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IIAI International Conference on Advanced Applied Informatics (IIAIAAI)

作者： Elisa Indriasari Haryono Soeparno Ford Lumban Gaol Tokuro Matsuo Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Professor at Advanced Institute of Industrial Technology Japan

The issues that relate with the automatics and intelligent tracing of the transaction and banking customer become the main issues nowadays. Whereas most of transaction that already moved to digital and online transaction that needed more intelligent features on the Predictive Analyst. The rise of predictive analytics (PA) is one of the biggest disruptions in financial services. In the last years, PA methods grew sharply to give the baseline of data driven decision-making process for forecasting future business situation. PA uses various algorithms to discover different patterns in the big data environment that might create more value for businesses including in financial institutions. The potential implementation of data science in financial industry is still to be explored. In this paper, we summarize work done on PA at financial industry. We also explore the potential application of PA and investigating how data science and big data is going to be used in financial institutions in the future.

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Author Correction: The landscape of viral associations in human cancers

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Nature genetics 2023年第6期55卷 1077页

作者： Marc Zapatka Ivan Borozan Daniel S Brewer Murat Iskar Adam Grundhoff Malik Alawi Nikita Desai Holger Sültmann Holger Moch Colin S Cooper Roland Eils Vincent Ferretti Peter Lichter Division of Molecular Genetics German Cancer Research Center (DKFZ) Heidelberg Germany. Informatics and Bio-computing Program Ontario Institute for Cancer Research Toronto Ontario Canada. Norwich Medical School University of East Anglia Norwich UK. Earlham Institute Norwich UK. Heinrich-Pette-Institute Leibniz Institute for Experimental Virology Hamburg Germany. German Center for Infection Research (DZIF) Partner Site Hamburg-Borstel-Lübeck-Riems Hamburg Germany. Bioinformatics Core University Medical Center Hamburg-Eppendorf Hamburg Germany. Bioinformatics Group Department of Computer Science University College London London UK. Biomedical Data Science Laboratory Francis Crick Institute London UK. National Center for Tumor Diseases (NCT) Heidelberg Heidelberg Germany. Division of Cancer Genome Research German Cancer Research Center (DKFZ) Heidelberg Germany. German Cancer Consortium (DKTK) Heidelberg Germany. Department of Pathology and Molecular Pathology University and University Hospital Zürich Zurich Switzerland. Institute of Cancer Research London UK. University of East Anglia Norwich UK. Division of Theoretical Bioinformatics German Cancer Research Center (DKFZ) Heidelberg Germany. Department of Bioinformatics and Functional Genomics Institute of Pharmacy and Molecular Biotechnology Heidelberg University and BioQuant Center Heidelberg Germany. Center for Digital Health Berlin Institute of Health and Charité Universitätsmedizin Berlin Berlin Germany. Ontario Institute for Cancer Research MaRS Centre Toronto Ontario Canada. Department of Biochemistry and Molecular Medicine University of Montreal Montreal Québec Canada. Division of Molecular Genetics German Cancer Research Center (DKFZ) Heidelberg Germany. peter.lichter@dkfz-heidelberg.de. German Cancer Consortium (DKTK) Heidelberg Germany. peter.lichter@dkfz-heidelberg.de.

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Detecting Hemileia vastatrix using Vision AI as Supporting to Food Security for Smallholder Coffee Commodities

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IOP Conference Series: Earth and Environmental Science 2022年第1期998卷

作者： Teddy Suparyanto Erick Firmansyah Tjeng Wawan Cenggoro Digdo Sudigyo Bens Pardamean Faculty of Agricultural Technology Institute of Agriculture STIPER Yogyakarta Indonesia 55282 Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

