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Unit of study_
HTIN4005: Applied Healthcare Data Science
2024 unit information
The current health data revolution promises transformative advancements in healthcare services and delivery. However, the data generated are vast and complex. Extracting actionable understanding requires cross-disciplinary engagement between data science with healthcare. This unit explores the computational technologies involved in integrating and making sense of the breath of health data, and their use in better understanding the patient. Students will understand the data challenges presented by the various assays in which patients are quantified, spanning genetic testing to organ imaging. Students will explore how computational and machine learning models can span health data to derive integrated understanding of the links and patterns across them. They will employ such models in performing diagnosis and forecasting disease progression and intervention outcomes, thus enabling personalised medicine and supporting clinical decision making. This unit will develop students' understanding of current healthcare challenges, how these can be framed as data science questions, and how they can engage and apply their knowledge in cross-disciplinary ventures to improve healthcare.
Unit details and rules
Managing faculty or University school:
Computer Science
| Code | HTIN4005 |
|---|---|
| Academic unit | Computer Science |
| Credit points | 6 |
|
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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HTIN5005 |
| Assumed knowledge:
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None |
At the completion of this unit, you should be able to:
- LO1. Demonstrate knowledge of the broad range of computational problems in healthcare.
- LO2. Use machine learning tools to address the needs in healthcare data science.
- LO3. Evaluate the performance of machine learning tools in computational healthcare problems.
- LO4. Apply and tailor known machine learning tools for solving new challenging healthcare problems.
- LO5. Present the design and evaluation of a machine learning prototype for healthcare data science problems, defining the requirements and describing the design processes and evaluation.
- LO6. Communicate and collaborate effectively across technological and healthcare/medicine disciplinary boundaries.
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 ? |
|---|---|---|---|
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Semester 2 2024
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Normal evening | Camperdown/Darlington, Sydney |
Outline unavailable
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Modes of attendance (MoA)
This refers to the Mode of attendance (MoA) for the unit as it appears when you’re selecting your units in Sydney Student. Find more information about modes of attendance on our website.
