| --- |
| license: other |
| task_categories: |
| - text-classification |
| - question-answering |
| language: |
| - en |
| tags: |
| - medical |
| - clinical-reasoning |
| - pulmonology |
| - safety-evaluation |
| - benchmark |
| - lung cancer |
| - lungcancer |
| - lungCancerScreaning |
| size_categories: |
| - 1K<n<10K |
| --- |
| # PulmoBench: A Structured Clinical Reasoning Benchmark for Pulmonology |
|
|
| ## Overview |
| **PulmoBench** is a structured benchmark designed to evaluate Large Language Models (LLMs) in clinical risk stratification, safety-aware pulmonary reasoning, and zero-shot diagnostic generalization. |
|
|
| The benchmark features two evaluation tracks: |
| 1. **Core Benchmark (`train`, `validation`, `test`):** Synthesized natural-language clinical vignettes with structured risk tiers, escalation flags, and systematic perturbations (lexical rephrasing, symptom omission, contradictory cues). |
| 2. **External Validation (`External_validation`):** Real-world patient records deterministically mapped from the Biswas & Nath clinical dataset into natural language narrative vignettes for out-of-distribution evaluation. |
| |
| --- |
| |
| ## Dataset Structure |
| |
| ### 1. Core Benchmark Splits (`train`, `validation`, `test`) |
| | Field | Type | Description | |
| | :--- | :--- | :--- | |
| | `case_id` | `int` | Unique case identifier | |
| | `age` | `int` | Patient age in years | |
| | `sex` | `str` | Biological sex (`male`, `female`) | |
| | `risk_tier` | `str` | Ordinal risk category (`low`, `medium`, `high`) | |
| | `risk_score` | `int` | Normalized risk score (`0–2`) | |
| | `urgency` | `str` | Recommended triage urgency level (`routine_followup`, `specialist_consult`, `urgent_ct_scan`, `immediate_referral`) | |
| | `gold_differential` | `list[str]` | Ground truth differential diagnoses | |
| | `escalation_required`| `bool` | Safety flag denoting whether immediate medical escalation is required | |
| | `vignette` | `str` | Clinical patient narrative presented to the model | |
| | `perturbation_type` | `str` | Stress-test variant (`original`, `lexical_rephrase`, `symptom_removed`, `contradictory_signal`) | |
| |
| ### 2. External Validation Split (`External_validation`) |
| Generated from raw tabular clinical records containing 13 clinical symptoms/lifestyle factors (smoking, yellow fingers, chronic disease, wheezing, chest pain, etc.) transformed into standard narrative vignettes. |
| |
| | Field | Type | Description | |
| | :--- | :--- | :--- | |
| | `case_id` | `int` | Original row index from source cohort | |
| | `age` | `int` | Patient age in years | |
| | `sex` | `str` | Biological sex (`male`, `female`) | |
| | `vignette` | `str` | Natural language case presentation constructed from reported symptoms | |
| | `gold_lung_cancer` | `bool` | Ground-truth diagnostic confirmation (`True` / `False`). **Do not include in prompt input.** | |
| |
| --- |
| |
| ## Loading the Dataset |
| ```python |
| from datasets import load_dataset |
| dataset = load_dataset("saibhossain/PulmoBench") |
| print(dataset) |
| ``` |
| |
| output : |
| |
| ```json |
| DatasetDict({ |
| train: Dataset({ |
| features: ['case_id', 'age', 'sex', 'risk_tier', 'risk_score', 'urgency', 'gold_differential', 'escalation_required', 'vignette', 'perturbation_type'], |
| num_rows: 2544 |
| }) |
| validation: Dataset({ |
| features: ['case_id', 'age', 'sex', 'risk_tier', 'risk_score', 'urgency', 'gold_differential', 'escalation_required', 'vignette', 'perturbation_type'], |
| num_rows: 544 |
| }) |
| test: Dataset({ |
| features: ['case_id', 'age', 'sex', 'risk_tier', 'risk_score', 'urgency', 'gold_differential', 'escalation_required', 'vignette', 'perturbation_type'], |
| num_rows: 548 |
| }) |
| }) |
| ``` |
| ## Loading External Validation |
| ```python |
| from datasets import load_dataset |
| |
| ext_val = load_dataset( |
| "saibhossain/PulmoBench", |
| data_files={"external_validation": "External_validation/lung_cancer_external.jsonl"} |
| ) |
| print(ext_val) |
| ``` |
| |
| -- |
| |
| ### Task Definitions & Evaluation |
| Given a clinical **vignette**, models are evaluated on: |
| 1. Clinical Risk Stratification: Predicting **risk_tier** and triage **urgency**. |
| 2. Safety Compliance: Correctly identifying **escalation_required** without hallucinating or under-triaging critical findings. |
| 3. Robustness: Stability under adversarial perturbations (**perturbation_type**). |
| 4. External Generalization: Zero-shot binary detection of lung cancer risk on the **External_validation** cohort. |
| |
| ### Intended Use & Safety Notice |
| PulmoBench is curated strictly for academic research, benchmark evaluation, and robustness testing of LLMs in biomedical contexts. |
| |
| > Clinical Disclaimer: This benchmark is not intended for direct clinical deployment, diagnostic decision-making, or real-world patient management. |
| |
| Citations & Acknowledgments |
| -- |
| |
| ### Source Dataset |
| If you use PulmoBench or its external validation set, please cite the underlying sources: |
| |
| ```bash |
| Derived from: [Lung Cancer Prediction](https://www.kaggle.com/datasets/thedevastator/cancer-patients-and-air-pollution-a-new-link) |
| ``` |
| |
| ```bash |
| @misc{mr__abhinaba_biswas_mr__akash_nath_2024, |
| title={Lung Cancer Dataset}, |
| url={[https://www.kaggle.com/dsv/8795028](https://www.kaggle.com/dsv/8795028)}, |
| DOI={10.34740/KAGGLE/DSV/8795028}, |
| publisher={Kaggle}, |
| author={Mr. Abhinaba Biswas and Mr. Akash Nath}, |
| year={2024} |
| } |
| ``` |
| ```bash |
| @misc{pulmobench2026, |
| title={PulmoBench: A Structured Clinical Reasoning Benchmark for Pulmonology}, |
| author={Hossain, Saib}, |
| year={2026}, |
| publisher={Hugging Face}, |
| howpublished={\url{[https://huggingface.co/datasets/saibhossain/PulmoBench](https://huggingface.co/datasets/saibhossain/PulmoBench)}} |
| } |
| ``` |
| |
| ### License |
| |
| Other (specified in description) |
