ORCID Profile
0000-0002-9977-8247
Current Organisation
Monash University
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
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.
Artificial Intelligence and Image Processing | Pattern Recognition and Data Mining | Computer Vision | Computer Vision | Pattern Recognition | Adaptive Agents and Intelligent Robotics | Machine learning | Machine learning not elsewhere classified | Information Systems | Computer vision | Data security and protection | Conceptual Modelling | Health Informatics | Software Engineering | Knowledge Representation and Machine Learning | Image Processing | Computer-Human Interaction | Cognitive Science | Cybersecurity and privacy not elsewhere classified | Deep learning | Cybersecurity and privacy |
Expanding Knowledge in the Information and Computing Sciences | Application tools and system utilities | Computer Software and Services not elsewhere classified | Information and Communication Services not elsewhere classified | Information processing services | Specific Population Health (excl. Indigenous Health) not elsewhere classified | Expanding Knowledge in the Medical and Health Sciences | Information Processing Services (incl. Data Entry and Capture) | Application Tools and System Utilities |
Publisher: Springer Science and Business Media LLC
Date: 16-11-2013
Publisher: Springer International Publishing
Date: 2020
Publisher: IEEE
Date: 10-2016
DOI: 10.1109/ICHI.2016.26
Publisher: IEEE
Date: 12-2016
Publisher: IEEE
Date: 2005
Publisher: Springer Berlin Heidelberg
Date: 2004
Publisher: IEEE
Date: 07-2020
Publisher: ACM
Date: 11-08-2013
Publisher: IEEE
Date: 2005
Publisher: Springer International Publishing
Date: 2018
Publisher: IEEE
Date: 03-2016
Publisher: ACM
Date: 19-07-2018
Publisher: IEEE
Date: 08-2014
Publisher: IEEE
Date: 2006
Publisher: Springer International Publishing
Date: 2015
Publisher: JMIR Publications Inc.
Date: 16-12-2016
DOI: 10.2196/JMIR.5870
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2022
Publisher: IEEE
Date: 12-2011
DOI: 10.1109/ICDM.2011.51
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: Elsevier BV
Date: 07-2017
Publisher: IEEE
Date: 11-2017
DOI: 10.1109/ICDM.2017.35
Publisher: Elsevier BV
Date: 12-2018
Publisher: Springer International Publishing
Date: 2019
Publisher: Association for Computing Machinery (ACM)
Date: 11-2008
Abstract: We present online algorithms to extract social context: Social spheres are labeled locations of significance, represented as convex hulls extracted from GPS traces. Colocation is determined from Bluetooth and GPS to extract social rhythms, patterns in time, duration, place, and people corresponding to real-world activities. Social ties are formulated from proximity and shared spheres and rhythms. Quantitative evaluation is performed for 10+ million s les over 45 man-months. Applications are presented with assessment of perceived utility: Socio-Graph , a video and photo browser with filters for social metadata, and Jive , a blog browser that uses rhythms to discover similarity between entries automatically.
Publisher: IEEE
Date: 12-2016
Publisher: IEEE
Date: 10-2016
DOI: 10.1109/DSAA.2016.48
Publisher: BMJ
Date: 04-2016
Publisher: Springer International Publishing
Date: 2016
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 08-2019
Abstract: We propose a new formulation for learning generative adversarial networks (GANs) using optimal transport cost (the general form of Wasserstein distance) as the objective criterion to measure the dissimilarity between target distribution and learned distribution. Our formulation is based on the general form of the Kantorovich duality which is applicable to optimal transport with a wide range of cost functions that are not necessarily metric. To make optimising this duality form amenable to gradient-based methods, we employ a function that acts as an amortised optimiser for the innermost optimisation problem. Interestingly, the amortised optimiser can be viewed as a mover since it strategically shifts around data points. The resulting formulation is a sequential min-max-min game with 3 players: the generator, the critic, and the mover where the new player, the mover, attempts to fool the critic by shifting the data around. Despite involving three players, we demonstrate that our proposed formulation can be trained reasonably effectively via a simple alternative gradient learning strategy. Compared with the existing Lipschitz-constrained formulations of Wasserstein GAN on CIFAR-10, our model yields significantly better ersity scores than weight clipping and comparable performance to gradient penalty method.
