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Deep learning to attend to risk in ICU 2

Deep learning to attend to risk in ICU

引用

2nd International Workshop on Knowledge Discovery in Healthcare data, KDH at IJCAI 2017

作者： Nguyen, Phuoc Tran, Truyen Venkatesh, Svetha Centre for Pattern Recognition and Data Analytics Deakin University Geelong Australia

Modeling physiological time-series in ICU is of high clinical importance. However, data collected within ICU are irregular in time and often contain missing measurements. Since absence of a measure would signify its lack of importance, the missingness is indeed informative and might reflect the decision making by the clinician. Here we propose a deep learning architecture that can effectively handle these challenges for predicting ICU mortality outcomes. The model is based on Long Short-Term Memory, and has layered attention mechanisms. At the sensing layer, the model decides whether to observe and incorporate parts of the current measurements. At the reasoning layer, evidences across time steps are weighted and combined. The model is evaluated on the PhysioNet 2012 dataset showing competitive and interpretable results.

关键词： Deep learning

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Outlier detection on mixed-type data: An energy-based approach 12th

Outlier detection on mixed-type data: An energy-based approa...

引用

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

作者： Do, Kien Tran, Truyen Phung, Dinh Venkatesh, Svetha Centre for Pattern Recognition and Data Analytics Deakin University Geelong Australia

ISBN: (纸本)9783319495859

Outlier detection amounts to finding data points that differ significantly from the norm. Classic outlier detection methods are largely designed for single data type such as continuous or discrete. However, real world data is increasingly heterogeneous, where a data point can have both discrete and continuous attributes. Handling mixed-type data in a disciplined way remains a great challenge. In this paper, we propose a new unsupervised outlier detection method for mixed-type data based on Mixed-variate Restricted Boltzmann Machine (***). The *** is a principled probabilistic method that models data density. We propose to use free-energy derived from *** as outlier score to detect outliers as those data points lying in low density regions. The method is fast to learn and compute, is scalable to massive datasets. At the same time, the outlier score is identical to data negative log-density up-to an additive constant. We evaluate the proposed method on synthetic and real-world datasets and demonstrate that (a) a proper handling mixed-types is necessary in outlier detection, and (b) free-energy of *** is a powerful and efficient outlier scoring method, which is highly competitive against state-of-the-arts. © Springer International Publishing AG 2016.

关键词： Free energy

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Matching objects across the textured-smooth continuum

Matching objects across the textured-smooth continuum

引用

2012 Australasian Conference on Robotics and Automation, ACRA 2012

作者： Arandjelović, Ognjen Centre for Pattern Recognition and Data Analytics Deakin University Geelong VIC 3220 Australia

ISBN: (纸本)9780980740431

The problem of 3D object recognition is of immense practical importance, with the last decade witnessing a number of breakthroughs in the state of the art. Most of the previous work has focused on the matching of textured objects using local appearance descriptors extracted around salient image points. The recently proposed bag of boundaries method was the first to address directly the problem of matching smooth objects using boundary features. However, no previous work has attempted to achieve a holistic treatment of the problem by jointly using textural and shape features which is what we describe herein. Due to the complementarity of the two modalities, we fuse the corresponding matching scores and learn their relative weighting in a data specific manner by optimizing discriminative performance on synthetically distorted data. For the textural description of an object we adopt a representation in the form of a histogram of SIFT based visual words. Similarly the apparent shape of an object is represented by a histogram of discretized features capturing local shape. On a large public database of a diverse set of objects, the proposed method is shown to outperform significantly both purely textural and purely shape based approaches for matching across viewpoint variation.

关键词： Graphic methods

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Computer simulation based parameter selection for resistance exercise

Computer simulation based parameter selection for resistance...

引用

24th IASTED International Conference on Modelling and Simulation, MS 2013

作者： Arandjelović, Ognjen Centre for Pattern Recognition and Data Analytics Deakin University Geelong 3216 VIC Australia

ISBN: (纸本)9780889869561

In contrast to most scientific disciplines, sports science research has been characterized by comparatively little effort investment in the development of relevant phenomenologi-cal models. Scarcer yet is the application of said models in practice. We present a framework which allows resistance training practitioners to employ a recently proposed neu-romuscular model in actual training program design. The first novelty concerns the monitoring aspect of coaching. A method for extracting training performance characteristics from loosely constrained video sequences, effortlessly and with minimal human input, using computer vision is described. The extracted data is subsequently used to fit the underlying neuromuscular model. This is achieved by solving an inverse dynamics problem corresponding to a particular exercise. Lastly, a computer simulation of hypothetical training bouts, using athlete-specific capability parameters, is used to predict the effected adaptation and changes in performance. The software described here allows the practitioner to manipulate hypothetical training parameters and immediately see their effect on predicted adaptation for a specific athlete. Thus, this work presents a holistic view of the monitoring-assessment-adjustment loop.

