Add ClinicalJargonDataset and ClinicalJargonVerification benchmark task

docs/api/datasets.rst

@@ -240,6 +240,7 @@ Available Datasets
     datasets/pyhealth.datasets.ChestXray14Dataset
     datasets/pyhealth.datasets.TUABDataset
     datasets/pyhealth.datasets.TUEVDataset
+    datasets/pyhealth.datasets.ClinicalJargonDataset
     datasets/pyhealth.datasets.ClinVarDataset
     datasets/pyhealth.datasets.COSMICDataset
     datasets/pyhealth.datasets.TCGAPRADDataset

docs/api/datasets/pyhealth.datasets.ClinicalJargonDataset.rst

@@ -0,0 +1,21 @@
+pyhealth.datasets.ClinicalJargonDataset
+=======================================
+
+Public clinical jargon benchmark dataset backed by the released MedLingo and
+CASI assets from Jia et al. (CHIL 2025).
+
+Example
+-------
+
+.. code-block:: python
+
+    from pyhealth.datasets import ClinicalJargonDataset
+
+    dataset = ClinicalJargonDataset(root="/tmp/clinical_jargon", download=True)
+    task = dataset.default_task
+    samples = dataset.set_task(task)
+
+.. autoclass:: pyhealth.datasets.ClinicalJargonDataset
+    :members:
+    :undoc-members:
+    :show-inheritance:

docs/api/tasks.rst

@@ -227,6 +227,7 @@ Available Tasks
     Benchmark EHRShot <tasks/pyhealth.tasks.benchmark_ehrshot>
     ChestX-ray14 Binary Classification <tasks/pyhealth.tasks.ChestXray14BinaryClassification>
     ChestX-ray14 Multilabel Classification <tasks/pyhealth.tasks.ChestXray14MultilabelClassification>
+    Clinical Jargon Verification <tasks/pyhealth.tasks.ClinicalJargonVerification>
     Variant Classification (ClinVar) <tasks/pyhealth.tasks.VariantClassificationClinVar>
     Mutation Pathogenicity (COSMIC) <tasks/pyhealth.tasks.MutationPathogenicityPrediction>
     Cancer Survival Prediction (TCGA) <tasks/pyhealth.tasks.CancerSurvivalPrediction>

docs/api/tasks/pyhealth.tasks.ClinicalJargonVerification.rst

@@ -0,0 +1,25 @@
+pyhealth.tasks.ClinicalJargonVerification
+=========================================
+
+Binary candidate-verification task for the public MedLingo and CASI clinical
+jargon benchmarks.
+
+Example
+-------
+
+.. code-block:: python
+
+    from pyhealth.datasets import ClinicalJargonDataset
+    from pyhealth.tasks import ClinicalJargonVerification
+
+    dataset = ClinicalJargonDataset(root="/tmp/clinical_jargon", download=True)
+    task = ClinicalJargonVerification(
+        benchmark="medlingo",
+        medlingo_distractors=1,
+    )
+    samples = dataset.set_task(task)
+
+.. autoclass:: pyhealth.tasks.ClinicalJargonVerification
+    :members:
+    :undoc-members:
+    :show-inheritance:

