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arXiv 2017年

作者： Nguyen, Tu Dinh Tran, Truyen Phung, Dinh Venkatesh, Svetha Center for Pattern Recognition and Data Analytics School of Information Technology Deakin University Geelong Australia Institute for Multi-Sensor Processing and Content Analysis Curtin University Australia

The analysis of mixed data has been raising challenges in statistics and machine learning. One of two most prominent challenges is to develop new statistical techniques and methodologies to effectively handle mixed data by making the data less heterogeneous with minimum loss of information. The other challenge is that such methods must be able to apply in large-scale tasks when dealing with huge amount of mixed data. To tackle these challenges, we introduce parameter sharing and balancing extensions to our recent model, the mixed-variate restricted Boltzmann machine (***) which can transform heterogeneous data into homogeneous representation. We also integrate structured sparsity and distance metric learning into RBM-based models. Our proposed methods are applied in various applications including latent patient profile modelling in medical data analysis and representation learning for image retrieval. The experimental results demonstrate the models perform better than baseline methods in medical data and outperform state-of-the-art rivals in image dataset. Copyright © 2017, The Authors. All rights reserved.

关键词： Machine learning

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$\mathtt {Deepr}$: A Convolutional Net for Medical Records

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IEEE journal of biomedical and health informatics 2017年第1期21卷 22-30页

作者： Phuoc Nguyen Truyen Tran Nilmini Wickramasinghe Svetha Venkatesh Centre for Pattern Recognition and Data Analytics Faculty of Science and Technology Deakin University Geelong Vic Australia Health Informatics Management Deakin University Geelong Vic Australia Health Information Management Epworth HealthCare Richmond Vic Australia

Feature engineering remains a major bottleneck when creating predictive systems from electronic medical records. At present, an important missing element is detecting predictive regular clinical motifs from irregular episodic records. We present Deepr (short for Deep record), a new end-to-end deep learning system that learns to extract features from medical records and predicts future risk automatically. Deepr transforms a record into a sequence of discrete elements separated by coded time gaps and hospital transfers. On top of the sequence is a convolutional neural net that detects and combines predictive local clinical motifs to stratify the risk. Deepr permits transparent inspection and visualization of its inner working. We validate Deepr on hospital data to predict unplanned readmission after discharge. Deepr achieves superior accuracy compared to traditional techniques, detects meaningful clinical motifs, and uncovers the underlying structure of the disease and intervention space.

关键词： Medical diagnostic imaging Machine learning Diseases Feature extraction Hospitals Electronic medical records

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SECC: Simultaneous extraction of context and community from pervasive signals 14

SECC: Simultaneous extraction of context and community from ...

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14th IEEE International Conference on Pervasive Computing and Communications, PerCom 2016

作者： Nguyen, Thuong Nguyen, Vu Salim, Flora D. Phung, Dinh Computer Science and Information Technology RMIT University Australia Centre for Pattern Recognition and Data Analytics Deakin University Australia

ISBN: (纸本)9781467387798

Understanding user contexts and group structures plays a central role in pervasive computing. These contexts and community structures are complex to mine from data collected in the wild due to the unprecedented growth of data, noise, uncertainties and complexities. Typical existing approaches would first extract the latent patterns to explain the human dynamics or behaviors and then use them as the way to consistently formulate numerical representations for community detection, often via a clustering method. While being able to capture highorder and complex representations, these two steps are performed separately. More importantly, they face a fundamental difficulty in determining the correct number of latent patterns and communities. This paper presents an approach that seamlessly addresses these challenges to simultaneously discover latent patterns and communities in a unified Bayesian nonparametric framework. Our Simultaneous Extraction of Context and Community (SECC) model roots in the nested Dirichlet process theory which allows nested structure to be built to explain data at multiple levels. We demonstrate our framework on three public datasets where the advantages of the proposed approach are validated. © 2016 IEEE.

关键词： Ubiquitous computing

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Process-constrained batch Bayesian optimisation 17

Process-constrained batch Bayesian optimisation

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Proceedings of the 31st International Conference on Neural Information Processing Systems

作者： Pratibha Vellanki Santu Rana Sunil Gupta David Rubin Alessandra Sutti Thomas Dorin Murray Height Paul Sandars Svetha Venkatesh Centre for Pattern Recognition and Data Analytics Deakin University Geelong Australia Institute for Frontier Materials GTP Research Deakin University Geelong Australia Materials Science and Engineering Michigan Technological University

ISBN: (纸本)9781510860964

Prevailing batch Bayesian optimisation methods allow all control variables to be freely altered at each iteration. Real-world experiments, however, often have physical limitations making it time-consuming to alter all settings for each recommendation in a batch. This gives rise to a unique problem in BO: in a recommended batch, a set of variables that are expensive to experimentally change need to be fixed, while the remaining control variables can be varied. We formulate this as a process-constrained batch Bayesian optimisation problem. We propose two algorithms, pc-BO(basic) and pc-BO(nested). pc-BO(basic) is simpler but lacks convergence guarantee. In contrast pc-BO(nested) is slightly more complex, but admits convergence analysis. We show that the regret of pc-BO(nested) is sublinear. We demonstrate the performance of both pc-BO(basic) and pc-BO(nested) by optimising benchmark test functions, tuning hyper-parameters of the SVM classifier, optimising the heat-treatment process for an Al-Sc alloy to achieve target hardness, and optimising the short polymer fibre production process.

