ACT Health Directorate - Using Natural Language Analysis for Classifying Clinical Notes

Develop natural language analysis to classify clinical (medical) notes.

label Opportunity type
College approved Internship
schedule Application date
31 Aug 2023 | 9am - 15 Sep 2023 | 5pm
school Level
Bachelor
Master

About

ACT Health Directorate (ACTHD) provides a comprehensive range of health services to the people of the Australian Capital Territory. ACTHD sets health policy, and plans the delivery of health services while ensuring these services meet community needs. ACTHD also funds a range of non-Government organisations to provide vital healthcare services to the people of the ACT and surrounding region.

Project: Using Natural Language Analysis for Classifying Clinical Notes

Internship details

Internship Availability

Semester 1,  2024

Internship Discipline/s
  • Public Health
  • Epidemiology
  • Computer Science

Internship Level

3rd yr undergraduate, or PG coursework

Available to International Students

No

Preferred Project Skills:

Data science skills including at minimum basic familiarity with languages such as R or Python

Clearances Required

Will need to be approved via ACT Health's InPlace student intake system for non-clinical student placements 

Host Supervisor

Glenn Draper, Senior Epidemiologist, ACT Health;  glenn.draper@act.gov.au;

Dr Louise Freebairn, Manager, Knowledge Translation and Heath Outcomes, ACT Health; louise.freebairn@act.gov.au.

Please cc PHXchange@anu.edu.au and helen.skeat@anu.edu.au

Location

ACT Health Directorate staff follow a hybrid working arrangement.  Some in-office time (one day per week) will be offered located at Bowes St, Woden, ACT.

Project Opportunities/Benefits for the Intern

The intern will receive mentoring from the co-supervisors and will gain an understanding of developing and applying data science skills in a government environment with real world application.

Summary:

Development of natural language analysis to classify clinical (medical) notes, using statistical tools such as R or Python. The core aim of the project is to automate the classification of potential self-harm Emergency Department presentations via key-word searches.