---
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)