Personalized decision support for cardiology based on deep learning: an overview

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


Cardiovascular disease is the leading global cause of death. With the availability of a rapidly increasing amount of medical data, physicians are now faced with a challenge and an opportunity. The challenge is to decipher this data efficiently and at the same time it presents an opportunity to make more informed and personalized decisions. For this purpose, many computerized Clinical Decision Support Systems (CDSSs) have been developed to assist medical professionals in a range of decision-making tasks, from diagnosis, screening, prescription, treatment to care strategies. In the past decade, Deep Learning technology has brought forward a paradigm shift to the advances of Artificial Intelligence, starting with computer vision, natural language processing to a wide range of application areas including medicine. In this chapter, we will give an overview of how deep learning has been utilized in personalized clinical decision support in cardiology. Particularly, we will look at different modalities of cardiac data, related learning tasks, and recently developed deep learning models. Since this is a fast-growing research field and there have been good review papers published on related topics in recent years, this chapter will pay more attention to recent works published in the past two years and direct readers to relevant review papers for earlier studies.

Original languageEnglish
Title of host publicationEdge-of-Things in Personalized Healthcare Support Systems
Number of pages31
ISBN (Electronic)9780323905855
ISBN (Print)9780323907088
StatePublished - 1 Jan 2022


  • Cardiovascular disease
  • clinical decision support systems
  • computerized tomography
  • coronary heart disease
  • deep learning
  • electrocardiogram
  • heart failure
  • magnetic resonance imaging


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