Privacy-Preserving Synthetic Healthcare Data Generation for Causal Analysis
The project focuses on developing a framework to generate synthetic healthcare records that accurately preserve the causal relationships present in real data while ensuring privacy. This research aims to address the challenges of using real healthcare data in research, such as privacy concerns and strict legal and regulatory frameworks like GDPR and HIPAA.
Mapping clinical classification systems using Natural Language processing and machine learning techniques
The goal is to develop a system/algorithm for automatic mapping of different versions of the International Classification of Disease (ICD).
Privacy-Preserving Data Analysis and Computation on Electronic Medical Record (EMR) Documents
Privacy-Preserving Data Analysis and Computation on Electronic Medical Record (EMR) Documents
Distributed Knowledge Based Clinical Auto-Coding System
The aim is to develop a knowledge-based clinical auto-coding system that utilise appropriate Natural Language Processing and Machine Learning techniques to assign ICD-10-AM and ACHI codes to clinical records while adhering to Australian Coding Standard that get updated and validated continuously.
MobileEYE: Deep Learning based Mobile Device Eye Tracking Solution for Dynamic Visuals
This project represents a cutting-edge exploration into the realms of human-computer interaction and advanced technology. Leveraging the power of mobile devices, this research initiative seeks to develop a groundbreaking system for tracking and accurately estimating a user’s gaze while they engage with dynamic visual content.
In a world inundated with multimedia and immersive experiences, the accurate tracking of user gaze is crucial for tailoring content, improving accessibility, and enhancing user experiences. By harnessing the capabilities of edge computing, the project aims to deliver real-time and precise gaze estimation directly on mobile devices, reducing latency and enhancing privacy.