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arXiv 2021年

作者： Khalafi, Sahar Ghadiri, Nasser Moradi, Milad Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan84156-83111 Iran Institute for Artificial Intelligence and Decision Support Center for Medical Statistics Informatics and Intelligent Systems Medical University of Vienna Vienna Austria

MSC Codes 68T50, 68T07Background and objectives: Identifying patient cohorts from clinical notes in secondary electronic health records is a fundamental task in clinical information management. The patient cohort identification needs to identify the patient phenotypes. However, with the growing number of clinical notes, it becomes challenging to analyze the data manually. Therefore, automatic extraction of clinical concepts would be an essential task to identify the patient phenotypes correctly. This paper proposes a novel hybrid model for automatically extracting patient phenotypes using natural language processing and deep learning models to determine the patient phenotypes without dictionaries and human intervention. Methods: The proposed hybrid model is based on a neural bidirectional sequence model (BiLSTM or BiGRU) and a Convolutional Neural Network (CNN) for identifying patient's phenotypes in discharge reports. Furthermore, to extract more features related to each phenotype, an extra CNN layer is run parallel to the hybrid proposed model. The extra CNN layer improves the model in identifying different phenotypes. We used pre-trained embeddings such as FastText and Word2vec separately as the input layers to evaluate other embedding's performance in identifying patient phenotypes. We also measured the effect of applying additional data cleaning steps on discharge reports to identify patient phenotypes by deep learning models. Results: We used discharge reports in the Medical Information Mart for Intensive Care III (MIMIC III) database in our experiments to identify patient phenotypes. Experimental results in internal comparison demonstrate that the proposed model achieved significant performance improvement over existing models. In addition, we pre-trained embeddings using FastText and Word2vce on discharge reports in MIMIC III. The enhanced version of our model with an extra CNN layer obtained a relatively higher F1-score than the original hybrid model. We al

关键词： Embeddings

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Enterprise Architecture for the Sensing Enterprise a Research Framework

Enterprise Architecture for the Sensing Enterprise a Researc...

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International Conference of Information and Communication Technology (ICoICT)

作者： Erda Guslinar Perdana Husni S Sastramihardja Iping Supriana Suwardi School of Applied Science Institut Teknologi Bandung (ITB) Bandung Indonesia Faculty of Computer Science Esa Unggul University Jakarta Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology (ITB) Bandung Indonesia

ISBN: (纸本)9781538645734

Internet of Things (IoT) will change many things, open up opportunities and enabling something that was not possible before. IoT provides sensing capability so that enterprise has better global context awareness. This is the Sensing Enterprise, an enterprise that obtains multidimensional information from physical or virtual objects in a connected environment. This sensing capability is expected to increase the capacity and capability of the enterprise in responding to sustainability challenges. This paper proposes a research framework as an alternative guide for researchers to produce various artifacts or theories to realize The Sensing Enterprise for sustainability achievement. The method used in developing the framework is modification/adaptation of transdisciplinary research model and information system research model. This framework has three main area consist of scientific base, enterprise architecture research process and sustainability achievement.

关键词： Sustainable development Sensors computer architecture Internet of Things Standards Information systems Adaptation models

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Lecture Notes in computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface

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Lecture Notes in computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2012年 6599 LNAI卷 V-VI页

作者： Collier, Rem Dix, Jürgen Novák, Peter University College Dublin College of Science School of Computer Science and Informatics Belfield Dublin 4 Ireland Technische Universität Institut für Informatik Julius-Albert-Straße 4 38678 Clausthal-Zellerfeld Germany Czech Technical University Faculty of Electrical Engineering Department of Computer Science and Engineering Karlovo namesti 13 121 35 Prague 2 Czech Republic

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Performance Analysis of Apache Hadoop for Generating Candidates of Acronym and Expansion Pairs and Their Numerical Features

Performance Analysis of Apache Hadoop for Generating Candida...