The loss of food crops during the COVID-19 pandemic threatens food security in Indonesia as one of the world's top coffee producers. This affects the insecurity of coffee commodities which is influenced by several factors other than the pandemic such as pests, plant diseases, and extreme weather. Plant diseases, such as leaf rust, are a significant factor in the insecurity problem in coffee commodities. The fungus Hemileia vastatrix B et Br causes leaf rust disease, which is a pest that frequently damages coffee plants. This disease not only interferes with plant growth but also causes a decrease in coffee quality and quantity. This initial research aims to carry out early prevention of these diseases as supporting to food security in smallholder coffee commodities. An AI-based visual detection application is the result of this research. We collected 100 images of coffee leaves from various coffee plants. The image is reshaped to 256 x 256 pixels and randomly trimmed to 224 x 224 pixels to fulfill the size requirements of a standard Deep Learning technique. Each image was classified into two classes by a plant pest and disease specialist. The dataset was divided into training, validation, and testing series with a ratio of 60:20:20 for training procedures. The Convolutional Neural Network (CNN) research method used a variation of the ResNet CNN model with 18 layers. The best model validation was 59%.

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Author Correction: Large-scale benchmarking of circRNA detection tools reveals large differences in sensitivity but not in precision

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Nature methods 2025年第2期22卷 448页

作者： Marieke Vromman Jasper Anckaert Stefania Bortoluzzi Alessia Buratin Chia-Ying Chen Qinjie Chu Trees-Juen Chuang Roozbeh Dehghannasiri Christoph Dieterich Xin Dong Paul Flicek Enrico Gaffo Wanjun Gu Chunjiang He Steve Hoffmann Osagie Izuogu Michael S Jackson Tobias Jakobi Eric C Lai Justine Nuytens Julia Salzman Mauro Santibanez-Koref Peter Stadler Olivier Thas Eveline Vanden Eynde Kimberly Verniers Guoxia Wen Jakub Westholm Li Yang Chu-Yu Ye Nurten Yigit Guo-Hua Yuan Jinyang Zhang Fangqing Zhao Jo Vandesompele Pieter-Jan Volders OncoRNALab Cancer Research Institute Ghent (CRIG) Department of Biomolecular Medicine Ghent University Ghent Belgium. Department of Molecular Medicine University of Padova Padova Italy. Genomics Research Center Academia Sinica Taipei City Taiwan. Institute of Crop Science and Institute of Bioinformatics Zhejiang University Zhejiang China. Department of Biomedical Data Science and of Biochemistry Stanford University Stanford CA USA. Klaus Tschira Institute for Integrative Computational Cardiology Department of Internal Medicine III University Hospital Heidelberg German Center for Cardiovascular Research (DZHK) Heidelberg Germany. School of Basic Medical Science Department of Medical Genetics Wuhan University Wuhan China. EMBL-EBI Hinxton UK. Collaborative Innovation Center of Jiangsu Province of Cancer Prevention and Treatment of Chinese Medicine School of Artificial Intelligence and Information Technology Nanjing University of Chinese Medicine Nanjing China. Computational Biology Group Leibniz Institute on Aging - Fritz Lipmann Institute (FLI) Jena Germany. Biosciences Institute Faculty of Medical Sciences Newcastle University Newcastle UK. Translational Cardiovascular Research Center University of Arizona - College of Medicine Phoenix Phoenix AZ USA. Sloan Kettering Institute New York NY USA. Bioinformatics Group Department of Computer Science and Interdisciplinary Center for Bioinformatics Universität Leipzig Leipzig Germany. Data Science Institute I-Biostat Hasselt University Hasselt Belgium. State Key Laboratory of Bioelectronics School of Biological Sciences and Medical Engineering Southeast University Nanjing China. Department of Biochemistry and Biophysics National Bioinformatics Infrastructure Sweden Science for Life Laboratory Stockholm University Stockholm Sweden. Center for Molecular Medicine Children's Hospital Fudan University and Shanghai Key Laboratory of Medical Epigenetics International Laboratory of Medical Epigenetics and Metab