Publisher: Elsevier BV
Date: 06-2016
Publisher: IEEE
Date: 07-2012
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 08-2017
Abstract: A typical online kernel learning method faces two fundamental issues: the complexity in dealing with a huge number of observed data points (a.k.a the curse of kernelization) and the difficulty in learning kernel parameters, which often assumed to be fixed. Random Fourier feature is a recent and effective approach to address the former by approximating the shift-invariant kernel function via Bocher's theorem, and allows the model to be maintained directly in the random feature space with a fixed dimension, hence the model size remains constant w.r.t. data size. We further introduce in this paper the reparameterized random feature (RRF), a random feature framework for large-scale online kernel learning to address both aforementioned challenges. Our initial intuition comes from the so-called "reparameterization trick" [Kingma et al., 2014] to lift the source of randomness of Fourier components to another space which can be independently s led, so that stochastic gradient of the kernel parameters can be analytically derived. We develop a well-founded underlying theory for our method, including a general way to reparameterize the kernel, and a new tighter error bound on the approximation quality. This view further inspires a direct application of stochastic gradient descent for updating our model under an online learning setting. We then conducted extensive experiments on several large-scale datasets where we demonstrate that our work achieves state-of-the-art performance in both learning efficacy and efficiency.
Publisher: IEEE
Date: 08-2014
Publisher: Springer International Publishing
Date: 2015
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 08-2017
Abstract: We propose a general framework for discriminative Bayesian nonparametric clustering to promote the inter-discrimination among the learned clusters in a fully Bayesian nonparametric (BNP) manner. Our method combines existing BNP clustering and discriminative models by enforcing latent cluster indices to be consistent with the predicted labels resulted from probabilistic discriminative model. This formulation results in a well-defined generative process wherein we can use either logistic regression or SVM for discrimination. Using the proposed framework, we develop two novel discriminative BNP variants: the discriminative Dirichlet process mixtures, and the discriminative-state infinite HMMs for sequential data. We develop efficient data-augmentation Gibbs s lers for posterior inference. Extensive experiments in image clustering and dynamic location clustering demonstrate that by encouraging discrimination between induced clusters, our model enhances the quality of clustering in comparison with the traditional generative BNP models.
Publisher: Elsevier BV
Date: 09-2015
Publisher: Springer Science and Business Media LLC
Date: 20-11-2015
Publisher: ACM
Date: 22-04-2008
Publisher: Elsevier BV
Date: 10-2017
Publisher: Springer International Publishing
Date: 2016
Publisher: BMJ
Date: 24-03-2015
Abstract: The WHO framework for non-communicable disease (NCD) describes risks and outcomes comprising the majority of the global burden of disease. These factors are complex and interact at biological, behavioural, environmental and policy levels presenting challenges for population monitoring and intervention evaluation. This paper explores the utility of machine learning methods applied to population-level web search activity behaviour as a proxy for chronic disease risk factors. Web activity output for each element of the WHO's Causes of NCD framework was used as a basis for identifying relevant web search activity from 2004 to 2013 for the USA. Multiple linear regression models with regularisation were used to generate predictive algorithms, mapping web search activity to Centers for Disease Control and Prevention (CDC) measured risk factor/disease prevalence. Predictions for subsequent target years not included in the model derivation were tested against CDC data from population surveys using Pearson correlation and Spearman's r. For 2011 and 2012, predicted prevalence was very strongly correlated with measured risk data ranging from fruits and vegetables consumed (r=0.81 95% CI 0.68 to 0.89) to alcohol consumption (r=0.96 95% CI 0.93 to 0.98). Mean difference between predicted and measured differences by State ranged from 0.03 to 2.16. Spearman's r for state-wise predicted versus measured prevalence varied from 0.82 to 0.93. The high predictive validity of web search activity for NCD risk has potential to provide real-time information on population risk during policy implementation and other population-level NCD prevention efforts.
