AI-Based Disease Risk Prediction System Development
Standard screenings miss up to 40% of cases at early stages—a patient with three risk factors receives intervention only after the disease has developed. We build AI models that analyze medical history, genetics, and lifestyle: the system flags risk 2–5 years before clinical onset. For cardiovascular risk, our model achieves an AUC of 0.86—15% higher than the classic SCORE2—and through calibration, the Brier Score decreases by 20%. Preventive medicine becomes tangible: on a population of 100,000 patients, the system can identify up to 2,000 high-risk individuals, preventing hundreds of hospitalizations and saving up to 25 million rubles per year.
Risk Prediction Tasks
- Population screening – identifying high-risk individuals among the entire enrolled population. Applications: type 2 diabetes, CVD, oncology, chronic kidney disease.
- Individual prediction – 10-year cardiovascular event risk. ML models outperform classic risk scores (Framingham, SCORE2) by 15–20% in AUC (a recent meta-study) due to nonlinear interactions and a larger number of predictors.
- Disease progression – early-stage patient: when will they transition to severe? We use survival models (Cox PH, Random Survival Forest, DeepHit) with time-to-event endpoints.
How the AI Model Incorporates Genetic Data?
Genomic data—SNPs for polygenic risk scores. Polygenic risk score (PRS) – a weighted sum of thousands of SNPs. The ML challenge: optimal weighting for a specific population. In our projects, we combine PRS with EHR data: a multimodal model yields an AUC increase of 5–8%.
Why Calibration Matters More Than Accuracy?
A risk score of "68%" must actually mean a 68% probability. After training, we apply Platt scaling or isotonic regression. A calibration plot (reliability diagram) is mandatory. Without calibration, the model can confidently err, which is clinically dangerous.
What Data Sources Are Used?
- Structured EHR data: diagnoses (ICD-10), labs (glucose, HbA1c, lipids), medication orders, vitals, demographics.
- Genomic data: SNPs for polygenic scores. BRCA1/2, ApoE4, PCSK9.
- Lifestyle and social factors: smoking, BMI, diet, stress, education level. From EMR, questionnaires, wearable devices.
| Data Type | Examples | Role in Model |
|---|---|---|
| EHR | Diagnoses, lab, orders | Primary predictors, temporal dynamics |
| Genomic | SNPs, PRS | Additional signal for hereditary risks |
| Lifestyle | Smoking, BMI, activity | Modifiable factors, improve calibration |
How We Build Models: Stack and Approach
- Preprocessing: imputation of missing values, encoding diagnoses, aggregation of time series.
- Feature engineering: creating combined features (e.g., Charlson comorbidity index, lab value time windows).
- Training: XGBoost or LightGBM for tabular data with SHAP interpretation. For time series – Transformer-based models (BEHRT, Med-BERT) pretrained on EMR.
- Calibration: Platt scaling or isotonic regression.
- Validation: temporal split and external validation on data from another clinic.
Comparison: XGBoost + SHAP yields a 1.5x improvement in AUC over logistic regression on the same data, and through calibration the Brier Score decreases by 20%.
Models and Validation
Technical validation details
For tabular EHR data, we use XGBoost and LightGBM—they dominate real medical data. Advantages: handling missing values, interpretability via SHAP, efficiency on small samples. For time series – Transformer-based models (BEHRT, Med-BERT). Pretraining on EMR databases → fine-tuning on specific risk tasks.Evaluation Metrics
| Metric | Clinical Meaning |
|---|---|
| AUC-ROC | Discrimination: separates sick from healthy |
| AUC-PR | Under strong class imbalance (rare events) |
| Brier Score | Overall accuracy of probabilistic predictions |
| Net Benefit / Decision Curve | Clinical utility at threshold decisions |
| NRI, IDI | Improvement vs. existing risk score |
External validation on data from another clinic is mandatory before deployment.
Population health deployment: stratification by risk score → high risk → active outreach (call, screening invitation). In the EHR, the score appears with a SHAP explanation: "CVD risk: 23% (high). Factors: hypertension, dyslipidemia, smoking." Economic impact: on a population of 100,000, we identify 1,500–2,000 high-risk individuals → intervention prevents 200–400 hospitalizations, yielding net savings of 10 to 25 million rubles per year.
What's Included in the Work
- Clinic data and EMR structure analysis.
- Development of a predictive model (XGBoost/Transformer) for the specific outcome.
- Calibration and external validation.
- Integration of risk score into the EMR (REST API + SHAP widget).
- Documentation, clinician training, post-release support for 6 months.
Our team's experience – 5+ years in medical AI, 30+ projects in prediction and EHR processing. We guarantee methodological rigor: all models undergo external validation and calibration.
Get a consultation on deployment: we will send examples of implemented risk score systems under NDA. Contact us for a project assessment.