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ERGODIC THEOREMS FOR DYNAMIC IMPRECISE PROBABILITY KINEMATICS

arXiv

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arXiv 2020年

作者： Caprio, Michele Mukherjee, Sayan PRECISE Center Dept. of Computer and Information Science University of Pennsylvania 3330 Walnut Street PhiladelphiaPA19104 United States Center for Scalable Data Analytics and Artificial Intelligence Universität Leipzig Humboldtstraße 25 Leipzig04105 Germany The Max Planck Institute for Mathematics in the Sciences Inselstraße 22 Leipzig04103 Germany Dept. of Statistical Science Mathematics Computer Science and Biostatistics & Bioinformatics Duke University DurhamNC27708 United States

We formulate an ergodic theory for the (almost sure) limit PE co Ε͂ of a sequence (PcoΕn) of successive dynamic imprecise probability kinematics (DIPK, introduced in [10]) updates of a set Pco Ε0 representing the initial beliefs of an agent. As a consequence, we formulate a strong law of large *** Codes 60A99, 37A50, 60F15, 60G05 © 2020, CC BY.

关键词： Kinematics

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Sustainability of the Water Footprint of Various Soil Types on Oil Palm Plantations

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IOP Conference Series: Earth and Environmental Science 2022年第1期998卷

作者： Lisma Safitri Hermantoro Valensi Kautsar Teddy Suparyanto Alam Ahmad Hidayat Bens Pardamean Agricultural Engineering Department Institute of Agricultural STIPER Yogyakarta 55282 Indonesia School of Earth and Environment University of Leeds Woodhouse Leeds LS2 9J Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

There are significant studies that have quantified oil palm water footprint as an indicator of environmental sustainability but an estimation of water footprint under varying soil types furthermore is still limited. The objectives of the study were to estimate whether annual variations of soil type and yields significantly effects for the oil palm water footprint. The data from three types of soil (spodosol, inceptisol, ultisol) were collected from an oil palm plantation in Pundu village, Central Kalimantan, Indonesia. To perform the water footprint analysis we utilize water balance accounting equations via application Cropwat 8.0. From that, we determine the crop irrigation scheduling to compute the blue, green, and grey water footprint of oil palm fresh fruit bunch in the area. Our analysis found that the actual evapotranspiration of spodosol and inceptisol have the same value: 1242 mm/year whilst ultisol is 1239 mm/year. The total water footprint of oil palm varied considerably with the largest value being 1310.04 m3/ton for ultisol. The actual evapotranspiration of spodosol and inceptisol have the same value of 1242 mm/year whilst ultisol is 1239 mm/year. The higher production resulted in a lower water footprint and vice versa. Moreover, the total water footprint from ultisol soil type has the highest value due to the lowest yields. The difference in evapotranspiration value resulted in the insignificant value of total water footprint. The lower water availability, the lower water use, and the higher actual irrigation requirement in oil palm plantation yet showed the unnotable impact on water footprint in different soil types for the oil palm plantation.

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Benchmarking Emergency Department Triage Prediction Models with Machine Learning and Large Public Electronic Health Records

arXiv

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arXiv 2021年

作者： Xie, Feng Zhou, Jun Lee, Jin Wee Tan, Mingrui Li, Siqi Rajnthern, Logasan S.O. Chee, Marcel Lucas Chakraborty, Bibhas Wong, An-Kwok Ian Dagan, Alon Ong, Marcus Eng Hock Gao, Fei Liu, Nan Centre for Quantitative Medicine and Programme in Health Services and Systems Research Duke-NUS Medical School Singapore Singapore Singapore Singapore School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore Faculty of Medicine Nursing and Health Sciences Monash University VIC Australia Department of Statistics and Data Science National University of Singapore Singapore Singapore Department of Biostatistics and Bioinformatics Duke University DurhamNC United States Division of Pulmonary Allergy and Critical Care Medicine Duke University DurhamNC United States Department of Emergency Medicine Beth Israel Deaconess Medical Center Harvard Medical School BostonMA United States MIT Critical Data Laboratory for Computational Physiology Institute for Medical Engineering and Science Massachusetts Institute of Technology CambridgeMA United States Department of Emergency Medicine Singapore General Hospital Singapore Singapore SingHealth AI Health Program Singapore Health Services Singapore Singapore Institute of Data Science National University of Singapore Singapore Singapore

