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Unit of study_

HTIN5005: Applied Healthcare Data Science

The current health data revolution promises transformative advancements in healthcare services and delivery. However, the data generated are vast and complex. Extracting actionable understanding requires cross-disciplinary engagement between data science with healthcare. This unit explores the computational technologies involved in integrating and making sense of the breath of health data, and their use in better understanding the patient. Students will understand the data challenges presented by the various assays in which patients are quantified, spanning genetic testing to organ imaging. Students will explore how computational and machine learning models can span health data to derive integrated understanding of the links and patterns across them. They will employ such models in performing diagnosis and forecasting disease progression and intervention outcomes, thus enabling personalised medicine and supporting clinical decision making. This unit will develop students' understanding of current healthcare challenges, how these can be framed as data science questions, and how they can engage and apply their knowledge in cross-disciplinary ventures to improve healthcare.

Details

Academic unit Computer Science
Unit code HTIN5005
Unit name Applied Healthcare Data Science
Session, year
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Semester 2, 2022
Attendance mode Normal evening
Location Camperdown/Darlington, Sydney
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 Chang Xu, c.xu@sydney.edu.au
Tutor(s) Linwei Tao , linwei.tao@sydney.edu.au
Administrative staff Keiko Narushima provides education support for the Master of Health Technology Innovation. keiko.narushima@sydney.edu.au
Type Description Weight Due Length
Final exam (Record+) Type B final exam Final exam
Final exam
60% Formal exam period 2 hours
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6
Assignment group assignment Assignment 1
Take-home assignment.
20% Week 07 Released in Week 2.
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6
Assignment group assignment Assignment 2
Take-home assignment.
20% Week 12 Released in Week 7.
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6
group assignment = group assignment ?
Type B final exam = Type B final exam ?

Assessment criteria

Fail:
It is a policy of the School of Computer Science that in order to pass this unit, a student must achieve at least 40% in the written examination. For subjects without a final exam, the 40% minimum requirement applies to the corresponding major assessment component specified by the lecturer. A student must also achieve an overall final mark of 50 or more. Any student not meeting these requirements may be given a maximum final mark of no more than 45 regardless of their average.
 

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 Introduction Lecture and tutorial (3 hr) LO1 LO6
Week 02 The Machine Learning Landscape Lecture and tutorial (3 hr) LO2 LO3
Week 03 Visual Analytics in Healthcare Lecture and tutorial (3 hr) LO2 LO4
Week 04 Clinical Prediction Models Lecture and tutorial (3 hr) LO2 LO4 LO5
Week 05 Genomic Data Analysis Lecture and tutorial (3 hr) LO2 LO3 LO4 LO5
Week 06 Biomedical Signal Processing Lecture and tutorial (3 hr) LO2 LO4 LO5
Week 07 Biomedical Image Analysis Lecture and tutorial (3 hr) LO1 LO2 LO4 LO5
Week 08 Drug Discovery Lecture and tutorial (3 hr) LO2 LO3 LO5 LO6
Week 09 Temporal Data Mining for Healthcare Data Lecture and tutorial (3 hr) LO2 LO4 LO5
Week 10 Natural Language Processing for Clinical Text Lecture and tutorial (3 hr) LO1 LO2 LO3 LO4 LO5
Week 11 Social Media Analytics for Healthcare Lecture and tutorial (3 hr) LO2 LO4 LO5 LO6
Week 12 Data Privacy in Healthcare Lecture and tutorial (3 hr) LO2 LO3 LO4 LO5
Week 13 Course Review 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.

Required readings

Please find them on the Canvas website. 

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. Demonstrate knowledge of the broad range of computational problems in healthcare.
  • LO2. Use machine learning tools to address the needs in healthcare data science.
  • LO3. Evaluate the performance of machine learning tools in computational healthcare problems.
  • LO4. Apply and tailor known machine learning tools for solving new challenging healthcare problems.
  • LO5. Present the design and evaluation of a machine learning prototype for healthcare data science problems, defining the requirements and describing the design processes and evaluation.
  • LO6. Communicate and collaborate effectively across technological and healthcare/medicine disciplinary boundaries.

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
This unit is redeveloped for 2022 S2.

IMPORTANT: School policy relating to Academic Dishonesty and Plagiarism.
In assessing a piece of submitted work, the School of Computer Science may reproduce it entirely, may provide a copy to another member of faculty, and/or to an external plagiarism checking service or in-house computer program and may also maintain a copy of the assignment for future checking purposes and/or allow an external service to do so.
 
Computer programming assignments may be checked by specialist code similarity detection software. The Faculty of Engineering currently uses the MOSS similarity detection engine (see http://theory.stanford.edu/~aiken/moss/), or the similarity report available in ED (edstem.org). These programs work in a similar way to TurnItIn in that they check for similarity against a database of previously submitted assignments and code available on the internet, but they have added functionality to detect cases of similarity of holistic code structure in cases such as global search and replace of variable names, reordering of lines, changing of comment lines, and the use of white space.
 
All written assignments submitted in this unit of study will be submitted to the similarity detecting software program known as Turnitin. Turnitin searches for matches between text in your written assessment task and text sourced from the Internet, published works and assignments that have previously been submitted to Turnitin for analysis.There will always be some degree of text-matching when using Turnitin. Text-matching may occur in use of direct quotations, technical terms and phrases, or the listing of bibliographic material. This does not mean you will automatically be accused of academic dishonesty or plagiarism, although Turnitin reports may be used as evidence in academic dishonesty and plagiarism decision-making processes.

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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