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HealthMap: A visual platform for patient suicide risk review

HealthMap: A visual platform for patient suicide risk review

引用

2014 Scientific Stream at Big data, BD 2014

作者： Rana, Santu Luo, Wei Tran, Truyen Phung, Dinh Venkatesh, Svetha Harvey, Richard Centre for Pattern Recognition and Data Analytics Deakin University Geelong Australia Barwon Health Geelong Australia

Misjudging suicide risk can be fatal. Risk assessment is complicated by multiplicity of risk factors, none of which individually can reliably predict risk. This paper addresses the need for better clinical support, visualising risk factors scattered in raw electronic medical records. HealthMap is a visual tool that helps clinicians effectively examine patient histories during a suicide risk assessment. We characterise the information visualisation problems accompanying suicide risk assessments. A design driven by visualisation principles was implemented. The prototype was evaluated by clinicians and accepted into daily clinical work-flow. Copyright © 2014 for the individual papers by the papers' authors.

关键词： Risk assessment

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A novel technique to investigate the effect of ageing on ventricular repolarization characteristics in healthy and LQTS subjects

A novel technique to investigate the effect of ageing on ven...

引用

Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： Mohammad H Imam Chandan K Karmakar M Palanaiswami Ahsan H Khandoker the Department of Electrical & Electronic Engineering The University of Melbourne Melbourne VIC 3010 Australia. the centre for Pattern Recognition and Data Analytics Deakin University Geelong Australia the Department of Biomedical Engineering Khalifa University of Science Technology and Research Abu Dhabi UAE

ISBN: (纸本)9781424492695

Ventricular repolarization(VR) characteristics is affected by ageing alongside several other factors like Heart rate(HR),respiration, modulation of autonomic nervous system, different drug effects, genetical factors affecting the cardiac ion channel characteristics, gender etc. Therefore, total VR variability (i.e. QT interval variability in surface ECG) consists of two components: one dependent on HR variability (HRV) and another independent of HRV. Analysis of QT interval variability (QTV) is crucial for both healthy and pathological conditions as increase in VR variability measured by QTV increases cardiac repolarization instability, which might lead to arrhythmogenesis. Analyzing the effect of ageing using a widely used measure of QTV (i.e. QTVI) is reported inconsistently in Healthy subjects whereas the same for Long QT Syndrome (LQTS) subjects is not widely reported. In this study, we propose a novel time domain measure from beat-to-beat QT-RR distribution to analyze how ageing affects VR in both Healthy and a group of genotyped LQTS1 subjects. A total of 139 Healthy subjects and 134 LQTS1 subjects of three different age groups (i.e. Young: age 20-35, Middle-aged: 40-55 and Old: age>60) were analyzed for this study. The proposed measure is also compared with other existing widely used measures of QTV like SDQT and QTVI in differentiating different age groups. The proposed measure stands out to be more discriminatory than other existing variability measures of QT interval.

关键词： LQTS HRV SDQT

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A Bayesian Nonparametric Framework for Activity recognition Using Accelerometer data

A Bayesian Nonparametric Framework for Activity Recognition ...

引用

International Conference on pattern recognition

作者： Thuong Nguyen Sunil Gupta Svetha Venkatesh Dinh Phung Centre for Pattern Recognition and Data Analytics Deakin University Australia

ISBN: (纸本)9781479952106

Monitoring daily physical activity of human plays an important role in preventing diseases as well as improving health. In this paper, we demonstrate a framework for monitoring the physical activity levels in daily life. We collect the data using accelerometer sensors in a realistic setting without any supervision. The ground truth of activities is provided by the participants themselves using an experience sampling application running on mobile phones. The original data is discretized by the hierarchical Dirichlet process (HDP) into different activity levels and the number of levels is inferred automatically. We validate the accuracy of the extracted patterns by using them for the multi-label classification of activities and demonstrate the high performances in various standard evaluation metrics. We further show that the extracted patterns are highly correlated to the daily routine of users.