The demand for emergency department (ED) services is increasing across the globe, particularly during the current COVID-19 pandemic. Clinical triage and risk assessment have become increasingly challenging due to the shortage of medical resources and the strain on hospital infrastructure caused by the pandemic. As a result of the widespread use of electronic health records (EHRs), we now have access to a vast amount of clinical data, which allows us to develop predictive models and decision support systems to address these challenges. To date, however, there are no widely accepted benchmark ED triage prediction models based on large-scale public EHR data. An open-source benchmarking platform would streamline research workflows by eliminating cumbersome data preprocessing, and facilitate comparisons among different studies and methodologies. In this paper, based on the Medical Information Mart for Intensive Care IV Emergency Department (MIMIC-IV-ED) database, we developed a publicly available benchmark suite for ED triage predictive models and created a benchmark dataset that contains over 400,000 ED visits from 2011 to 2019. We introduced three ED-based outcomes (hospitalization, critical outcomes, and 72-hour ED reattendance) and implemented a variety of popular methodologies, ranging from machine learning methods to clinical scoring systems. We evaluated and compared the performance of these methods against benchmark tasks. Our codes are open-source, allowing anyone with MIMIC-IV-ED data access to perform the same steps in data processing, benchmark model building, and experiments. This study provides future researchers with insights, suggestions, and protocols for managing raw data and developing risk triaging tools for emergency care. © 2021, CC BY.

关键词： Learning systems

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Association between sodium-glucose cotransporter-2 (SGLT2) inhibitors and macular degeneration in patients with diabetes: a nationwide population-based study in Taiwan

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Acta diabetologica 2024年第9期61卷 1161-1168页

作者： Min-Yen Hsu Kai-Shin Luo Chien-Chih Chou Yu-Hsiang Lin Yu-Chien Hung Wu-Lung Chuang Stella Chin-Shaw Tsai Heng-Jun Lin Teng-Shun Yu Fuu-Jen Tsai Kuang-Hsi Chang School of Medicine Chung Shan Medical University Taichung City 402 Taiwan. Department of Ophthalmology Chung Shan Medical University Hospital Taichung City 402 Taiwan. Graduate Institute of Clinical Medicine College of Medicine National Taiwan University Taipei Taiwan. School of Medicine National Yang Ming Chiao Tung University Taipei Taiwan. Department of Ophthalmology Taichung Veterans General Hospital Taichung Taiwan. Department of Post-Baccalaureate Medicine College of Medicine National Chung Hsing University Taichung Taiwan. Division of Endocrinology and Metabolism Department of Internal Medicine Changhua Christian Hospital Changhua City 500 Taiwan. Division of Endocrinology and Metabolism Department of Internal Medicine Lukang Christian Hospital Changhua County 505 Taiwan. Department of Otolaryngology Tungs' Taichung MetroHarbor Hospital Taichung 435 Taiwan. Rong Hsing Research Center for Translational Medicine College of Life Sciences National Chung Hsing University Taichung 402 Taiwan. Management Office for Health Data China Medical University Hospital Taichung 404 Taiwan. College of Medicine China Medical University Taichung City 404 Taiwan. School of Chinese Medicine College of Chinese Medicine China Medical University Taichung City 404 Taiwan. Department of Medical Research China Medical University Hospital China Medical University Taichung City 404 Taiwan. Division of Medical Genetics China Medical University Children's Hospital Taichung City 404 Taiwan. Department of Biotechnology and Bioinformatics Asia University Taichung City 413 Taiwan. Department of Medical Research Tungs' Taichung MetroHarbor Hospital Taichung City 435 Taiwan. kuanghsichang@***. Center for General Education China Medical University Taichung City 404 Taiwan. kuanghsichang@***. General Education Center Nursing and Management Jen-Teh Junior College of Medicine Miaoli County 356 Taiwan. kuanghsichang@gmail.com.

