Centre for Youth Mental Health - Research Publications
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ItemNo Preview AvailableDesigning an App for Pregnancy Care for a Culturally and Linguistically Diverse Community(The Association for Computing Machinery, 2017)We report a study to design and evaluate an app to support pregnancy information provided to women through an Australian health service. As part of a larger project to provide prenatal resources for culturally and linguistically diverse groups, this study focused on the design and reception of an app with the local Vietnamese community and health professionals of a particular hospital. Our study had three stages: an initial design workshop with the hospital; prototype design and development; prototype-based interviews with health professionals and focus groups with Vietnamese women. We explore how an app of this sort must be designed for a range of different use scenarios, considering its use by consumers with a multiplicity of differing viewpoints about its nature and purpose in relation to pregnancy care.
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ItemMachine Learning for Large-Scale Quality Control of 3D Shape Models in Neuroimaging(SPRINGER INTERNATIONAL PUBLISHING AG, 2017)As very large studies of complex neuroimaging phenotypes become more common, human quality assessment of MRI-derived data remains one of the last major bottlenecks. Few attempts have so far been made to address this issue with machine learning. In this work, we optimize predictive models of quality for meshes representing deep brain structure shapes. We use standard vertex-wise and global shape features computed homologously across 19 cohorts and over 7500 human-rated subjects, training kernelized Support Vector Machine and Gradient Boosted Decision Trees classifiers to detect meshes of failing quality. Our models generalize across datasets and diseases, reducing human workload by 30-70%, or equivalently hundreds of human rater hours for datasets of comparable size, with recall rates approaching inter-rater reliability.
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ItemIncreases in controlled-release oxycodone utilisation following the subsidy of oxycodone with naloxone formulations: An Australian population-based study(WILEY, 2019-01)PURPOSE: Despite increasing use of oxycodone/naloxone controlled-release (CR) in Australia, little is known about how it has affected the overall oxycodone CR market since its subsidy in 2011. METHODS: We used Pharmaceutical Benefits Scheme dispensing claims (2006-2016) and interrupted time series analysis to examine changes in the quarterly rates of dispensing of oral oxycodone CR formulations (oxycodone/naloxone CR and single-ingredient oxycodone CR) and new oxycodone CR treatment episodes. We also performed a retrospective cohort study in a sample of people initiating a new oxycodone CR treatment episode in 2009, 2012/2013, and 2016 to compare opioid utilisation patterns over time. RESULTS: The subsidy of oxycodone/naloxone CR was associated with a 1.6-fold increase in the growth rate of oxycodone CR dispensing, resulting from rapid uptake of low strength (≤5 mg) oxycodone/naloxone CR. In our cohort of initiators, the number of new oxycodone CR treatment episodes increased 2.1-fold between 2009 and 2016; in 2016, 91.4% of new treatment episodes involved oxycodone/naloxone CR. Comparing 2016 with 2009, we observed an increase in people initiating with a tablet strength less than or equal to 5-mg (risk difference [RD] = 21.1%, 95% CI, 19.9%-22.4%) in people initiating with no other opioid dispensing 90 days prior to initiation (RD = 5.2%, 3.8%-6.6%) and with no further opioid dispensing 90 days after initiation (RD = 8.8%, 7.4%-10.2%). CONCLUSIONS: After its subsidy, the uptake of low-dose oxycodone/naloxone CR was greater than expected if it were substituting the single-ingredient oxycodone CR, resulting in an expansion of the oxycodone CR market.
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ItemA chatbot architecture for promoting youth resilience(IOS Press, 2021-04-19)E-health technologies have potential to provide scalable and accessible interventions for youth mental health. As part of an ecosystem of e-screening and e-therapy tools for New Zealand young people, a dialog agent, Headstrong, has been designed to promote resilience with methods grounded in cognitive behavioral therapy and positive psychology. This paper describes the architecture underlying the chatbot. The architecture supports a range of over 20 activities delivered in a 4-week program by relatable personas. The architecture provides a visual authoring interface to its content management system. In addition to supporting the original adolescent resilience chatbot, the architecture has been reused to create a 3-week ‘stress-detox’ intervention for undergraduates, and subsequently for a chatbot to support young people with the impacts of the COVID-19 pandemic, with all three systems having been used in field trials. The Headstrong architecture illustrates the feasibility of creating a domain-focused authoring environment in the context of e-therapy that supports non-technical expert input and rapid deployment.
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ItemClassification of major depressive disorder via multi-site weighted LASSO model(Springer, 2017-01-01)Large-scale collaborative analysis of brain imaging data, in psychiatry and neurology, offers a new source of statistical power to discover features that boost accuracy in disease classification, differential diagnosis, and outcome prediction. However, due to data privacy regulations or limited accessibility to large datasets across the world, it is challenging to efficiently integrate distributed information. Here we propose a novel classification framework through multi-site weighted LASSO: each site performs an iterative weighted LASSO for feature selection separately. Within each iteration, the classification result and the selected features are collected to update the weighting parameters for each feature. This new weight is used to guide the LASSO process at the next iteration. Only the features that help to improve the classification accuracy are preserved. In tests on data from five sites (299 patients with major depressive disorder (MDD) and 258 normal controls), our method boosted classification accuracy for MDD by 4.9% on average. This result shows the potential of the proposed new strategy as an effective and practical collaborative platform for machine learning on large scale distributed imaging and biobank data.