Computer-assisted Clinical Guidelines For The Management Of Manifestations Of Anxiety, Aggression And Depression
Funder
National Health and Medical Research Council
Funding Amount
$354,032.00
Summary
This project focuses on creating a new approach to integration of clinical guidelines and the development of a computer-assisted tool to support medical reasoning in psychogeriatrics. The primary focus of this research is on helping medical practitioners to better manage dementia patients with symptoms of anxiety, aggression and depression living in nursing homes. It has the potential to fundamentally improve the way guidelines are utilised in clinical practice
Learning to Reason in Reinforcement Learning. Deep Reinforcement Learning (RL) uses deep neural networks to represent and learn optimal decision-making policies for intelligent agents in complex environments. However, most RL approaches require millions of episodes to converge to good policies, making it difficult for RL to be applied in real-world scenarios taking significant resources. This project aims to equip RL with capabilities such as counterfactual reasoning and outcome anticipation to ....Learning to Reason in Reinforcement Learning. Deep Reinforcement Learning (RL) uses deep neural networks to represent and learn optimal decision-making policies for intelligent agents in complex environments. However, most RL approaches require millions of episodes to converge to good policies, making it difficult for RL to be applied in real-world scenarios taking significant resources. This project aims to equip RL with capabilities such as counterfactual reasoning and outcome anticipation to significantly reduce the number of interactions required, improve generalisation, and provide the agent with the capability to consider the cause-effects. These improvements would narrow the gap between AI and human capabilities and broaden the adoption of RL in real-world applications.Read moreRead less
Small Scalable Natural Language Models using Explicit Memory. Deep neural networks have had spectacular success in natural language processing, seeing wide-spread deployment as part of automatic assistant devices in homes and cars, and across many valuable industries including finance, medicine and law. Fueling this success is the use of ever larger models, with exponentially increasing training resources, accompanying hardware and energy demands. This project aims to develop more compact models ....Small Scalable Natural Language Models using Explicit Memory. Deep neural networks have had spectacular success in natural language processing, seeing wide-spread deployment as part of automatic assistant devices in homes and cars, and across many valuable industries including finance, medicine and law. Fueling this success is the use of ever larger models, with exponentially increasing training resources, accompanying hardware and energy demands. This project aims to develop more compact models, based on the incorporation of an explicit searchable memory, which will dramatically reduce model size, hardware requirements and energy usage. This will make modern natural language processing more accessible, while also providing greater flexibility, allowing for more adaptable and portable technologies.Read moreRead less
Towards knowledge discovery from imperfect and evolving data. Information extraction from data is critical, both to analyse and protect consumer data. However, many learning techniques are developed using perfect, static datasets, quite different to messy, ever-changing real-world data. This project aims to develop data analytics techniques that can extract accurate information in complex structures from imperfect/incomplete data that changes over time. Expected outcomes are a prototype tool, te ....Towards knowledge discovery from imperfect and evolving data. Information extraction from data is critical, both to analyse and protect consumer data. However, many learning techniques are developed using perfect, static datasets, quite different to messy, ever-changing real-world data. This project aims to develop data analytics techniques that can extract accurate information in complex structures from imperfect/incomplete data that changes over time. Expected outcomes are a prototype tool, tested on real datasets, that combines new techniques in data modelling, algorithm development, and system design. Likely benefits are enhanced Australia's competence in data science through student training and new, robust data tools relevant to critical sectors such as cybersecurity, healthcare, and defence.Read moreRead less
Short Sequence Representation Learning with Limited Supervision . Predicting events based on short text and video data is widely found in real-world applications such as online crime detection, cyber-attack identification, and public security protection. However, to develop such an effective prediction model is very difficult due to the problems such as limited supervision, heterogeneous multiple sources, and missing and low-quality data. This project is to tackle these challenges. Expected outc ....Short Sequence Representation Learning with Limited Supervision . Predicting events based on short text and video data is widely found in real-world applications such as online crime detection, cyber-attack identification, and public security protection. However, to develop such an effective prediction model is very difficult due to the problems such as limited supervision, heterogeneous multiple sources, and missing and low-quality data. This project is to tackle these challenges. Expected outcome of this project will lay a theoretical foundation for effective short sequence representation learning and build next-generation intelligent systems. This should benefit our society and economy through the applications of multimodality-integrated video technologies for cybersecurity and public safety. Read moreRead less
Improving Research Evidence Quality Using Individual Patient Data, Prospective Meta-analysis And Trial Registration
Funder
National Health and Medical Research Council
Funding Amount
$387,489.00
Summary
The quality of evidence we use to make health care decisions can be improved if we use systematic reviews that are planned ahead, that use raw data from each participant and include all the trials that have looked at the clinical problem. This research program will utilise these three ways of obtaining better quality data and will thus make research results more reliable. In particular, we will use these techniques to address health problems in mothers and babies.
Fluid resuscitation is widely used in the management of critically ill patients. There are a variety of different fluids available to doctors but there is little evidence regarding how effective they are. One of the most commonly used fluids, a hydroxyethyl starch was recently approved by the TGA for use in Australia. This project aims to compare how effective and safe this fluid is compared to another widely used fluid, saline, for resuscitation of critically ill patients in intensive care.
Cancer is now the leading cause of death in our community. Dramatic progress in genomic technologies is impacting on cancer treatment and risk management internationally. My vision is an Australian Genomic Cancer Medicine Program (AGCMP), uniting than 15 cancer centres and three major medical research institutes in all states and territories, and bringing genomics through research into the clinic to improve health outcomes for all Australians.
Increasing Value, Reducing Waste From Incomplete Or Unusable Reports Of Medical Research
Funder
National Health and Medical Research Council
Funding Amount
$788,486.00
Summary
We estimated that the avoidable waste in research - from design flaws, non-publication, and inadequate reporting - results in over $85 Billion annual loss. I will research innovations to reduce this waste. My focus is particularly on non-drug interventions - exercises, dietary changes, self-monitoring, e-health applications – which are often effective but more difficult to use in clinical practice, and being compiled in my recently founded Handbook of Non-Drug Interventions (see RACGP website).