Publisher: Elsevier BV
Date: 09-2018
Publisher: Springer Science and Business Media LLC
Date: 30-01-2016
Publisher: Elsevier BV
Date: 10-2017
Publisher: Elsevier BV
Date: 05-2009
Publisher: Springer Berlin Heidelberg
Date: 2008
Publisher: Association for Computing Machinery (ACM)
Date: 27-01-2020
DOI: 10.1145/3372121
Abstract: The effective training of supervised Person Re-identification (Re-ID) models requires sufficient pairwise labeled data. However, when there is limited annotation resource, it is difficult to collect pairwise labeled data. We consider a challenging and practical problem called Early Active Learning, which is applied to the early stage of experiments when there is no pre-labeled s le available as references for human annotating. Previous early active learning methods suffer from two limitations for Re-ID. First, these instance-based algorithms select instances rather than pairs, which can result in missing optimal pairs for Re-ID. Second, most of these methods only consider the representativeness of instances, which can result in selecting less erse and less informative pairs. To overcome these limitations, we propose a novel pair-based active learning for Re-ID. Our algorithm selects pairs instead of instances from the entire dataset for annotation. Besides representativeness, we further take into account the uncertainty and the ersity in terms of pairwise relations. Therefore, our algorithm can produce the most representative, informative, and erse pairs for Re-ID data annotation. Extensive experimental results on five benchmark Re-ID datasets have demonstrated the superiority of the proposed pair-based early active learning algorithm.
Publisher: Springer Science and Business Media LLC
Date: 03-2015
Publisher: Informa UK Limited
Date: 19-07-2013
DOI: 10.3109/17518423.2013.784817
Abstract: To investigate use patterns and learning outcomes associated with the use of Therapy Outcomes By You (TOBY. Playpad, an early intervention iPad application. Participants were 33 families with a child with an autism spectrum disorder (ASD) aged 16 years or less, and with a diagnosis of autism or pervasive developmental disorder - not otherwise specified, and no secondary diagnoses. Families were provided with TOBY and asked to use it for 4-6 weeks, without further prompting or coaching. Dependent variables included participant use patterns and initial indicators of child progress. Twenty-three participants engaged extensively with TOBY, being exposed to at least 100 complete learn units and completing between 17% and 100% of the curriculum. TOBY may make a useful contribution to early intervention programming for children with ASD delivering high rates of appropriate learning opportunities. Further research evaluating the efficacy of TOBY in relation to independent indicators of functioning is warranted.
Publisher: ACM
Date: 25-11-2014
Publisher: Springer International Publishing
Date: 2016
Publisher: Springer International Publishing
Date: 2015
Publisher: Wiley
Date: 10-04-2015
DOI: 10.1002/SAM.11262
Publisher: Springer International Publishing
Date: 2016
Publisher: Association for Computing Machinery (ACM)
Date: 07-2010
Abstract: The blogosphere has grown to be a mainstream forum of social interaction as well as a commercially attractive source of information and influence. Tools are needed to better understand how communities that adhere to in idual blogs are constituted in order to facilitate new personal, socially-focused browsing paradigms, and understand how blog content is consumed, which is of interest to blog authors, big media, and search. We present a novel approach to blog subcommunity characterization by modeling in idual blog readers using mixtures of an extension to the LDA family that jointly models phrases and time, Ngram Topic over Time (NTOT), and cluster with a number of similarity measures using Affinity Propagation. We experiment with two datasets: a small set of blogs whose authors provide feedback, and a set of popular, highly commented blogs, which provide indicators of algorithm scalability and interpretability without prior knowledge of a given blog. The results offer useful insight to the blog authors about their commenting community, and are observed to offer an integrated perspective on the topics of discussion and members engaged in those discussions for unfamiliar blogs. Our approach also holds promise as a component of solutions to related problems, such as online entity resolution and role discovery.
Publisher: Springer International Publishing
Date: 2017
Publisher: Springer International Publishing
Date: 2016
Publisher: IEEE
Date: 08-2018
Publisher: IEEE
Date: 12-2016
Publisher: IEEE
Date: 12-2016
Publisher: Springer Science and Business Media LLC
Date: 19-02-2016
Publisher: JMIR Publications Inc.
