["label"],
                            "text": [d["text"]],
                            "offsets": d["spans"],
                            "normalized": [
                                {
                                    "db_name": "ICD10-PCS"
                                    if d["label"] == "PROCEDIMIENTO"
                                    else "ICD10-CM",
                                    "db_id": d["code"],
                                }
                            ],
                        }
                    )

                yield guid, example

        elif (
            self.config.name == "codiesp_D_source"
            or self.config.name == "codiesp_P_source"
        ):
            df = pd.read_csv(paths[self.config.subset_id], sep="\t", header=None)

            file_codes_dict = defaultdict(list)
            for idx, row in df.iterrows():
                file, code = row[0], row[1]
                file_codes_dict[file].append(code)

            for guid, (file, codes) in enumerate(file_codes_dict.items()):
                example = {
                    "id": guid,
                    "document_id": file,
                    "text": Path(
                        os.path.join(paths["text_files"], f"{file}.txt")
                    ).read_text(),
                    "labels": codes,
                }

                yield guid, example

        elif self.config.name == "codiesp_X_source":
            df = pd.read_csv(paths[self.config.subset_id], sep="\t", header=None)
            file_codes_dict = defaultdict(list)
            for idx, row in df.iterrows():
                file, label, code, text, spans = row[0], row[1], row[2], row[3], row[4]
                appearances = spans.split(";")
                spans = []
                for a in appearances:
                    spans.append([int(a.split()[0]), int(a.split()[1])])
                file_codes_dict[file].append(
                    {"label": label, "code": code, "text": text, "spans": spans[0]}
                )

            for guid, (file, codes) in enumerate(file_codes_dict.items()):
                example = {
                    "id": guid,
                    "document_id": file,
                    "text": Path(
                        os.path.join(paths["text_files"], f"{file}.txt")
                    ).read_text(),
                    "task_x": file_codes_dict[file],
                }

                yield guid, example

        elif "extra" in self.config.name:
            with open(filepath) as file:
                json_data = json.load(file)

            if "mesh" in self.config.name:
                for guid, article in enumerate(json_data["articles"]):
                    example = {
                        "id": str(guid),
                        "document_id": article["pmid"],
                        "text": str(article["title"])
                        + " <SEP> "
                        + str(article["abstractText"]),
                        "labels": [mesh["Code"] for mesh in article["Mesh"]],
                    }
                    yield guid, example

            else:  # CIE ID codes
                for guid, article in enumerate(json_data["articles"]):
                    example = {
                        "id": str(guid),
                        "document_id": article["pmid"],
                        "text": str(article["title"])
                        + " <SEP> "
                        + str(article["abstractText"]),
                        "labels": [
                            code
                            for mesh in article["Mesh"]
                            if "CIE" in mesh
                            for code in mesh["CIE"]
                        ],
                    }
                    yield guid, example

        else:
            raise ValueError(f"Invalid config: {self.config.name}")