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Tuberculosis screening among children and adolescents in China:insights from a mathematical model

Intelligent Medicine 2023年第3期3卷 157-163页

作者： Janne Estill Yangqin Xu Shouyuan Wu Lidong Hu Nan Yang Shu Yang Yaolong Chen Guobao Li Evidence-Based Medicine Center School of Basic Medical SciencesLanzhou UniversityLanzhouGansu 730000China Institute of Global Health University of GenevaGenevaSwitzerland School of Public Health Lanzhou UniversityLanzhouGansu 730000China Department of Rheumatology and Immunology The First Medical CenterChinese PLA General HospitalBejing 100853China College of Medical Information Engineering Chengdu University of Traditional Chinese MedicineChengduSichuan 610000China Research Unit of Evidence-Based Evaluation and Guidelines Chinese Academy of Medical Sciences(2021RU017)School of Basic Medical SciencesLanzhou UniversityLanzhouGansu 730000China Lanzhou University Institute of Health Data Science LanzhouGansu 730000China WHO Collaborating Centre for Guideline Implementation and Knowledge Translation LanzhouGansu 730000China Department of Lung Disease Shenzhen Third People's HospitalThe Second Affiliated HospitalSouthern University of Science and TechnologyShenzhen Guangdong 518112China

Background Tuberculosis(TB)continues to be prevalent in China also among children and adolescents in *** built a dynamic mathematical model for TB transmission in China,and applied it to compare the epidemic trends 2021–2030 under a range of screening interventions focusing on children and *** We developed a dynamic mathematical model with a flexible *** model can be applied either stochastically or deterministically,and can encompass arbitrary age structure and resistance *** the present version,we used the deterministic version excluding resistance but including age structure with six groups:0–5,6–11,12–14,15–17,18–64,and 65 years and *** parameterized the model by literature data and fitting it to case and death estimates provided by the World Health *** compared the new TB cases and TB-related deaths in each age group over the period 2021–2030 in 10 scenarios that involved intensified screening of particular age groups of children,adolescents,or young adults,or decreased or increased diagnostic accuracy of the *** Screening the entire age class of 18-year-old persons would prevent 517,000 TB cases and 14,600 TB-related deaths between years 2021 and 2030,corresponding to 6.6%and 5.5%decrease from the standard of care projection,*** screening of children aged 6–11 and,to a lesser extent,0–5 years,also reduced TB incidence and mortality,particularly among children of the respective ages but also in other age *** contrast,intensified screening of adolescents did not have a major *** with a simpler and less accurate method resulted in worsened outcomes,which could not be offset by more intensive *** accurate screening and better sensitivity to detect latent TB could prevent 2.3 million TB cases and 68,500 TB deaths in the coming 10 *** Routine screening in schools can efficiently reduce the burden of TB in *** should be intensifie

关键词： Tuberculosis China Children and adolescents Mathematical model Screening

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Automated SSIM Regression for Detection and Quantification of Motion Artefacts in Brain MR Images

arXiv

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arXiv 2022年

作者： Sciarra, Alessandro Chatterjee, Soumick Dünnwald, Max Placidi, Giuseppe Nürnberger, Andreas Speck, Oliver Oeltze-Jafra, Steffen The Medicine and Digitalization - MedDigit Group Department of Neurology Medical Faculty University Hospital Magdeburg Germany the Department of Biomedical Magnetic Resonance Faculty of Natural Sciences Otto von Guericke University Germany The Data and Knowledge Engineering Group Faculty of Computer Science Otto von Guericke University Germany The Genomics Research Centre Human Technopole Milan Italy The Faculty of Computer Science Otto von Guericke University Germany The Department of Life Health and Environmental Sciences University of L'Aquila Italy The Center for Behavioral Brain Sciences Magdeburg Germany The German Center for Neurodegenerative Disease Magdeburg Germany

