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Machine Learning for Mobile Health

An introduction to artificial intelligence and the concepts of mobile health

Julius-Maximilians-University Würzburg (JMU) |
Institute for Clinical Epidemiology & Biometry | Medical Informatics

This module is part of the certificate ”Secure Development of Medical Information Systems”

Teaching Language: English or German depending on class attendance

Workload: 0h presence / 35h online / 145h self-study = 180h total1 (6 ETCS)

Target Group: The course is aimed at medical students, physicians or medical scientists and computer science students

Pretty young student studying at home sitting at her dining table with a large binder of notes checking something on the screen of her laptop computer
top view of Medicine doctor hand working with modern computer and smart phone on wooden desk as medical concept-1
Focused classmates studying together and using laptop in library

Consultation & Registration:
If you have any questions, please do not hesitate to contact us:

Form of teaching: Lecture | Hands-on Tutorials | Group projects with guidance  

Mobile Health (mHealth) and Machine Learning are both topics that have become very popular in recent years due to the availability of mobile data, sensors, and more computational power of local machines. The widespread adoption of smartphones creates an enormous potential to improve healthcare services.

More than 17,000 mHealth apps now are available for smart phones and other devices, and they do everything from monitoring urine flow for patients with enlarged prostates to reminding people prone to kidney stones to drink more water. mHealth has a tremendous potential to change health care, and artificial intelligence is a key element for it.

Content Overview

          • Mobile Health Basics and Ecological Momentary Assessments
          • Health apps and the MARS evaluation
          • Artificial intelligence in Medicine
          • Limitations, chances and biases in artificial intelligence
          • Introduction to Neural Networks
          • Decision Trees, Random Forests and Gradient Boosting Machines
          • Machine Learning and Data Science with Python


1The times serve as rough orientation. The real times may differ.