关键词： Accelerometers Feature extraction Sensors Accuracy data models Legged locomotion

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Nonparametric Discovery of Learning patterns and Autism Subgroups from Therapeutic data

Nonparametric Discovery of Learning Patterns and Autism Subg...

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International Conference on pattern recognition

作者： Pratibha Vellanki Thi Duong Svetha Venkatesh Dinh Phung Centre for Pattern Recognition and Data Analytics Deakin University {pvellank

ISBN: (纸本)9781479952106

Autism Spectrum Disorder (ASD) is growing at a staggering rate, but, little is known about the cause of this condition. Inferring learning patterns from therapeutic performance data, and subsequently clustering ASD children into subgroups, is important to understand this domain, and more importantly to inform evidence-based intervention. However, this data-driven task was difficult in the past due to insufficiency of data to perform reliable analysis. For the first time, using data from a recent application for early intervention in autism (TOBY Play pad), whose download count is now exceeding 4500, we present in this paper the automatic discovery of learning patterns across 32 skills in sensory, imitation and language. We use unsupervised learning methods for this task, but a notorious problem with existing methods is the correct specification of number of patterns in advance, which in our case is even more difficult due to complexity of the data. To this end, we appeal to recent Bayesian nonparametric methods, in particular the use of Bayesian Nonparametric Factor Analysis. This model uses Indian Buffet Process (IBP) as prior on a binary matrix of infinite columns to allocate groups of intervention skills to children. The optimal number of learning patterns as well as subgroup assignments are inferred automatically from data. Our experimental results follow an exploratory approach, present different newly discovered learning patterns. To provide quantitative results, we also report the clustering evaluation against K-means and Nonnegative matrix factorization (NMF). In addition to the novelty of this new problem, we were able to demonstrate the suitability of Bayesian nonparametric models over parametric rivals.

关键词： Variable speed drives Entropy Bayes methods Autism data analysis Education Computational modeling

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Fixed-lag particle filter for continuous context discovery using Indian Buffet Process

Fixed-lag particle filter for continuous context discovery u...

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IEEE International Conference on Pervasive Computing and Communications (PerCom)

作者： Thuong Nguyen Sunil Gupta Svetha Venkatesh Dinh Phung Centre for Pattern Recognition and Data Analytics Deakin University Australia

Exploiting context from stream data in pervasive environments remains a challenge. We aim to extract proximal context from Bluetooth stream data, using an incremental, Bayesian nonparametric framework that estimates the number of contexts automatically. Unlike current approaches that can only provide final proximal grouping, our method provides proximal grouping and membership of users over time. Additionally, it provides an efficient online inference. We construct co-location matrix over time using Bluetooth data. A Poisson-exponential model is used to factorize this matrix into a factor matrix, interpreted as proximal groups, and a coefficient matrix that indicates factor usage. The coefficient matrix follows the Indian Buffet Process prior, which estimates the number of factors automatically. The non-negativity and sparsity of factors are enforced by using the exponential distribution to generate the factors. We propose a fixed-lag particle filter algorithm to process data incrementally. We compare the incremental inference (particle filter) with full batch inference (Gibbs sampling) in terms of normalized factorization error and execution time. The normalized error obtained through our incremental inference is comparable to that of full batch inference, whilst the execution time is more than 100 times faster. The discovered factors have similar meaning to the results of the popular Louvain method for community detection.

关键词： Context Bluetooth data models Bayes methods Matrix decomposition Exponential distribution Adaptation models

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Analysis of circadian rhythms from online communities of individuals with affective disorders

Analysis of circadian rhythms from online communities of ind...

