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Investigation of the visual attention role in clinical bioethics decision-making using machine learning algorithms

Procedia computer science 2017年 108卷 1165-1174页

作者： Daniel L. Fernandes Rodrigo Siqueira-Batista Andréia P. Gomes Camila R. Souza Israel T. da Costa Felippe da S.L. Cardoso João V. de Assis Gustavo H.L. Caetano Fabio R. Cerqueira Graduate Program in Computer Science Universidade Federal de Viçosa Minas Gerais Brazil Department of Medicine and Nursing Universidade Federal de Viçosa Minas Gerais Brazil Department of Physical Education Universidade Federal de Viçosa Minas Gerais Brazil Department of Production Engineering Universidade Federal Fluminense Rio de Janeiro Brazil

This study proposes the use of a computational approach based on machine learning (ML) algorithms to build predictive models using eye tracking data. Our intention is to provide results that may support the study of medical investigation in the decision-making process in clinical bioethics, particularly in this work, in cases of euthanasia. The data used in the approach were collected from 75 students of the nursing undergraduate course using an eye tracker. The available data were processed through feature selection methods, and were later used to create models capable of predicting the euthanasia decision through ML algorithms. Statistical experiments showed that the predictive model resultant from the multilayer perceptron (MLP) algorithm led to the best performance compared with the other tested algorithms, presenting an accuracy of 90.7% and a mean area under the ROC curve of 0.90. Interesting knowledge (patterns and rules) for the studied bioethical decision-making was extracted using simulations with MLP models and inspecting the obtained decision-tree rules. The good performance shown by the obtained MLP predictive model demonstrates that the proposed investigation approach may be used to test scientific hypotheses related to visual attention and decision-making.

关键词： Visual attention Decision-making in bioethics Mobile eye tracking Machine learning in medicine

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Author Correction: Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

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Nature communications 2020年第1期11卷 6232页

作者： Matthew H Bailey William U Meyerson Lewis Jonathan Dursi Liang-Bo Wang Guanlan Dong Wen-Wei Liang Amila Weerasinghe Shantao Li Yize Li Sean Kelso Gordon Saksena Kyle Ellrott Michael C Wendl David A Wheeler Gad Getz Jared T Simpson Mark B Gerstein Li Ding The McDonnell Genome Institute at Washington University St. Louis MO 63108 USA. Division of Oncology Department of Medicine Washington University School of Medicine St. Louis MO 63108 USA. Alvin J. Siteman Cancer Center Washington University School of Medicine St. Louis MO 63108 USA. Yale School of Medicine Yale University New Haven CT 06520 USA. Program in Computational Biology and Bioinformatics Yale University New Haven CT 06520 USA. Computational Biology Program Ontario Institute for Cancer Research Toronto ON M5G 0A3 Canada. The Hospital for Sick Children Toronto ON M5G 1X8 Canada. Broad Institute of MIT and Harvard Cambridge MA 02142 USA. Biomedical Engineering Oregon Health and Science University Portland OR 97239 USA. Department of Mathematics Washington University in St. Louis St. Louis MO 63130 USA. Department of Genetics Washington University School of Medicine St.Louis MO 63110 USA. Human Genome Sequencing Center Baylor College of Medicine Houston TX 77030 USA. Department of Molecular and Human Genetics Baylor College of Medicine Houston TX 77030 USA. Harvard Medical School Boston MA 02115 USA. Center for Cancer Research Massachusetts General Hospital Boston MA 02114 USA. Department of Pathology Massachusetts General Hospital Boston MA 02114 USA. Department of Computer Science University of Toronto Toronto ON M5S Canada. Program in Computational Biology and Bioinformatics Yale University New Haven CT 06520 USA. mark.gerstein@yale.edu. Department of Computer Science Yale University New Haven CT 06520 USA. mark.gerstein@yale.edu. Department of Molecular Biophysics and Biochemistry Yale University New Haven CT 06520 USA. mark.gerstein@yale.edu. The McDonnell Genome Institute at Washington University St. Louis MO 63108 USA. lding@wustl.edu. Division of Oncology Department of Medicine Washington University School of Medicine St. Louis MO 63108 USA. lding@wustl.edu. Alvin J. Siteman Cancer Center Wash

Correction to this paper has been published: https://***/10.1038/s41467-020-20128-w.