Motion artefacts in magnetic resonance brain images can have a strong impact on diagnostic confidence. The assessment of MR image quality is fundamental before proceeding with the clinical diagnosis. Motion artefacts can alter the delineation of structures such as the brain, lesions or tumours and may require a repeat scan. Otherwise, an inaccurate (e.g. correct pathology but wrong severity) or incorrect diagnosis (e.g. wrong pathology) may occur."Image quality assessment" as a fast, automated step right after scanning can assist in deciding if the acquired images are diagnostically sufficient. An automated image quality assessment based on the structural similarity index (SSIM) regression through a residual neural network is proposed in this work. Additionally, a classification into different groups - by subdividing with SSIM ranges - is evaluated. Importantly, this method predicts SSIM values of an input image in the absence of a reference ground truth image. The networks were able to detect motion artefacts, and the best performance for the regression and classification task has always been achieved with ResNet-18 with contrast augmentation. The mean and standard deviation of residuals' distribution were µ = −0.0009 and σ = 0.0139, respectively. Whilst for the classification task in 3, 5 and 10 classes, the best accuracies were 97, 95 and 89%, respectively. The results show that the proposed method could be a tool for supporting neuro-radiologists and radiographers in evaluating image quality quickly. Copyright © 2022, The Authors. All rights reserved.

关键词： Image quality

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Using mapping languages for building legal knowledge graphs from XML files

Using mapping languages for building legal knowledge graphs ...

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2019 Blockchain Enabled Semantic Web Workshop and Contextualized knowledge Graphs Workshop, BlockSW-CKG 2019

作者： Junior, Ademar Crotti Orlandi, Fabrizio O'Sullivan, Declan Dirschl, Christian Reul, Quentin ADAPT Centre for Digital Content Platform Research Knowledge and Data Engineering Group School of Computer Science and Statistics Trinity College Dublin Dublin 2 Ireland Wolters Kluwer Deutschland Munchen Germany Wolters Kluwer N.V. Chicago United States

This paper presents our experience on building RDF knowledge graphs for an industrial use case in the legal domain. The information contained in legal information systems are often accessed through simple keyword interfaces and presented as a simple list of hits. In order to improve search accuracy one may avail of knowledge graphs, where the semantics of the data can be made explicit. Significant research effort has been invested in the area of building knowledge graphs from semi-structured text documents, such as XML, with the prevailing approach being the use of mapping languages. In this paper, we present a semantic model for representing legal documents together with an industrial use case. We also present a set of use case requirements based on the proposed semantic model, which are used to compare and discuss the use of state-of-the-art mapping languages for building knowledge graphs for legal data. © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： XML

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P12-49 Finding the roadmap of a liver cell developing non-alcoholic fatty liver disease

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Toxicology Letters 2022年 368卷 S194-S194页

作者： C. Pieterman J. Jiang M. Gerards G. Ertaylan R. Peeters T. de Kok Maastricht University Maastricht Centre for Systems Biology Maastricht Netherlands Vrije Universiteit Brussel Pharmaceutical and Pharmacological Sciences Brussel Belgium VITO Flemish Institute for Technological Research Mol Belgium Maastricht University Department of Data Science and Knowledge Engineering Maastricht Netherlands Maastricht University Toxicogenomics Maastricht Netherlands

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On Training Classifiers for Linking Event Templates 27

On Training Classifiers for Linking Event Templates

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2018 Events and Stories in the News Workshop, EventStory 2018, colocated with the 27th International Conference on Computational Linguistics, COLING 2018

作者： Piskorski, Jakub Šarić, Fredi Zavarella, Vanni Atkinson, Martin Text and Data Mining Unit Joint Research Centre of the European Ispra Italy Text Analysis and Knowledge Engineering Lab University of Zagreb Croatia

ISBN: (纸本)9781948087599

The paper reports on exploring various machine learning techniques and a range of textual and meta-data features to train classifiers for linking related event templates automatically extracted from online news. With the best model using textual features only we achieved 94.7% (92.9%) F1 score on GOLD (SILVER) dataset. These figures were further improved to 98.6% (GOLD) and 97% (SILVER) F1 score by adding meta-data features, mainly thanks to the strong discriminatory power of automatically extracted geographical information related to events. © 2018 EventStory 2018 - Events and Stories in the News, Proceedings of the Workshop. All rights reserved.