AIMS:Evidence showed that SGLT2 inhibitors have greater protective effects against retinal diseases compared to other hypoglycemic agents. Thus, we explore the association between SGLT2 inhibitor usage and macular degeneration (MD) in Taiwanese patients with diabetes. METHODS:The National Health Insurance (NHI) program's claim data are released as the National Health Insurance Research database (NHIRD). This database covers more than 99% of the residents in Taiwan. We included data on patients who were newly diagnosed with type 2 diabetes mellitus (ICD-9-CM: 250, exclude 250.1x; ICD-10-CM: E11), with an age at diagnosis of over 20 years as our study population. Patients who received (sodium-glucose cotransporter 2 inhibitor) SGLT2i (ATC code: A10BK) over 90 days in 2016-2019 were defined as the SGLT2i cohort. Conversely, patients who did never received SGLT2i were defined as the non-SGLT2i cohort. The exclusion criteria were having MD before the index date, receiving SGLT2i within 1-89 days, and missing data on sex, age, or days of SGLT2i usage. Two cohorts were matched by 1:1 propensity score matching, which was based on age, sex, payroll bracket grade, urbanization, comorbidities, and medications. RESULTS:Compared to non-SGLT2i cohort, patients who received SGLT2i had a significantly lower risk of MD (adjusted hazard ratio = 0.70, 95%CI = 0.66-0.75). CONCLUSIONS:We found that SGLT2is has a strong protective effect against MD in patients with diabetes. SGLT2is may have benefits beyond glycemic control in patients with DR. However, additional clinical and experimental studies are required.

关键词： Macular degeneration (MD) National Health Insurance Research database (NHIRD) Sodium-glucose cotransporter-2 (SGLT2)

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FINITE ADMIXTURE MODELS: A BRIDGE WITH STOCHASTIC GEOMETRY AND CHOQUET THEORY

arXiv

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arXiv 2020年

作者： Caprio, Michele Mukherjee, Sayan PRECISE Center Dept. of Computer and Information Science University of Pennsylvania 3330 Walnut Street PhiladelphiaPA19104 United States Center for Scalable Data Analytics and Artificial Intelligence Universität Leipzig Humboldtstraße 25 Leipzig04105 Germany the Max Planck Institute for Mathematics in the Sciences Inselstraße 22 Leipzig04103 Germany Dept. of Statistical Science Mathematics Computer Science and Biostatistics & Bioinformatics Duke University DurhamNC27708 United States

In the context of a finite admixture model whose components and weights are unknown, if the number of identifiable components is a function of the amount of data collected, we use techniques from stochastic convex geometry to find the growth rate of its expected value. In addition, when the components are known but the weights are not, we provide an application of the classic Glivenko-Cantelli’s theorem that allows us to retrieve the Choquet measure supported on the identifiable admixture components. In turn, this gives us the identifiable admixture weights. Finally, we propose a novel algorithm that estimates the model capturing the complexity of the data using only the strictly necessary number of *** Codes 52B11, 60D05, 62G20 © 2020, CC BY.

关键词： Stochastic systems

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POS-309 Prediction of Non-Responsiveness to Pre-dialysis Care Program in Patients with Chronic Kidney Disease: A Retrospective Cohort Analysis

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Kidney International Reports 2021年第4期6卷 S133-S133页

作者： E. Kim D. CHANG M.H. Hsieh C.T. Lu C.C. Wang H.Y. Chiang C.C. Kuo Saint Bear Incorporated Department of Medical Media Design and Application Taichung City Taiwan China Medical University Hospital Division of Nephrology- Department of Internal Medicine Taichung City Taiwan Asia University Department of Bioinformatics and Medical Engineering Taichung City Taiwan China Medial University Hospital Big Data Center Taichung City Taiwan China Medical University Hospital Big Data Center Taichung City Taiwan

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