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Extracting key challenges in achieving sobriety through shared subspace learning 12th

Extracting key challenges in achieving sobriety through shar...

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12th International Conference on Advanced data Mining and Applications, ADMA 2016

作者： Harikumar, Haripriya Nguyen, Thin Rana, Santu Gupta, Sunil Kaimal, Ramachandra Venkatesh, Svetha Centre for Pattern Recognition and Data Analytics Deakin University Geelong Australia Computer Science and Engineering Department Amrita University Kollam India

ISBN: (纸本)9783319495859

Alcohol abuse is quite common among all people without any age restrictions. The uncontrolled use of alcohol affects both the individual and society. Alcohol addiction leads to a huge increase in crime, suicide, health related problems and financial crisis. Research has shown that certain behavioral changes can be effective towards staying abstained. The analysis of behavioral changes of quitters and those who are at the beginning phase of quitting can be useful for reducing the issues related to alcohol addiction. Most of the conventional approaches are based on surveys and, therefore, expensive in both time and cost. Social media has lend itself as a source of large, diverse and unbiased data for analyzing social behaviors. Reddit is a social media platform where a large number of people communicate with each other. It has many different sub-groups called subreddits categorized based on the subject. We collected more than 40,000 self reported user’s data from a subreddit called ‘/r/stopdrinking’. We divide the data into two groups, short-term with abstinent days less than 30 and long-term abstainers with abstinent days greater than 365 based on badge days at the time of post submission. Common and discriminative topics are extracted from the data using JS-NMF, a shared subspace non-negative matrix factorization method. The validity of the extracted topics are demonstrated through predictive performance. © Springer International Publishing AG 2016.

关键词： Non-negative matrix factorization

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Understanding behavioral differences between short and long-term drinking abstainers from social media 12th

Understanding behavioral differences between short and long-...

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12th International Conference on Advanced data Mining and Applications, ADMA 2016

作者： Harikumar, Haripriya Nguyen, Thin Gupta, Sunil Rana, Santu Kaimal, Ramachandra Venkatesh, Svetha Centre for Pattern Recognition and Data Analytics Deakin University Geelong Australia Computer Science and Engineering Department Amrita University Kollam India

ISBN: (纸本)9783319495859

Drinking alcohol has high cost on society. The journey from being a regular drinker to a successful quitter may be a long and hard journey, fraught with the risk to relapse. Research has shown that certain behavioral changes can be effective towards staying abstained. Traditional way to conduct research on drinking abstainers uses questionnaire based approach to collect data from a curated group of people. However, it is an expensive approach in both cost and time and often results in small data with less diversity. Recently, social media has emerged as a rich data source. Reddit is one such social media platform that has a community (‘subreddit’) with an interest to quit drinking. The discussions among the group dates back to year 2011 and contain more than 40,000 posts. This large scale data is generated by users themselves and without being limited by any survey questionnaires. The most predictive factors from the features (unigrams, topics and LIWC) associated with short-term and long-term abstinence are identified using Lasso. It is seen that many common patterns manifest in unigrams, topics and LIWC. Whilst topics provided much richer associations between a group of words and the outcome, unigrams and LIWC are found to be good at finding highly predictive solo and psycho linguistically important words. Combining them we have found that many interesting patterns that are associated with the successful attempt made by the long-term abstainer, at the same time finding many of the common issues faced during the initial period of abstinence. © Springer International Publishing AG 2016.

关键词： Surveys

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The influence of pharmacological autonomic blockades on multi-scale measures of heart rate variability

The influence of pharmacological autonomic blockades on mult...

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Joint Conference of the European Medical and Biological Engineering Conference, EMBEC 2017 and Nordic-Baltic Conference on Biomedical Engineering and Medical Physics, NBC 2107

作者： Marzbanrad, Faezeh Karmakar, C.K. Khandoker, A.H. Palaniswami, M. Moritani, T. Jelinek, H.F. Department of Electrical and Computer Systems Engineering Monash University ClaytonVIC3168 Australia Centre for Research in Complex Systems and School of Community Health Charles Sturt University Albury Australia Centre of Pattern Recognition and Data Analytics Deakin University GeelongVIC Australia Khalifa University of Science Technology and Research Abu Dhabi United Arab Emirates The University of Melbourne ParkvilleVIC3010 Australia Kyoto University Kyoto Japan

