Machine Learning for Health Research [ESP80]

Detailed information about this course:

Description

Faculty: Dr. Gennady Roshchupkin, PhD, Dr. Daniel Bos, MD, PhD and Prof. Kamran Ikram, MD PhD

Machine learning (and data science in general) is a multi-disciplinary field in which – besides domain-specific expertise – several fundamental scientific disciplines converge, including mathematics, statistics, computer science, engineering and epidemiology. The aim of this field is to combine scientific methods and algorithms to extract knowledge and insights from structured and unstructured data. Recent advances in technology allow for the collection of enormous amounts of health-related data. Consequently, skills pertaining to handle and manipulate these data and to extract relevant information have become crucial to perform high quality research. Unfortunately, many (clinical) researchers without a technical background frequently experience troubles obtaining or developing these skills. The aim of this course is to bridge this gap in knowledge by providing an interactive and hands-on program about machine learning and how it can be applied in health research.

Objectives

• Understand that epidemiology is at the core of machine learning in healthcare;
• Understand the basics of machine learning methods and neural network algorithms;
• Understand the biases and fairness related to handling and analyzing health-related data.
  

Participant profile

Clinical researchers, clinical epidemiologists, computer scientists, those in health technology assessment or value-based healthcare.

Assessment

Attendance