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Global, regional, and national incidence of six major immune-mediated inflammatory diseases: findings from the global burden of disease study 2019

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eClinicalMedicine 2023年 64卷 102193-102193页

作者： Wu, Dongze Jin, Yingzhao Xing, Yuhan Abate, Melsew Dagne Abbasian, Mohammadreza Abbasi-Kangevari, Mohsen Abbasi-Kangevari, Zeinab Abd-Allah, Foad Abdelmasseh, Michael Abdollahifar, Mohammad-Amin Abdulah, Deldar Morad Abedi, Aidin Abedi, Vida Abidi, Hassan Aboagye, Richard Gyan Abolhassani, Hassan Abuabara, Katrina Abyadeh, Morteza Addo, Isaac Yeboah Adeniji, Kayode Nelson Adepoju, Abiola Victor Adesina, Miracle Ayomikun Adnani, Qorinah Estiningtyas Sakilah Afarideh, Mohsen Aghamiri, Shahin Agodi, Antonella Agrawal, Anurag Arriagada, Constanza Elizabeth Aguilera Ahmad, Aqeel Ahmad, Danish Ahmad, Sohail Ahmadi, Ali Ahmed, Ayman Aithala, Janardhana P. Ajadi, Abdullateef Abiodun Ajami, Marjan Akbarzadeh-Khiavi, Mostafa Alahdab, Fares AlBataineh, Mohammad T. Alemi, Sharifullah Al-Gheethi, Adel Ali Saeed Ali, Liaqat Alif, Sheikh Mohammad Almazan, Joseph Uy Almustanyir, Sami Alqahtani, Jaber S. Alqasmi, Ibrahim Altaf, Ihsan Ullah Khan Alvis-Guzman, Nelson Alvis-Zakzuk, Nelson J. Al-Worafi, Yaser Mohammed Aly, Hany Amani, Reza Amu, Hubert Amusa, Ganiyu Adeniyi Andrei, Catalina Liliana Ansar, Adnan Ansariniya, Hossein Anyasodor, Anayochukwu Edward Arabloo, Jalal Arefnezhad, Reza Arulappan, Judie Asghari-Jafarabadi, Mohammad Ashraf, Tahira Atata, Jamila Abdulhamid Athari, Seyyed Shamsadin Atlaw, Daniel Atout, Maha Moh'd Wahbi Aujayeb, Avinash Awan, Asma Tahir Ayatollahi, Haleh Azadnajafabad, Sina Azzam, Ahmed Y. Badawi, Alaa Badiye, Ashish D. Bagherieh, Sara Baig, Atif Amin Bantie, Berihun Bantie Barchitta, Martina Bardhan, Mainak Barker-Collo, Suzanne Lyn Barone-Adesi, Francesco Batra, Kavita Bayileyegn, Nebiyou Simegnew Behnoush, Amir Hossein Belgaumi, Uzma Iqbal Bemanalizadeh, Maryam Bensenor, Isabela M. Beyene, Kebede A. Bhagavathula, Akshaya Srikanth Bhardwaj, Pankaj Bhaskar, Sonu Bhat, Ajay Nagesh Bitaraf, Saeid Bitra, Veera R. Boloor, Archith Bora, Kaustubh Botelho, João Silva Buchbinder, Rachelle Calina, Daniela Cámera, Luis Alberto Carvalho, Andre F. Chan, Jeffrey Shi Kai Chattu, Vijay Kumar Abebe, Endeshaw Chekol Department of Rheumatology and Immunology Sichuan Provincial People's Hospital School of Medicine University of Electronic Science and Technology of China China Department of Medicine & Therapeutics The Chinese University of Hong Kong Hong Kong Department of Paediatrics The Chinese University of Hong Kong Hong Kong School of Pharmacy The Chinese University of Hong Kong Hong Kong Jockey Club School of Public Health and Primary Care The Chinese University of Hong Kong Hong Kong Department of Nursing Woldia University Woldia Ethiopia Department of Orthopedic Surgery Harvard University Boston MA United States Postgraduate Medical Education Harvard University Boston MA United States Maternal Fetal Care Center Harvard University Boston MA United States Department of Health Policy and Oral Epidemiology Harvard University Boston MA United States Division of General Internal Medicine Harvard University Boston MA United States Department of Orthopaedic Surgery Shahid Beheshti University of Medical Sciences Tehran Iran Social Determinants of Health Research Center Shahid Beheshti University of Medical Sciences Tehran Iran Department of Biotechnology Shahid Beheshti University of Medical Sciences Tehran Iran Department of Epidemiology Shahid Beheshti University of Medical Sciences Tehran Iran National Nutrition and Food Technology Research Institute Shahid Beheshti University of Medical Sciences Tehran Iran Department of Immunology Shahid Beheshti University of Medical Sciences Tehran Iran Department of Neurology Shahid Beheshti University of Medical Sciences Tehran Iran Department of Medical Genetics Shahid Beheshti University of Medical Sciences Tehran Iran Center for Comprehensive Genetic Services Shahid Beheshti University of Medical Sciences Tehran Iran Cancer Research Center Shahid Beheshti University of Medical Sciences Tehran Iran Department of Health Economics and Statistics Shahid Beheshti University of Medical Sciences Tehran Iran Functional Neuro

