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Unit of study_

HTIN5006: Foundations of Healthcare Data Science

The transformation of medicine and health by big data and artificial intelligence is already underway, with ever more routine data collection and its linkage through electronic means. Herein lies the potential to supply real-time personalised healthcare, deep clinical phenotyping and diagnostic capabilities, and prognostic predictions of disease and intervention outcomes. Data science techniques underpin these approaches. This unit will provide a deep dive into understanding the entire end-to-end data cycle / pipeline of healthcare data: from its acquisition (e.g., health records, imaging, sensors etc), to its processing (e.g., cleaning, feature extraction, data linkage etc), to analysing the data (e.g., decision support / computer aided diagnosis) and finally to use the data for prediction (e.g., prognosis and modelling). We will also study the importance of using the data to its stakeholders (patients, clinicians, society etc.) by taking into account of the ethics, privacy, security and measurable benefits from the use of the data. On completion of this unit, students will have a solid understanding of how the healthcare data is now being exploited, through data science principles and tools, to provide improved healthcare delivery. Students will also learn practical skills in healthcare data analysis using Python programming language.

Details

Academic unit Computer Science
Unit code HTIN5006
Unit name Foundations of Healthcare Data Science
Session, year
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Semester 1, 2022
Attendance mode Normal evening
Location Remote
Credit points 6

Enrolment rules

Prohibitions
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None
Prerequisites
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None
Corequisites
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None
Available to study abroad and exchange students

Yes

Teaching staff and contact details

Coordinator Jinman Kim, jinman.kim@sydney.edu.au
Type Description Weight Due Length
Final exam (Record+) Type B final exam Final Exam
Written and closed-book exam
55% Formal exam period 2 hours
Outcomes assessed: LO1 LO2 LO3 LO4 LO5
Assignment Individual report on Healthcare Data Science
Written report
10% Week 05 max 10 pages
Outcomes assessed: LO1 LO2 LO6
Online task Mid-term Quiz
Closed-book Quiz
5% Week 06 30 minutes
Outcomes assessed: LO1 LO2 LO3 LO4
Assignment Individual report on Healthcare Data Science – Solution Proposal
Written report
10% Week 08 5 pages
Outcomes assessed: LO3 LO4 LO5 LO6
Presentation Presentation
Oral presentation
10% Week 12 30 minutes
Outcomes assessed: LO1 LO2 LO3 LO4 LO6
Assignment Individual report on Healthcare Data Science - Data Analysis
Written report
10% Week 12 10 pages
Outcomes assessed: LO3 LO5 LO6
Type B final exam = Type B final exam ?

Detailed information for each assessment can be found on Canvas.

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.

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 honesty, academic dishonesty, 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 dishonesty or plagiarism seriously.

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

WK Topic Learning activity Learning outcomes
Week 01 Lecture: Introduction to Data Science in Healthcare: Benefits, Challenges and Opportunities Tutorial: Emergence of data-driven AI and automation in healthcare (discussion on recent publications / news) Lecture and tutorial (3 hr) LO1
Week 02 Lecture: Understanding Healthcare ‘big’ data sources and basic analytics Tutorial: Data flow within a hospital / local health district / national infrastructure Lecture and tutorial (3 hr) LO2
Week 03 Lecture: Working with healthcare data – Stakeholder engagement, Ethics, Privacy, Security and Equality Tutorial: Data representations and their consequences Lecture and tutorial (3 hr) LO4
Week 04 Lecture: End-to-end with Healthcare data: Part I: Data lifecycle, Acquisition, Cleaning and Storing Data Lab: Introduction to Python – loading and viewing health data Lecture and tutorial (3 hr) LO3
Week 05 Lecture: End-to-end with Healthcare data: Part II: Querying and summarising data Lab: Health data exploration Lecture and tutorial (3 hr) LO3
Week 06 Lecture: End-to-end with Healthcare data: Part III: Hypothesis testing and evaluation Lab: Health data exploration II Lecture and tutorial (3 hr) LO3
Week 07 Lecture: Major types of healthcare data analytics I: Association rules, dimensionality reduction and data clustering (Data Mining) Lab: Predictive analytics example with EMR data Lecture and tutorial (3 hr) LO5
Week 08 Lecture: Major types of healthcare data analytics II: Regression and Classification (Machine Learning) Lab: Predictive analytics example with EMR data II Lecture and tutorial (3 hr) LO5
Week 09 Lecture: Dealing with unstructured healthcare data, uncertainty in the data and the predictive models Lab: Using visualisation tool for healthcare data Lecture and tutorial (3 hr) LO5
Week 10 Lecture: Applications and Practical Systems of healthcare data science I Tutorial: Round table discussion – understanding the role of data in healthcare application Lecture and tutorial (3 hr) LO2 LO3 LO4
Week 11 Lecture: Applications and Practical Systems of healthcare data science II Tutorial: Round table discussion – implementation of AI tools in healthcare Lecture and tutorial (3 hr) LO2 LO3 LO4
Week 12 Lecture: Healthcare data science research, tools, datasets, and the community Tutorial: Oral presentation Lecture and tutorial (3 hr) LO1 LO2 LO3 LO6
Week 13 Lecture: Unit of study review Tutorial: Oral presentation II Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5 LO6

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.

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. Identify contemporary healthcare challenges and how novel artificial intelligence-based solutions can address them.
  • LO2. Identify and understand the sources of healthcare data, and how they together pertain to health.
  • LO3. Understand the full pipeline of health data generation, collation, processing, analytics and predictive modelling, and presentation to healthcare stakeholders so as to support decision making.
  • LO4. Understand ethical and security best practices as they apply to the use of healthcare data.
  • LO5. Create and execute machine learning-based models around a real health dataset, visualise, interpret and present the results.
  • LO6. Communicate effectively across data science and healthcare disciplines in understanding a healthcare challenge, devising a data science solution and interpreting its results.

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

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.