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Unit of study_

HTIN5006: Foundations of Healthcare Data Science

2025 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 Postgraduate
Academic unit Computer Science
Credit points 6
Prerequisites:
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None
Corequisites:
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None
Prohibitions:
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HTIN4006
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

This section lists the session, attendance modes and locations the unit is available in. There is a unit outline for each of the unit availabilities, which gives you information about the unit including assessment details and a schedule of weekly activities.

The outline is published 2 weeks before the first day of teaching. You can look at previous outlines for a guide to the details of a unit.

Session MoA ?  Location Outline ? 
Semester 1 2024
Normal evening Camperdown/Darlington, Sydney
Session MoA ?  Location Outline ? 
Semester 1 2025
Normal evening Camperdown/Darlington, Sydney
Outline unavailable
Session MoA ?  Location Outline ? 
Semester 1 2022
Normal evening Camperdown/Darlington, Sydney
Semester 1 2022
Normal evening Remote
Semester 1 2023
Normal evening Camperdown/Darlington, Sydney
Semester 1 2023
Normal evening Remote

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Modes of attendance (MoA)

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