Useful links
Unit of study_
HTIN4006: Foundations of Healthcare Data Science
2024 unit information
The transformation of medicine and health by big data and artificial intelligence is already underway, with ever more routine data collection and its linkage through electronic means. Herein lies the potential to supply real-time personalised healthcare, deep clinical phenotyping and diagnostic capabilities, and prognostic predictions of disease and intervention outcomes. Data science techniques underpin these approaches. This unit will provide a deep dive into understanding the entire end-to-end data cycle / pipeline of healthcare data: from its acquisition (e.g., health records, imaging, sensors etc), to its processing (e.g., cleaning, feature extraction, data linkage etc), to analysing the data (e.g., decision support / computer aided diagnosis) and finally to use the data for prediction (e.g., prognosis and modelling). We will also study the importance of using the data to its stakeholders (patients, clinicians, society etc.) by taking into account of the ethics, privacy, security and measurable benefits from the use of the data. On completion of this unit, students will have a solid understanding of how the healthcare data is now being exploited, through data science principles and tools, to provide improved healthcare delivery. Students will also learn practical skills in healthcare data analysis using Python programming language.
Unit details and rules
Managing faculty or University school:
Engineering
| Study level | Undergraduate |
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| Academic unit | Computer Science |
| Credit points | 6 |
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Prerequisites:
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None |
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Corequisites:
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Enrolment in a thesis unit. INFO4001 or INFO4911 or INFO4991 or INFO4992 or AMME4111 or BMET4111 or CHNG4811 or CIVL4022 or ELEC4712 or COMP4103 or SOFT4103 or DATA4103 or ISYS4103 |
| Prohibitions:
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HTIN5006 |
| Assumed knowledge:
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None |
At the completion of this unit, you should be able to:
- LO1. Identify contemporary healthcare challenges and how novel artificial intelligence-based solutions can address them.
- LO2. Identify and understand the sources of healthcare data, and how they together pertain to health.
- LO3. Understand the full pipeline of health data generation, collation, processing, analytics and predictive modelling, and presentation to healthcare stakeholders so as to support decision making.
- LO4. Understand ethical and security best practices as they apply to the use of healthcare data.
- LO5. Create and execute machine learning-based models around a real health dataset, visualise, interpret and present the results.
- LO6. Communicate effectively across data science and healthcare disciplines in understanding a healthcare challenge, devising a data science solution and interpreting its results.
Unit availability
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Modes of attendance (MoA)
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