ORCID Profile
0000-0001-6531-8907
Current Organisations
St. Francis Xavier University
,
Deakin University
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Research Topics
In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Top 5 Research Topics
ANZSRC Field of Research (FoR)
Health Information Systems (incl. Surveillance) | Cognitive Science | Pattern Recognition and Data Mining | Medical Devices | Software Engineering | Knowledge Representation and Machine Learning | Computer Software
ANZSRC Socio-Economic Objective (SEO)
Behaviour and Health | Expanding Knowledge in the Information and Computing Sciences | Integrated Circuits and Devices | Application Software Packages (excl. Computer Games) |
Related Links
Publications
Building Teacher Capacity and Leadership in Elementary Mathematics Classrooms in Nova Scotia: Review of the Certificate in Elementary Mathematics Pedagogy
Publisher: Springer Science and Business Media LLC
Date: 12-2021
Catastrophic forgetting and mode collapse in GANs
Publisher: IEEE
Date: 07-2020
GEFA: Early Fusion Approach in Drug-Target Affinity Prediction
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2022
Neural Reasoning, Fast and Slow, for Video Question Answering
Publisher: IEEE
Date: 07-2020
A Deep Learning Model for Estimating Story Points
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2019
Consistency of the Health of the Nation Outcome Scales (HoNOS) at inpatient-to-community transition
Publisher: BMJ
Date: 04-2016
Dynamic Reasoning for Movie QA: A Character-Centric Approach
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Protocol for a bandit-based response adaptive trial to evaluate the effectiveness of brief self-guided digital interventions for reducing psychological distress in university students: The Vibe Up Stu
Publisher: Cold Spring Harbor Laboratory
Date: 06-12-2022
DOI: 10.1101/2022.12.05.22283129
Abstract: Meta-analytic evidence confirms a range of interventions, including mindfulness, physical activity and sleep hygiene, can reduce psychological distress in university students. However, it is unclear which intervention is most effective. Artificial intelligence (AI) driven adaptive trials may be an efficient method to determine what works best and for whom. The primary purpose of the study is to rank the effectiveness of mindfulness, physical activity, sleep hygiene and an active control on reducing distress, using a multi-arm contextual bandit-based AI-adaptive trial method. Furthermore, the study will explore which interventions have the largest effect for students with different levels of baseline distress severity. The Vibe Up study is a pragmatically-oriented, decentralised AI-adaptive group sequential randomised controlled trial (RCT) comparing the effectiveness of one of three brief, two week digital self-guided interventions (mindfulness, physical activity, or sleep hygiene) or active control (ecological momentary assessment) in reducing self-reported psychological distress in Australian university students. The adaptive trial methodology involves up to 12 sequential mini-trials that allow for the optimisation of allocation ratios. The primary outcome is change in psychological distress (DASS-21 total score) from pre-intervention to post-intervention. Secondary outcomes include change in depression, anxiety, and stress (measured by DASS-21 subscales) from pre-intervention to post-intervention. Planned contrasts will compare the four groups (i.e., the three intervention and control) using self-reported psychological distress at pre-specified time points for interim analyses. The study aims to determine the best performing intervention, as well as ranking of other interventions. Ethical approval was sought and obtained from the UNSW Sydney Human Research Ethics Committee (HREC A, HC200466). A trial protocol adhering to the requirements of the Guideline for Good Clinical Practice [1] was prepared for and approved by the Sponsor, UNSW Sydney (Protocol number: HC200466_CTP). The trial is registered with the Australian New Zealand Clinical Trials Registry (AC-TRN12621001223820). The study addresses an important clinical question using novel, advanced methods The trial uses short-duration interventions designed to improve coping responses to transient stressors, which addresses the most common needs of university students A value of information analysis is included to compare the value of the new trial methods with traditionalapproaches Digital phenotyping is used to explore smartphone sensor information with clinical outcomes More than 12 mini-trials might be required to determine the ranking for the interventions The interventions may prove to be of the same level of effectiveness for each level of severity Interventions other than those examined in this study, such as CBT, may be more effective and remain untested The methodology assumes that the three digital interventions are configured to deliver similar doses and/or have approximate fidelity with standard methods
Energy-based anomaly detection for mixed data
Publisher: Springer Science and Business Media LLC
Date: 12-02-2018
Predicting Risk of Suicide Attempt Using History of Physical Illnesses From Electronic Medical Records
Publisher: JMIR Publications Inc.
Date: 11-07-2016
DOI: 10.2196/MENTAL.5475
Abstract: Although physical illnesses, routinely documented in electronic medical records (EMR), have been found to be a contributing factor to suicides, no automated systems use this information to predict suicide risk. The aim of this study is to quantify the impact of physical illnesses on suicide risk, and develop a predictive model that captures this relationship using EMR data. We used history of physical illnesses (except chapter V: Mental and behavioral disorders) from EMR data over different time-periods to build a lookup table that contains the probability of suicide risk for each chapter of the International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10) codes. The lookup table was then used to predict the probability of suicide risk for any new assessment. Based on the different lengths of history of physical illnesses, we developed six different models to predict suicide risk. We tested the performance of developed models to predict 90-day risk using historical data over differing time-periods ranging from 3 to 48 months. A total of 16,858 assessments from 7399 mental health patients with at least one risk assessment was used for the validation of the developed model. The performance was measured using area under the receiver operating characteristic curve (AUC). The best predictive results were derived (AUC=0.71) using combined data across all time-periods, which significantly outperformed the clinical baseline derived from routine risk assessment (AUC=0.56). The proposed approach thus shows potential to be incorporated in the broader risk assessment processes used by clinicians. This study provides a novel approach to exploit the history of physical illnesses extracted from EMR (ICD-10 codes without chapter V-mental and behavioral disorders) to predict suicide risk, and this model outperforms existing clinical assessments of suicide risk.
