DATA430-19S2 (C) Semester Two 2019

Medical Data Informatics

15 points
15 Jul 2019 - 10 Nov 2019

Description

This course explores statistical models, algorithms, and programming platforms for medical data including imaging, clinical and research text reports, lab results, and patient records.

Special Topic: Medical Data Informatics This course explores statistical models, algorithms, and programming platforms for medical data including imaging, clinical and research text reports, lab results, and patient records. The course will explore detailed aspects of data science research and applications such as reinforcement learning, natural language processing, deep learning, model selection, visualisation, and parallel programming. This course will offer students an opportunity to deepen their understanding in these topics using medical data to explore active research and applications to improve quality of medical care and advance knowledge of big data's role in diagnosis and treatment. Topics include statistical models, algorithms, and programming platforms for processing medical data including medical imaging data (ECG, CT, MRI, fMRI, ultrasound), medical texts (clinical notes, lab reports, published research), and patient medical records (EHR). Students will complete lab assignments to show competency in usage of software platforms for visualisation, parallel processing, and model selection. Students will design and implement a project using machine learning to research possible solutions to real-world problems in the medical data domain. Students will self-reflect on aspects of data science to improve quality, access, and efficacy in medical care.

Learning Outcomes

  • Students who successfully complete this course will be able to:
  • Demonstrate knowledge of models and algorithms for reinforcement learning, natural language processing, and deep learning
  • Show competency in usage of software platforms for visualisation, parallel processing, and model selection
  • Show competency in applying skills to process medical imaging data (ECG, CT, MRI, fMRI, ultrasound), medical texts (clinical notes, lab reports, published research), and patient medical records (EHR)
  • Apply data science competencies to design and implement a research project using machine learning to research possible solutions to real-world problems in the medical data domain.
  • Self-reflect on selected aspects of data science to improve quality, access, and efficacy in medical care

Pre-requisites

Subject to approval of the Head of Department of Computer Science and Software Engineering.

Timetable 2019

Students must attend one activity from each section.

Lecture A
Activity Day Time Location Weeks
01 Monday 15:00 - 16:00 Jack Erskine 340 15 Jul - 25 Aug
9 Sep - 20 Oct
Lecture B
Activity Day Time Location Weeks
01 Tuesday 09:00 - 10:00 Jack Erskine 443 15 Jul - 25 Aug
9 Sep - 20 Oct
Computer Lab A
Activity Day Time Location Weeks
01 Thursday 11:00 - 13:00 Jack Erskine 131 Lab 1 15 Jul - 25 Aug
9 Sep - 20 Oct

Course Coordinator

James Atlas

Indicative Fees

Domestic fee $923.00

* Fees include New Zealand GST and do not include any programme level discount or additional course related expenses.

For further information see Computer Science and Software Engineering.

All DATA430 Occurrences

  • DATA430-19S2 (C) Semester Two 2019