Clinical prediction models are statistical and computational tools developed to assess disease risk, anticipate treatment outcomes, and support clinical decision-making.
Machine learning methods expand what is possible in model development, but building a reliable model is not only a matter of choosing the right algorithm.
Data preparation, model validation, performance measurement, calibration, interpretability, and clinical utility assessment are core parts of the development process.
Data4Care’s Clinical Prediction Models and Machine Learning training teaches participants how to develop and evaluate prediction models in clinical research through hands-on practice.
Training Content
1. Introduction to Clinical Prediction Models
- Diagnostic and prognostic prediction models
- Risk prediction and classification problems
- Differences between statistical models and machine learning
- Defining the clinical research question
2. Data Preparation and Model Planning
- Defining the target variable and predictors
- Missing data management
- Feature engineering
- Preventing data leakage
- Creating training and test datasets
3. Model Development
- Logistic regression
- Regularized regression models
- Decision trees and Random Forest
- Gradient Boosting and XGBoost
- Hyperparameter optimization
- Comparing models
4. Model Validation
- Cross-validation
- Bootstrap methods
- Internal and external validation
- Assessing overfitting
- Model generalizability
5. Evaluating Model Performance
- ROC curve and AUC
- Sensitivity and specificity
- Positive and negative predictive values
- Calibration analysis
- Prediction errors
- Threshold selection
- Decision curve analysis and clinical utility
6. Explainability and Scientific Reporting
- Variable importance
- SHAP analyses
- Clinical interpretation of model results
- TRIPOD+AI framework
- Reporting model development and validation results in scientific papers
Who Can Attend?
Clinical researchers, physicians, academics, biostatisticians, health data analysts, graduate researchers, and professionals who want to develop models with clinical data.
Prerequisites
Basic statistics knowledge is recommended. Beginner-level knowledge of R or Python is helpful for the applied programming modules.
Our Training Approach
The core stages of model development are examined through clinical research problems in a practice-oriented way.
Participants learn to evaluate not only prediction accuracy, but also validation results, calibration, interpretability, and potential clinical use.
After the Training
Participants should be able to plan a clinical prediction model development process, compare basic statistical and machine learning models, and evaluate model performance with scientific criteria.
Want to learn how to build machine learning models with your clinical data?
Get information on training content, practice level, and institutional program options.