Background: The causes for immune-mediated inflammatory diseases (IMIDs) are diverse and the incidence trends of IMIDs from specific causes are rarely studied. The study aims to investigate the pattern and trend of IMIDs from 1990 to 2019. Methods: We collected detailed information on six major causes of IMIDs, including asthma, inflammatory bowel disease, multiple sclerosis, rheumatoid arthritis, psoriasis, and atopic dermatitis, between 1990 and 2019, derived from the Global Burden of Disease study in 2019. The average annual percent change (AAPC) in number of incidents and age standardized incidence rate (ASR) on IMIDs, by sex, age, region, and causes, were calculated to quantify the temporal trends. Findings: In 2019, rheumatoid arthritis, atopic dermatitis, asthma, multiple sclerosis, psoriasis, inflammatory bowel disease accounted 1.59%, 36.17%, 54.71%, 0.09%, 6.84%, 0.60% of overall new IMIDs cases, respectively. The ASR of IMIDs showed substantial regional and global variation with the highest in High SDI region, High-income North America, and United States of America. Throughout human lifespan, the age distribution of incident cases from six IMIDs was quite different. Globally, incident cases of IMIDs increased with an AAPC of 0.68 and the ASR decreased with an AAPC of −0.34 from 1990 to 2019. The incident cases increased across six IMIDs, the ASR of rheumatoid arthritis increased (0.21, 95% CI 0.18, 0.25), while the ASR of asthma (AAPC = −0.41), inflammatory bowel disease (AAPC = −0.72), multiple sclerosis (AAPC = −0.26), psoriasis (AAPC = −0.77), and atopic dermatitis (AAPC = −0.15) decreased. The ASR of overall and six individual IMID increased with SDI at regional and global level. Countries with higher ASR in 1990 experienced a more rapid decrease in ASR. Interpretation: The incidence patterns of IMIDs varied considerably across the world. Innovative prevention and integrative management strategy are urgently needed to mitigate the increasing ASR of rh

关键词： Global burden of disease study Immune-mediated inflammatory disease Incidence Trend

High-performance and lightweight real-time deep face emotion recognition

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High-performance and lightweight real-time deep face emotion...

