Datasets:
license: mit
language:
- en
tags:
- healthcare
- clinical-ai
- synthetic-data
- algorithmic-fairness
- clinical-decision-support
- model-evaluation
pretty_name: RISED Synthetic Clinical Cohort (10K)
size_categories:
- 10K<n<100K
task_categories:
- tabular-classification
configs:
- config_name: default
data_files: synthetic_cohort_10k.csv
RISED Synthetic Clinical Cohort (10,000 patients)
A fully synthetic adult clinical cohort generated deterministically
(random_state = 42) by a Synthea-inspired computational model implemented in
the rised Python
package. No real patient records were used at any stage. The cohort is
intended as a methodological testbed for the RISED Framework and is
demographically heterogeneous to support subgroup-level evaluation.
This is the reference dataset used in the demonstration application of the RISED Framework — a five-dimension pre-deployment evaluation framework for clinical AI decision-support systems (Reliability, Inclusivity, Sensitivity, Equity, Deployability).
Dataset Summary
| Attribute | Value |
|---|---|
| Patients (rows) | 10,000 |
| Features (cols) | 26 |
| Outcome prevalence | 30.0% positive class (3,000 patients) |
| License | MIT |
| Source code | github.com/anonymousreview111/rised-healthcare-eval |
| Generator | Synthea-inspired, deterministic (seed = 42) |
Demographic Composition
| Group | Full cohort | Outcome=1 (n=3,000) |
|---|---|---|
| Age 18–44 | 18.4% | 0.2% |
| Age 45–64 | 25.0% | 9.2% |
| Age 65–74 | 28.2% | 31.6% |
| Age 75+ | 28.4% | 59.0% |
| Female / Male | 55.5% / 44.5% | 55.8% / 44.2% |
| White | 63.8% | 63.3% |
| Black | 13.4% | 13.5% |
| Hispanic | 13.0% | 13.1% |
| Asian | 5.7% | 5.9% |
| Other | 4.1% | 4.3% |
| Insurance: Public-major | 47.4% | 74.2% |
| Insurance: Public-secondary | 14.4% | 7.7% |
| Insurance: Private | 29.8% | 13.8% |
| Insurance: Uninsured | 8.4% | 4.4% |
Mean Charlson Comorbidity Index: 0.99 ± 1.20 (full); 1.86 ± 1.33 (outcome=1).
Features
Demographics: age, sex_male, age_group, sex, race, insurance,
ins_medicare, ins_medicaid, ins_private (insurance-type indicators
included as a demographic axis on which to evaluate subgroup performance)
Clinical: cci_score, has_hypertension, has_diabetes, has_chf,
has_ckd, has_copd, has_mi, has_cvd, has_dementia, has_cancer,
has_metastatic, prior_hosp_count, ed_visits_count, bmi
Neighborhood: adi_score (deprivation index, 1–100 scale)
Outcome: high_need (binary; 1 = top-30% derived clinical risk score).
The column is named high_need for backward compatibility with earlier
versions of the codebase; it represents a generic adverse-clinical-outcome
label and the cohort is not specific to any particular clinical use case
or deployed risk-stratification program.
Outcome Definition
The binary outcome label is derived from a logistic transformation of age, diabetes, congestive heart failure, chronic kidney disease, COPD, prior myocardial infarction, CCI, prior hospitalization count, ED utilization count, and the deprivation index, with additive Gaussian noise (σ=0.5). Patients in the top 30% of the predicted score receive label = 1.
Important: Because the outcome is derived directly from the feature space, this dataset is suitable for evaluation framework demonstrations but not for benchmarking model accuracy on a real-world prediction task. Real EHR cohorts introduce distribution shifts and access-barrier distortions absent from synthetic data.
Intended Use
- Primary: Demonstrating the RISED Framework for pre-deployment evaluation of clinical AI decision-support systems.
- Secondary: Teaching, methodological development, and reproducibility benchmarking for fairness, calibration, and sensitivity tooling.
Not intended for: training production clinical models, benchmarking discrimination performance against real-world systems, or any deployed clinical use.
Usage
from datasets import load_dataset
ds = load_dataset("anonymousreview111/rised-healthcare-eval-dataset")
df = ds["train"].to_pandas()
print(df.shape) # (10000, 26)
print(df["high_need"].mean()) # 0.30
Or load directly with pandas:
import pandas as pd
df = pd.read_csv(
"hf://datasets/anonymousreview111/rised-healthcare-eval-dataset/synthetic_cohort_10k.csv"
)
To regenerate from source (deterministic):
from rised.datasets import generate_synthea_cohort
df = generate_synthea_cohort(n=10000, random_state=42)
Citation
If you use this dataset, please cite the accompanying paper:
@article{anonymous2026rised,
author = {Anonymous, Author},
title = {{RISED}: A Pre-Deployment Safety Evaluation Framework for Clinical {AI}
Decision-Support Systems},
year = {2026},
journal = {Artificial Intelligence in Medicine (under review)},
url = {https://github.com/anonymousreview111/rised-healthcare-eval}
}
License
MIT. The dataset is fully synthetic and contains no information derived from real patients; redistribution and derivative works are unrestricted under MIT terms.
Limitations
- Synthetic only. Distributions reflect a generative model, not real-world epidemiology. Results obtained on this cohort do not generalize to real clinical populations without further validation.
- Self-derived outcome. The outcome label is a function of the feature space, so high accuracy is expected and does not indicate real predictive skill. Use for methodology evaluation only.
- Single random seed. All values are deterministic at seed = 42; future versions may include alternative seeds.