The University of Sydney - Engineering

Unit:	BMET2925: AI, Data, and Society in Health (6 CP)
Mode:	Normal-Day
On Offer:	Yes
Level:	Intermediate
Faculty/School:	School of Biomedical Engineering
Unit Coordinator/s:	Dr Kumar, Ashnil
Session options:	Semester 1
Versions for this Unit:	2021 - Semester 1 Go

Campus:	Westmead
Pre-Requisites:	None.
Prohibitions:	BMET9925.
Brief Handbook Description:	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.
Assumed Knowledge:	Familiarity with general mathematical and statistical concepts. Online learning modules will be provided to support obtaining this knowledge.
Additional Notes:	Departmental permission will only be required for the first offering of this unit during the Semester 1 2021, when this unit will be piloted. From 2022, the unit will be available to all students via Table S.
Department Permission	Department permission is required for enrollment in this session.

Timetable:	BMET2925 Timetable
Time Commitment:	# Activity Name Hours per Week Sessions per Week Weeks per Semester 1 E-Learning 2.00 1 13 2 Tutorial 2.00 1 9 3 Laboratory 2.00 1 9 4 Datathon 2.00 2 4 5 Independent Study 3.00 1 13
T&L Activities:	E-Learning (Weeks 1-13): Video lectures, online worksheets, and other learning materials will cover the core conceptual knowledge each week. Students will be expected to complete this work prior to class. Tutorials (Weeks 1-9): E-learning content review, with problem-based activities to support consolidation of knowledge. Laboratory (Weeks 1-9): Practical exercises that will provide practical experience in AI and data analytics techniques applied to health. Studio Datathon (Weeks 10-13): Sessions where students will work in teams to propose and prototype a solution to a real-world health data problem.

Attributes listed here represent the key course goals (see Course Map tab) designated for this unit. The list below describes how these attributes are developed through practice in the unit. See Learning Outcomes and Assessment tabs for details of how these attributes are assessed.

Attribute Development Method	Attribute Developed
Laboratories will take a problem-based focus, developing practical skills through applying a range of data analytics and AI techniques on real-world datasets. A set of intensive Datathon sessions will allow teams of students will work together to solve a larger scale real-world challenge. During tutorial sessions, students will discuss the theories behind data analytics and AI algorithms, focusing on conceptual explanations and examining the appropriateness of each technique to different problems.	(2) Engineering/ IT Specialisation (Level 2)
During the Datathon activities, students will draw upon their knowledge to create data-driven solutions to an open-ended health problem. The solution they will produce will require them to consider not just technical challenges, but the social and ethical considerations that may affect various stakeholders in the healthcare ecosystem. Assessments will include ongoing exercises where students will be asked to propose solutions to practical health problems.	(3) Problem Solving and Inventiveness (Level 2)
During the Datathon, students will work in a team environment to collaboratively develop a data-driven solution to a health problem. They will present their findings to convince a non-expert audience about the validity of their proposed solution. Through lectures and tutorials, students will learn and debate ethical, social, and legal impacts that AI and data analytics solutions can have for the healthcare stakeholders. The Case Study assessment will develop students ability to critically examine and communicate the ethical, social, and legal impacts that AI and data analytics solutions can have for the healthcare stakeholders.	(5) Interdisciplinary, Inclusiveness, Influence (Level 2)
The Case Study assessment will require students to conduct research and write a written report detailing the ethical and social considerations that apply to a data-driven solution in current practice. As part of the Datathon, the students will produce a flyer to communicate the technical elements of their solution as well as explain why it is appropriate to the specific healthcare context. As part of the Datathon, students will create an oral presentation to communicate their findings to a non-expert audience.	(6) Communication and Inquiry/ Research (Level 3)

For explanation of attributes and levels see Engineering & IT Graduate Outcomes Table 2018.

Learning outcomes are the key abilities and knowledge that will be assessed in this unit. They are listed according to the course goal supported by each. See Assessment Tab for details how each outcome is assessed.

