ORCID Profile
0000-0002-9977-8247
Current Organisation
Monash 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)
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 |
ANZSRC Socio-Economic Objective (SEO)
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 |
Related Links
Publications
Regularized nonnegative shared subspace learning
Publisher: Springer Science and Business Media LLC
Date: 16-11-2013
Improving Adversarial Robustness by Enforcing Local and Global Compactness
Publisher: Springer International Publishing
Date: 2020
Forecasting Patient Outflow from Wards having No Real-Time Clinical Data
Publisher: IEEE
Date: 10-2016
DOI: 10.1109/ICHI.2016.26
Stable clinical prediction using graph support vector machines
Publisher: IEEE
Date: 12-2016
Learning and detecting activities from movement trajectories using the hierarchical hidden markov model
Publisher: IEEE
Date: 2005
Content structure discovery in educational videos using shared structures in the hierarchical hidden markov models
Publisher: Springer Berlin Heidelberg
Date: 2004
Code Pointer Network for Binary Function Scope Identification
Publisher: IEEE
Date: 07-2020
An integrated framework for suicide risk prediction
Publisher: ACM
Date: 11-08-2013
Factored State-Abstract Hidden Markov Models for activity recognition using pervasive multi-modal sensors
Publisher: IEEE
Date: 2005
Jointly predicting affective and mental health scores using deep neural networks of visual cues on the web
Publisher: Springer International Publishing
Date: 2018
SECC: Simultaneous extraction of context and community from pervasive signals
Publisher: IEEE
Date: 03-2016
Robust Bayesian Kernel Machine via Stein Variational Gradient Descent for Big Data
Publisher: ACM
Date: 19-07-2018
Regularizing topic discovery in emrs with side information by using hierarchical bayesian models
Publisher: IEEE
Date: 08-2014
Human behavior recognition with generic exponential family duration modeling in the hidden semi-Markov model
Publisher: IEEE
Date: 2006
Hierarchical dirichlet process for tracking complex topical structure evolution and its application to autism research literature
Publisher: Springer International Publishing
Date: 2015
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
Robust Variational Learning for Multiclass Kernel Models With Stein Refinement
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2022
Detection of cross-channel anomalies from multiple data channels
Publisher: IEEE
Date: 12-2011
DOI: 10.1109/ICDM.2011.51
Split-Merge Augmented Gibbs Sampling for Hierarchical Dirichlet Processes
Publisher: Springer Berlin Heidelberg
Date: 2013
A Simultaneous Extraction of Context and Community from pervasive signals using nested Dirichlet process
Publisher: Elsevier BV
Date: 07-2017
GoGP: Fast online regression with Gaussian processes
Publisher: IEEE
Date: 11-2017
DOI: 10.1109/ICDM.2017.35
Model-based learning for point pattern data
Publisher: Elsevier BV
Date: 12-2018
Sqn2Vec: Learning sequence representation via sequential patterns with a gap constraint
Publisher: Springer International Publishing
Date: 2019
Sensing and using social context
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.
Model-based classification and novelty detection for point pattern data
Publisher: IEEE
Date: 12-2016
Learning multifaceted latent activities from heterogeneous mobile data
Publisher: IEEE
Date: 10-2016
DOI: 10.1109/DSAA.2016.48
Consistency of the Health of the Nation Outcome Scales (HoNOS) at inpatient-to-community transition
Publisher: BMJ
Date: 04-2016
Neural choice by elimination via highway networks
Publisher: Springer International Publishing
Date: 2016
Three-player Wasserstein GaN via amortised duality
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.
Screening for post 32-week preterm birth risk: how helpful is routine perinatal data collection?
Publisher: Elsevier BV
Date: 06-2016
Learning Boltzmann distance metric for face recognition
Publisher: IEEE
Date: 07-2012
Large-scale online kernel learning with random feature reparameterization
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.
A Bayesian Nonparametric Framework for Activity Recognition Using Accelerometer Data
Publisher: IEEE
Date: 08-2014
Stable Feature Selection with Support Vector Machines
Publisher: Springer International Publishing
Date: 2015
Discriminative Bayesian nonparametric clustering
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.