关键词： Gold

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Exploration of Interpretability Techniques for Deep COVID-19 Classification using Chest X-ray Images

arXiv

引用

arXiv 2020年

作者： Chatterjee, Soumick Saad, Fatima Sarasaen, Chompunuch Ghosh, Suhita Krug, Valerie Khatun, Rupali Mishra, Rahul Desai, Nirja Radeva, Petia Rose, Georg Stober, Sebastian Speck, Oliver Nürnberger, Andreas Data and Knowledge Engineering Group Otto von Guericke University Magdeburg Germany Faculty of Computer Science Otto von Guericke University Magdeburg Germany Genomics Research Centre Human Technopole Italy Institute for Medical Engineering Otto von Guericke University Magdeburg Germany Research Campus STIMULATE Otto von Guericke University Magdeburg Germany Biomedical Magnetic Resonance Otto von Guericke University Magdeburg Germany Artificial Intelligence Lab Otto von Guericke University Magdeburg Germany Department of Mathematics and Computer Science University of Barcelona Barcelona Spain Translational Radiobiology Department of Radiation Oncology Universitätsklinikum Erlangen Erlangen Germany Apollo Hospitals Bilaspur India HCG Cancer Centre Vadodara India Computer Vision Centre Cerdanyola Barcelona Spain Centre for Behavioural Brain Sciences Magdeburg Germany German Centre for Neurodegenerative Diseases Magdeburg Germany

The outbreak of COVID-19 has shocked the entire world with its fairly rapid spread and has challenged different sectors. One of the most effective ways to limit its spread is the early and accurate diagnosing infected patients. Medical imaging, such as X-ray and Computed Tomography (CT), combined with the potential of Artificial Intelligence (AI), plays an essential role in supporting medical personnel in the diagnosis process. Thus, in this article five different deep learning models (ResNet18, ResNet34, InceptionV3, InceptionResNetV2 and DenseNet161) and their ensemble, using majority voting have been used to classify COVID-19, pneumoniæ and healthy subjects using chest X-ray images. Multilabel classification was performed to predict multiple pathologies for each patient, if present. Firstly, the interpretability of each of the networks was thoroughly studied using local interpretability methods - occlusion, saliency, input X gradient, guided backpropagation, integrated gradients, and DeepLIFT, and using a global technique - neuron activation profiles. The mean Micro-F1 score of the models for COVID-19 classifications ranges from 0.66 to 0.875, and is 0.89 for the ensemble of the network models. The qualitative results showed that the ResNets were the most interpretable models. This research demonstrates the importance of using interpretability methods to compare different models before making a decision regarding the best performing model. Copyright © 2020, The Authors. All rights reserved.

关键词： COVID-19

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Using mapping languages for building legal knowledge graphs from XML files?

arXiv

引用

arXiv 2019年

作者： Junior, Ademar Crotti Orlandi, Fabrizio O’Sullivan, Declan Dirschl, Christian Reul, Quentin ADAPT Centre for Digital Content Platform Research Knowledge & Data Engineering Group School of Computer Science and Statistics Trinity College Dublin Dublin 2 Ireland Wolters Kluwer Deutschland Munchen Germany Wolters Kluwer N.V. Chicago United States

This paper presents our experience on building RDF knowledge graphs for an industrial use case in the legal domain. The information contained in legal information systems are often accessed through simple keyword interfaces and presented as a simple list of hits. In order to improve search accuracy one may avail of knowledge graphs, where the semantics of the data can be made explicit. Significant research effort has been invested in the area of building knowledge graphs from semi-structured text documents, such as XML, with the prevailing approach being the use of mapping languages. In this paper, we present a semantic model for representing legal documents together with an industrial use case. We also present a set of use case requirements based on the proposed semantic model, which are used to compare and discuss the use of state-of-the-art mapping languages for building knowledge graphs for legal data. Copyright © 2019, The Authors. All rights reserved.

关键词： Mapping

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Multi-perspective ant colony optimization for mining and understanding the topology oriented big data

Multi-perspective ant colony optimization for mining and und...

引用

2017 World Congress on engineering, WCE 2017

作者： Hiba, Khalid Usman, Qamar Mazhar, Hameed Knowledge and Data Engineering Research Centre Department of Computer Engineering College of Electrical and Mechanical Engineering NUST Pakistan

ISBN: (纸本)9789881404749

Big data has the potential to transform how data can be used to manipulate, gather, collect, enforce and contribute towards data centric applications. data can have meaning and big data has meaning hidden inside it which can be used to empower multiple applications. Big data has its challenges;this massive amount of data can be used to extract a few lines or a few million lines that actually serve the purpose and meaning. For such outputs the algorithmic techniques needs to be devised that can efficiently extract meaning from a topological shape based on the nature of application and the information that is being looked for. The search for hidden meaning in topology of data is a challenge. Many techniques are employed to discover the hidden meaning and patterns in the data. This paper presents an algorithm for mining the clustered topological data using ant colony optimization.