(5) Interdisciplinary, Inclusiveness, Influence (Level 2)
(6) Communication and Inquiry/ Research (Level 3)
(3) Problem Solving and Inventiveness (Level 2)
(2) Engineering/ IT Specialisation (Level 2)

Assessment Methods:	# Name Group Weight Due Week Outcomes 1 Exercises No 10.00 Multiple Weeks 5, 6, 8, 9, 10, 2 Quiz No 10.00 Week 7 3, 5, 8, 9, 3 Case Study No 15.00 Week 9 2, 3, 7, 4 Datathon - Final Presentation Yes 20.00 Week 13 1, 2, 4, 5, 6, 10, 5 Datathon - Solution Flyer Yes 15.00 Week 13 1, 2, 4, 5, 7, 9, 6 Take Home Exam No 30.00 Exam Period 2, 6, 7, 8, 9,
Assessment Description:	Exercises: Ongoing practical work in tutorials and laboratories, assessed based on progress and effort rather than correctness. Quiz: A short in-class covering conceptual content from the first half of semester. Case Study: A written report that examines the social and ethical considerations about existing data analytics and AI solutions that are currently being used, the challenges that exist, and how these can be overcome. Datathon - Final Presentation: A combined oral presentation and practical demonstration where the team of students will showcase the solution they developed during the Datathon, explain how it works, and discuss the social and ethical impacts on the stakeholders for whom the solution was developed. Datathon - Solution Flyer: Teams will produce a short one-pager flyer the describes the solution they developed during the Datathon. Take Home Exam: A time-limited exam that will test conceptual knowledge and practical skills in data interpretation.
Assessment Feedback:	Feedback for assessments will be provided via Canvas and through in-class discussions.
Grading:	Grade Type Description Standards Based Assessment Final grades in this unit are awarded at levels of HD for High Distinction, DI (previously D) for Distinction, CR for Credit, PS (previously P) for Pass and FA (previously F) for Fail as defined by University of Sydney Assessment Policy. Details of the Assessment Policy are available on the Policies website at http://sydney.edu.au/policies . Standards for grades in individual assessment tasks and the summative method for obtaining a final mark in the unit will be set out in a marking guide supplied by the unit coordinator.
Policies & Procedures:	See the policies page of the faculty website at http://sydney.edu.au/engineering/student-policies/ for information regarding university policies and local provisions and procedures within the Faculty of Engineering and Information Technologies.

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Course Relations

The following is a list of courses which have added this Unit to their structure.

Course	Year(s) Offered
Biomedical Mid-Year	2018, 2019, 2020, 2021, 2022
Biomedical/ Project Management	2019, 2020, 2021, 2022
Biomedical	2018, 2019, 2020, 2021, 2022
Biomedical / Arts	2017, 2018, 2019, 2020, 2021, 2022
Biomedical / Commerce	2017, 2018, 2019, 2020, 2021, 2022
Biomedical / Medical Science	2017
Biomedical / Music Studies	2017
Biomedical / Project Management	2017, 2018
Biomedical /Science	2017, 2018, 2019, 2020, 2021, 2022
Biomedical/Science (Health)	2018, 2019, 2020, 2021, 2022
Biomedical / Law	2017, 2018, 2019, 2020, 2021, 2022
Biomedical/Science (Medical Science Stream)	2018, 2019, 2020, 2021, 2022

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Course Goals

This unit contributes to the achievement of the following course goals:

Attribute	Practiced	Assessed
(5) Interdisciplinary, Inclusiveness, Influence (Level 2)	Yes	19.5%
(6) Communication and Inquiry/ Research (Level 3)	Yes	17%
(4) Design (Level 2)	No	0%
(3) Problem Solving and Inventiveness (Level 2)	Yes	22.5%
(2) Engineering/ IT Specialisation (Level 2)	Yes	41%
(1) Maths/ Science Methods and Tools (Level 2)	No	0%

These goals are selected from Engineering & IT Graduate Outcomes Table 2018 which defines overall goals for courses where this unit is primarily offered. See Engineering & IT Graduate Outcomes Table 2018 for details of the attributes and levels to be developed in the course as a whole. Percentage figures alongside each course goal provide a rough indication of their relative weighting in assessment for this unit. Note that not all goals are necessarily part of assessment. Some may be more about practice activity. See Learning outcomes for details of what is assessed in relation to each goal and Assessment for details of how the outcome is assessed. See Attributes for details of practice provided for each goal.