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MedIntel-clinical-risk-models
Sushantak17/MedIntel-clinical-risk-models
MedIntel-clinical-risk-models is a tabular classification model from Sushantak17. Use it for the tabular classification task on the model card, and read the license before you ship it in a product. It is set up for xgboost. The card lists the license as mit.
Three calibrated XGBoost models for predicting risk of Diabetes, Chronic Kidney Disease (CKD), and Anemia from standard laboratory blood tests.
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Public
Updated Jul 6, 2026
Repo size
9.1 MB
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Repo makeup
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.joblib9.1 MB · 100%
At a glance
- Task
- Tabular Classification
- Library
- xgboost
- License
- mit
- Access
- Public
- Created
- Jul 6, 2026
- Updated
- Jul 6, 2026
- SHA
- b75a19b1
Datasets
Model card
From the Hugging Face model README
MedIntel — Clinical Risk Prediction Models
Three calibrated XGBoost models for predicting risk of Diabetes, Chronic Kidney Disease (CKD), and Anemia from standard laboratory blood tests.
Model Details
| Property | Value |
|---|---|
| Algorithm | XGBoost (Gradient Boosting) |
| Calibration | Platt Scaling (CalibratedClassifierCV) |
| Features | 35 (13 base + 22 trend) |
| Training Data | NHANES 2017-2018 (~5,000 adults) |
| Explainability | SHAP TreeExplainer |
| Framework | scikit-learn, XGBoost, joblib |
Performance (Test Set, n=4,909)
| Model | AUROC | Brier | F1 | Threshold |
|---|---|---|---|---|
| Diabetes | 1.0000 | 0.0077 | 0.9918 | 0.402 |
| CKD | 0.9999 | 0.0026 | 0.9962 | 0.465 |
| Anemia | 1.0000 | 0.0010 | 0.9981 | 0.892 |
Files
| File | Description | Size |
|---|---|---|
xgb_diabetes.joblib | Raw XGBoost diabetes model | ~563 KB |
xgb_ckd.joblib | Raw XGBoost CKD model | ~449 KB |
xgb_anemia.joblib | Raw XGBoost anemia model | ~279 KB |
calibrator_diabetes.joblib | Platt-calibrated diabetes model | ~3.3 MB |
calibrator_ckd.joblib | Platt-calibrated CKD model | ~2.6 MB |
calibrator_anemia.joblib | Platt-calibrated anemia model | ~1.7 MB |
evaluation_report.txt | Full evaluation metrics | — |
Usage
import joblib
import numpy as np
# Load calibrated model
data = joblib.load("calibrator_diabetes.joblib")
model = data["calibrated_model"]
features = data["features"]
# 35-feature input vector (13 base + 22 trend features)
# Base: age, sex_encoded, bmi, hba1c, creatinine, albumin, egfr,
# wbc, rbc, hemoglobin, hematocrit, systolic_bp, diastolic_bp
# Trend: {param}_slope, {param}_delta for each lab parameter
X = np.array([[50, 0, 31.4, 8.2, 1.0, 4.0, 91.7, 7.2, 4.9, 14.5, 43.0, 142, 88,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])
prob = model.predict_proba(X)[:, 1]
print(f"Diabetes risk: {prob[0]:.1%}") # ~94%
SHAP Explainability
import shap
raw = joblib.load("xgb_diabetes.joblib")
explainer = shap.TreeExplainer(raw["model"])
shap_values = explainer.shap_values(X)
# Positive SHAP = pushes toward high risk
# Negative SHAP = pushes toward low risk
Feature Importance (by SHAP)
| Diabetes | CKD | Anemia |
|---|---|---|
| HbA1c | eGFR | Hemoglobin |
| Age | RBC | Hematocrit |
| BMI | BMI | Sex |
| Creatinine | Creatinine | Albumin |
| WBC | Age | RBC |
Training Pipeline
Fully reproducible 9-step pipeline in the MedIntel repository:
- Download NHANES 2017-2018 from CDC
- Merge 8 tables, compute eGFR (CKD-EPI 2021), impute missing values
- Construct labels (ADA, KDIGO, WHO criteria)
- Synthetic longitudinal augmentation (15% of patients)
- Train XGBoost with stratified 5-fold CV
- Platt scaling calibration
- Full evaluation (AUROC, PR-AUC, Brier, subgroup analysis)
- Download BC5CDR corpus for NER
- Fine-tune BioBERT for biomedical NER
Intended Use
- Clinical decision support for healthcare providers
- Risk screening in telemedicine settings
- Research on multi-disease prediction
Limitations
- Trained on US population (NHANES) — may not generalize globally
- Trend features are synthetic (NHANES is cross-sectional)
- Not a substitute for clinical diagnosis
Citation
@software{medintel2026,
title={MedIntel: AI-Powered Clinical Risk Intelligence Platform},
author={Jha, Sushantak Parashar},
year={2026},
url={https://github.com/Sushantak17/MedIntel}
}
- Task
- Tabular Classification
- Library
- xgboost
- License
- mit
- Languages
- en
- Created
- Jul 6, 2026
- Updated
- Jul 6, 2026