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International Conference on Information Technology, Information Systems and electrical engineering (ICITISEE)

作者： Taufik Fuadi Abidin Ramzi Adriman Ridha Ferdhiana Department of Informatics Syiah Kuala University Banda Aceh Indonesia Dept. of Electrical and Computer Engineering Syiah Kuala University Banda Aceh Indonesia Department of Statistics Syiah Kuala University Banda Aceh Indonesia

ISBN: (纸本)9781538670835;9781538670828

Mining information from web pages to automatically determine the acronym and its expansion is an important and challenging task. It is considered not easy to do because the acronyms writing rules and forms are very diverse for each language. The task consists of several important steps i.e. generating candidates of an acronym and its expansion, creating their numerical features, and determining the correct acronyms and expansions using machine learning algorithm. In this work, we evaluate and compare the performance analysis, in terms of speed, to generate the candidates of acronym and expansion pairs and create their numerical features when the processes are running on Hadoop cluster with different data nodes and on a single machine. The results show that Hadoop cluster outperforms a single machine when generating almost 52 million candidates of acronym and expansion pair and their numerical features. When Hadoop cluster was set up from two to three data nodes, the performance improved on average by 65.81%.

关键词： Terminology Cluster computing Big Data Web pages Task analysis Videos Data mining

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A Complete Resource Theory of Quantum Incompatibility as Quantum Programmability

arXiv

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arXiv 2019年

作者： Buscemi, Francesco Chitambar, Eric Zhou, Wenbin Graduate School of Informatics Nagoya University Chikusa-kuNagoya464-8601 Japan Department of Electrical and Computer Engineering Coordinated Science Laboratory University of Illinois at Urbana-Champaign UrbanaIL61801 United States

Measurement incompatibility describes two or more quantum measurements whose expected joint outcome on a given system cannot be defined. This purely non-classical phenomenon provides a necessary ingredient in many quantum information tasks such violating a Bell Inequality or non-locally steering part of an entangled state. In this paper, we characterize incompatibility in terms of programmable measurement devices and the general notion of quantum programmability. This refers to the temporal freedom a user has in issuing programs to a quantum device. For devices with a classical control and classical output, measurement incompatibility emerges as the essential quantum resource embodied in their functioning. Based on the processing of programmable measurement devices, we construct a quantum resource theory of incompatibility. A complete set of convertibility conditions for programmable devices is derived based on quantum state discrimination with post-measurement information. Copyright © 2019, The Authors. All rights reserved.

关键词： Quantum entanglement

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A comparative study of invariant descriptors for shape retrieval

A comparative study of invariant descriptors for shape retri...

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IEEE International Workshop on Imaging Systems and Techniques (IST)

作者： A. Amanatiadis V.G. Kaburlasos A. Gasteratos S.E. Papadakis Laboratory of Electronics Department of Electrical and Computer Engineering Democritus University of Thrace Xanthi Greece Division of Computing Systems Department of Industrial Informatics Technological Educational Institute of Kavala Kavala Greece Laboratory of Robotics and Automation Department of Production and Management Engineering Democritus University of Thrace Xanthi Greece

This paper presents a comparative study between scale, rotation and translation invariant descriptors for shape representation and retrieval. Specifically, we studied Fourier, angular radial transform and image moment descriptors for shape representation. Since shape is one of the most widely used image feature exploited in content-based image retrieval systems, we studied for each descriptor, the number of coefficients needed for indexing and their retrieval performance. Results showed that moment descriptors present the best performance in both terms of shape representation quality as well as in the amount of required coefficients.

关键词： Shape Fourier transforms Image retrieval Subspace constraints Laboratories Character recognition Content based retrieval Data mining computer industry electrical products industry

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Wavelet usage for feature extraction for crack localization

Wavelet usage for feature extraction for crack localization

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Mediterranean Conference on Control and Automation (MED)

作者： George Georgoulas Chrysostomos Stylios Vassilios Kappatos Evangelos Dermatas Dept. of Computer Applications in Finance and Management TEI of Ionian Islands Lefkas Greece Electrical Engineering Department University of Patras Patras Hellas Dept. of Informatics & Communications Technology TEI of Epirus Arta Greece

In this research work we investigate the analysis of Acoustic Emission (AE) signals using wavelet decomposition to locate a single event (crack), which usually takes place in three typical areas in a ship hull. The problem is a typical classification problem relying on the use of novel features extracted from the AE time series. As in most classification problems the extraction and selection of the most appropriate set of features plays a major role in the overall performance of the method and it is by no means a trivial task. Once a suitable set of features is extracted even ldquosimplerdquo classification models can perform adequately whereas a non-informative set of features even combined with sophisticated classifiers can lead to disappointing results. Here, we exploit the multi-resolution capabilities of wavelet decomposition, so that a set of features is extracted which it is then combined with a simple classifier. The proposed method gives superior classification rates for noisy environments compared to our previous work where conventional methods for feature extraction were deployed.