Mixed-norm sparse representation for multi view face recognition
Publisher: Elsevier BV
Date: 09-2015
Tree-based iterated local search for Markov random fields with applications in image analysis
Publisher: Springer Science and Business Media LLC
Date: 20-11-2015
Computable social patterns from sparse sensor data
Publisher: ACM
Date: 22-04-2008
Kernel-based features for predicting population health indices from geocoded social media data
Publisher: Elsevier BV
Date: 10-2017
Sparse Adaptive Multi-hyperplane Machine
Publisher: Springer International Publishing
Date: 2016
Web search activity data accurately predict population chronic disease risk in the USA
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.
Using spatiotemporal distribution of geocoded Twitter data to predict US county-level health indices
Publisher: Elsevier BV
Date: 09-2018
Multiple task transfer learning with small sample sizes
Publisher: Springer Science and Business Media LLC
Date: 30-01-2016
Streaming clustering with Bayesian nonparametric models
Publisher: Elsevier BV
Date: 10-2017
Efficient duration and hierarchical modeling for human activity recognition
Publisher: Elsevier BV
Date: 05-2009
Constrained sequence classification for lexical disambiguation
Publisher: Springer Berlin Heidelberg
Date: 2008
Pair-based Uncertainty and Diversity Promoting Early Active Learning for Person Re-identification
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.
Bayesian Nonparametric Approaches to Abnormality Detection in Video Surveillance
Publisher: Springer Science and Business Media LLC
Date: 03-2015
TOBY play-pad application to teach children with ASD – A pilot trial
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.
Unsupervised inference of significant locations from WiFi data for understanding human dynamics
Publisher: ACM
Date: 25-11-2014
Large-scale stylistic analysis of formality in academia and social media
Publisher: Springer International Publishing
Date: 2016
Differentiating sub-groups of online depression-related communities using textual cues
Publisher: Springer International Publishing
Date: 2015
A predictive framework for modeling healthcare data with evolving clinical interventions
Publisher: Wiley
Date: 10-04-2015
DOI: 10.1002/SAM.11262
Textual cues for online depression in community and personal settings
Publisher: Springer International Publishing
Date: 2016
Discovery of latent subcommunities in a blog's readership
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.
Energy-based localized anomaly detection in video surveillance
Publisher: Springer International Publishing
Date: 2017
Outlier detection on mixed-type data: An energy-based approach
Publisher: Springer International Publishing
Date: 2016
Bayesian Multi-Hyperplane Machine for Pattern Recognition
Publisher: IEEE
Date: 08-2018
Distributed data augmented support vector machine on Spark
Publisher: IEEE
Date: 12-2016
Transfer learning for rare cancer problems via Discriminative Sparse Gaussian Graphical model
Publisher: IEEE
Date: 12-2016
A new transfer learning framework with application to model-agnostic multi-task learning
Publisher: Springer Science and Business Media LLC
Date: 19-02-2016
Usefulness of Wearable Cameras as a Tool to Enhance Chronic Disease Self-Management: Scoping Review
Publisher: JMIR Publications Inc.
Date: 03-01-2019
DOI: 10.2196/10371
DeepCare: A Deep Dynamic Memory Model for Predictive Medicine
Publisher: Springer International Publishing
Date: 2016
Nonnegative shared subspace learning and its application to social media retrieval
Publisher: ACM
Date: 25-07-2010
Automatically learning structural units in educational videos with the hierarchical hidden markov models
Publisher: IEEE
Date: 2004
Learning Multi-faceted Activities from Heterogeneous Data with the Product Space Hierarchical Dirichlet Processes
Publisher: Springer International Publishing
Date: 2016
Analysing the history of autism spectrum disorder using topic models
Publisher: IEEE
Date: 10-2016
DOI: 10.1109/DSAA.2016.65
A Bayesian framework for learning shared and individual subspaces from multiple data sources
Publisher: Springer Berlin Heidelberg
Date: 2011
Introduction: special issue of selected papers from ACML 2014
Publisher: Springer Science and Business Media LLC
Date: 08-04-2016
Animal recognition and identification with deep convolutional neural networks for automated wildlife monitoring
Publisher: IEEE
Date: 10-2017
DOI: 10.1109/DSAA.2017.31
What shall i share and with Whom? - A multi-task learning formulation using multi-faceted task relationships
Publisher: Society for Industrial and Applied Mathematics
Date: 30-06-2015