Date: 03-01-2019
DOI: 10.2196/10371
Publisher: Springer International Publishing
Date: 2016
Publisher: ACM
Date: 25-07-2010
Publisher: IEEE
Date: 2004
Publisher: Springer International Publishing
Date: 2016
Publisher: IEEE
Date: 10-2016
DOI: 10.1109/DSAA.2016.65
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: Springer Science and Business Media LLC
Date: 08-04-2016
Publisher: IEEE
Date: 10-2017
DOI: 10.1109/DSAA.2017.31
Publisher: Society for Industrial and Applied Mathematics
Date: 30-06-2015
Publisher: Springer Science and Business Media LLC
Date: 22-06-2014
Publisher: Elsevier BV
Date: 12-2012
Publisher: IEEE
Date: 07-2013
Publisher: Elsevier
Date: 2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2008
Publisher: Elsevier BV
Date: 05-2017
Publisher: Springer Science and Business Media LLC
Date: 12-12-2016
Publisher: IEEE
Date: 2005
Publisher: ACM
Date: 04-08-2023
Publisher: ACM Press
Date: 2019
Publisher: Springer Science and Business Media LLC
Date: 12-06-2013
Publisher: IEEE
Date: 2003
Publisher: Springer International Publishing
Date: 2016
Publisher: IEEE
Date: 03-2014
Publisher: Springer Science and Business Media LLC
Date: 20-10-2013
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 07-2018
Abstract: Training model to generate data has increasingly attracted research attention and become important in modern world applications. We propose in this paper a new geometry-based optimization approach to address this problem. Orthogonal to current state-of-the-art density-based approaches, most notably VAE and GAN, we present a fresh new idea that borrows the principle of minimal enclosing ball to train a generator G\\left(\\bz\\right) in such a way that both training and generated data, after being mapped to the feature space, are enclosed in the same sphere. We develop theory to guarantee that the mapping is bijective so that its inverse from feature space to data space results in expressive nonlinear contours to describe the data manifold, hence ensuring data generated are also lying on the data manifold learned from training data. Our model enjoys a nice geometric interpretation, hence termed Geometric Enclosing Networks (GEN), and possesses some key advantages over its rivals, namely simple and easy-to-control optimization formulation, avoidance of mode collapsing and efficiently learn data manifold representation in a completely unsupervised manner. We conducted extensive experiments on synthesis and real-world datasets to illustrate the behaviors, strength and weakness of our proposed GEN, in particular its ability to handle multi-modal data and quality of generated data.
Publisher: Elsevier BV
Date: 02-2016
DOI: 10.1016/J.JBI.2015.11.012
Abstract: Emerging Electronic Medical Records (EMRs) have reformed the modern healthcare. These records have great potential to be used for building clinical prediction models. However, a problem in using them is their high dimensionality. Since a lot of information may not be relevant for prediction, the underlying complexity of the prediction models may not be high. A popular way to deal with this problem is to employ feature selection. Lasso and l1-norm based feature selection methods have shown promising results. But, in presence of correlated features, these methods select features that change considerably with small changes in data. This prevents clinicians to obtain a stable feature set, which is crucial for clinical decision making. Grouping correlated variables together can improve the stability of feature selection, however, such grouping is usually not known and needs to be estimated for optimal performance. Addressing this problem, we propose a new model that can simultaneously learn the grouping of correlated features and perform stable feature selection. We formulate the model as a constrained optimization problem and provide an efficient solution with guaranteed convergence. Our experiments with both synthetic and real-world datasets show that the proposed model is significantly more stable than Lasso and many existing state-of-the-art shrinkage and classification methods. We further show that in terms of prediction performance, the proposed method consistently outperforms Lasso and other baselines. Our model can be used for selecting stable risk factors for a variety of healthcare problems, so it can assist clinicians toward accurate decision making.