关键词： Topology

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Publisher Correction: Global estimates on the number of people blind or visually impaired by cataract: a meta-analysis from 2000 to 2020

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Eye (London, England) 2024年第11期38卷 2229-2231页

作者： Konrad Pesudovs Hugh R. Taylor Tabassom Sedighi Rupert R. A. Bourne Shahina Pardhan Van Charles Lansingh John H. Kempen Ian Tapply Arthur G. Fernandes Maria Vittoria Cicinelli Alessandro Arrigo Nicolas Leveziel Serge Resnikoff Seth Flaxman Mukkharram M. Bikbov Tasanee Braithwaite Alain Bron Ching-Yu Cheng Monte A. Del Monte Joshua R. Ehrlich Leon B. Ellwein David Friedman João M. Furtado Gus Gazzard Ronnie George M. Elizabeth Hartnett Jost B. Jonas Rim Kahloun Moncef Khairallah Rohit C. Khanna Janet Leasher Julie-Anne Little Vinay Nangia Michal Nowak Tunde Peto Pradeep Ramulu Fotis Topouzis Mitiadis Tsilimbaris Ya Xing Wang Ningli Wang Paul Svitil Briant Theo Vos Katrin Burkart Kaleb Coberly Xiaochen Dai Stephen S. Lim Tomislav Mestrovic Ali H. Mokdad Christopher J. L. Murray Jaimie D. Steinmetz Awoke Misganaw Yohannes Habtegiorgis Abate Mohammad Abdollahi Mozhan Abdollahi Ayele Mamo Abebe Olumide Abiodun Richard Gyan Aboagye Woldu Aberhe Abrha Hasan Abualruz Hiwa Abubaker Ali Eman Abu-Gharbieh Salahdein Aburuz Tadele Girum Girum Adal Mesafint Molla Adane Isaac Yeboah Addo Qorinah Estiningtyas Sakilah Adnani Muhammad Sohail Afzal Muhammad Umair Shahin Aghamiri Bright Opoku Ahinkorah Aqeel Ahmad Sajjad Ahmad Ali Ahmadi Siamak Sabour Ayman Ahmed Haroon Ahmed Ahmad Samir Alfaar Abid Ali Syed Shujait Shujait Ali Awais Altaf Hubert Amu Emmanuel Manu Sofia Androudi Rodrigo Anguita Abhishek Anil Rimple Jeet Kaur Muhammad Aaqib Shamim Saeid Anvari Anayochukwu Edward Anyasodor Francis Appiah Jalal Arabloo Haleh Ayatollahi Mosab Arafat Damelash Areda Reza Arefnezhad Brhane Berhe Aregawi Akeza Awealom Asgedom Tahira Ashraf Seyyed Shamsadin Athari Bantalem Tilaye Tilaye Atinafu Birhan Tsegaw Taye Maha Moh’d Wahbi Atout Alok Atreya Ahmed Y. Azzam Hassan Babamohamadi Sara Bagherieh Yogesh Bahurupi Atif Amin Baig Shumaila Nargus Biswajit Banik Mainak Bardhan Saurav Basu Kavita Batra Nebiyou Simegnew Bayileyegn Fatemeh Bazvand Addisu Shunu Beyene Awoke Masrie Asrat Derese Devidas S. Bhagat Akshaya Srikanth Bhagavathula Pankaj Bh Medicine & Health University of New South Wales Sydney NSW Australia School of Optometry and Vision Science University of New South Wales Sydney NSW Australia School of Population and Global Health University of Melbourne Carlton VIC Australia Vision and Eye Research Institute Anglia Ruskin University Cambridge UK HelpMeSee Instituto Mexicano de Oftalmologia Santiago de Querétaro Mexico University of Miami Gables FL USA University of Utah Salt Lake City UT USA Chief Medical Office HelpMeSee New York NY USA Mexican Institute of Ophthalmology Queretaro Mexico Department of Ophthalmology Massachusetts Eye and Ear/Shepens Eye Research Institute/Harvard Medical School Boston MA USA Eye Unit MyungSung Christian Medical Center (MCM) Comprehensive Specialized Hospital and MyungSun Medical College Addis Ababa Ethiopia Department of Ophthalmology Addis Ababa University Addis Ababa Ethiopia Sight for Souls Bellevue WA USA Department of Ophthalmology Harvard University Boston MA USA Eye Unit MyungSung Medical College Addis Ababa Ethiopia Addenbrooke’s Hospital Cambridge UK Department of Ophthalmology Cambridge University Hospitals Cambridge UK Federal University of Sao Paolo Sao Paolo SP Brazil University of Calgary Calgary AB Canada Department of Ophthalmology and Visual Sciences Federal University of São Paulo Sao Paulo Brazil School of Medicine Vita-Salute San Raffaele University Milan Italy Department of Ophthalmology IRCCS San Raffaele Scientific Institute Milan Italy Department of Ophthalmology San Raffaele Scientific Institute Milano Italy IRCCS San Raffaele Scientific Institute Vita-Salute University Milan Italy Scientific Institute San Raffaele Hospital Vita-Salute University Milan Italy University of Poitiers Poitiers France CHU de Poitiers Poitiers France Ophthalmology Department CHU de Poitiers (Poitiers University Hospital) Poitiers France Unité 1084 National Institute of Health and Medical Research (INSERM) Poitiers France