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International Conference on data Science and Advanced analytics (DSAA)

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

The circadian system regulates 24 hour rhythms in biological creatures. It impacts mood regulation. The disruptions of circadian rhythms cause destabilization in individuals with affective disorders, such as depression and bipolar disorders. Previous work has examined the role of the circadian system on effects of light interactions on mood-related systems, the effects of light manipulation on brain, the impact of chronic stress on rhythms. However, such studies have been conducted in small, preselected populations. The deluge of data is now changing the landscape of research practice. The unprecedented growth of social media data allows one to study individual behavior across large and diverse populations. In particular, individuals with affective disorders from online communities have not been examined rigorously. In this paper, we aim to use social media as a sensor to identify circadian patterns for individuals with affective disorders in online *** use a large scale study cohort of data collecting from online affective disorder communities. We analyze changes in hourly, daily, weekly and seasonal affect of these clinical groups in contrast with control groups of general communities. By comparing the behaviors between the clinical groups and the control groups, our findings show that individuals with affective disorders show a significant distinction in their circadian rhythms across the online activity. The results shed light on the potential of using social media for identifying diurnal individual variation in affective state, providing key indicators and risk factors for noninvasive wellbeing monitoring and prediction.

关键词： Communities Mood Circadian rhythm Media Blogs Positron emission tomography Pragmatics

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Individualized arrhythmia detection with ECG signals from wearable devices

Individualized arrhythmia detection with ECG signals from we...

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International Conference on data Science and Advanced analytics (DSAA)

作者： Thanh-Binh Nguyen Wei Lou Terry Caelli Svetha Venkatesh Dinh Phung Centre for Pattern Recognition and Data Analytics Deakin University National ICT Australia

Low cost pervasive electrocardiogram (ECG) monitors is changing how sinus arrhythmia are diagnosed among patients with mild symptoms. With the large amount of data generated from long-term monitoring, come new data science and analytical challenges. Although traditional rule-based detection algorithms still work on relatively short clinical quality ECG, they are not optimal for pervasive signals collected from wearable devices-they don't adapt to individual difference and assume accurate identification of ECG fiducial points. To overcome these short-comings of the rule-based methods, this paper introduces an arrhythmia detection approach for low quality pervasive ECG signals. To achieve the robustness needed, two techniques were applied. First, a set of ECG features with minimal reliance on fiducial point identification were selected. Next, the features were normalized using robust statistics to factors out baseline individual differences and clinically irrelevant temporal drift that is common in pervasive ECG. The proposed method was evaluated using pervasive ECG signals we collected, in combination with clinician validated ECG signals from Physiobank. Empirical evaluation confirms accuracy improvements of the proposed approach over the traditional clinical rules.

关键词： Electrocardiography Heart beat Monitoring Biomedical monitoring Robustness Accuracy

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Multi-Lag HRV analysis discriminates disease progression of postinfarct people with no diabetes versus diabetes

Multi-Lag HRV analysis discriminates disease progression of ...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： Chandan Karmakar Herbert F. Jelinek Ahsan Khandoker Mikko Tulppo Timo Makikallio Antti Kiviniemi Heikki Huikuri Marimuthu Palaniswami Centre of Pattern Recognition and Data Analytics (PRaDA) Deakin University Geelong VIC 3220 Australia School of Community Health and Centre for Research in Complex Systems Charles Stuart University NSW 2640 Australia Department of Biomedical Engineering Khalifa University Abu Dhabi UAE Department of Exercise and Medical Physiology Verve Oulu Finland Division of Cardiology Department of Medicine University of Oulu Oulu Finland Electrical & Electronic Engineering Department University of Melbourne Parkville Melbourne VIC 3010 Australia

