Technologies / Artificial Intelligence

Machine Learning in biomedical data analysis with R

Comprehensive training in applying Machine Learning to biomedical data analysis using R. Participants will learn caret and tidymodels frameworks for biomedical classification, survival prediction methods, genomic feature selection, clinical decision support systems, and model interpretability (SHAP/LIME) in a medical context.

Why choose this training?

The application of Machine Learning in medicine and bioinformatics opens new possibilities in diagnostics, treatment outcome prediction, and therapy personalization. The growing availability of genomic, clinical, and imaging data makes ML methods an indispensable tool in modern biomedical analysis. At the same time, the specifics of medical data require a particular approach to model interpretability and regulatory compliance.

During the training, participants will learn caret and tidymodels frameworks for biomedical classification, survival prediction methods, genomic feature selection, and clinical decision support system development with emphasis on model interpretability (SHAP/LIME) in a medical context.

After completing the training, participants will be able to: build classification models for biomedical data using caret and tidymodels, apply ML methods for patient survival prediction, perform feature selection from high-dimensional genomic data, design ML-based clinical decision support systems. These competencies directly translate into higher efficiency in IT project execution.

This training is particularly valuable for: data analysts and data scientists in the medical sector, bioinformaticians applying machine learning methods, clinical researchers interested in treatment outcome prediction.

What sets our approach apart?

At EITT, we believe the best learning happens through practice. During 3 days of intensive training, participants work on real-world examples and scenarios, ensuring not only theoretical understanding but above all the ability to apply it in practice.

With over 2,500 trainings in our portfolio and a 4.8/5 rating from participants, EITT is a trusted partner in competency development for organizations of all sizes. Our trainers are practitioners with years of experience who share current knowledge and proven solutions.

Looking for training tailored to your team’s needs? Contact us — we’ll prepare a program customized to your requirements.

Benefits

Build classification models for biomedical data using caret and tidymodels
Apply ML methods for patient survival prediction
Perform feature selection from high-dimensional genomic data
Design ML-based clinical decision support systems
Interpret ML model results using SHAP and LIME
Validate clinical models for calibration and discrimination
Integrate multi-omics data in machine learning models
Document ML models in compliance with audit requirements

Who is this training for?

Data analysts and data scientists in the medical sector

Bioinformaticians applying machine learning methods

Clinical researchers interested in treatment outcome prediction

AI specialists in pharmaceutical and biotechnology companies

R programmers developing biomedical ML competencies

Medical statisticians expanding their knowledge of ML methods

Research project managers with AI components

Prerequisites

Intermediate knowledge of the R language
Basic understanding of statistics and machine learning
Familiarity with biomedical data analysis basics (recommended)
Own laptop with R and RStudio installed

Training program

Specifics of machine learning in medicine and bioinformatics
Framework overview: caret, tidymodels, mlr3
Preparing biomedical data for modeling
Cross-validation and clinical data splitting strategies
Model evaluation metrics in a medical context (AUC, sensitivity, specificity)

Building classification models for clinical data
Random Forest, SVM, and XGBoost in medical diagnostics
Handling imbalanced classes in medical data
Hyperparameter tuning with tidymodels
Ensemble methods in biomedical classification

ML models for survival prediction (survival forests, DeepSurv)
Feature selection from high-dimensional genomic data
Regularization (Lasso, Elastic Net) for -omics data
Dimensionality reduction (PCA, t-SNE, UMAP) for biomedical data
Multi-omics data integration in ML models

Building predictive models for clinical endpoints
Nomograms and risk models in R
Clinical model validation (calibration, discrimination)
Integrating ML models into clinical workflows
Ethical aspects of applying AI in medicine

SHAP and LIME in the medical data context
Partial Dependence Plots and Feature Importance
Model documentation for regulatory audits
Reproducibility of ML analyses in biomedical research
Deploying ML models as clinical tools

Delivery Methods

Online

Convenience of participating from anywhere
Interactive live sessions with trainer
Materials available for 30 days
No travel costs

On-site

Direct contact with trainer and group
Intensive hands-on workshops
Networking with other participants
Full focus on learning

Training Topics

Frequently asked questions

What prerequisites do I need to meet before the training?

Intermediate knowledge of R and basic understanding of statistics and machine learning are required. Familiarity with biomedical data analysis basics is recommended but not mandatory — key concepts will be introduced during the training.

What format is the training delivered in and how long does it last?

The training lasts 3 days and is available in online (live) and on-site formats. The program combines theoretical lectures with intensive hands-on exercises using real biomedical datasets. Participants receive training materials, ready-to-use R scripts, and a completion certificate.

Who is this training designed for?

The training is designed for data analysts and data scientists in the medical sector, bioinformaticians applying ML methods, clinical researchers interested in treatment outcome prediction, and AI specialists in pharmaceutical and biotechnology companies. It is also suitable for medical statisticians looking to expand their competencies with machine learning methods.