Abstract
In this chapter the key concepts of artificial intelligence and machine learning are introduced. The importance of first identifying and defining the right problem is emphasised. A review is provided of different types of machine learning model, and pointers are provided about how to design and train a model to meet the requirements of the chosen problem. Important considerations regarding validating the trained model are also discussed. A review is provided of the context of AI and machine learning in cardiology, i.e. what imaging and non-imaging data sources are typically available for such models and what information can they provide? Within each of these data sources, some of the important applications and contributions of AI are highlighted. A practical tutorial is provided to introduce the reader to Jupyter notebooks and Python.
| Original language | English |
|---|---|
| Title of host publication | AI and Big Data in Cardiology |
| Subtitle of host publication | a Practical Guide |
| Editors | N. Duchateau, A.P. King |
| Publisher | Springer International Publishing |
| Pages | 11-33 |
| Number of pages | 23 |
| ISBN (Electronic) | 9783031050718 |
| ISBN (Print) | 9783031050701 |
| DOIs | |
| Publication status | Published - 2023 |
Keywords
- Artificial intelligence
- Computed tomography
- Data descriptors
- Data standardization
- Echocardiography
- Electrocardiogram
- Electronic health records
- Machine learning
- Magnetic resonance
- Positron emission tomography
- Validation