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DEA Model with Hesitant Fuzzy Polyhedral Set in Benchmarking

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Journal of Physics: Conference Series 2019年第1期1361卷

作者： Dahlan Abdullah H Hartono Cut Ita Erliana Defi Irwansyah Nurintan Asyiah Siregar Ayu Esteka Sari Abdurrozzaq Hasibuan Abdussakir Nuning Kurniasih Mas'ut Irma Yanti Rangkuti Nurmawati Erni Buston Apridar Abdurrahman Tita Septi Handayani Wiwien Hadikurniawati Edy Winarno Department of Informatics Universitas Malikussaleh Aceh Indonesia Department of Computer Science STMIK IBBI Medan Indonesia Department of Industrial Engineering Universitas Malikussaleh Aceh Indonesia Department of Management STIE Labuhanbatu Sumatera Utara Indonesia Department of Management STIE Sakti Alam Kerinci Jambi Indonesia Faculty of Engineering Universitas Islam Sumatera Utara Medan Indonesia Department of Mathematics Education State Islamic University of Malang MalangIndonesia Faculty of Communication Sciences Library and Information Science Program Universitas Padjadjaran Bandung Indonesia Faculty of Economic Universitas Islam Sumatera Utara Medan Indonesia Faculty of Medical Universitas Islam Sumatera Utara Medan Indonesia Faculty of Industry Technique University of 45 Surabaya Indonesia Departement of Nursing Politeknik Kesehatan Kementerian Bengkulu Indonesia Department of Management Universitas Malikussaleh Aceh Indonesia Departemen of Nursing Universitas Dehasen Bengkulu Indonesia Faculty of Information Technology Universitas Stikubank Semarang Indonesia

Data Envelopment Analysis (DEA) method is a linear programming approach that has been widely used as a framework for evaluating efficiency and measurement. DEA decision making is often faced with situations where the existing DMU has input and output that contains fuzzy and hesitant elements so that it is difficult to make efficiency measurements. This study will design a DEA model in the state of inputs and outputs that contain hesitant elements by using polyhedral uncertainty sets. The results of this study are expected to produce a benchmarking model with DEA that can overcome the hesitant element and have the advantage of using a polyhedral uncertainty set that can measure the linearity of the nominal problem.

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Intent Recognition in a Generalized Framework for Collaboration

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Procedia computer science 2016年 84卷 123-126页

作者： Rupam Bhattacharyya Shyamanta M. Hazarika Biomimetic and Cognitive Robotics Lab Department of Computer Science and Engineering Tezpur University Tezpur and 784028 India

For a collaborative assistive device, human intent recognition (IR) is one of the first and foremost requirements. Formalizing the complex process of human IR in a compact yet expressive mathematical model holds promise. We put forward a Hierarchical Finite State Machine (H-FSM) for human IR within a generalized framework for collaborative assistive devices. Visual and contextual observations drive the H-FSM to different levels of granularity.

关键词： Collaborative intent recognition assistive device H-FSM.

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Preventing Disclosure of Personal Data in IoT Networks

Preventing Disclosure of Personal Data in IoT Networks

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International IEEE Conference on Signal-Image Technologies and Internet-Based System

作者： Ilaria Torre Giovanni Adorni Frosina Koceva Odnan Sanchez Department of Computer Science Bioengineering Robotics and Systems Engineering University of Genoa Italy

Sharing data among applications is a growing phenomenon. With the IoT, this phenomenon becomes more significant. As already studied in social networks, data sharing has the drawback of privacy risks. Authorization protocols and cryptographic systems may not be enough to ensure that user data and metadata are not used for non-legitimate purposes. There are different scenarios and several personal data management proposals aimed to improve privacy protection. However, a risk that is always present is related to the possibility of processing and aggregating public and authorized data to infer sensitive information and data that the user may not want to share. These approaches, often called inference attacks, concern the disclosure of personal user data and have been widely studied in social networks. In this paper we describe the problem and some techniques to face it, showing its relevance in the IoT. Then we present the concept of an Adaptive Inference Discovery Service AID-S, conceived as a service that may support users to prevent this kind of information leakage and that can be integrated into personal data managers.