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: Elsevier BV
Date: 04-2015
DOI: 10.1016/J.JBI.2015.01.012
Abstract: Electronic medical record (EMR) offers promises for novel analytics. However, manual feature engineering from EMR is labor intensive because EMR is complex - it contains temporal, mixed-type and multimodal data packed in irregular episodes. We present a computational framework to harness EMR with minimal human supervision via restricted Boltzmann machine (RBM). The framework derives a new representation of medical objects by embedding them in a low-dimensional vector space. This new representation facilitates algebraic and statistical manipulations such as projection onto 2D plane (thereby offering intuitive visualization), object grouping (hence enabling automated phenotyping), and risk stratification. To enhance model interpretability, we introduced two constraints into model parameters: (a) nonnegative coefficients, and (b) structural smoothness. These result in a novel model called eNRBM (EMR-driven nonnegative RBM). We demonstrate the capability of the eNRBM on a cohort of 7578 mental health patients under suicide risk assessment. The derived representation not only shows clinically meaningful feature grouping but also facilitates short-term risk stratification. The F-scores, 0.21 for moderate-risk and 0.36 for high-risk, are significantly higher than those obtained by clinicians and competitive with the results obtained by support vector machines.
Publisher: Springer International Publishing
Date: 2017
Publisher: Springer International Publishing
Date: 2014
Publisher: BMJ
Date: 03-2014
Publisher: Society for Industrial and Applied Mathematics
Date: 26-04-2012
Publisher: Elsevier BV
Date: 06-2017
DOI: 10.1016/J.IJMEDINF.2017.03.013
Abstract: This work aims to estimate the degree of adverse drug reactions (ADR) for psychiatric medications from social media, including Twitter, Reddit, and LiveJournal. Advances in lightning-fast cluster computing was employed to process large scale data, consisting of 6.4 terabytes of data containing 3.8 billion records from all the media. Rates of ADR were quantified using the SIDER database of drugs and side-effects, and an estimated ADR rate was based on the prevalence of discussion in the social media corpora. Agreement between these measures for a s le of ten popular psychiatric drugs was evaluated using the Pearson correlation coefficient, r, with values between 0.08 and 0.50. Word2vec, a novel neural learning framework, was utilized to improve the coverage of variants of ADR terms in the unstructured text by identifying syntactically or semantically similar terms. Improved correlation coefficients, between 0.29 and 0.59, demonstrates the capability of advanced techniques in machine learning to aid in the discovery of meaningful patterns from medical data, and social media data, at scale.
Publisher: Springer International Publishing
Date: 2014
Publisher: Springer Science and Business Media LLC
Date: 12-2014
Publisher: Springer International Publishing
Date: 2014
Publisher: JMIR Publications Inc.
Date: 21-07-2016
Publisher: Elsevier BV
Date: 2016
Publisher: Society for Industrial and Applied Mathematics
Date: 28-04-2011
Publisher: Springer Science and Business Media LLC
Date: 21-12-2017
Publisher: IEEE
Date: 07-2020
Publisher: ACM
Date: 23-10-2009
Publisher: Springer International Publishing
Date: 2016
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: IEEE
Date: 2006
Publisher: IEEE
Date: 12-2016
Publisher: IEEE
Date: 03-2013
Publisher: ACM
Date: 06-11-2005
Publisher: Springer Science and Business Media LLC
Date: 17-03-2014
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2013
Publisher: Springer Berlin Heidelberg
Date: 2010
Publisher: IEEE
Date: 06-2012
Publisher: IEEE
Date: 2006
DOI: 10.1109/CVPR.2006.49
Publisher: IEEE
Date: 07-2013
Publisher: Springer International Publishing
Date: 2014
Publisher: ACM
Date: 25-10-2010
Publisher: Public Library of Science (PLoS)
Date: 04-05-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2011
Publisher: Elsevier BV
Date: 05-2017
DOI: 10.1016/J.JBI.2017.04.001
Abstract: Personalized predictive medicine necessitates the modeling of patient illness and care processes, which inherently have long-term temporal dependencies. Healthcare observations, stored in electronic medical records are episodic and irregular in time. We introduce DeepCare, an end-to-end deep dynamic neural network that reads medical records, stores previous illness history, infers current illness states and predicts future medical outcomes. At the data level, DeepCare represents care episodes as vectors and models patient health state trajectories by the memory of historical records. Built on Long Short-Term Memory (LSTM), DeepCare introduces methods to handle irregularly timed events by moderating the forgetting and consolidation of memory. DeepCare also explicitly models medical interventions that change the course of illness and shape future medical risk. Moving up to the health state level, historical and present health states are then aggregated through multiscale temporal pooling, before passing through a neural network that estimates future outcomes. We demonstrate the efficacy of DeepCare for disease progression modeling, intervention recommendation, and future risk prediction. On two important cohorts with heavy social and economic burden - diabetes and mental health - the results show improved prediction accuracy.