International Workshop on Semantic Media Adaptation and Personalization (SMAP)

作者： Justus Schwan Esam Ghaleb Enrique Hortal Stylianos Asteriadis Department of Data Science and Knowledge Engineering Maastricht University Maastricht Netherlands

ISBN: (纸本)9781538607572

Deep learning is used for all kinds of tasks which require human-like performance, such as voice and image recognition in smartphones, smart home technology, and self-driving cars. While great advances have been made in the field, results are often not satisfactory when compared to human performance. In the field of facial emotion recognition, especially in the wild, Convolutional Neural Networks (CNN) are employed because of their excellent generalization properties. However, while CNNs can learn a representation for certain object classes, an amount of (annotated) training data roughly proportional to the class's complexity is needed and seldom available. This work describes an advanced pre-processing algorithm for facial images and a transfer learning mechanism, two potential candidates for relaxing this requirement. Using these algorithms, a lightweight face emotion recognition application for Human-Computer Interaction with TurtleBot units was developed.

关键词： Face Training Feature extraction Real-time systems Emotion recognition Training data Face detection

A Retrieval-Based Dialogue System Utilizing Utterance and Context Embeddings

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A Retrieval-Based Dialogue System Utilizing Utterance and Co...

International Conference on Machine Learning and Applications (ICMLA)

作者： Alexander Bartl Gerasimos Spanakis Department of Data Science and Knowledge Engineering Maastricht University MD Netherlands

Finding semantically rich and computer-understandable representations for textual dialogues, utterances and words is crucial for dialogue systems (or conversational agents), as their performance mostly depends on understanding the context of conversations. In recent research approaches, responses have been generated utilizing a decoder architecture, given the distributed vector representation (embedding) of the current conversation. In this paper, the utilization of embeddings for answer retrieval is explored by using Locality-Sensitive Hashing Forest (LSH Forest), an Approximate Nearest Neighbor (ANN) model, to find similar conversations in a corpus and rank possible candidates. Experimental results on the well-known Ubuntu Corpus (in English) and a customer service chat dataset (in Dutch) show that, in combination with a candidate selection method, retrieval-based approaches outperform generative ones and reveal promising future research directions towards the usability of such a system.

关键词： Decoding Logic gates Context modeling Encoding Pipelines Training databases

Social emotion mining techniques for facebook posts reaction prediction

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arXiv 2017年

作者： Krebs, Florian Lubascher, Bruno Moers, Tobias Schaap, Pieter Spanakis, Gerasimos Department of Data Science and Knowledge Engineering Maastricht University Maastricht Netherlands

As of February 2016 Facebook allows users to express their experienced emotions about a post by using five so-called 'reactions'. This research paper proposes and evaluates alternative methods for predicting these reactions to user posts on public pages of firms/companies (like supermarket chains). For this purpose, we collected posts (and their reactions) from Facebook pages of large supermarket chains and constructed a dataset which is available for other researches. In order to predict the distribution of reactions of a new post, neural network architectures (convolutional and recurrent neural networks) were tested using pretrained word embeddings. Results of the neural networks were improved by introducing a bootstrapping approach for sentiment and emotion mining on the comments for each post. The final model (a combination of neural network and a baseline emotion miner) is able to predict the reaction distribution on Facebook posts with a mean squared error (or misclassification rate) of 0.135. Copyright © 2017, The Authors. All rights reserved.

关键词： Social networking (online)

Global investments in pandemic preparedness and COVID-19: development assistance and domestic spending on health between 1990 and 2026

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The Lancet Global Health 2023年第3期11卷 e385-e413页