关键词： Data privacy Data models Correlation Social network services Sensors Biomedical engineering Robots

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Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

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arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

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How will the Kenai fisheries respond to changing environmental conditions: scenario based studies of coupled socio-ecological systems dynamics using an agent-based model

How will the Kenai fisheries respond to changing environment...

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Oceans (Conference)

作者： Martin Cenek Maxwell Franklin E. Jamie Trammell Spencer Dahl Department of Computer Science University of Alaska Anchorage Anchorage AK 99508 Department of Civil Engineering University of Alaska Anchorage Anchorage AK 99508 Alaska Heritage Program University of Alaska Anchorage Anchorage AK 99508 Columbia University New York NY US

ISBN: (纸本)9781509064298;9780692946909

Alaska's Kenai fisheries are managed for maximum efficiency while ensuring fair allocation across multiple stakeholder groups and long term sustainability of the fishery. Managers must work to meet the fishery's goals while adapting their commercial and subsistence eco-service management practices to meet changing ecological conditions. Pre- and in-season fishery management practices are supported by historical and location-based management knowledge. Unexpected changes to environmental conditions may be outside the adaptive capacity of the management. Furthermore, managers currently lack a tool to test how proposed policy will alter fishery outcomes. We present an agent-based simulation framework designed for the Kenai case-study that incorporates stakeholder harvest and effort data, as well as Oncorhynchus nerka and Oncorhynchus tshawytscha salmon run-timing dynamics for the last 35 years. The resulting high-fidelity and verified model was used for scenario-based studies to understand how shifting ecological and social drivers effect the seasonal outcomes of fishery dynamics. Scenarios were determined from plausible future changes to system drivers identified during participatory stakeholder meetings and in scientific literature from southeast Alaska. Using our model, we uncovered recent instability in the Kenai fisheries coupled socio-ecological system dynamics, and determined equations that describe the strategy of fishery managers in responding to in-season drivers. Sensitivity of seasonal fishery outcomes to different drivers was quantified via the scenario-based studies. Our results show our agent-based simulation framework to be a capable decision support tool, one that resource managers might use for testing outcomes of proposed policy change in response to environmental change.

关键词： Data models Biological system modeling Stakeholders Rivers Correlation Electronic mail Timing

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Raman study of lattice vibrations in type II superlattice InAs/InAs1-xSbx

arXiv

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arXiv 2017年

作者： Liu, Henan Zhang, Yong Steenbergen, Elizabeth H. Liu, Shi Lin, Zhiyuan Zhang, Yong-Hang Kim, Jeomoh Ji, Mi-Hee Detchprohm, Theeradetch Dupuis, Russell D. Kim, Jin K. Hawkins, Samuel D. Klem, John F. Optical Science and Engineering Graduate Program Department of Electrical and Computer Engineering University of North Carolina at Charlotte Charlotte NC United States Center for Photonics Innovation and School of Electrical Computer and EnergyEngineering Arizona State University Tempe AZ United States Center for Compound Semiconductors and School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA United States Sandia National Laboratories Albuquerque NM United States