Publisher: Springer Science and Business Media LLC
Date: 20-07-2018
Publisher: Springer International Publishing
Date: 2020
Publisher: Springer International Publishing
Date: 2020
Publisher: IEEE
Date: 07-2020
Publisher: Society for Industrial and Applied Mathematics
Date: 28-04-2014
Publisher: Springer Berlin Heidelberg
Date: 2008
Publisher: Elsevier BV
Date: 11-2016
Publisher: IEEE
Date: 12-2016
Publisher: Elsevier BV
Date: 02-2015
DOI: 10.1016/J.JBI.2014.11.013
Abstract: Modern healthcare is getting reshaped by growing Electronic Medical Records (EMR). Recently, these records have been shown of great value towards building clinical prediction models. In EMR data, patients' diseases and hospital interventions are captured through a set of diagnoses and procedures codes. These codes are usually represented in a tree form (e.g. ICD-10 tree) and the codes within a tree branch may be highly correlated. These codes can be used as features to build a prediction model and an appropriate feature selection can inform a clinician about important risk factors for a disease. Traditional feature selection methods (e.g. Information Gain, T-test, etc.) consider each variable independently and usually end up having a long feature list. Recently, Lasso and related l1-penalty based feature selection methods have become popular due to their joint feature selection property. However, Lasso is known to have problems of selecting one feature of many correlated features randomly. This hinders the clinicians to arrive at a stable feature set, which is crucial for clinical decision making process. In this paper, we solve this problem by using a recently proposed Tree-Lasso model. Since, the stability behavior of Tree-Lasso is not well understood, we study the stability behavior of Tree-Lasso and compare it with other feature selection methods. Using a synthetic and two real-world datasets (Cancer and Acute Myocardial Infarction), we show that Tree-Lasso based feature selection is significantly more stable than Lasso and comparable to other methods e.g. Information Gain, ReliefF and T-test. We further show that, using different types of classifiers such as logistic regression, naive Bayes, support vector machines, decision trees and Random Forest, the classification performance of Tree-Lasso is comparable to Lasso and better than other methods. Our result has implications in identifying stable risk factors for many healthcare problems and therefore can potentially assist clinical decision making for accurate medical prognosis.
Publisher: Springer Science and Business Media LLC
Date: 21-03-2014
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 08-2019
Abstract: Generative Adversarial Networks (GANs) are a powerful class of deep generative models. In this paper, we extend GAN to the problem of generating data that are not only close to a primary data source but also required to be different from auxiliary data sources. For this problem, we enrich both GANs' formulations and applications by introducing pushing forces that thrust generated s les away from given auxiliary data sources. We term our method Push-and-Pull GAN (P2GAN). We conduct extensive experiments to demonstrate the merit of P2GAN in two applications: generating data with constraints and addressing the mode collapsing problem. We use CIFAR-10, STL-10, and ImageNet datasets and compute Fréchet Inception Distance to evaluate P2GAN's effectiveness in addressing the mode collapsing problem. The results show that P2GAN outperforms the state-of-the-art baselines. For the problem of generating data with constraints, we show that P2GAN can successfully avoid generating specific features such as black hair.
