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.
|Academic unit||Computer Science|
|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|
The learning outcomes for this unit will be available two weeks before the first day of teaching.
Unit outlines will be available 1 week before the first day of teaching for the relevant session.