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--- |
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license: apache-2.0 |
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task_categories: |
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- question-answering |
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- text-generation |
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language: |
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- en |
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tags: |
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- medical |
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- ehr |
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- synthetic |
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- dspy |
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- gepa |
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size_categories: |
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- n<1K |
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dataset_info: |
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features: |
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- name: query |
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dtype: string |
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- name: expected_answer |
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dtype: string |
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- name: expected_strategy |
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dtype: string |
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- name: expected_snomed_codes |
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list: string |
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- name: expected_demographics |
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struct: |
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- name: age_filter |
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dtype: string |
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- name: filter |
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dtype: string |
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- name: gender_filter |
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dtype: string |
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- name: expected_neo4j_count |
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dtype: int64 |
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- name: query_complexity |
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dtype: string |
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- name: medical_category |
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dtype: string |
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- name: expected_excluded_conditions |
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list: 'null' |
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splits: |
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- name: train |
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num_bytes: 35562 |
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num_examples: 210 |
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- name: validation |
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num_bytes: 8680 |
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num_examples: 45 |
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- name: test |
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num_bytes: 8629 |
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num_examples: 46 |
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download_size: 28142 |
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dataset_size: 52871 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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- split: validation |
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path: data/validation-* |
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- split: test |
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path: data/test-* |
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--- |
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# Medical EHR Training Dataset |
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Training dataset for Medical EHR GEPA-optimized module. |
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## Dataset Description |
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This dataset contains **382 medical EHR query examples** for training DSPy GEPA optimization. |
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### Dataset Structure |
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``` |
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{ |
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"query": "Show me diabetic patients", |
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"expected_strategy": "ENRICHMENT", |
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"expected_snomed_codes": ["73211009", "44054006"], |
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"expected_neo4j_count": 15, |
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"query_complexity": "simple", |
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"medical_category": "endocrine" |
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} |
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``` |
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### Splits |
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| Split | Examples | |
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|-------|----------| |
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| train | 267 | |
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| validation | 57 | |
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| test | 58 | |
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| **Total** | **382** | |
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### Complexity Distribution |
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- **Simple**: 58% (basic demographic or single-condition queries) |
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- **Moderate**: 40% (multi-filter or combined queries) |
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- **Complex**: 2% (multi-condition with exclusions and lab filters) |
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### Medical Categories |
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17 categories covering: |
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- Demographic filters (38%) |
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- Cardiovascular (12%) |
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- Neurological (11%) |
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- Respiratory (8%) |
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- Endocrine (8%) |
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- And 12 more... |
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## Usage |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset("dafesmi/medical-ehr-training-data") |
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# Access splits |
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train_data = dataset['train'] |
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val_data = dataset['validation'] |
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test_data = dataset['test'] |
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print(f"Training examples: {len(train_data)}") |
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``` |
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## License |
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Apache 2.0 |
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