作者： Micah, Angela E. Bhangdia, Kayleigh Cogswell, Ian E. Lasher, Dylan Lidral-Porter, Brendan Maddison, Emilie R. Nguyen, Trang Nhu Ngoc Patel, Nishali Pedroza, Paola Solorio, Juan Stutzman, Hayley Tsakalos, Golsum Wang, Yifeng Warriner, Wesley Zhao, Yingxi Zlavog, Bianca S. Abbafati, Cristiana Abbas, Jaffar Abbasi-Kangevari, Mohsen Abbasi-Kangevari, Zeinab Abdelmasseh, Michael Abdulah, Deldar Morad Abedi, Aidin Abegaz, Kedir Hussein Abhilash, E.S. Aboagye, Richard Gyan Abolhassani, Hassan Abrigo, Michael R.M. Abubaker Ali, Hiwa Abu-Gharbieh, Eman Adem, Mohammed Hussien Afzal, Muhammad Sohail Ahmadi, Ali Ahmed, Haroon Ahmed Rashid, Tarik Aji, Budi Akbarialiabad, Hossein Akelew, Yibeltal Al Hamad, Hanadi Alam, Khurshid Alanezi, Fahad Mashhour Alanzi, Turki M. Al-Hanawi, Mohammed Khaled Alhassan, Robert Kaba Aljunid, Syed Mohamed Almustanyir, Sami Al-Raddadi, Rajaa M. Alvis-Guzman, Nelson Alvis-Zakzuk, Nelson J. Amare, Azmeraw T. Ameyaw, Edward Kwabena Amini-Rarani, Mostafa Amu, Hubert Ancuceanu, Robert Andrei, Tudorel Anwar, Sumadi Lukman Appiah, Francis Aqeel, Muhammad Arabloo, Jalal Arab-Zozani, Morteza Aravkin, Aleksandr Y. Aremu, Olatunde Aruleba, Raphael Taiwo Athari, Seyyed Shamsadin Avila-Burgos, Leticia Ayanore, Martin Amogre Azari, Samad Baig, Atif Amin Bantie, Abere Tilahun Barrow, Amadou Baskaran, Pritish Basu, Sanjay Batiha, Abdul-Monim Mohammad Baune, Bernhard T. Berezvai, Zombor Bhardwaj, Nikha Bhardwaj, Pankaj Bhaskar, Sonu Boachie, Micheal Kofi Bodolica, Virginia Botelho, João Silva Botelho Braithwaite, Dejana Breitborde, Nicholas J.K. Busse, Reinhard Cahuana-Hurtado, Lucero Catalá-López, Ferrán Chansa, Collins Charan, Jaykaran Chattu, Vijay Kumar Chen, Simiao Chukwu, Isaac Sunday Dadras, Omid Dandona, Lalit Dandona, Rakhi Dargahi, Abdollah Debela, Sisay Abebe Denova-Gutiérrez, Edgar Desye, Belay Dharmaratne, Samath Dhamminda Diao, Nancy Doan, Linh Phuong Dodangeh, Milad dos Santos, Wendel Mombaque Doshmangir, Leila Dube, John Eini, Ebrahim El Sayed Zaki, Maysaa El Tantawi, Maha Enyew, Daniel Berhanie Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Applied Mathematics University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Nuffield Department of Medicine University of Oxford Oxford United Kingdom Department of Juridical and Economic Studies La Sapienza University Rome Italy Antai College of Economics Shanghai Jiao Tong University Shanghai China Non-communicable Diseases Research Center Tehran University of Medical Sciences Tehran Iran Research Center for Immunodeficiencies Tehran University of Medical Sciences Tehran Iran Multiple Sclerosis Research Center Tehran University of Medical Sciences Tehran Iran Children’s Medical Center Tehran University of Medical Sciences Tehran Iran Department of Epidemiology and Biostatistics Tehran University of Medical Sciences Tehran Iran Tehran Heart Center Tehran University of Medical Sciences Tehran Iran Faculty of Medicine Tehran University of Medical Sciences Tehran Iran National Institute for Health Research Tehran University of Medical Sciences Tehran Iran Department of Pharmacology Tehran University of Medical Sciences Tehran Iran Department of Cardiology Tehran University of Medical Sciences Tehran Iran Sina Trauma and Surgery Research Center Tehran University of Medical Sciences Tehran Iran Digestive Diseases Research Institute Tehran University of Medical Sciences Tehran Iran Department of Neurology Tehran University of Medical Sciences Tehran Iran Social Determinants of Health Research Center Shahid Beheshti University of Medical Sciences Tehran Iran Department of Epidemiology Shahid Beheshti University of Medical Sciences Tehran Iran Department of Health Economics and Statistics Shahid Beheshti University of Medical Sciences Tehran Iran Department of Neurosurgery Shahid Beheshti University of Medical Sciences Tehran Iran Ophthalmic R