Forecasting Daily Patient Outflow From a Ward Having No Real-Time Clinical Data
Publisher: JMIR Publications Inc.
Date: 21-07-2016
Hierarchical semi-Markov conditional random fields for deep recursive sequential data
Publisher: Elsevier BV
Date: 05-2017
Robust and Interpretable General Movement Assessment Using Fidgety Movement Detection
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2023
A Spatio-Temporal Attention-Based Model for Infant Movement Assessment From Videos
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2021
DeepTRIAGE: Interpretable and Individualised Biomarker Scores using Attention Mechanism for the Classification of Breast Cancer Sub-types
Publisher: Cold Spring Harbor Laboratory
Date: 29-01-2019
DOI: 10.1101/533406
Abstract: Breast cancer is a collection of multiple tissue pathologies, each with a distinct molecular signature that correlates with patient prognosis and response to therapy. Accurately differentiating between breast cancer sub-types is an important part of clinical decision-making. Already, this problem has been addressed using machine learning methods that separate tissue s les into distinct groups. However, there remains unexplained heterogeneity within the established sub-types that cannot be resolved by the commonly used classification algorithms. In this paper, we propose a novel deep learning architecture, called DeepTRIAGE (Deep learning for the TRactable In idualised Analysis of Gene Expression), which not only classifies cancer sub-types with comparable accuracy, but simultaneously assigns each patient their own set of interpretable and in idualised biomarker scores. These personalised scores describe how important each feature is in the classification of each patient, and can be analysed post-hoc to generate new hypotheses about intra-class heterogeneity. We apply the DeepTRIAGE framework to classify the gene expression signatures of luminal A and luminal B breast cancer sub-types, and illustrate its use for genes and gene set (i.e., GO and KEGG) features. Using DeepTRIAGE, we find that the GINS1 gene and the kinetochore organisation GO term are the most important features for luminal sub-type classification. Through classification, DeepTRIAGE simultaneously reveals heterogeneity within the luminal A biomarker scores that significantly associate with tumour stage, placing all luminal s les along a continuum of severity. The proposed model is implemented in Python using Py-Torch framework. The analysis is done in Python and R. All Methods and models are freely available from dham/BiomarkerAttend .
Graph-induced restricted Boltzmann machines for document modeling
Publisher: Elsevier BV
Date: 2016
Protocol for a bandit-based response adaptive trial to evaluate the effectiveness of brief self-guided digital interventions for reducing psychological distress in university students: the Vibe Up stu
Publisher: BMJ
Date: 04-2023
DOI: 10.1136/BMJOPEN-2022-066249
Abstract: Meta-analytical evidence confirms a range of interventions, including mindfulness, physical activity and sleep hygiene, can reduce psychological distress in university students. However, it is unclear which intervention is most effective. Artificial intelligence (AI)-driven adaptive trials may be an efficient method to determine what works best and for whom. The primary purpose of the study is to rank the effectiveness of mindfulness, physical activity, sleep hygiene and an active control on reducing distress, using a multiarm contextual bandit-based AI-adaptive trial method. Furthermore, the study will explore which interventions have the largest effect for students with different levels of baseline distress severity. The Vibe Up study is a pragmatically oriented, decentralised AI-adaptive group sequential randomised controlled trial comparing the effectiveness of one of three brief, 2-week digital self-guided interventions (mindfulness, physical activity or sleep hygiene) or active control (ecological momentary assessment) in reducing self-reported psychological distress in Australian university students. The adaptive trial methodology involves up to 12 sequential mini-trials that allow for the optimisation of allocation ratios. The primary outcome is change in psychological distress (Depression, Anxiety and Stress Scale, 21-item version, DASS-21 total score) from preintervention to postintervention. Secondary outcomes include change in physical activity, sleep quality and mindfulness from preintervention to postintervention. Planned contrasts will compare the four groups (ie, the three intervention and control) using self-reported psychological distress at prespecified time points for interim analyses. The study aims to determine the best performing intervention, as well as ranking of other interventions. Ethical approval was sought and obtained from the UNSW Sydney Human Research Ethics Committee (HREC A, HC200466). A trial protocol adhering to the requirements of the Guideline for Good Clinical Practice was prepared for and approved by the Sponsor, UNSW Sydney (Protocol number: HC200466_CTP). ACTRN12621001223820.
Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View
Publisher: JMIR Publications Inc.
Date: 16-12-2016
DOI: 10.2196/JMIR.5870
PAN: Personalized Annotation-Based Networks for the Prediction of Breast Cancer Relapse
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2021
Stabilizing High-Dimensional Prediction Models Using Feature Graphs
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2015
Learning Transferable Domain Priors for Safe Exploration in Reinforcement Learning
Publisher: IEEE
Date: 07-2020
Explaining Black Box Drug Target Prediction Through Model Agnostic Counterfactual Samples
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2023
Automatic Feature Learning for Predicting Vulnerable Software Components
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Collaborative filtering via sparse Markov random fields
Publisher: Elsevier BV
Date: 11-2016
Related Organisations
Deakin University - Geelong Campus At Waurn Ponds
Location: Australia
Related Funding Activities
Discovery Projects - Grant ID: DP210102798
Start Date: 02-2021
End Date: 01-2024
Amount: $361,000.00
Funder: Australian Research Council
View Funded ActivityIndustrial Transformation Research Hubs - Grant ID: IH170100013
Start Date: 03-2018
End Date: 10-2023
Amount: $2,962,655.00
Funder: Australian Research Council
View Funded Activity