HTIN5006: Semester 1, 2022

University home

Library

Current students

Staff intranet

Find an event

Give

Current students

Units HTIN5006 Semester 1 2022 [Normal evening]

Unit of study_

HTIN5006: Foundations of Healthcare Data Science

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

Overview

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.

Unit details and rules

Unit code	HTIN5006
Academic unit	Computer Science
Credit points	6
Prohibitions ?	None
Prerequisites ?	None
Corequisites ?	None
Assumed knowledge ?	None
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Jinman Kim, jinman.kim@sydney.edu.au

Type	Description	Weight	Due	Length
Final exam (Record+)	Final Exam Written and closed-book exam	55%	Formal exam period	2 hours
Outcomes assessed:
Assignment	Individual report on Healthcare Data Science Written report	10%	Week 05	max 10 pages
Outcomes assessed:
Online task	Mid-term Quiz Closed-book Quiz	5%	Week 06	30 minutes
Outcomes assessed:
Assignment	Individual report on Healthcare Data Science – Solution Proposal Written report	10%	Week 08	5 pages
Outcomes assessed:
Presentation	Presentation Oral presentation	10%	Week 12	30 minutes
Outcomes assessed:
Assignment	Individual report on Healthcare Data Science - Data Analysis Written report	10%	Week 12	10 pages
Outcomes assessed:
= Type B final exam ?

Assessment summary

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.

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.

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
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)
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)
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)
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)
Week 05	Lecture: End-to-end with Healthcare data: Part II: Querying and summarising data Lab: Health data exploration	Lecture and tutorial (3 hr)
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)
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)
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)
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)
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)
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)
Week 12	Lecture: Healthcare data science research, tools, datasets, and the community Tutorial: Oral presentation	Lecture and tutorial (3 hr)
Week 13	Lecture: Unit of study review Tutorial: Oral presentation II	Lecture and tutorial (3 hr)

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

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. 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
Learning outcomes

Responding to student feedback

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

N/A

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

Study commitment

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