EPM5029 · Introduction to health data analytics

Overview

In this unit you will gain foundational knowledge for the study of Health Data Analytics by introducing key health, statistical and machine learning concepts. You will be introduced to the key ethical and privacy issues when using health data. You will obtain a basic understanding of common statistics used in health data analytics and the use machine learning techniques and their applications using the R software package. You will be presented with the classification of health data analytics tasks into description, prediction and explanation. You will be introduced to the different sources of health data, including linked data, together with the appropriate methods for analysis and strengths and weaknesses surrounding their use. You will discuss the fast-changing nature of data analytics and current topics of major interest including precision and evidence-based medicine, and the surge in data-driven evaluation and policy.

Offerings

Assessment

The Handbook does not list a final examination among the assessment items. That is not a guarantee there is none.

Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.

Requisites

The Handbook lists no prerequisite, corequisite or prohibition for this unit.

Learning outcomes

1. Explain the appropriate use of health data analytics in health research.
2. Appraise and summarise ethics and privacy with respect to health data analytics.
3. Evaluate different health data sources for health data analytics.
4. Differentiate between descriptive, predictive and explanatory research problems, and statistical compared with machine learning models.
5. Evaluate the different uses of linked health data and explain their strengths and weaknesses.
6. Implement the steps in health data analytics.
7. Critically assess and effectively use artificial intelligence (AI) tools responsibly, with transparency and specific to health data analytics

Workload

Twelve hours per week, consisting of (on average) • 4 hours per week for reading core material • 1-hour online lecture materials (multiple videos) • 2-hour face-to-face tutorial • 2 hours per week for online communication via online moderated discussions, and • 3 hours per week for assignment and quiz preparation No residential component is required.

Student experience, not official rules. Nothing here changes what the Handbook says.