Publisher: Springer Science and Business Media LLC
Date: 18-03-2017
Publisher: IEEE
Date: 12-2016
Publisher: Springer Science and Business Media LLC
Date: 10-08-2018
Publisher: Society for Industrial and Applied Mathematics
Date: 07-05-2018
Publisher: ACM
Date: 07-05-2011
Publisher: IEEE
Date: 10-2015
Publisher: ACM
Date: 23-10-2006
Publisher: Springer International Publishing
Date: 2016
Publisher: ACM
Date: 19-10-2020
Publisher: Springer International Publishing
Date: 2015
Publisher: IEEE
Date: 2005
DOI: 10.1109/CVPR.2005.61
Publisher: Elsevier BV
Date: 12-2009
Publisher: IEEE
Date: 11-2015
Publisher: Elsevier BV
Date: 12-2018
Publisher: IEEE
Date: 04-2013
Publisher: Elsevier BV
Date: 2016
Publisher: Springer International Publishing
Date: 2015
Publisher: ACM
Date: 28-11-2011
Publisher: ACM
Date: 27-03-2023
Publisher: IEEE
Date: 03-2009
Publisher: Springer International Publishing
Date: 2015
Publisher: Springer Science and Business Media LLC
Date: 13-05-2015
Publisher: Springer International Publishing
Date: 2015
Publisher: IEEE
Date: 12-2012
DOI: 10.1109/ICDM.2012.46
Publisher: ACM
Date: 28-11-2011
Publisher: Springer International Publishing
Date: 2015
Publisher: ACM
Date: 16-12-2012
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: Elsevier BV
Date: 2015
Publisher: IEEE
Date: 07-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2023
Publisher: Society for Industrial and Applied Mathematics
Date: 02-05-2013
Publisher: IEEE
Date: 10-2017
DOI: 10.1109/DSAA.2017.78
Publisher: ACM
Date: 29-10-2012
Publisher: Elsevier BV
Date: 03-2018
Publisher: IEEE
Date: 2015
Publisher: JMIR Publications Inc.
Date: 13-03-2018
Abstract: elf-management is a critical component of chronic disease management and can include a host of activities, such as adhering to prescribed medications, undertaking daily care activities, managing dietary intake and body weight, and proactively contacting medical practitioners. The rise of technologies (mobile phones, wearable cameras) for health care use offers potential support for people to better manage their disease in collaboration with their treating health professionals. Wearable cameras can be used to provide rich contextual data and insight into everyday activities and aid in recall. This information can then be used to prompt memory recall or guide the development of interventions to support self-management. Application of wearable cameras to better understand and augment self-management by people with chronic disease has yet to be investigated. he objective of our review was to ascertain the scope of the literature on the use of wearable cameras for self-management by people with chronic disease and to determine the potential of wearable cameras to assist people to better manage their disease. e conducted a scoping review, which involved a comprehensive electronic literature search of 9 databases in July 2017. The search strategy focused on studies that used wearable cameras to capture one or more modifiable lifestyle risk factors associated with chronic disease or to capture typical self-management behaviors, or studies that involved a chronic disease population. We then categorized and described included studies according to their characteristics (eg, behaviors measured, study design or type, characteristics of the s le). e identified 31 studies: 25 studies involved primary or secondary data analysis, and 6 were review, discussion, or descriptive articles. Wearable cameras were predominantly used to capture dietary intake, physical activity, activities of daily living, and sedentary behavior. Populations studied were predominantly healthy volunteers, school students, and sports people, with only 1 study examining an intervention using wearable cameras for people with an acquired brain injury. Most studies highlighted technical or ethical issues associated with using wearable cameras, many of which were overcome. his scoping review highlighted the potential of wearable cameras to capture health-related behaviors and risk factors of chronic disease, such as diet, exercise, and sedentary behaviors. Data collected from wearable cameras can be used as an adjunct to traditional data collection methods such as self-reported diaries in addition to providing valuable contextual information. While most studies to date have focused on healthy populations, wearable cameras offer promise to better understand self-management of chronic disease and its context.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2015
Publisher: CSIRO Publishing
Date: 2014
DOI: 10.1071/AH14059
