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Teaching GuideTerm Faculty of Computer Science |
| Máster Universitario en Intelixencia Artificial |
 Subjects |
| AI in Health |
| Learning aims |
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| Identifying Data | 2023/24 | |||||||||||||
| Subject | AI in Health | Code | 614544022 | |||||||||||
| Study programme |
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| Descriptors | Cycle | Period | Year | Type | Credits | |||||||||
| Official Master's Degree | 1st four-month period |
Second | Optional | 3 | ||||||||||
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| Learning outcomes | Study programme competences | ||
| Developing sound capabilities for creating complex models that allow personalised diagnostics and clinic trends prediction based on heterogeneous sources | AC7 AC8 AC9 AC13 AC14 AC15 AC19 AC20 AC21 AC22 AC27 AC28 AC29 AC30 |
BC1 BC2 BC4 BC5 BC6 BC7 BC9 BC10 |
CC5 CC8 CC9 |
| Knowing the different standards for data treatment in the medical domain and developing the capability to integrate them in AI projects. Knowing the techniques for AI integration in medical devices | AC7 AC8 AC9 AC13 AC14 AC15 AC19 AC20 AC21 AC22 AC27 AC28 AC29 AC30 |
BC1 BC2 BC4 BC5 BC6 BC7 BC9 BC10 |
CC5 CC8 CC9 |
| Developing the capabilities to design web applications in e-Health based on AI models | AC7 AC8 AC13 AC14 AC15 AC19 AC20 AC21 AC22 AC27 AC28 AC29 AC30 |
BC1 BC2 BC4 BC5 BC6 BC7 BC9 BC10 |
CC5 CC8 CC9 |
| Knowing the specificities in the application fields for intelligent data monitoring and signals in e-health and their constraints in real time | AC7 AC8 AC9 AC13 AC14 AC15 AC19 AC20 AC21 AC22 AC27 AC28 AC29 AC30 |
BC1 BC2 BC4 BC5 BC6 BC7 BC9 BC10 |
CC5 CC8 CC9 |
| AC7 AC8 AC9 |
BC2 BC7 |
CC8 |
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