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Natural Language Processing for Health (COMP90090)
Graduate courseworkPoints: 12.5Dual-Delivery (Parkville)
To learn more, visit 2023 Course and subject delivery.
About this subject
- Overview
- Eligibility and requirements
- Assessment
- Dates and times
- Further information
- Timetable(opens in new window)
Contact information
Semester 1
Overview
Availability | Semester 1 - Dual-Delivery |
---|---|
Fees | Look up fees |
Natural Language Processing (NLP) 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 NLP using a range of different data sources and application areas.
INDICATIVE CONTENT
Topics covered may include: introduction to NLP and text analytics for health applications; introduction to healthcare and public health; development of NLP pipelines for clinical notes; best practice in data annotation; clinical information extraction using rule-based methods and machine learning; knowledge resources for clinical NLP; clinical NLP toolkits; utilization of NLP and social media for health applications; sentiment and stance analysis in social media data; data management, privacy, and ethical issues in health-related NLP
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 NLP pipelines using the Python programming language
- Demonstrate competence in the use of standard health-related NLP libraries and knowledge resources
- Demonstrate an ability to appraise ethical issues in health NLP
- Develop and evaluate an NLP pipeline for consumer-generated data using Python
- Demonstrate an ability to critically evaluate health NLP publications
Generic skills
- Develop critical thinking and analytical skills
- Enhance existing Python programming skills
- Presentation skills
- Improved writing skills
Last updated: 31 January 2024
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 2 (On Campus - Parkville)
Semester 1 (Dual-Delivery - Parkville)
Summer Term (Dual-Delivery - Parkville)
|
12.5 |
COMP90038 | Algorithms and Complexity |
Semester 2 (On Campus - Parkville)
Semester 1 (Dual-Delivery - Parkville)
|
12.5 |
Or equivalent
OR
Admission into or selection of one of the following:
- MC-IT Master of Information Technology
- MC-DATASC Master of Data Science
- MC-CS Master of Computer Science
- GD-CS Graduate Diploma in Computer Science
- 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: 31 January 2024
Assessment
Description | Timing | Percentage |
---|---|---|
Short essay on ethical issues in Health NLP - Equivalent to approximately 500 words in total
| Week 3 | 15% |
Two individual programming assignments – Equivalent to approximately 2,000 words in total (2x1,000 words). Due in Week 4 & 7.
| During the teaching period | 30% |
Participation in online discussion – Equivalent to 500 words in total
| From Week 1 to Week 10 | 10% |
Independent project (Individual) – Equivalent to 2,000 words. Due at the start of the exam period
| End of semester | 45% |
Last updated: 31 January 2024
Dates & times
- Semester 1
Coordinator Mike Conway Mode of delivery Dual-Delivery (Parkville) Contact hours 36 hours, comprising two 1-hour lectures and one 1-hour tutorial per week Total time commitment 200 hours Teaching period 27 February 2023 to 28 May 2023 Last self-enrol date 10 March 2023 Census date 31 March 2023 Last date to withdraw without fail 5 May 2023 Assessment period ends 23 June 2023 Semester 1 contact information
What do these dates mean
Visit this webpage to find out about these key dates, including how they impact on:
- Your tuition fees, academic transcript and statements.
- And for Commonwealth Supported students, your:
- Student Learning Entitlement. This applies to all students enrolled in a Commonwealth Supported Place (CSP).
Subjects withdrawn after the census date (including up to the ‘last day to withdraw without fail’) count toward the Student Learning Entitlement.
Last updated: 31 January 2024
Further information
- Texts
Prescribed texts
There are no specifically prescribed or recommended texts for this subject.
- Related Handbook entries
This subject contributes to the following:
Type Name Course Master of Information Technology Course Master of Data Science Course Graduate Diploma in Computer Science Course Master of Computer Science Course Master of Information Systems - 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: 31 January 2024