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