examples/clinical_jargon_clinical_jargon_verification_transformers.py

@@ -0,0 +1,130 @@
+"""Clinical jargon verification example with `TransformersModel`.
+
+This example is the course-facing ablation entrypoint for the public clinical
+jargon benchmark contribution. It supports three lightweight task ablations
+while keeping each source benchmark item in a single split:
+
+- switch between ``medlingo``, ``casi``, and ``all`` benchmark subsets
+- switch CASI between ``release62`` and ``paper59`` variants
+- vary MedLingo distractor count through ``--medlingo-distractors``
+
+Run this example from an environment where PyHealth is installed, such as
+``pip install -e .`` from the repository root.
+
+Example commands:
+    python3 examples/clinical_jargon_clinical_jargon_verification_transformers.py \
+        --benchmark medlingo --medlingo-distractors 1 --epochs 1
+
+    python3 examples/clinical_jargon_clinical_jargon_verification_transformers.py \
+        --benchmark casi --casi-variant paper59 --epochs 1
+"""
+
+import argparse
+from pathlib import Path
+
+PROJECT_ROOT = Path(__file__).resolve().parents[1]
+
+from pyhealth.datasets import ClinicalJargonDataset, get_dataloader, split_by_patient
+from pyhealth.models.transformers_model import TransformersModel
+from pyhealth.tasks import ClinicalJargonVerification
+from pyhealth.trainer import Trainer
+
+
+def parse_args() -> argparse.Namespace:
+    """Parse CLI arguments for the clinical jargon example.
+
+    Returns:
+        Parsed command-line arguments for dataset root, model choice, task
+        configuration, and training hyperparameters.
+    """
+    parser = argparse.ArgumentParser(
+        description="Clinical jargon verification example with TransformersModel.",
+        epilog=(
+            "Ablation knobs: --benchmark changes the benchmark subset, "
+            "--casi-variant changes the CASI filtering mode, and "
+            "--medlingo-distractors changes the number of negative MedLingo "
+            "candidates."
+        ),
+    )
+    parser.add_argument(
+        "--root",
+        type=str,
+        default=str(
+            PROJECT_ROOT / "test-resources" / "clinical_jargon"
+        ),
+        help="Dataset root containing clinical_jargon_examples.csv.",
+    )
+    parser.add_argument(
+        "--download",
+        action="store_true",
+        help="Fetch the public source assets when the normalized CSV is missing.",
+    )
+    parser.add_argument(
+        "--model-name",
+        type=str,
+        default="emilyalsentzer/Bio_ClinicalBERT",
+        help="Hugging Face model name used by TransformersModel.",
+    )
+    parser.add_argument(
+        "--benchmark",
+        choices=["all", "medlingo", "casi"],
+        default="medlingo",
+        help="Benchmark subset used for the run.",
+    )
+    parser.add_argument(
+        "--casi-variant",
+        choices=["release62", "paper59"],
+        default="release62",
+        help="CASI filtering mode when --benchmark includes CASI.",
+    )
+    parser.add_argument(
+        "--medlingo-distractors",
+        type=int,
+        default=3,
+        help="Number of negative MedLingo candidates retained per sample.",
+    )
+    parser.add_argument("--epochs", type=int, default=1, help="Training epochs.")
+    parser.add_argument("--batch-size", type=int, default=4, help="Batch size.")
+    return parser.parse_args()
+
+
+def main() -> None:
+    """Run one clinical jargon verification configuration."""
+    args = parse_args()
+    dataset = ClinicalJargonDataset(root=args.root, download=args.download)
+    task = ClinicalJargonVerification(
+        benchmark=args.benchmark,
+        casi_variant=args.casi_variant,
+        medlingo_distractors=args.medlingo_distractors,
+    )
+    samples = dataset.set_task(task)
+    print(
+        {
+            "benchmark": args.benchmark,
+            "casi_variant": args.casi_variant,
+            "medlingo_distractors": args.medlingo_distractors,
+            "num_samples": len(samples),
+            "model_name": args.model_name,
+        }
+    )
+    train_dataset, val_dataset, test_dataset = split_by_patient(
+        samples, [0.6, 0.2, 0.2], seed=42
+    )
+    train_loader = get_dataloader(train_dataset, batch_size=args.batch_size, shuffle=True)
+    val_loader = get_dataloader(val_dataset, batch_size=args.batch_size, shuffle=False)
+    test_loader = get_dataloader(test_dataset, batch_size=args.batch_size, shuffle=False)
+
+    model = TransformersModel(dataset=samples, model_name=args.model_name)
+    trainer = Trainer(model=model, enable_logging=False)
+    trainer.train(
+        train_dataloader=train_loader,
+        val_dataloader=val_loader,
+        test_dataloader=test_loader,
+        epochs=args.epochs,
+    )
+    scores = trainer.evaluate(test_loader)
+    print(scores)
+
+
+if __name__ == "__main__":
+    main()

pyhealth/datasets/__init__.py

@@ -65,6 +65,7 @@ def __init__(self, *args, **kwargs):
 from .shhs import SHHSDataset
 from .sleepedf import SleepEDFDataset
 from .bmd_hs import BMDHSDataset
+from .clinical_jargon import ClinicalJargonDataset
 from .support2 import Support2Dataset
 from .tcga_prad import TCGAPRADDataset
 from .splitter import (