Data Analytics in Healthcare

From Basics to Business

Data analytics is a very broad field which is being deployed in many domains as it provides us with unlimited possibilities for improvements and successes. Amazon uses it to improve how we shop, banks use it to detect credit card fraud. Many are eager to learn what data analytics can do to their professional domain.


This MOOC shows the different steps of approaching and using health data from the gathering of data part, to using understanding the medical reasoning behind it and finally to using it as a basis for entrepreneurship.


KU Leuven starts by addressing the possible medical knowledge that can be inferred from health data, then RWTH Aachen explains the basics on how to obtain data and what to do with it to make it visually acceptable, and finally the University of Maastricht provides insight how entrepreneurial initiative could result in the starting-up of businesses that deploy health data to create economic and societal value.


Thus knowledge concerning medicine and health, data and its mining and entrepreneurship and business creation is covered in this MOOC.

Developed in partnership with:


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Course outline


This course is suitable for Master and Doctoral students of medicine, engineering or computer science striving for a career in automated health data analysis, as well as for medical practitioners that want to expand their competences in health data analytics in a digitalized professional environment.


You will gain an overview of the state of the art in data mining methods and have a hands-on application of these methods to describe and make predictions about sets of data from various health-related application domains. You will learn:

  • How healthcare data analysis can be used to improve diagnosis, curing and caring
  • How to acquire, transform, classify, mine and visualize data
  • How to identify data analytics based entrepreneurial opportunities in healthcare and quantify it’s economic value
  • How to improve entrepreneurial opportunities and to create a rigorous business plan for your start up


In detail:


1. Data Science Learning Outcomes:
  • how to transfer contextual data from the health domain into objects which can be mathematically processed (Input)
  • how to pre-process data (Cleansing)
  • how to perform exploratory data analysis (Review)
  • different techniques of mathematical modeling (classification, regression, clustering)
  • different visualization techniques required e.g. of an emergency doctor and for Clinical Decision Support (CDS)


2. Expert Knowledge Learning Outcomes:
  • how to interpret different problems or phenomena within the treatment of diseases in relation to big data analyses (e.g. google.flu in reference to the incidents of flu over a region or improving influenza forecasting by analyzing twitter feeds)
  • knowledge on patient safety and risk management analysis by combining epidemiological data supporting the treatment of the critically ill patient with data on communication and information management along the process, human factors and crew resource management aspects
  • knowledge on preventive medicine, i.e. how to perform data analysis in order to predict e.g. the risk of malignancy in ovarian cancer
  • how distributed systems i.e. on the body of the patient (e.g. smart watches, fitness bands, smart clothes etc.) and additional systems in the environment, such as cameras, can independently arrive at hypotheses and intelligent insights in emergency medicine


3. Entrepreneurship Learning Outcomes:
  • how to assess the commercial potential of new services or new technology in order to bring them to the market
  • different techniques for the transformation of research based ideas into business plans
  • how to communicate business ideas effectively to different stakeholders, including investors, partners and the public
  • how to build up a brand and form a corporate identity basic concepts in budgeting and financial reporting
  • project management competence needed to effectively manage development projects that involve computer hardware, software and telecommunications technology

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