Abstract: Objective Readmission rates are high following acute myocardial infarction (AMI), but risk stratification has proved difficult because known risk factors are only weakly predictive. In the present study, we applied hospital data to identify the risk of unplanned admission following AMI hospitalisations. Methods The study included 1660 consecutive AMI admissions. Predictive models were derived from 1107 randomly selected records and tested on the remaining 553 records. The electronic medical record (EMR) model was compared with a seven-factor predictive score known as the HOSPITAL score and a model derived from Elixhauser comorbidities. All models were evaluated for the ability to identify patients at high risk of 30-day ischaemic heart disease readmission and those at risk of all-cause readmission within 12 months following the initial AMI hospitalisation. Results The EMR model has higher discrimination than other models in predicting ischaemic heart disease readmissions (area under the curve (AUC) 0.78 95% confidence interval (CI) 0.71–0.85 for 30-day readmission). The positive predictive value was significantly higher with the EMR model, which identifies cohorts that were up to threefold more likely to be readmitted. Factors associated with readmission included emergency department attendances, cardiac diagnoses and procedures, renal impairment and electrolyte disturbances. The EMR model also performed better than other models (AUC 0.72 95% CI 0.66–0.78), and with greater positive predictive value, in identifying 12-month risk of all-cause readmission. Conclusions Routine hospital data can help identify patients at high risk of readmission following AMI. This could lead to decreased readmission rates by identifying patients suitable for targeted clinical interventions. What is known about the topic? Many clinical and demographic risk factors are known for hospital readmissions following acute myocardial infarction, including multivessel disease, high baseline heart rate, hypertension, diabetes, obesity, chronic obstructive pulmonary disease and psychiatric morbidity. However, combining these risk factors into indices for predicting readmission had limited success. A recent study reported a C-statistic of 0.73 for predicting 30-day readmissions. In a recent American study, a simple seven-factor score was shown to predict hospital readmissions among medical patients. What does this paper add? This paper presents a way to predict readmissions following myocardial infarction using routinely collected administrative data. The model performed better than the recently described HOSPITAL score and a model derived from Elixhauser comorbidities. Moreover, the model uses only data generally available in most hospitals. What are the implications for practitioners? Routine hospital data available at discharges can be used to tailor preventative care for AMI patients, to improve institutional performance and to decrease the cost burden associated with AMI.
Publisher: Springer International Publishing
Date: 2015
Publisher: Springer Science and Business Media LLC
Date: 14-03-2014
Publisher: Springer International Publishing
Date: 2016
Publisher: Springer International Publishing
Date: 2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2015
Publisher: Springer International Publishing
Date: 2015
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: IEEE
Date: 07-2019
Publisher: Springer Science and Business Media LLC
Date: 11-04-2018
DOI: 10.1007/S10916-018-0951-4
Abstract: Evidence-based medicine often involves the identification of patients with similar conditions, which are often captured in ICD (International Classification of Diseases (World Health Organization 2013)) code sequences. With no satisfying prior solutions for matching ICD-10 code sequences, this paper presents a method which effectively captures the clinical similarity among routine patients who have multiple comorbidities and complex care needs. Our method leverages the recent progress in representation learning of in idual ICD-10 codes, and it explicitly uses the sequential order of codes for matching. Empirical evaluation on a state-wide cancer data collection shows that our proposed method achieves significantly higher matching performance compared with state-of-the-art methods ignoring the sequential order. Our method better identifies similar patients in a number of clinical outcomes including readmission and mortality outlook. Although this paper focuses on ICD-10 diagnosis code sequences, our method can be adapted to work with other codified sequence data.
Start Date: 2007
End Date: 12-2009
Amount: $360,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 06-2015
End Date: 07-2019
Amount: $384,700.00
Funder: Australian Research Council
View Funded ActivityStart Date: 2011
End Date: 12-2014
Amount: $430,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 10-2008
End Date: 12-2011
Amount: $483,400.00
Funder: Australian Research Council
View Funded ActivityStart Date: 2023
End Date: 12-2025
Amount: $420,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 04-2016
End Date: 12-2017
Amount: $250,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 2023
End Date: 12-2023
Amount: $440,145.00
Funder: Australian Research Council
View Funded ActivityStart Date: 2013
End Date: 12-2013
Amount: $210,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 2016
End Date: 12-2019
Amount: $410,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 06-2013
End Date: 12-2016
Amount: $495,000.00
Funder: Australian Research Council
View Funded Activity