关键词： Sports

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Exceptional contrast set mining: Moving beyond the deluge of the obvious 29th

引用

29th Australasian Joint Conference on Artificial Intelligence, AI 2016

作者： Nguyen, Dang Luo, Wei Phung, Dinh Venkatesh, Svetha School of Information Technology Centre for Pattern Recognition and Data Analytics Deakin University Geelong Australia

ISBN: (纸本)9783319501260

data scientists, with access to fast growing data and computing power, constantly look for algorithms with greater detection power to discover "novel" knowledge. But more often than not, their algorithms give them too many outputs that are either highly speculative or simply confirming what the domain experts already know. To escape this dilemma, we need algorithms that move beyond the obvious association analyses and leverage domain analytic objectives (aka. KPIs) to look for higher order connections. We propose a new technique Exceptional Contrast Set Mining that first gathers a succinct collection of affirmative contrast sets based on the principle of redundant information elimination. Then it discovers exceptional contrast sets that contradict the affirmative contrast sets. The algorithm has been successfully applied to several analytic consulting projects. In particular, during an analysis of a state-wide cancer registry, it discovered a surprising regional difference in breast cancer screening. © Springer International Publishing AG 2016.

关键词： Diseases

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Individualized arrhythmia detection with ECG signals from wearable devices

Individualized arrhythmia detection with ECG signals from we...

引用

2014 IEEE International Conference on data Science and Advanced analytics, DSAA 2014

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

ISBN: (纸本)9781479969913

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. © 2014 IEEE.

关键词： Electrocardiograms

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A novel embedding model for knowledge base completion based on convolutional neural network

A novel embedding model for knowledge base completion based ...

引用

2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2018

作者： Nguyen, Dai Quoc Nguyen, Tu Dinh Nguyen, Dat Quoc Phung, Dinh Deakin University Geelong Australia Centre for Pattern Recognition and Data Analytics University of Melbourne Melbourne Australia

ISBN: (纸本)9781948087292

In this paper, we propose a novel embedding model, named ConvKB, for knowledge base completion. Our model ConvKB advances state-of-the-art models by employing a convolutional neural network, so that it can capture global relationships and transitional characteristics between entities and relations in knowledge bases. In ConvKB, each triple (head entity, relation, tail entity) is represented as a 3- column matrix where each column vector represents a triple element. This 3-column matrix is then fed to a convolution layer where multiple filters are operated on the matrix to generate different feature maps. These feature maps are then concatenated into a single feature vector representing the input triple. The feature vector is multiplied with a weight vector via a dot product to return a score. This score is then used to predict whether the triple is valid or not. Experiments show that ConvKB achieves better link prediction performance than previous state-of-the-art embedding models on two benchmark datasets WN18RR and FB15k-237. © 2018 Association for Computational Linguistics.

关键词： Embeddings

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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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Multi-modal abnormality detection in video with unknown data segmentation

Multi-modal abnormality detection in video with unknown data...

引用

21st International Conference on pattern recognition, ICPR 2012

作者： Nguyen, Tien Vu Phung, Dinh Rana, Santu Pham, Duc Son Venkatesh, Svetha Centre for Pattern Recognition and Data Analytics Deakin University VIC Australia Department of Computing Curtin University WA Australia

ISBN: (纸本)9784990644109

This paper examines a new problem in large scale stream data: abnormality detection which is localized to a data segmentation process. Unlike traditional abnormality detection methods which typically build one unified model across data stream, we propose that building multiple detection models focused on different coherent sections of the video stream would result in better detection performance. One key challenge is to segment the data into coherent sections as the number of segments is not known in advance and can vary greatly across cameras;and a principled way approach is required. To this end, we first employ the recently proposed infinite HMM and collapsed Gibbs inference to automatically infer data segmentation followed by constructing abnormality detection models which are localized to each segmentation. We demonstrate the superior performance of the proposed framework in a real-world surveillance camera data over 14 days. © 2012 ICPR Org Committee.

关键词： pattern recognition

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

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

引用

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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