PRECISION MEDICINE DATA PLATFORM (PMDP) Projects
Funded by Advance Queensland and Datarwe Pty Ltd

Available through Griffith University
Datarwe PhD TopUp Scholarships

Datarwe Pty Ltd in association with Advance Queensland has formed a collaborative research agreement with Griffith University’s Institute for Integrated and Intelligent Systems.

As part of this collaboration seven research projects using Datarwe’s Precision Medicine Data Platform (PMDP) have been selected by Griffith University for PhD completion. Successful applicants will be eligible to receive PhD top-up scholarships from Advance Queensland and Datarwe Pty Ltd. Top-Up Scholarships will be for one year, with options to renew in subsequent years.

Data science research using data within the Datarwe platform has the potential to unlock AI insights and innovation for critical care.  These PhD top-up scholarships will provide students internships at Datarwe’s head office collaborating with an experienced data science and engineering team, and financial support in their data preparation and enrichment tasks for their research topic.

Following are the seven Precision Medicine Data Platform (PMDP) projects being offered commencing in 2021. Internal candidates apply as listed or contact Datarwe directly at info@datarwe.com . Applications now being received !

Can pathological indicators determine severity of bacteremia and/or predict antimicrobial resistant clones?

Can pathological measures such as inflammatory cascades be matched to clinical observations to guide antimicrobial choice in intensive care? Can these patterns predict disease severity and/or potential microbial clones and therefore likely patterns of resistance? Can this data detect or predict clinical response to guide changes in antimicrobial coverage or [...]

AI analysis of Antibiotic Prescribing

Antimicrobial resistance is driven high volume, inappropriate prescribing. Antibiotic usage (AU) and antimicrobial resistance (AMR) data alone are incapable of reporting appropriate use of antimicrobials. There is a need to create deeper data driven analysis of appropriate antibiotic prescribing in real-time. Data readily available through electronic records could be mined [...]

Augmented stream learning of medical time series data for continuous outcome prognosis

This project aims to develop novel stream learning algorithms for continuous patient outcome prognosis by taking into account patient's data collected during ICU admission in a unified manner. The algorithms are expected to integrate high frequency time series data with patient's demographic data, lab data, diagnosis data, prescription data, etc. [...]

Knowledge Empowered Decision Making in Medical Data Processing

This research project focuses on developing explainable AI solutions to decision support systems, by combining knowledge graphs, machine reasoning and machine learning. Knowledge graphs is a promising data and knowledge organisation, synthesis and management approach, and we have developed scalable reasoning tools for knowledge graphs coupled with ontological rules that [...]

Using machine learning techniques to help improve intensive care outcomes in traumatic brain injuries

This project aims to develop a real time application for prediction of intensive care outcomes in traumatic brain injuries. It utilises heart rate variability parameters, applies ECG signal analysis techniques in combination with machine learning and feature selection algorithms. The candidate will investigate time series analysis and various classification methodologies, [...]

Artificial Intelligence facilitates ICU Resource Management against Infectious Diseases

Respiratory infections and antibiotic resistance bacterial infection are some of the conditions that have significantly stressed our hospital ICU. A number of risk factors have been reported to associate with severe diseases, which includes age, pre-existing conditions, pathogen setpoints, responsiveness to therapeutic strategy. Any single risk factor is unlikely to [...]

Safe reinforcement learning for medical applications

This project aims to develop safer and more secure AI decisions-making systems for the medical domain. We plan to develop a new learning approach which combines probabilistic model checking and reinforcement learning and provides formal safety guarantees for the learned policies. This learning approach will be integrated into an adversarial [...]