Background: The COVID-19 pandemic highlighted gaps in health surveillance systems, disease prevention, and treatment globally. Among the many factors that might have led to these gaps is the issue of the financing of national health systems, especially in low-income and middle-income countries (LMICs), as well as a robust global system for pandemic preparedness. We aimed to provide a comparative assessment of global health spending at the onset of the pandemic;characterise the amount of development assistance for pandemic preparedness and response disbursed in the first 2 years of the COVID-19 pandemic;and examine expectations for future health spending and put into context the expected need for investment in pandemic preparedness. Methods: In this analysis of global health spending between 1990 and 2021, and prediction from 2021 to 2026, we estimated four sources of health spending: development assistance for health (DAH), government spending, out-of-pocket spending, and prepaid private spending across 204 countries and territories. We used the Organisation for Economic Co-operation and Development (OECD)'s Creditor Reporting System (CRS) and the WHO Global Health Expenditure database (GHED) to estimate spending. We estimated development assistance for general health, COVID-19 response, and pandemic preparedness and response using a keyword search. Health spending estimates were combined with estimates of resources needed for pandemic prevention and preparedness to analyse future health spending patterns, relative to need. Findings: In 2019, at the onset of the COVID-19 pandemic, US$9·2 trillion (95% uncertainty interval [UI] 9·1–9·3) was spent on health worldwide. We found great disparities in the amount of resources devoted to health, with high-income countries spending $7·3 trillion (95% UI 7·2–7·4) in 2019;293·7 times the $24·8 billion (95% UI 24·3–25·3) spent by low-income countries in 2019. That same year, $43·1 billion in development assistance was provided

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MultiFarm: A Benchmark for Multilingual Ontology Matching

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SSRN

SSRN 2018年

作者： Meilicke, Christian García-Castro, Raúl Freitas, Fred van Hage, Willem Robert Montiel-Ponsoda, Elena de Azevedo, Ryan Ribeiro Stuckenschmidt, Heiner Šváb-Zamazal, Ondřej Svátek, Vojtěch Tamilin, Andrei Trojahn, Cássia Wang, Shenghui Universidade Federal de Pernambuco University of Mannheim Universidad Politécnica de Madrid Web & Media Group Ontology Engineering Group Data and Web Science Group Department of Information and Knowledge Engineering Fondazione Bruno Kessler Grenoble - Rhône-Alpes VU University Amsterdam

In this paper we present the MultiFarm dataset, which has been designed as a benchmark for multilingual ontology matching. The MultiFarm dataset is composed of a set of ontologies translated in different languages and the corresponding alignments between these ontologies. It is based on the OntoFarm dataset, which has been used successfully for several years in the Ontology Alignment Evaluation Initiative (OAEI). By translating the ontologies of the OntoFarm dataset into eight different languages – Chinese, Czech, Dutch, French, German, Portuguese, Russian, and Spanish - we created a comprehensive set of realistic test cases. Based on these test cases, it is possible to evaluate and compare the performance of matching approaches with a special focus on multilingualism. © 2018, The Authors. All rights reserved.

关键词： Ontology

Effects of synaptic and myelin plasticity on learning in a network of kuramoto phase oscillators

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arXiv 2019年

作者： Karimian, M. Dibenedetto, D. Moerel, M. Burwick, T. Westra, R.L. De Weerd, P. Senden, M. Maastricht University Maastricht6229 ER Netherlands Department of Cognitive Neuroscience Faculty of Psychology and Neuroscience Maastricht University P.O. Box 616 Maastricht6200 MD Netherlands Maastricht Brain Imaging Centre Faculty of Psychology and Neuroscience Maastricht University P.O. Box 616 Maastricht6200 MD Netherlands Department of Data Science and Knowledge Engineering Maastricht University Maastricht6211 LH Netherlands Frankfurt Institute for Advanced Studies Goethe University Frankfurt Ruth-Moufang-Str. 1 Frankfurt am Main60438 Germany

