Course Code: BME363
Synopsis
BME363 introduces the concept of artificial intelligence through data analytics, which can be used to analyse large amount of data, in order to provide useful information for decision-making. In the context of healthcare, large amounts of data such as electronic health records (EHRs) and “omics” data have become available through various stakeholders of the healthcare industry (payers, providers, pharmaceuticals). These data can be used to derive insights for improving medical care, disease staging and therapy monitoring. This course involves the use of open-source Python software and related toolboxes for data processing and also highlights specific biomedical applications in the field of bioinformatics, personalised medicine, diagnostics and medical informatics.
Level: 3
Credit Units: 5
Presentation Pattern: EVERY JULY
Topics
- Big Data in Healthcare
- Big Data in Healthcare Informatics
- Data Sources
- Big Data Platforms
- Machine Learning Models
- Business Analytics in Healthcare
- Patient Flow Management
- Telemedicine
- Patient Monitoring Devices
- Precision Medicine
- EHR for Risk Profiling
- Current and Future Perspectives
Learning Outcome
- Demonstrate a good understanding of key concepts and terminology in healthcare
- Illustrate and describe major characteristics and potential applications of various AI-based platforms, tools and techniques
- Examine and analyse major issues faced in implementing AI applications in healthcare
- Discuss current AI trends and predict future trends in healthcare
- Analyse key data challenges in healthcare and develop data science proposals with clear objectives towards overcoming these challenges
- Design AI frameworks and leverage on existing Python toolboxes and techniques for solving data science problems in healthcare