Outline

Overview

Unprecedented growth in computing power, the advent of artificial intelligence (AI)/machine learning technologies, and global data platforms are changing the way in which we approach real-world healthcare challenges. This interdisciplinary unit will introduce students from different backgrounds to the fundamental concepts of data analytics and AI, and their practical applications in healthcare. Throughout the unit, students will learn about the key concepts in data analytics and AI techniques, and obtain hands-on experience in applying these techniques to a broad range of healthcare problems. At the same time, they will develop an understanding of the ethical considerations in health data analytics and AI, and how their use impacts society: from the patient, to the doctor, to the broader community. A key element of the learning process will be a team-based Datathon project where students will deploy their knowledge to address an open-ended healthcare problem, in particular developing a practical solution and analysing how it's use may change things in the healthcare domain. Upon completion of this unit, students will understand and be able to enlist data analytics and AI tools to design solutions to healthcare problems.

Unit details and rules

Unit code	BMET2925
Academic unit	Biomedical Engineering
Credit points	6
Prohibitions ?	BMET9925
Prerequisites ?	None
Corequisites ?	None
Assumed knowledge ?	Familiarity with general mathematical and statistical concepts. Online learning modules will be provided to support obtaining this knowledge
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Hamish Fernando, hamish.fernando@sydney.edu.au
Lecturer(s)	Hamish Fernando, hamish.fernando@sydney.edu.au

Type	Description	Weight	Due	Length
Final exam (Take-home extended release)	Take-home exam Exam with conceptual and practical elements. Approx. required time 3 hours.	30%	Formal exam period	24 hours
Outcomes assessed:
Online task	Weekly Exercises Set of quizzes and exercises to be completed each week.	10%	Multiple weeks	15-30 mins (see Canvas)
Outcomes assessed:
Tutorial quiz	Quiz A small quiz on content covered in class.	10%	Week 07	1-2 hours (see Canvas)
Outcomes assessed:
Assignment	Research Report A report written based on independent research.	15%	Week 08	2000 words
Outcomes assessed:
Assignment	Health Datathon Group Project A group project developing a solution for an open-ended health data task.	35%	Week 13	N/A
Outcomes assessed:
= group assignment ? = Type E final exam ?

Assessment summary

Weekly Exercises: A set of weekly exercises delivered via Canvas. This will include short quizzes as well as submissions based on work undertaken during the tutorials and laboratories.
Quiz: An online quiz covering concepts covered in class.
Research Report: A report based on individual report based on a topic provided via Canvas.
Health Datathon Group Project: An open-ended project where students will work in teams to create a solution for a health challenge. Students will demonstrate their work during a presentation, and create a small flyer/brochure describing the benefits of their solution.
Take-home exam: An exam comprising both conceptual questions, as well as practical data analysis questions.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name	Mark range	Description
High distinction	85 - 100
Distinction	75 - 84
Credit	65 - 74
Pass	50 - 64
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see sydney.edu.au/students/guide-to-grades.

For more information see guide to grades.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

A late penalty of 5% per day late (or part thereof) will be applied. After 10 days, a mark of zero will be given unless special consideration has been received.

Academic integrity

The Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Weekly schedule

WK	Topic	Learning activity
Ongoing	Weekly personal study and review	Individual study (2 hr)
Week 01	Online E-learning: Introduction to Health Data and AI	Independent study (2 hr)
	Health Data Stakeholders	Tutorial (2 hr)
	Data Loading, Access, and Plotting	Computer laboratory (2 hr)
Week 02	Online E-Learning: Health Data Context	Independent study (2 hr)
	Understanding Health Stakeholder Needs	Tutorial (2 hr)
	Combining and Extracting Health Data	Computer laboratory (2 hr)
Week 03	Online E-Learning: Health Data Quality	Independent study (2 hr)
	The Impact of Health Data Quality	Tutorial (2 hr)
	Health Data Cleaning	Computer laboratory (2 hr)
Week 04	Online E-Learning: Exploratory Data Analysis	Independent study (2 hr)
	Review of Descriptive Statistics	Tutorial (2 hr)
	Exploratory Data Analysis	Computer laboratory (2 hr)
Week 05	Online E-Learning: Predictive Analysis	Independent study (2 hr)
	Predictive Analysis	Tutorial (2 hr)
	Implementing Predictive Models	Computer laboratory (2 hr)
Week 06	Online E-Learning: Health Image Data	Independent study (2 hr)
	Image Features and Radiomics	Tutorial (2 hr)
	Image Classification in Medicine	Computer laboratory (2 hr)
Week 07	Online E-Learning: Evaluating AI and Data Solutions	Independent study (2 hr)
	Quiz	Tutorial (2 hr)
	Cross Validation	Computer laboratory (2 hr)
Week 08	Online E-Learning: Toolboxes and Methods for Different Health Data	Independent study (2 hr)
	Toolboxes and Methods for Different Health Data	Tutorial (2 hr)
	Toolboxes and Methods for Different Health Data	Computer laboratory (2 hr)
Week 09	Online E-Learning: Toolboxes and Methods for Different Health Data	Independent study (2 hr)
	Health Datathon Group Project	Project (2 hr)
	Health Datathon Group Project	Project (2 hr)
Week 10	Online E-Learning: Implementation and Impact in Health	Independent study (2 hr)
	Health Datathon Group Project	Project (2 hr)
	Health Datathon Group Project	Project (2 hr)
Week 11	Online E-Learning: AI Systems in Data and Health - Today	Independent study (2 hr)
	Health Datathon Group Project	Project (2 hr)
	Health Datathon Group Project	Project (2 hr)
Week 12	Online E-Learning: AI Systems in Data and Health - Future	Independent study (2 hr)
	Health Datathon Group Project	Project (2 hr)
	Health Datathon Group Project	Project (2 hr)
Week 13	Online E-Learning: Unit Review	Independent study (2 hr)
	Unit Review and Health Datathon Group Project	Tutorial (2 hr)
	Health Datathon Group Project	Presentation (2 hr)

Attendance and class requirements

Students are expected to attend all classes, as per the Faculty Resolutions: https://www.sydney.edu.au/handbooks/engineering/rules/faculty_resolutions.shtml

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Required readings

References and readings will be provided on Canvas.

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

Outcomes
Graduate qualities

At the completion of this unit, you should be able to:

LO1. Discuss the importance of data and AI for modern society in health, using appropriate literature to explain their reasoning.
LO2. Articulate the challenges in working with real-world health datasets and select an appropriate data analytics or AI solution for a given health problem, with sufficient justification for the choice.
LO3. Characterise the impact of AI and data analytics solutions on different health stakeholder groups, in terms of technical, legal, ethical, economic, and social benefits and limitations.
LO4. Apply machine learning techniques such as support vector machines and neural networks to solve problems on health datasets.
LO5. Communicate the results of a data analytics pipeline in an oral and written form to an audience that may comprise non-experts.
LO6. Understand and apply fundamental data analytics processes such as problem definition, data collection, data cleaning, exploratory data analysis, modelling, and visualisation.
LO7. Use code libraries and toolboxes for simple data analysis and machine learning tasks in health.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

No changes have been made since this unit was last offered. This unit has received excellent feedback in the previous semester. Most of the areas of improvement involve a change in the delivery of the content, and not in the structure of the unit (which was very well received). Therefore, I have opted to continue this unit with no changes.

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect

Current students

BMET2925: AI, Data, and Society in Health

Semester 1, 2022 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect