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arXiv 2019年

作者： Hyland, Stephanie L. Faltys, Martin Hüser, Matthias Lyu, Xinrui Gumbsch, Thomas Esteban, Cristóbal Bock, Christian Horn, Max Moor, Michael Rieck, Bastian Zimmermann, Marc Bodenham, Dean Borgwardt, Karsten Rätsch, Gunnar Merz, Tobias M. Department of Computer Science ETH Zürich Zürich Switzerland Computational Biology Program Memorial Sloan Kettering Cancer Center New York United States Tri-Institutional PhD Program in Computational Biology and Medicine Weill Cornell Medicine New York United States Medical Informatics Group Zürich University Hospital Zürich Switzerland Department of Intensive Care Medicine University Hospital University of Bern Bern Switzerland Department of Biosystems and Engineering ETH Zürich Basel Switzerland Department of Biology ETH Zürich Zürich Switzerland Swiss Institute for Bioinformatics Lausanne Switzerland Cardiovascular Intensive Care Unit Auckland City Hospital Auckland New Zealand

Intensive care clinicians are presented with large quantities of patient information and measurements from a multitude of monitoring systems. The limited ability of humans to process such complex information hinders physicians to readily recognize and act on early signs of patient deterioration. We used machine learning to develop an early warning system for circulatory failure based on a high-resolution ICU database with 240 patient-years of data. This automatic system predicts 90.0% of circulatory failure events (prevalence 3.1%), with 81.8% identified more than two hours in advance, resulting in an area under the receiver operating characteristic curve of 94.0% and area under the precision recall curve of 63.0%. The model was externally validated in a large independent patient cohort. Copyright © 2019, The Authors. All rights reserved.

关键词： Intensive care units

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Multiscale based nonlinear fault detection of industrial processes

Multiscale based nonlinear fault detection of industrial pro...

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Fuels and Petrochemicals Division 2017 - Core programming Area at the 2017 AIChE Spring Meeting and 13th Global Congress on Process Safety

作者： Botre, Chiranjivi Mansouri, Majdi Nounou, Hazem Nounou, Mohamed Karim, M. Nazmul Artie McFerrin Dept. of Chemical Engineering Texas AandM University College StationTX77843 United States Electrical and Computer Engineering Program Texas AandM University at Qatar Doha Qatar Chemical Engineering Department Texas AandM University at Qatar Doha Qatar

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A Machine Learning-Based Approach for Classification of Alzheimer’s Disease and its Risk Prediction

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Alzheimer's & Dementia 2022年第S11期18卷

作者： Payam Hosseinzadeh Kasani Jung-Kyeom Kim Peyman Hosseinzadeh Kassani Yeshin Kim Jeong-Ah Kim Chihyun Park Cheol-Heui Yun Seong Hye Choi Sang-Ah Lee Seo-Young Lee Jae-Won Jang Department of Neurology Kangwon National University Hospital Chuncheon Korea Republic of (South) Interdisciplinary Graduate Program in Medical Bigdata Convergence Kangwon National University Chuncheon Korea Republic of (South) Department of Neurology and Neurological Sciences Stanford University Stanford CA USA College of Medicine Kangwon National University Chuncheon Korea Republic of (South) Department of Ophthalmology Kangwon National University Hospital Chuncheon Korea Republic of (South) Department of Computer Science and Engineering Kangwon National University Chuncheon Korea Republic of (South) Department of Agricultural Biotechnology Seoul National University Seoul Korea Republic of (South) Research Institute of Agriculture and Life Sciences Seoul National University Seoul Korea Republic of (South) Department of Neurology Inha University School of Medicine Incheon Korea Republic of (South) Department of Preventive Medicine Kangwon National University School of Medicine Chuncheon Korea Republic of (South)

Background Dementia, a clinical syndrome with progressive neurodegenerative disease, is one of the inevitable aging consequences. Alzheimer’s disease (AD) is the most common cause of dementia. Accurate detection and classification of Alzheimer’s disease alongside its conversion from Mild Cognitive Impairment (MCI) to AD at an early stage is of significant clinical importance. However, a reliable diagnosis with a multi-label classification approach remains a challenging task. This study aimed to measure the effectiveness of machine learning algorithm integrated with feature importance techniques to constitutes a multi-label classification of AD prediction for clinical practice. Method The machine learning algorithms are validated using a nationwide cohort dataset, the Korean Brain Aging Study for the early diagnosis and prediction of AD (KBASE) to identify the most important risk factors, where a database is classified into three classes namely, normal cognition (NC, n = 91), mild cognitive impairment (MCI, n = 61), and Alzheimer’s disease (AD, n = 49). Applying eight state-of-the-art supervised machine learning algorithms namely Support Vector Machine, Naive Bayes, XGBoost, Decision Tree, Logistic Regression, Random Forest, Bagging, and AdaBoost. A stratified 10-fold cross-validation was used for every train/test split and several performance measures, including accuracy, precision, recall and f1-score were used to compare the classifier performances. Result Based on the performance evaluation, the XGBoost classifier significantly outperformed all other models and achieved 82.55% accuracy followed by the Bagging classifier (81%). According to the XGBoost model, calculating risk factors to predict performance, the Consortium to Establish a Registry for AD (CERAD) subsets is among the highest ranked feature. It was intriguing that the parameters including job, job levels and retirement achieved a minimal, if any, impact in the final performance. Conclusion Collectivel

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Blockchain technology for managing an architectural model of decentralized medical record

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Journal of Physics: Conference Series 2019年第7期1402卷

作者： M Harika S Rahmadika D R Ramdania Department of Computer and Informatics Engineering Politeknik Negeri Bandung Indonesia Interdisciplinary Program of Information Security Graduate School PKNU Department of IT Convergence and Application Engineering Pukyong National University Busan South Korea Department of Informatics UIN Sunan Gunung Djati Bandung Indonesia

Medical record (MedRec) of the patient is derived from several healthcare providers i.e. hospital, the national provider, and laboratories which contains personally identifiable information test yet referred to therapeutic procedures and medications of the patient. MedRec has been compiled and maintained by several providers thus resulting in separated and disseminated the data among the parties. Mostly, the MedRecs are stored in provider's database and the patients have no control to manage and access their data. Hence, in this paper, we propose a conceptual model for managing the data of medical record using blockchain technology in the peer-to-peer network with several protocols are embedded in the system. The proposed model aims to facilitate the patients in order to manage their data from various providers into a single view. Blockchain offers the promise of an immutable, single source of truth from multiple sources and facilitates a trust as well as validates an identity without the third-party involvement.

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NTEP-DB 1.0: A relational database for the national turfgrass evaluation program

International Turfgrass Society Research Journal

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International Turfgrass Society Research Journal 2021年第1期14卷

作者： Yiqun Xie Majid Farhadloo Ning Guo Shashi Shekhar Eric Watkins Len Kne Han Bao Aaron J. Patton Kevin Morris Dep. of Computer Science and Engineering Univ. of Minnesota Minneapolis MN 55455 USA Dep. of Geographical Sciences Univ. of Maryland College Park MD 20740 USA Center for Geospatial Information Science Univ. of Maryland College Park MD 20740 USA Dep. of Horticultural Science Univ. of Minnesota St. Paul MN 55108 USA U-Spatial Univ. of Minnesota Minneapolis MN 55455 USA Dep. of Horticulture and Landscape Architecture Purdue Univ. West Lafayette IN 47907 USA National Turfgrass Evaluation Program Beltsville MD 20705 USA

Given field experiment data collected by the National Turfgrass Evaluation program (NTEP), we aim to design and create a relational database to store the data and support efficient queries. As one of the most widely-known turfgrass research programs in the world, NTEP has generated large volumes of data on turfgrass cultivars and experimental germplasm since the early 1980s, providing invaluable information for a variety of user groups (e.g., homeowners, seed companies, golf course managers, retailers, turfgrass researchers) to select cultivars that best fit their needs (e.g., winter survival, pest tolerance, turf quality). The datasets have historically been stored in large sets of text files and spreadsheets. Currently, NTEP data are delivered to users through a website ( *** ) as summary reports and it can be extremely tedious (e.g., hundreds of clicks, data merging, jargon) to perform a simple query (e.g., best cultivar selection with typical conditions). This significantly limits the use of NTEP data and hides its value from the public. To address these limitations, we carried out an interdisciplinary effort with horticulture and computer science researchers to design and create the first NTEP database – NTEP-DB 1.0 – to reduce the manual efforts and expert knowledge currently required to extract meaningful information from the data. Experiments confirm that the query results are correct, and that the database can greatly reduce manual efforts. Anticipating next-generation advances, we also recommend incorporating spatial data types and analytical techniques into future designs of the database.

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The Use of Relations and Functions Games Based on Balanced Design in Mathematics Subjects to Improve Student Learning Outcomes

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Journal of Physics: Conference Series 2019年第1期1175卷

作者： Diena Rauda Ramdania Maisevli Harika Sandi Rahmadika Gina Giftia Azmiana Informatics Engineering UIN Sunan Gunung Djati Bandung Computer Engineering and Informatics Politeknik Negeri Bandung Interdisciplinary Program of Information Security Graduate School PKNU Pukyong National University Busan South Korea Department of Phyloshopy UIN Sunan Gunung Djati Bandung

Game Relations and Functions are digital game-based learning in junior high school mathematics subjects. In this study will discuss how to use the game Relationships and Functions that have been designed using the Balanced Design method. The research method used was a quasi-experiment with the One group Pretest Post-test design. A total of 40 junior high school students were involved in this study to determine differences in student learning outcomes in the cognitive domain before and after using the Relationship and Function games. The instrument used is a type of multiple choice objective test of 20 questions with details of 8 questions related to relations material and 12 questions related to functions material. The results showed that there were differences in the improvement of student learning outcomes before and after using the game Relations and Functions in Mathematics subjects.

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Abstracts

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JOURNAL OF SPINAL CORD MEDICINE 2021年第SUPPL 1期44卷 S278-S324页

作者： [Anonymous] Department of Medicine Division of Physical Medicine and Rehabilitation University of Toronto Toronto ON CA KITE - Toronto Rehabilitation Institute University Health Network Toronto ON CA Institute for Health Policy Management and Evaluation University of Toronto Toronto ON CA Rehabilitation Sciences Institute University of Toronto Toronto ON CA Institute of Medical Sciences University of Toronto Toronto ON CA. Rehabilitation Sciences Institute University of Toronto ON CA KITE Research Institute-Toronto Rehab-University Health Network Toronto ON CA Physical Therapy Department University of Toronto ON CA Praxis Spinal Cord Institute Vancouver BC Université de Montréal (École de réadaptation) Montreal QC CA Institut de réadaptation Gingras-Linsay-de-Montréal Montreal QC CA Division of Physical Medicine & Rehabilitation Department of Medicine Faculty of Medicine & Dentistry University of Alberta Edmonton AB CA University of Calgary Calgary AB CA Alberta Health Services Centre for Family Medicine Kitchener ON CA McMaster University Department of Family Medicine Hamilton ON CA Dept. of Physical Therapy University of Toronto Toronto ON CA KITE-University Health Network Toronto ON CA Praxis Spinal Cord Institute Vancouver BC CA Center for Interdisciplinary Research in Rehabilitation and Social Integration (CIRRIS) Quebec City QC CA Dept. of Rehabilitation Laval University Quebec City QC CA Institute of Health Policy Management and Evaluation University of Toronto Toronto ON CA School of Rehabilitation Science University of Saskatchewan Saskatoon SK CA Ontario Neurotrauma Foundation Toronto ON CA Canadian Spinal Research Organization Toronto ON CA CIUSSS du Centre-Sud-de-l ‘Île-de-Montréal (Installation Gingras-Lindsay) Montréal QC CA École de réadaptation Université de Montréal Montréal QC CA First Steps Wellness Centre Regina SK CA Stan Cassidy Centre for Rehabilitation Horizon Health Network Fredericton NB CA Departmen

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An implemented, initialization algorithm for many-dimension, Monte Carlo circuit simulations using Spice

An implemented, initialization algorithm for many-dimension,...

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IEEE Annual Computing and Communication Workshop and Conference (CCWC)

作者： Michael A. Turi José G. Delgado-Frias Computer Engineering Program California State University Fullerton CA School of Electrical Engineering and Computer Science Washington State University Pullman

ISBN: (纸本)9781509042296

We present an algorithm for generating Spice netlists with varied parameters/variables in order to support Monte Carlo simulations. A number of data structures are required to randomly or pseudo-randomly vary transistor parameters or simulation parameters/variables. This algorithm ensures that Spice sub-circuits and transistor fingers are expanded so that every transistor and transistor finger of the circuit is independently varied. Some versions of Spice do not have built-in support of Monte Carlo simulations. Therefore, this algorithm can enable engineers operating these Spice versions to generate netlists for executing Monte Carlo simulations. This is important since Monte Carlo simulations are commonly used to test the performance or operation of a circuit affected by parameter variations caused during the manufacturing process.

关键词： Transistors Monte Carlo methods Data models Integrated circuit modeling Fingers Standards Data structures

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Parametric Analysis of Iterated Game Environments as Social Interaction Model for Genetic Algorithm to Solve Constrained engineering Problems

Parametric Analysis of Iterated Game Environments as Social ...

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Congress on Evolutionary Computation

作者： Rodrigo Lisbôa Pereira Marco A. Florenzano Mollinetti Mário Tasso Ribeiro Serra Neto Adilson de Almeida Neto Daniel Leal Souza Edson Koiti Yasojima Otávio Noura Teixeira Roberto Célio Limão de Oliveira Federal Rural University of Amazon (UFRA) Federal University of Pará (UFPA) Pará Brazil Systems Optimization Laboratory Tsukuba University Tsukuba Ibaraki Japan University Centre of the State of Pará (CESUPA) Belém Pará Brazil Electrical Engineering Post Graduation Program Federal University of Pará (UFPA) Belém Pará Brazil Electrical Engineering Post Graduation Program Federal University of Pará (UFPA) Pará Brazil Computer Engineering Post Graduation Program Federal University of Pará (UFPA) Pará Brazil Laboratory of Bionspired Computing (LCBIO) Federal University of Pará (UFPA) Belém Pará Brazil

This article presents a parameter study on the applied game theory in Genetic Algorithm (GA), performing an analysis of the game Prisoner's Dilemma applied in the solution of four constrained engineering problems. Simulations were applied in four different variations of the game, in order to find the best configuration for the analyzed problems. Then, after obtaining the best configuration of the game, we performed an analysis of the incidence of Alpha and Beta weights, present in GA fitness with Social Interaction, using the game that best suited each problem, employing sixteen different weight configurations. At end, the results obtained authenticated the influence of the Social Interaction process in the essence of GA.

关键词： Genetic algorithms Games Game theory Optimization Biological cells Sociology Statistics

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Democratising Knowledge Representation with BioCypher

arXiv

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arXiv 2022年

作者： Lobentanzer, Sebastian Aloy, Patrick Baumbach, Jan Bohar, Balazs Charoentong, Pornpimol Danhauser, Katharina Doğan, Tunca Dreo, Johann Dunham, Ian Fernandez-Torras, Adrià Gyori, Benjamin M. Hartung, Michael Hoyt, Charles Tapley Klein, Christoph Korcsmaros, Tamas Maier, Andreas Mann, Matthias Ochoa, David Pareja-Lorente, Elena Popp, Ferdinand Preusse, Martin Probul, Niklas Schwikowski, Benno Sen, Bünyamin Strauss, Maximilian T. Turei, Denes Ulusoy, Erva Heidrun Wodke, Judith Andrea Saez-Rodriguez, Julio Heidelberg University Faculty of Medicine Heidelberg University Hospital Institute for Computational Biomedicine Bioquant Heidelberg Germany The Barcelona Institute of Science and Technology Catalonia Barcelona Spain Catalonia Barcelona Spain Institute for Computational Systems Biology University of Hamburg Germany Earlham Institute Norwich United Kingdom Biological Research Centre Szeged Hungary Heidelberg University Im Neuenheimer Feld 267 Heidelberg69120 Germany Im Neuenheimer Feld 460 Heidelberg69120 Germany Department of Pediatrics Dr. von Hauner Children’s Hospital University Hospital LMU Munich Germany Biological Data Science Lab Department of Computer Engineering Hacettepe University Ankara Turkey Department of Bioinformatics Graduate School of Health Sciences Hacettepe University Ankara Turkey Computational Systems Biomedicine Lab Department of Computational Biology Institut Pasteur Université Paris Cité Paris France Bioinformatics and Biostatistics Hub Institut Pasteur Université Paris Cité Paris France Wellcome Genome Campus Cambridgeshire HinxtonCB10 1SD United Kingdom Open Targets Wellcome Genome Campus Cambridgeshire HinxtonCB10 1SD United Kingdom Laboratory of Systems Pharmacology Harvard Medical School Boston United States Imperial College London London United Kingdom Quadram Institute Bioscience Norwich United Kingdom Proteomics Program Novo Nordisk Foundation Centre for Protein Research University of Copenhagen Copenhagen Denmark Department of Proteomics and Signal Transduction Max Planck Institute of Biochemistry Martinsried Germany Im Neuenheimer Feld 460 Heidelberg69120 Germany Neuherberg Germany Medical Informatics Laboratory University Medicine Greifswald Germany

Standardising the representation of biomedical knowledge among all researchers is an insurmountable task, hindering the effectiveness of many computational methods. To facilitate harmonisation and interoperability despite this fundamental challenge, we propose to standardise the framework of knowledge graph creation instead. We implement this standardisation in BioCypher, a FAIR (findable, accessible, interoperable, reusable) framework to transparently build biomedical knowledge graphs while preserving provenances of the source data. Mapping the knowledge onto biomedical ontologies helps to balance the needs for harmonisation, human and machine readability, and ease of use and accessibility to nonspecialist researchers. We demonstrate the usefulness of this framework on a variety of use cases, from maintenance of task-specific knowledge stores, to interoperability between biomedical domains, to on-demand building of task-specific knowledge graphs for federated learning. BioCypher (https://***) frees up valuable developer time;we encourage further development and usage by the community. © 2022, CC BY-NC-ND.

关键词： Ontology

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