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Unit of study_

BMET2925: AI, Data, and Society in Health

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.

Details

Academic unit Biomedical Engineering
Unit code BMET2925
Unit name AI, Data, and Society in Health
Session, year
? 
Semester 1, 2022
Attendance mode Normal day
Location Camperdown/Darlington, Sydney
Credit points 6

Enrolment rules

Prohibitions
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BMET9925
Prerequisites
? 
None
Corequisites
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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 and contact details

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) Type E final exam Take-home exam
Exam with conceptual and practical elements. Approx. required time 3 hours.
30% Formal exam period 24 hours
Outcomes assessed: LO1 LO7 LO6 LO5 LO4 LO3 LO2
Online task Weekly Exercises
Set of quizzes and exercises to be completed each week.
10% Multiple weeks 15-30 mins (see Canvas)
Outcomes assessed: LO1 LO2 LO3 LO5 LO6
Tutorial quiz Quiz
A small quiz on content covered in class.
10% Week 07 1-2 hours (see Canvas)
Outcomes assessed: LO1 LO2 LO3 LO6
Assignment Research Report
A report written based on independent research.
15% Week 08 2000 words
Outcomes assessed: LO1 LO2 LO3 LO6
Assignment group 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: LO2 LO7 LO6 LO5 LO4 LO3
group assignment = group assignment ?
Type E final exam = Type E final exam ?
  • 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.

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.

Special consideration

If you experience short-term circumstances beyond your control, such as illness, injury or misadventure or if you have essential commitments which impact your preparation or performance in an assessment, you may be eligible for special consideration or special arrangements.

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.

WK Topic Learning activity Learning outcomes
Ongoing Weekly personal study and review Individual study (2 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7
Week 01 Online E-learning: Introduction to Health Data and AI Independent study (2 hr) LO1 LO3
Health Data Stakeholders Tutorial (2 hr) LO1 LO3
Data Loading, Access, and Plotting Computer laboratory (2 hr) LO7
Week 02 Online E-Learning: Health Data Context Independent study (2 hr) LO1 LO2 LO3
Understanding Health Stakeholder Needs Tutorial (2 hr) LO1 LO3 LO6
Combining and Extracting Health Data Computer laboratory (2 hr) LO7
Week 03 Online E-Learning: Health Data Quality Independent study (2 hr) LO2 LO3
The Impact of Health Data Quality Tutorial (2 hr) LO2 LO3 LO6
Health Data Cleaning Computer laboratory (2 hr) LO6 LO7
Week 04 Online E-Learning: Exploratory Data Analysis Independent study (2 hr) LO2 LO6
Review of Descriptive Statistics Tutorial (2 hr) LO5 LO6
Exploratory Data Analysis Computer laboratory (2 hr) LO5 LO6 LO7
Week 05 Online E-Learning: Predictive Analysis Independent study (2 hr) LO4 LO5 LO6
Predictive Analysis Tutorial (2 hr) LO2 LO4 LO6
Implementing Predictive Models Computer laboratory (2 hr) LO4 LO6 LO7
Week 06 Online E-Learning: Health Image Data Independent study (2 hr) LO2 LO4 LO5 LO6
Image Features and Radiomics Tutorial (2 hr) LO2 LO4 LO5 LO6
Image Classification in Medicine Computer laboratory (2 hr) LO4 LO6 LO7
Week 07 Online E-Learning: Evaluating AI and Data Solutions Independent study (2 hr) LO3 LO5 LO6
Quiz Tutorial (2 hr) LO1 LO2 LO3 LO6
Cross Validation Computer laboratory (2 hr) LO3 LO4 LO5 LO6 LO7
Week 08 Online E-Learning: Toolboxes and Methods for Different Health Data Independent study (2 hr) LO2 LO3 LO4 LO6
Toolboxes and Methods for Different Health Data Tutorial (2 hr) LO2 LO3 LO4 LO6
Toolboxes and Methods for Different Health Data Computer laboratory (2 hr) LO3 LO4 LO5 LO6 LO7
Week 09 Online E-Learning: Toolboxes and Methods for Different Health Data Independent study (2 hr) LO2 LO3 LO4 LO6
Health Datathon Group Project Project (2 hr) LO2 LO4 LO5 LO6 LO7
Health Datathon Group Project Project (2 hr) LO2 LO4 LO5 LO6 LO7
Week 10 Online E-Learning: Implementation and Impact in Health Independent study (2 hr) LO1 LO2 LO3 LO6
Health Datathon Group Project Project (2 hr) LO2 LO4 LO5 LO6 LO7
Health Datathon Group Project Project (2 hr) LO2 LO4 LO5 LO6 LO7
Week 11 Online E-Learning: AI Systems in Data and Health - Today Independent study (2 hr) LO1 LO2 LO3
Health Datathon Group Project Project (2 hr) LO2 LO4 LO5 LO6 LO7
Health Datathon Group Project Project (2 hr) LO2 LO4 LO5 LO6 LO7
Week 12 Online E-Learning: AI Systems in Data and Health - Future Independent study (2 hr) LO1 LO2 LO3
Health Datathon Group Project Project (2 hr) LO2 LO4 LO5 LO6 LO7
Health Datathon Group Project Project (2 hr) LO2 LO4 LO5 LO6 LO7
Week 13 Online E-Learning: Unit Review Independent study (2 hr) LO1 LO2 LO3 LO5 LO6
Unit Review and Health Datathon Group Project Tutorial (2 hr) LO2 LO4 LO5 LO6 LO7
Health Datathon Group Project Presentation (2 hr) LO2 LO4 LO5 LO6 LO7

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 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.

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
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9
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.

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