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In Vivo Validation of Electrocardiographic Imaging

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JACC: Clinical Electrophysiology 2017年第3期3卷 232-242页

作者： Cluitmans, Matthijs J.M. Bonizzi, Pietro Karel, Joël M.H. Das, Marco Kietselaer, Bas L.J.H. de Jong, Monique M.J. Prinzen, Frits W. Peeters, Ralf L.M. Westra, Ronald L. Volders, Paul G.A. Department of Cardiology Cardiovascular Research Institute Maastricht Maastricht University Medical Centre Maastricht Netherlands Department of Data Science and Knowledge Engineering Maastricht University Maastricht Netherlands Department of Radiology Cardiovascular Research Institute Maastricht Maastricht University Medical Centre Maastricht Netherlands Department of Physiology Cardiovascular Research Institute Maastricht Maastricht University Medical Centre Maastricht Netherlands

Objectives The purpose of this study was to evaluate the accuracy of noninvasive reconstructions of epicardial potentials, electrograms, activation and recovery isochrones, and beat origins by simultaneously performing electrocardiographic imaging (ECGI) and invasive epicardial electrography in intact animals. Background Noninvasive imaging of electrical potentials at the epicardium, known as ECGI, is increasingly applied in patients to assess normal and abnormal cardiac electrical activity. Methods Body-surface potentials and epicardial potentials were recorded in normal anesthetized dogs. Computed tomography scanning provided a torso-heart geometry that was used to reconstruct epicardial potentials from body-surface potentials. Results Electrogram reconstructions attained a moderate accuracy compared with epicardial recordings (median correlation coefficient: 0.71), but with considerable variation (interquartile range: 0.36 to 0.86). This variation could be explained by a spatial mismatch (overall resolution was <20 mm) that was most apparent in regions with electrographic transition. More accurate derivation of activation times (Pearson R: 0.82), recovery times (R: 0.73), and the origin of paced beats (median error: 10 mm;interquartile range: 7 to 17 mm) was achieved by a spatiotemporal approach that incorporates the characteristics of the respective electrogram and neighboring electrograms. Reconstruction of beats from repeated single-site pacing showed a stable localization of origin. Cardiac motion, currently ignored in ECGI, correlates negatively with reconstruction accuracy. Conclusions ECGI shows a decent median accuracy, but variability in electrogram reconstruction can be sizable. At present, therefore, clinical interpretations of ECGI should not be made on the basis of single electrograms only. Incorporating local spatiotemporal characteristics allows for accurate reconstruction of epicardial activation and recovery patterns, and beat origin localization

关键词： cardiac electrophysiology electrocardiography inverse problem of electrocardiography noninvasive electrocardiographic imaging

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