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Text analytics (also known as natural language processing) is becoming increasingly important in clinical and public health given the near-ubiquitous adoption of text-based Electronic Health Record (EHR) systems in clinical care, and the widespread use of social media and online communities for health-related peer support. This subject aims to provide students with a grounding in applied health-related text analytics using a range of different data sources and application areas.
Topics covered may include: introduction to text analytics and text analytics for health applications; introduction to healthcare and public health; development of text analytics pipelines for clinical notes; best practice in data annotation; clinical information extraction using rule-based methods and machine learning; knowledge resources for clinical text analytics; clinical text analytics toolkits; utilization of text analytics and social media for health applications; sentiment and stance analysis in social media data; data management, privacy, and ethical issues in health-related text analytics.
Intended learning outcomes
On completion of this subject, students should be able to:
- Demonstrate an ability to evaluate a range of health-related text data sources
- Develop and evaluate text analytics pipelines using the Python programming language
- Demonstrate competence in the use of standard health-related text analytics libraries and knowledge resources
- Demonstrate an ability to appraise ethical issues in health-related text analytics
- Develop and evaluate a text analytics pipeline for consumer-generated data using Python
- Demonstrate an ability to critically evaluate health text analytics publications
- Develop critical thinking and analytical skills
- Enhance existing Python programming skills
- Presentation skills
- Improved writing skills
Last updated: 31 January 2024