ISBN: (纸本)9789811051210

Multi-scale measures have been used extensively to characterize non-linear dynamics of heart beats. However a physiological interpretation of the exponents associated with various scales of these measures and the role of the sympathetic and parasympathetic activities have not been attempted. In this study three multi-scale measures were compared at different scales in a pharmacological autonomic blockade experiment, starting with a control phase. It was then followed by injection of propranolol causing sympathetic blockade and injection of atropine resulting in the blockade of both sympathetic and parasympathetic pathways. Multiscale Entropy (MSE), Multi Fractal Detrended Fluctuation Analysis (MFDFA) and Renyi Entropy (RE) were compared using a paired test statistics. The measures performed differently depending on the scale factors and RE showed the best results indicating a good discrimination particularly at higher scale exponents. This study provides a basis for understanding the effect of autonomic activity on multi-scale measures with respect to changes in parasympathetic and sympathetic activity. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Entropy

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Cascade Bayesian optimization 29th

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29th Australasian Joint Conference on Artificial Intelligence, AI 2016

作者： Nguyen, Thanh Dai Gupta, Sunil Rana, Santu Nguyen, Vu Venkatesh, Svetha Deane, Kyle J. Sanders, Paul G. Center for Pattern Recognition and Data Analytics Deakin University Geelong3216 Australia Materials Science and Engineering Michigan Technological University Houghton United States

ISBN: (纸本)9783319501260

Multi-stage cascade processes are fairly common, especially in manufacturing industry. Precursors or raw materials are transformed at each stage before being used as the input to the next stage. Setting the right control parameters at each stage is important to achieve high quality products at low cost. Finding the right parameters via trial and error approach can be time consuming. Bayesian optimization is an efficient way to optimize costly black-box function. We extend the standard Bayesian optimization approach to the cascade process through formulating a series of optimization problems that are solved sequentially from the final stage to the first stage. Epistemic uncertainties are effectively utilized in the formulation. Further, cost of the parameters are also included to find cost-efficient solutions. Experiments performed on a simulated testbed of Al-Sc heat treatment through a three-stage process showed considerable efficiency gain over a naïve optimization approach. © Springer International Publishing AG 2016.

关键词： Uncertainty analysis

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Stable clinical prediction using graph support vector machines

Stable clinical prediction using graph support vector machin...

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International Conference on pattern recognition

作者： Iman Kamkar Sunil Gupta Cheng Li Dinh Phung Svetha Venkatesh Centre for Pattern Recognition and Data Analytics Deakin University Australia

ISBN: (纸本)9781509048489

The stability matters in clinical prediction models because it makes the model to be interpretable and generalizable. It is paramount for high dimensional data, which employ sparse models with feature selection ability. We propose a new method to stabilize sparse support vector machines using intrinsic graph structure of the electronic medical records. The graph structure is exploited using the Jaccard similarity among features. Our method employs a convex function to penalize the pairwise l ∞ -norm of connected feature coefficients in the graph. We apply the alternating direction method of multipliers to solve the proposed formulation. Our experiments are conducted on a synthetic and three real-world hospital datasets. We show that our proposed method is more stable than the state-of-the-art feature selection and classification techniques in terms of three stability measures namely, Jaccard similarity measure, Spearman's rank correlation coefficient and Kuncheva index. We further show that our method has resulted in better classification performance compared to the baselines.

关键词： Support vector machines Stability criteria Mathematical model Correlation Optimization Indexes

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Effect of social capital on emotion, language style and latent topics in online depression community

Effect of social capital on emotion, language style and late...

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IEEE International Conference on Research, Innovation and Vision for the Future

作者： Bo Dao Thin Nguyen Svetha Venkatesh Dinh Phung Centre for Pattern Recognition and Data Analytics Deakin University Australia

ISBN: (纸本)9781509041350

Social capital is linked to mental illness. It has been proposed that higher social capital is associated with better mental well-being in both individuals and groups in offline setting. However, in online settings, the association between online social capital and mental health conditions has not yet been explored. Social media offer us a rich opportunity to determine the link between social capital and aspects of mental wellbeing. In this paper, we examine social capital based on levels of social connectivity of bloggers can be connected to aspects of depression in individuals and online depression community. We explore apparent properties of textual contents, including expressed emotions, language styles and latent topics, of a large corpus of blog posts, to analyze the aspect of social capital in the community. Using data collected from online Livejoumal depression community, we apply both statistical tests and machine learning approaches to examine how predictive factors vary between low and high social capital groups. Significant differences are found between low and high social capital groups when characterized by a set of latent topics, language features derived from blog posts, suggesting discriminative features, proved to be useful in the classification task. This shows that linguistic styles are better predictors than latent topics as features. The findings indicate the potential of using social media as a sensor for monitoring mental well-being in online settings.

关键词： Blogs Feature extraction Pragmatics Social network services Adaptation models Monitoring Sociology

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