Master the complete ML pipeline on the InsilicoΣ platform. No programming required. Real clinical context throughout. Walk away building, evaluating, and interpreting clinical ML models.
From clinical data literacy to critical appraisal - a complete foundation
Explain what clinical ML is, when it is appropriate, and when it is not
Perform systematic EDA on clinical tabular data
Apply imputation, encoding, scaling, and feature selection strategies
Train classification, regression, and survival models on the platform
Select the right module: structured, time-series, NLP, or imaging
Interpret outputs using SHAP values, feature importance, and decision curves
Detect and quantify algorithmic bias across demographic subgroups
Critically evaluate published clinical ML studies
This workshop is for anyone who has clinical data and wants to build ML models - no programming needed. Everything is done through an intuitive UI with point-and-click selections.
MDs and PhDs who have collected patient data and want to apply ML to predict outcomes, classify diseases, or find patterns - using a guided, code-free interface.
Students working on research projects or theses who need to apply ML methods to their datasets. Learn by doing - no prior ML experience needed.
Biomedical and health sciences researchers who want to add ML analysis to their publications. The platform handles preprocessing, training, and interpretation for you.
Data managers and research nurses who collect clinical data and want to understand how it translates to ML models - and how data quality impacts results.
Prerequisites: Comfort with spreadsheets and basic statistics (p-values, sensitivity/specificity). No Python, R, or ML knowledge required.
A complete clinical ML environment - from raw data to interpretable, validated models
Upload your clinical data in any of these four formats
Classic CSV or Excel datasets with rows (patients) and columns (features)
Temporal data with timestamps - ICU monitoring, wearable sensors, longitudinal records
Unstructured clinical text - notes, reports, discharge summaries for classification or NER
Image-based data for classification, segmentation, or detection tasks
Choose from 15+ algorithms - the platform handles the rest
Predict categories - disease vs healthy, drug response, risk stratification
Predict continuous values - dosage, survival time, biomarker levels
Optimize your model automatically with smart search strategies
Everything you need to understand, trust, and present your model
Comprehensive evaluation with confidence intervals
Understand why your model makes each prediction
Ensure your model is fair, calibrated, and clinically useful
Publication-ready charts and plots generated automatically
Compare multiple algorithms side-by-side with statistical tests
April 29–30, 2026 · Lectures in the morning, hands-on labs in the afternoon
| Time | Session | Topic |
|---|---|---|
| 08:30 - 09:00 | Registration | Welcome, credentials, dataset download |
| 09:00 - 09:30 | Plenary | Introduction: Why Clinical ML Now? |
| 09:30 - 10:30 | Session 1 | The Clinical ML Landscape - terminology, workflow, common pitfalls |
| 10:30 - 10:50 | Break | |
| 10:50 - 12:00 | Session 2 | Understanding Your Clinical Data - types, quality, bias sources |
| 12:00 - 13:00 | Break | |
| 13:00 - 14:00 | Session 3 | EDA on the Platform - demo + guided lab |
| 14:15 - 15:15 | Session 4 | Preprocessing: Imputation, Encoding, Scaling |
| 15:30 - 16:30 | Session 5 | Your First Model: Train, Evaluate, Export |
| 16:30 - 17:30 | Labs 1 & 2 | Hands-on: EDA + Preprocessing + First Model (heart failure dataset) |
| 17:30 - 18:00 | Debrief | Q&A, discussion, preview of Day 2 |
| Time | Session | Topic |
|---|---|---|
| 08:30 - 09:00 | Welcome | Day 1 recap, Q&A from overnight |
| 09:00 - 10:00 | Session 6 | Choosing the Right Module - structured vs time-series vs NLP vs imaging |
| 10:20 - 11:20 | Session 7 | Optimization: Cross-validation, hyperparameters, class imbalance |
| 11:20 - 12:00 | Lab 3 | Hands-on: Optimization Lab (sepsis dataset) |
| 12:00 - 13:00 | Break | |
| 13:00 - 14:00 | Session 8 | Model Interpretation: SHAP, PDP, Decision Curve Analysis |
| 14:00 - 15:00 | Session 9 | Bias, Fairness & Subgroup Performance |
| 15:20 - 16:00 | Lab 4 | Hands-on: Interpretation + Bias Lab |
| 16:45 - 17:15 | Case Discussion | Live critique of a published clinical ML paper |
| 17:15 - 18:00 | Closing | Key takeaways, next steps, certificates |
Includes 30-day full access to the InsilicoΣ Clinical ML platform after the workshop
Per participant
Per participant
April 29–30, 2026
Complete the form below. We'll send confirmation and payment details to your email.
Chrome, Firefox, or Edge. The platform is fully browser-based, no installations needed.
Comfortable with p-values, confidence intervals, sensitivity/specificity concepts.
No prior ML experience required. We start from zero and build up systematically.