InAs/InAs1-xSbx superlattice system differ distinctly from two well-studied superlattice systems GaAs/AlAs and InAs/GaSb, in terms of electronic band alignment, common-element at the interface, and phonon spectrum overlapping of the constituents. This fact leads to unique electronic and vibrational properties of the InAs/InAs1-xSbx system when compared to the other two systems. In this work, we report a polarized Raman study on the vibrational properties of the InAs/InAs1-xSbx SLs as well as selected InAs1-xSbx alloys, all grown on GaSb substrates by either MBE or MOCVD, from both growth surface and cleaved edge. In the SL, from the (001) backscattering geometry, an InAs-like LO mode is observed as the primary feature and its intensity is found to increase with increasing Sb composition;from the (110) cleaved edge backscattering geometry, an InAs-like TO mode is observed as the main feature in two cross-polarization configurations, but an additional InAs-like "forbidden" LO mode was observed in two parallel-polarization configurations. InAs1-xSbx alloys lattice-matched to the substrate (xSb ~ 0.09) grown by MBE were also found to exhibit the "forbidden" LO mode, implying the existence of some unexpected [001] modulation, but the strained xSb ~ 0.35 samples grown by MOCVD were found to behavior like a disordered alloy. The primary conclusions are (1) the InAs-like LO or TO mode could be either a confined or quasi-confined mode in the InAs layers of the SL or extended mode of the whole structure, depending on the Sb composition;(2) InAs/InAsSb and InAs/GaSb SLs exhibit significantly different behaviors in the cleaved edge geometry, but qualitatively similar in the (001) geometry;and (3) the appearance of the "forbidden" LO-like mode is a universal signature for SLs and bulk systems resulting from mixing of phonon modes due to structural modulation or symmetry reduction. Copyright © 2017, The Authors. All rights reserved.

关键词： III-V semiconductors

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Optimal Learning for Sequential Decision Making for Expensive Cost Functions with Stochastic Binary Feedbacks

arXiv

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arXiv 2017年

作者： Wang, Yingfei Wang, Chu Powell, Warren Department of Computer Science Princeton University PrincetonNJ08540 United States Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Department of Operations Research and Financial Engineering Princeton University PrincetonNJ08544 United States

We consider the problem of sequentially making decisions that are rewarded by "successes" and "failures" which can be predicted through an unknown relationship that depends on a partially controllable vector of attributes for each instance. The learner takes an active role in selecting samples from the instance pool. The goal is to maximize the probability of success in either offline (training) or online (testing) phases. Our problem is motivated by real-world applications where observations are time consuming and/or expensive. We develop a knowledge gradient policy using an online Bayesian linear classifier to guide the experiment by maximizing the expected value of information of labeling each alternative. We provide a finite-time analysis of the estimated error and show that the maximum likelihood estimator based produced by the KG policy is consistent and asymptotically normal. We also show that the knowledge gradient policy is asymptotically optimal in an offline setting. This work further extends the knowledge gradient to the setting of contextual bandits. We report the results of a series of experiments that demonstrate its efficiency. Copyright © 2017, The Authors. All rights reserved.

关键词： Maximum likelihood estimation

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A data fusion approach based on systems integration for axle bearing monitoring

A data fusion approach based on systems integration for axle...

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作者： Fumeo, E. Terribile, S. Anguita, D. Massara, E. Mazzino, N. Department of Computer Science Bioengineering Robotics and Systems Engineering Polytechnic School University of Genoa Italy Innovation Project Ansaldo STS S.p.A. Genoa Italy

In the context of condition-based maintenance (CBM) for railway transportation systems, the correlation of data coming from several heterogeneous sensors is gaining growing research interest: for this purpose, it is extremely important to study and deploy data fusion methodologies able to merge information coming both from wayside train monitoring and diagnostic systems and from on-board train monitoring systems. This paper describes an innovative data fusion approach based on a software systems integration for axle bearing CBM: a data fusion system composed of three modules, namely the data fusion application, the system integrator and a visualization system referred as the human machine interface have been developed. The results presented in this paper show the benefits in terms of maintenance costs reduction, risk management and safety, and the system proves to have a positive impact all over the railways systems. © Civil-Comp Press, 2016.

关键词： Condition based maintenance

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