Social reader: Towards browsing the social web
Publisher: Springer Science and Business Media LLC
Date: 22-06-2014
Pervasive multimedia for autism intervention
Publisher: Elsevier BV
Date: 12-2012
Learning sparse latent representation and distance metric for image retrieval
Publisher: IEEE
Date: 07-2013
Learning Latent Activities from Social Signals with Hierarchical Dirichlet Processes
Publisher: Elsevier
Date: 2014
you tube and i find - Personalizing multimedia content access
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2008
Hierarchical semi-Markov conditional random fields for deep recursive sequential data
Publisher: Elsevier BV
Date: 05-2017
Modelling multilevel data in multimedia: A hierarchical factor analysis approach
Publisher: Springer Science and Business Media LLC
Date: 12-12-2016
Efficient Coxian duration modelling for activity recognition in smart environments with the hidden semi-Markov model
Publisher: IEEE
Date: 2005
Feature-based Learning for Diverse and Privacy-Preserving Counterfactual Explanations
Publisher: ACM
Date: 04-08-2023
Prediction of population health indices from social media using kernel-based textual and temporal features
Publisher: ACM Press
Date: 2019
Detection of cross-channel anomalies
Publisher: Springer Science and Business Media LLC
Date: 12-06-2013
On the extraction of thematic and dramatic functions of content in educational videos
Publisher: IEEE
Date: 2003
Discriminative cues for different stages of smoking cessation in online community
Publisher: Springer International Publishing
Date: 2016
Fixed-lag particle filter for continuous context discovery using Indian Buffet Process
Publisher: IEEE
Date: 03-2014
Event extraction using behaviors of sentiment signals and burst structure in social media
Publisher: Springer Science and Business Media LLC
Date: 20-10-2013
Geometric enclosing networks
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.
Stabilizingl 1
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: 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.
Prediction of age, sentiment, and connectivity from social media text
Publisher: Springer Berlin Heidelberg
Date: 2011
Learning vector representation of medical objects via EMR-driven nonnegative restricted Boltzmann machines (eNRBM)
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.
Estimating support scores of autism communities in large-scale web information systems
Publisher: Springer International Publishing
Date: 2017
Affective, linguistic and topic patterns in online autism communities
Publisher: Springer International Publishing
Date: 2014
Machine-learning prediction of cancer survival: a retrospective study using electronic administrative records and a cancer registry
Publisher: BMJ
Date: 03-2014
A bayesian nonparametric joint factor model for learning shared and individual subspaces from multiple data sources
Publisher: Society for Industrial and Applied Mathematics
Date: 26-04-2012
Estimation of the prevalence of adverse drug reactions from social media
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.
iPoll: Automatic Polling Using Online Search
Publisher: Springer International Publishing
Date: 2014
A framework for feature extraction from hospital medical data with applications in risk prediction
Publisher: Springer Science and Business Media LLC
Date: 12-2014
Intervention-driven predictive framework for modeling healthcare data
Publisher: Springer International Publishing
Date: 2014
Forecasting Daily Patient Outflow From a Ward Having No Real-Time Clinical Data
Publisher: JMIR Publications Inc.
Date: 21-07-2016
Nonparametric discovery of movement patterns from accelerometer signals
Publisher: Elsevier BV
Date: 2016
Probabilistic models over ordered partitions with applications in document ranking and collaborative filtering
Publisher: Society for Industrial and Applied Mathematics
Date: 28-04-2011
Using linguistic and topic analysis to classify sub-groups of online depression communities
Publisher: Springer Science and Business Media LLC
Date: 21-12-2017
Stein Variational Gradient Descent with Variance Reduction
Publisher: IEEE
Date: 07-2020
Social reader: Following social networks in the wilds of the blogosphere
Publisher: ACM
Date: 23-10-2009
Exceptional Contrast Set Mining: Moving Beyond the Deluge of the Obvious
Publisher: Springer International Publishing
Date: 2016
Latent patient profile modelling and applications with mixed-variate restricted Boltzmann machine
Publisher: Springer Berlin Heidelberg
Date: 2013
A probabilistic model with parsinomious representation for sensor fusion in recognizing activity in pervasive environment
Publisher: IEEE
Date: 2006