ISBN: (纸本)9781424492695

Diabetes mellitus is associated with multi-organ system dysfunction including the cardiovascular and autonomic nervous system. Although it is well documented that postinfarct patients are at higher risk of sudden cardiac death, diabetes adds an additional risk associated with autonomic neuropathy. However it is not known how the presence of diabetes in post-infarct patients affects cardiac rhythm. The majority of HRV algorithms for determining cardiac inter-beat interval changes describe only beat-to-beat variation determined over the whole heart rate recording and therefore do not consider the ability of a heart beat to influence a train of succeeding beats nor whether or how the temporal dynamics of the inter-beat intervals changes. This study used Poincaré Plot derived features and incorporated increased lag intervals to compare post-infarct patients with no history of prior infarct with or without diabetes and found that for the nondiabetic post-infarct patients only increased lag of short term correlation (SD1) predicted mortality, whereas in the diabetic post-infarct group only long-term correlations (SD2) significantly predicted mortality at a follow-up period of eight years. Temporal dynamics measured as a complex correlation measure (CCM) was also a significant predictor of mortality only in the diabetic post-infarct cohort. This study highlights the different pathophysiological progression and risk profile associated with presence of diabetes in a post-infarct patient population at eight year follow-up.

关键词： presence diabetes post-infarct patients Diabetes Mellitus Disease Progression Post infarct angina

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data-mining twitter and the autism spectrum disorder: A Pilot study

Data-mining twitter and the autism spectrum disorder: A Pilo...

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International Conference on Advances in Social Network Analysis and Mining, ASONAM

作者： Adham Beykikhoshk Ognjen Arandjelović Dinh Phung Svetha Venkatesh Terry Caelli Centre for Pattern Recognition and Data Analytics Deakin Univeristy Australia National ICT Australia (NICTA) Melbourne Australia

The autism spectrum disorder (ASD) is increasingly being recognized as a major public health issue which affects approximately 0.5-0.6% of the population. Promoting the general awareness of the disorder, increasing the engagement with the affected individuals and their carers, and understanding the success of penetration of the current clinical recommendations in the target communities, is crucial in driving research as well as policy. The aim of the present work is to investigate if Twitter, as a highly popular platform for information exchange, can be used as a data-mining source which could aid in the aforementioned challenges. Specifically, using a large data set of harvested tweets, we present a series of experiments which examine a range of linguistic and semantic aspects of messages posted by individuals interested in ASD. Our findings, the first of their nature in the published scientific literature, strongly motivate additional research on this topic and present a methodological basis for further work.

关键词： Variable speed drives Twitter Autism Communities data mining Pragmatics Conferences

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Extraction of latent patterns and contexts from social honest signals using hierarchical Dirichlet processes

Extraction of latent patterns and contexts from social hones...

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11th IEEE International Conference on Pervasive Computing and Communications, PerCom 2013

作者： Nguyen, Thuong Phung, Dinh Gupta, Sunil Venkatesh, S. Centre for Pattern Recognition and Data Analytics Deakin University Australia

ISBN: (纸本)9781467345750

A fundamental task in pervasive computing is reliable acquisition of contexts from sensor data. This is crucial to the operation of smart pervasive systems and services so that they might behave efficiently and appropriately upon a given context. Simple forms of context can often be extracted directly from raw data. Equally important, or more, is the hidden context and pattern buried inside the data, which is more challenging to discover. Most of existing approaches borrow methods and techniques from machine learning, dominantly employ parametric unsupervised learning and clustering techniques. Being parametric, a severe drawback of these methods is the requirement to specify the number of latent patterns in advance. In this paper, we explore the use of Bayesian nonparametric methods, a recent data modelling framework in machine learning, to infer latent patterns from sensor data acquired in a pervasive setting. Under this formalism, nonparametric prior distributions are used for data generative process, and thus, they allow the number of latent patterns to be learned automatically and grow with the data - as more data comes in, the model complexity can grow to explain new and unseen patterns. In particular, we make use of the hierarchical Dirichlet processes (HDP) to infer atomic activities and interaction patterns from honest signals collected from sociometric badges. We show how data from these sensors can be represented and learned with HDP. We illustrate insights into atomic patterns learned by the model and use them to achieve high-performance clustering. We also demonstrate the framework on the popular Reality Mining dataset, illustrating the ability of the model to automatically infer typical social groups in this dataset. Finally, our framework is generic and applicable to a much wider range of problems in pervasive computing where one needs to infer high-level, latent patterns and contexts from sensor data. © 2013 IEEE.

关键词： Ubiquitous computing

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