Workshop "AI in Biomedicine: Concepts and Applications" at the Life Science PhD Meeting

This interactive workshop introduces participants to the core concepts of artificial intelligence (AI), with a focus on its models and applications in biomedicine. Guided by the instructors, participants will work on AI-related assignments and present their results to the group. The workshop is open to beginners and those with some prior knowledge, but an interest in mathematical models is recommended. More information can be found here.

Organizers:

• Univ.-Prof. Dr. Clemens Dlaska, PhD
• Univ.-Prof. Dr. Sebastian Schönherr

Instructors:

• Priv.-Doz. Matteo Cesari, PhD
• Matthias Perkonigg, PhD
• Patrick Rockenschaub, PhD

Prerequisites

• Bring your own laptop and have a Google Account set up
• Set up Eduroam

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General

The AI in Biomedicine: Concepts and Applications workshop consists of specialized sessions, designed to introduce students to key applications of artificial intelligence in the biomedical field. Following this first session, students will be divided into three groups.

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Foundations of AI

Instructors:

• Clemens Dlaska – Digital Cardiology Lab, University Clinic of Internal Medicine III, Medical University of Innsbruck
• Sebastian Schönherr – Institute of Genetic Epidemiology, Medical University of Innsbruck

The workshop begins with a general introduction session, where all participants will come together to gain a foundational understanding of AI and machine learning. This session will cover core concepts, methodologies, and real-world applications, ensuring that students have the necessary background before diving into specific topics.

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Hands-on Machine Learning for Automatic Sleep Analysis

Instructor

• Matteo Cesari – Department of Neurology, Medical University of Innsbruck

Description

During this practical session, students will learn how sleep is recorded and scored and will use a validated automatic machine learning algorithm to analyze sleep recordings. A step-by-step analysis will guide the students to understand how the machine learning algorithm works, its pros and its cons. —

Machine Learning Tools for Medical Image Analysis

Instructor

• Matthias Perkonigg – Institute of Clinical Epidemiology, Public Health, Health Economics, Medical Statistics and Informatics, Medical University of Innsbruck

Description

In this session, students will explore several machine learning-based tools for analyzing medical images, with a focus on radiological and histopathological images. We will examine the strengths and limitations of these tools and discuss their potential applications in the participants’ research areas.

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Machine Learning for Electronic Health Records

Instructor

• Patrick Rockenschaub – Institute of Clinical Epidemiology, Public Health, Health Economics, Medical Statistics and Informatics, Medical University of Innsbruck

Description

Electronic health records (EHR) contain a wealth of patient information, including diagnoses, lab results, and medications. In this session, students will explore how machine learning can be applied to EHR data for clinical predictions and decision support. Through a hands-on case study and interactive discussion of intensive care data, we will examine key challenges and opportunities in this field.