Models of learning typically focus on synaptic plasticity. However, learning is the result of both synaptic and myelin plasticity. Specifically, synaptic changes often co-occur and interact with myelin changes, leading to complex dynamic interactions between these processes. Here, we investigate the implications of these interactions for the coupling behavior of a system of Kuramoto oscillators. To that end, we construct a fully connected, one-dimensional ring network of phase oscillators whose coupling strength (reflecting synaptic strength) as well as conduction velocity (reflecting myelination) are each regulated by a Hebbian learning rule. We evaluate the behavior of the system in terms of structural (pairwise connection strength and conduction velocity) and functional connectivity (local and global synchronization behavior). We find that for conditions in which a system limited to synaptic plasticity develops two distinct clusters both structurally and functionally, additional adaptive myelination allows for functional communication across these structural clusters. Hence, dynamic conduction velocity permits the functional integration of structurally segregated clusters. Our results confirm that network states following learning may be different when myelin plasticity is considered in addition to synaptic plasticity, pointing towards the relevance of integrating both factors in computational models of *** Codes 34C15 Copyright © 2019, The Authors. All rights reserved.

关键词： Synchronization

A retrieval-based dialogue system utilizing utterance and context embeddings

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arXiv 2017年

作者： Bartl, Alexander Spanakis, Gerasimos Department of Data Science and Knowledge Engineering Maastricht University MD6200 Netherlands

Finding semantically rich and computerunderstandable representations for textual dialogues, utterances and words is crucial for dialogue systems (or conversational agents), as their performance mostly depends on understanding the context of conversations. Recent research aims at finding distributed vector representations (embeddings) for words, such that semantically similar words are relatively close within the vector-space. Encoding the "meaning" of text into vectors is a current trend, and text can range from words, phrases and documents to actual human-to-human conversations. In recent research approaches, responses have been generated utilizing a decoder architecture, given the vector representation of the current conversation. In this paper, the utilization of embeddings for answer retrieval is explored by using Locality-Sensitive Hashing Forest (LSH Forest), an Approximate Nearest Neighbor (ANN) model, to find similar conversations in a corpus and rank possible candidates. Experimental results on the well-known Ubuntu Corpus (in English) and a customer service chat dataset (in Dutch) show that, in combination with a candidate selection method, retrieval-based approaches outperform generative ones and reveal promising future research directions towards the usability of such a system. Copyright © 2017, The Authors. All rights reserved.

关键词： Nearest neighbor search

Massive open online courses temporal profiling for dropout prediction

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arXiv 2017年

作者： Hagedoorn, Tom Rolandus Spanakis, Gerasimos Department of Data Science and Knowledge Engineering Maastricht University 6200MD Netherlands

Massive Open Online Courses (MOOCs) are attracting the attention of people all over the world. Regardless the platform, numbers of registrants for online courses are impressive but in the same time, completion rates are disappointing. Understanding the mechanisms of dropping out based on the learner profile arises as a crucial task in MOOCs, since it will allow intervening at the right moment in order to assist the learner in completing the course. In this paper, the dropout behaviour of learners in a MOOC is thoroughly studied by first extracting features that describe the behavior of learners within the course and then by comparing three classifiers (Logistic Regression, Random Forest and AdaBoost) in two tasks: Predicting which users will have dropped out by a certain week and predicting which users will drop out on a specific week. The former has showed to be considerably easier, with all three classifiers performing equally well. However, the accuracy for the second task is lower, and Logistic Regression tends to perform slightly better than the other two algorithms. We found that features that reflect an active attitude of the user towards the MOOC, such as submitting their assignment, posting on the Forum and filling their Profile, are strong indicators of persistence. Copyright © 2017, The Authors. All rights reserved.

关键词： Forecasting