MCNC: Multi-Channel Nonparametric Clustering from heterogeneous data
Publisher: IEEE
Date: 12-2016
Extraction of latent patterns and contexts from social honest signals using hierarchical Dirichlet processes
Publisher: IEEE
Date: 03-2013
Topic transition detection using hierarchical hidden Markov and Semi-Markov models
Publisher: ACM
Date: 06-11-2005
Stabilized sparse ordinal regression for medical risk stratification
Publisher: Springer Science and Business Media LLC
Date: 17-03-2014
Topic Model Kernel: An Empirical Study towards Probabilistically Reduced Features for Classification
Publisher: Springer Berlin Heidelberg
Date: 2013
Connectivity, online social capital, and mood: A bayesian nonparametric analysis
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2013
Classification and pattern discovery of mood in weblogs
Publisher: Springer Berlin Heidelberg
Date: 2010
Improved subspace clustering via exploitation of spatial constraints
Publisher: IEEE
Date: 06-2012
AdaBoost.MRF: Boosted Markov random forests and application to multilevel activity recognition
Publisher: IEEE
Date: 2006
DOI: 10.1109/CVPR.2006.49
Analysis of psycholinguistic processes and topics in online autism communities
Publisher: IEEE
Date: 07-2013
Effect of Mood, Social Connectivity and Age in Online Depression Community via Topic and Linguistic Analysis
Publisher: Springer International Publishing
Date: 2014
Hyper-community detection in the blogosphere
Publisher: ACM
Date: 25-10-2010
Is Demography Destiny? Application of Machine Learning Techniques to Accurately Predict Population Health Outcomes from a Minimal Demographic Dataset
Publisher: Public Library of Science (PLoS)
Date: 04-05-2015
Labeled Random Finite Sets and the Bayes Multi-Target Tracking Filter
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2011
Predicting healthcare trajectories from medical records: A deep learning approach
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.
GoGP: scalable geometric-based Gaussian process for online regression
Publisher: Springer Science and Business Media LLC
Date: 20-07-2018
Code Action Network for Binary Function Scope Identification
Publisher: Springer International Publishing
Date: 2020
Dual-Component Deep Domain Adaptation: A New Approach for Cross Project Software Vulnerability Detection
Publisher: Springer International Publishing
Date: 2020
OptiGAN: Generative Adversarial Networks for Goal Optimized Sequence Generation
Publisher: IEEE
Date: 07-2020
Keeping up with Innovation: A Predictive Framework for Modeling Healthcare Data with Evolving Clinical Interventions
Publisher: Society for Industrial and Applied Mathematics
Date: 28-04-2014
Learning discriminative sequence models from partially labelled data for activity recognition
Publisher: Springer Berlin Heidelberg
Date: 2008
Collaborative filtering via sparse Markov random fields
Publisher: Elsevier BV
Date: 11-2016
Faster training of very deep networks via p-norm gates
Publisher: IEEE
Date: 12-2016
Stable feature selection for clinical prediction: Exploiting ICD tree structure using Tree-Lasso
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.
Mood sensing from social media texts and its applications
Publisher: Springer Science and Business Media LLC
Date: 21-03-2014
Learning generative adversarial networks from multiple data sources
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.
Effective sparse imputation of patient conditions in electronic medical records for emergency risk predictions
Publisher: Springer Science and Business Media LLC
Date: 18-03-2017
Clustering for point pattern data
Publisher: IEEE
Date: 12-2016
Discovering topic structures of a temporally evolving document corpus
Publisher: Springer Science and Business Media LLC
Date: 10-08-2018
Learning Graph Representation via Frequent Subgraphs
Publisher: Society for Industrial and Applied Mathematics
Date: 07-05-2018
A context-sensitive device to help people with autism cope with anxiety
Publisher: ACM
Date: 07-05-2011
Exploiting feature relationships towards stable feature selection
Publisher: IEEE
Date: 10-2015
Extraction of social context and application to personal multimedia exploration
Publisher: ACM
Date: 23-10-2006
Toxicity prediction in cancer using multiple instance learning in a multi-task framework
Publisher: Springer International Publishing
Date: 2016
A Capsule Network-based Model for Learning Node Embeddings
Publisher: ACM
Date: 19-10-2020
Understanding Toxicities and Complications of Cancer Treatment: A Data Mining Approach
Publisher: Springer International Publishing
Date: 2015
Activity recognition and abnormality detection with the switching hidden semi-Markov model
