Add comprehensive dataset card

README.md

@@ -1,37 +1,126 @@
 ---
-dataset_info:
-  features:
-  - name: prompt
-    dtype: string
-  - name: prompt_type
-    dtype: string
-  - name: choices
-    sequence: string
-  - name: answer_idx
-    dtype: int64
-  - name: completion
-    dtype: string
-  - name: id
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 164339986
-    num_examples: 17158
-  - name: validation
-    num_bytes: 35359225
-    num_examples: 3754
-  - name: test
-    num_bytes: 35126706
-    num_examples: 3683
-  download_size: 32988983
-  dataset_size: 234825917
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: validation
-    path: data/validation-*
-  - split: test
-    path: data/test-*
+license: mit
+task_categories:
+- question-answering
+- multiple-choice
+language:
+- en
+tags:
+- medical
+- healthcare
+- ehr
+- diagnosis
+- medication
+- clinical
+size_categories:
+- 10K<n<100K
 ---
+
+# Medical Question Answering Dataset (QA Pairs MVD 10K)
+
+## Dataset Description
+
+This dataset contains medical question-answering tasks based on Electronic Health Record (EHR) data. The dataset focuses on three main prediction tasks in clinical settings:
+
+1. **Missing Medication MCQ**: Predicting which medication should be added to a patient's current regimen
+2. **Next Diagnosis MCQ**: Predicting the most likely future diagnosis for a patient  
+3. **Next Measurement Value MCQ**: Predicting future laboratory or vital sign values
+
+## Dataset Structure
+
+### Data Splits
+
+| Split | Examples |
+|-------|----------|
+| Train | 17,158 |
+| Validation | 3,754 |
+| Test | 3,683 |
+| **Total** | **24,595** |
+
+### Data Fields
+
+- `prompt`: The full question prompt including patient medical history
+- `prompt_type`: Type of question (missing_medication_mcq, next_diagnosis_mcq, next_measurement_value_mcq)
+- `choices`: List of multiple choice options (typically 5 options A-E)
+- `answer_idx`: Index of the correct answer (0-based)
+- `completion`: The correct answer choice letter (A, B, C, D, or E)
+- `id`: Unique identifier for each example
+
+### Example
+
+```json
+{
+  "prompt": "You are an assistant tasked with analyzing medical histories to determine which medication is missing from the patient's current regimen....",
+  "prompt_type": "missing_medication_mcq",
+  "choices": ["Cisplatin 50 MG Injection", "Tacrine 10 MG Oral Capsule", ...],
+  "answer_idx": 4,
+  "completion": "E",
+  "id": "2543984390693637980missing_medication_mcq"
+}
+```
+
+## Task Types
+
+### 1. Missing Medication MCQ
+Analyzes a patient's medical history including demographics, visits, measurements, procedures, and current medications to predict which medication should be added to their regimen.
+
+### 2. Next Diagnosis MCQ  
+Predicts the most likely future diagnosis based on a patient's medical trajectory and history.
+
+### 3. Next Measurement Value MCQ
+Predicts future laboratory values or vital signs based on historical measurement trends.
+
+## Patient Data Structure
+
+Each prompt includes structured patient data with:
+- **Demographics**: Race, gender, year of birth
+- **Visit History**: Outpatient visits, ER visits with dates
+- **Measurements**: Height, weight, BMI, blood pressure, lab values with timestamps
+- **Procedures**: Medical procedures performed with dates
+- **Medications**: Current and past medications with start/end dates
+- **Diagnoses**: Prior medical conditions with dates
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+# Load the dataset
+dataset = load_dataset("your_username/qa-pairs-mvd-10k")
+
+# Access different splits
+train_data = dataset['train']
+val_data = dataset['validation'] 
+test_data = dataset['test']
+
+# Example usage
+example = train_data[0]
+print(f"Question type: {example['prompt_type']}")
+print(f"Prompt: {example['prompt'][:200]}...")
+print(f"Choices: {example['choices']}")
+print(f"Correct answer: {example['completion']}")
+```
+
+## Ethical Considerations
+
+This dataset contains synthetic or anonymized medical data. Users should:
+- Ensure compliance with healthcare data regulations (HIPAA, etc.)
+- Use the dataset responsibly for research and educational purposes
+- Not use for actual medical diagnosis without proper validation
+- Consider potential biases in the synthetic data generation process
+
+## Citation
+
+If you use this dataset in your research, please cite:
+
+```bibtex
+@dataset{qa_pairs_mvd_10k,
+  title={Medical Question Answering Dataset (QA Pairs MVD 10K)},
+  year={2024},
+  url={https://huggingface.co/datasets/your_username/qa-pairs-mvd-10k}
+}
+```
+
+## License
+
+This dataset is released under the MIT License.