关键词： Feature extraction Marine vehicles Fatigue Welding Signal processing Inspection Surface cracks Neural networks Wavelet analysis Acoustic emission

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Automatic registration of large set of microscopic images using high-level features

Automatic registration of large set of microscopic images us...

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IEEE International Symposium on Biomedical Imaging

作者： J. Prescott M. Clary G. Wiet T. Pan Kun Huang Department of Electrical and Computer Engineering Ohio State Uinversity Columbus OH USA Department of Otolaryngology Ohio State Uinversity Columbus OH USA Department of Biomedical Informatics Ohio State Uinversity Columbus OH USA

In this paper, we present a novel method for automatic registration of large set of microscopic images by automatically match high-level region features via finding cyclic structures in a matching graph. The use of high-level features (e.g., regions, landmarks, objects) significantly reduced the computation and provides accurate initialization, which further allows fast convergence of the maximum mutual information algorithm. The scheme is a universal one as it works for other types of high-level features and the matching process is very computationally efficient. We have applied our method in 3-D reconstruction of a unique human cochlear sample and are also applying it to two other set of large microscopic images

关键词： Microscopy Mutual information Biomedical informatics Three dimensional displays Visualization Convergence Humans Ear computer vision NP-complete problem

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Monitoring Patients in Hospital Beds Using Unobtrusive Depth Sensors

Monitoring Patients in Hospital Beds Using Unobtrusive Depth...

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

作者： Tanvi Banerjee Moein Enayati James M. Keller Marjorie Skubic Mihail Popescu Marilyn Rantz Electrical and Computer Engineering Department University of Missouri Columbia MO University of Missouri System Columbia MO US Health Management and Informatics University of Missouri Columbia MO School of Nursing University of Missouri Columbia MO

We present an approach for patient activity recognition in hospital rooms using depth data collected using a Kinect sensor. Depth sensors such as the Kinect ensure that activity segmentation is possible during day time as well as night while addressing the privacy concerns of patients. It also provides a technique to remotely monitor patients in a non-intrusive manner. An existing fall detection algorithm is currently generating fall alerts in several rooms in the University of Missouri Hospital (MUH). In this paper we describe a technique to reduce false alerts such as pillows falling off the bed or equipment movement. We do so by detecting the presence of the patient in the bed for the times when the fall alert is generated. We test our algorithm on 96 hours obtained in two hospital rooms from MUH.

关键词： Hospitals Sensors Monitoring Detection algorithms Feature extraction Image edge detection Three-dimensional displays

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Predictive modeling of exercise response in CVD patients under rehabilitation

Predictive modeling of exercise response in CVD patients und...

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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： Filos, Dimitris Triantafyllidis, A. Manolios, V. Livitckaia, K. Claes, J. Buys, R. Cornelissen, V. Kouidi, E. Maglaveras, N. Chouvarda, I. Centre for Research and Technology Hellas Institute of Applied Biosciences Thessaloniki Greece Lab of Computing Medical Informatics & Biomedical lmaging Technologies School of Medicine Aristotle University of Thessaloniki Thessaloniki Greece Katholieke Universiteit Leuven Research Group for Cardiovascular and Respiratory Rehabilitation Belgium Laboratory of Sports Medicine Aristotle University of Thessaloniki Thermi Greece Department of Electrical Engineering and Computer Science Northwestern University EvanstonIL United States

ISBN: (纸本)9789811051210

Exercise-based rehabilitation plays a key role for patients with cardiovascular disease (CVD) in improving their well-being and reducing their symptoms. Monitoring and assessing the exercise response at an individual level is critical toward achieving better health outcomes. 15 exercise sessions performed by 5 CVD patients and 9 sessions from 3 regularly active individuals were monitored, and heart rate (HR) data were acquired. A model based on the HR dynamics during exercising at different intensities was built, and simulations were performed to assess performance in different scenarios of exercise selection. Our results show that the application of simple rules in exercise selection, which consider both the HR and the beneficial HR zones of individuals, can lead to beneficial execution of exercise programs (%time spent in beneficial HR zones: 60.6±27.5 for CVD patients). Personalized guidance during exercise has the potential to significantly contribute in the beneficial execution of exercise-based cardiac rehabilitation programs. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Patient rehabilitation

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