Publisher: IEEE
Date: 2005
DOI: 10.1109/CVPR.2005.61
Unsupervised context detection using wireless signals
Publisher: Elsevier BV
Date: 12-2009
Multi-View Subspace Clustering for Face Images
Publisher: IEEE
Date: 11-2015
LTARM: A novel temporal association rule mining method to understand toxicities in a routine cancer treatment
Publisher: Elsevier BV
Date: 12-2018
Interactive browsing system for anomaly video surveillance
Publisher: IEEE
Date: 04-2013
Graph-induced restricted Boltzmann machines for document modeling
Publisher: Elsevier BV
Date: 2016
Stabilizing Sparse Cox Model Using Statistic and Semantic Structures in Electronic Medical Records
Publisher: Springer International Publishing
Date: 2015
Eventscapes: Visualizing events over time with emotive facets
Publisher: ACM
Date: 28-11-2011
A Vietnamese Spelling Correction System
Publisher: ACM
Date: 27-03-2023
High accuracy context recovery using clustering mechanisms
Publisher: IEEE
Date: 03-2009
Learning conditional latent structures from multiple data sources
Publisher: Springer International Publishing
Date: 2015
Modelling human preferences for ranking and collaborative filtering: a probabilistic ordered partition approach
Publisher: Springer Science and Business Media LLC
Date: 13-05-2015
Collaborating differently on different topics: A multi-relational approach to multi-task learning
Publisher: Springer International Publishing
Date: 2015
Sparse subspace representation for spectral document clustering
Publisher: IEEE
Date: 12-2012
DOI: 10.1109/ICDM.2012.46
Cognitive intervention in autism using multimedia stimulus
Publisher: ACM
Date: 28-11-2011
A Bayesian Nonparametric Approach to Multilevel Regression
Publisher: Springer International Publishing
Date: 2015
Large-scale statistical modeling of motion patterns: A Bayesian nonparametric approach
Publisher: ACM
Date: 16-12-2012
Emotional reactions to real-world events in social networks
Publisher: Springer Berlin Heidelberg
Date: 2012
Continuous discovery of co-location contexts from Bluetooth data
Publisher: Elsevier BV
Date: 2015
Exploiting side information in distance dependent Chinese restaurant processes for data clustering
Publisher: IEEE
Date: 07-2013
VulExplainer: A Transformer-Based Hierarchical Distillation for Explaining Vulnerability Types
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2023
Sparse subspace clustering via group sparse coding
Publisher: Society for Industrial and Applied Mathematics
Date: 02-05-2013
Forward-Backward smoothing for hidden markov models of point pattern data
Publisher: IEEE
Date: 10-2017
DOI: 10.1109/DSAA.2017.78
Funniest thing i've seen since [href=http://flic.kr/p/KGEGB]: Shifting perspectives from multimedia artefacts to utterances
Publisher: ACM
Date: 29-10-2012
Academia versus social media: A psycho-linguistic analysis
Publisher: Elsevier BV
Date: 03-2018
Visual object clustering via mixed-norm regularization
Publisher: IEEE
Date: 2015
Usefulness of Wearable Cameras as a Tool to Enhance Chronic Disease Self-Management: Scoping Review (Preprint)
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.
Autism Blogs: Expressed Emotion, Language Styles and Concerns in Personal and Community Settings
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2015
Predicting unplanned readmission after myocardial infarction from routinely collected administrative hospital data
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.
Fast One-Class Support Vector Machine for novelty detection
Publisher: Springer International Publishing
Date: 2015
Risk stratification using data from electronic medical records better predicts suicide risks than clinician assessments
Publisher: Springer Science and Business Media LLC
Date: 14-03-2014
Stabilizing Linear Prediction Models Using Autoencoder
Publisher: Springer International Publishing
Date: 2016
Deep Cost-Sensitive Kernel Machine for Binary Software Vulnerability Detection
Publisher: Springer International Publishing
Date: 2020
Stabilizing High-Dimensional Prediction Models Using Feature Graphs
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2015
Small-Variance Asymptotics for Bayesian Nonparametric Models with Constraints
Publisher: Springer International Publishing
Date: 2015
Clustering patient medical records via sparse subspace representation
Publisher: Springer Berlin Heidelberg
Date: 2013
Deep Domain Adaptation for Vulnerable Code Function Identification
Publisher: IEEE
Date: 07-2019
Effective Identification of Similar Patients Through Sequential Matching over ICD Code Embedding
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.
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