Text Analytics for Health (COMP90090)

Graduate courseworkPoints: 12.5On Campus (Parkville)

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Overview

Availability
Semester 1
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AIMS

Text analytics (also known as natural language processing) is becoming increasingly important in clinical and public health given the near-ubiquitous adoption of text-based Electronic Health Record (EHR) systems in clinical care, and the widespread use of social media and online communities for health-related peer support. This subject aims to provide students with a grounding in applied health-related text analytics using a range of different data sources and application areas. 

INDICATIVE CONTENT 

Topics covered may include: introduction to text analytics and text analytics for health applications; introduction to healthcare and public health; development of text analytics pipelines for clinical notes; best practice in data annotation; clinical information extraction using rule-based methods and machine learning; knowledge resources for clinical text analytics;  clinical text analytics toolkits; utilization of text analytics and social media for health applications; sentiment and stance analysis in social media data; data management, privacy, and ethical issues in health-related text analytics. 

Intended learning outcomes

On completion of this subject, students should be able to:

  • Demonstrate an ability to evaluate a range of health-related text data sources
  • Develop and evaluate text analytics pipelines using the Python programming language
  • Demonstrate competence in the use of standard health-related text analytics libraries and knowledge resources
  • Demonstrate an ability to appraise ethical issues in health-related text analytics
  • Develop and evaluate a text analytics pipeline for consumer-generated data using Python
  • Demonstrate an ability to critically evaluate health text analytics publications

Generic skills

  • Develop critical thinking and analytical skills
  • Enhance existing Python programming skills
  • Presentation skills
  • Improved writing skills

Last updated: 4 March 2025

Eligibility and requirements

Prerequisites

Permission of the coordinator is required prior to enrolling in this subject (some programming experience in Python is required)

OR

Completion of one of the subjects below

Code Name Teaching period Credit Points
COMP90059 Introduction to Programming
Semester 1 (On Campus - Parkville)
Semester 2 (On Campus - Parkville)
 12.5
COMP90038 Algorithms and Complexity
Semester 2 (On Campus - Parkville)
Semester 1 (On Campus - Parkville)
 12.5

Or equivalent

OR

Admission into or selection of one of the following:

  • Admission into or selection of one of the following:
    • MC-IT Master of Information Technology
    Admission into or selection of one of the following:
    • MC-CS Master of Computer Science
    Admission into or selection of one of the following:
    • GD-CS Graduate Diploma in Computer Science
    Admission into or selection of one of the following:
    • MC-IS Master of Information Systems

Corequisites

None

Non-allowed subjects

None

Recommended background knowledge

Some experience with Python and data science methods

Inherent requirements (core participation requirements)

The University of Melbourne is committed to providing students with reasonable adjustments to assessment and participation under the Disability Standards for Education (2005), and the Assessment and Results Policy (MPF1326). Students are expected to meet the core participation requirements for their course. These can be viewed under Entry and Participation Requirements for the course outlines in the Handbook.

Further details on how to seek academic adjustments can be found on the Student Equity and Disability Support website: http://services.unimelb.edu.au/student-equity/home

Last updated: 4 March 2025

Assessment

DescriptionTimingPercentage

Short essay on ethical issues in health text analytics - Equivalent to approximately 500 words in total

  • 10 hours (of work required)
Week 315%

Two individual programming assignments – Equivalent to approximately 2,000 words in total (2x1,000 words). Due in Week 4 & 7.

  • 40 hours (of work required)
Hurdle requirement: 50% hurdle – Students are required to obtain at least 50% collectively on the three programming assignments. 		During the teaching period30%

Participation in online discussion – Equivalent to 500 words in total

  • 10 hours (of work required)
From Week 1 to Week 1010%

Independent project (Individual) – Equivalent to 2,000 words. Due at the start of the exam period

  • 40 hours (of work required)
Hurdle requirement: 50% hurdle – Students are required to obtain at least 50% on their individual, independent project. 		End of semester45%

Last updated: 4 March 2025

Dates & times

  • Semester 1
    CoordinatorMike Conway
    Mode of deliveryOn Campus (Parkville)
    Contact hours36 hours, comprising two 1-hour lectures and one 1-hour tutorial per week
    Total time commitment200 hours
    Teaching period 3 March 2025 to 1 June 2025
    Last self-enrol date14 March 2025
    Census date31 March 2025
    Last date to withdraw without fail 9 May 2025
    Assessment period ends27 June 2025

    Semester 1 contact information

    mike.conway@unimelb.edu.au

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  • Your tuition fees, academic transcript and statements.
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Last updated: 4 March 2025

Further information

  • Texts

    Prescribed texts

    There are no specifically prescribed or recommended texts for this subject.

  • Subject notes

    LEARNING AND TEACHING METHODS

    The subject is delivered through a combination of lectures and tutorials. 

  • Related Handbook entries

    This subject contributes to the following:

    TypeName
    CourseMaster of Data Science
    CourseMaster of Information Systems
    CourseMaster of Computer Science
    CourseMaster of Information Technology
    CourseGraduate Diploma in Computer Science
  • Available to Study Abroad and/or Study Exchange Students

    This subject is available to students studying at the University from eligible overseas institutions on exchange and study abroad. Students are required to satisfy any listed requirements, such as pre- and co-requisites, for enrolment in the subject.

Last updated: 4 March 2025