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10th International Conference on Computer, Control, informatics and its Applications, IC3INA 2023

作者： Ramdhani, Taufik Iqbal Basuki, Akbari Indra Susanto, Heru Umbara, Raditya Panji Arifin, Samsul Faiza, Ninon Nurul Research Center for Artificial Intelligence and Cyber Security National Research and Innovation Agency Bandung Indonesia Research Center for Geological Disaster National Research and Innovation Agency Tangerang Selatan Indonesia Statistics Department School of Computer Science Bina Nusantara University Jakarta Indonesia Data and Information Center National Research and Innovation Agency Jakarta Indonesia Faculty Computer Science Institut Teknologi Tangerang Selatan Indonesia Department of Informatics Faculty of Science Nad Technology Universitas Al Azhar Indonesia Jakarta Indonesia Department of Informatics Universitas Bunda Mulia Tangerang Selatan Indonesia

ISBN: (纸本)9798350394870

This paper addresses the challenges in the current centralized research funding selection system and proposes a solution using permissioned blockchain technology. The existing system suffers from drawbacks such as concealed data modification and lack of interoperability. The Blockchain approach, based on the principles of Bitcoin technology, offers high security, auditability, traceability, and integrity. By utilizing interconnected blocks and cryptography, the proposed system ensures the authenticity and consistency of data over time. The chosen blockchain platform, Hyperledger Fabric, provides crash-fault-tolerance consensus and private communication options for enhanced security and efficiency. The objective of this study is to develop a consortium blockchain-based research funding platform that enables collaboration among researchers from public agencies, universities, and companies, with funding provided by financial organizations. The proposed architecture model ensures tamper-proof data, accountability, and interoperability, benefiting researchers, reviewers, auditors, funders, and companies. © 2023 IEEE.

关键词： Smart contract

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Who Influences Cancer Conversations on Twitter?: A Comparative Surveillance of Cancer Communications 18

Who Influences Cancer Conversations on Twitter?: A Comparati...

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18th World Congress on Medical and Health informatics: One World, One Health - Global Partnership for Digital Innovation, MEDINFO 2021

作者： Jain, Nishant Zachary, Iris Boren, Suzanne Mu Institute of Data Science and Informatics University of Missouri ColumbiaMO United States Department of Health Management and Informatics University of Missouri ColumbiaMO United States Missouri Cancer Registry & Research Center University of Missouri ColumbiaMO United States

ISBN: (纸本)9781643682648

In this study, we used social network analysis to compare the Twitter social networks of top five cancers in the United States (as ranked by the CDC) to determine the key influencers in cancer-related conversations. We find that organizations and groups geared toward patients that provide patient support, promote cancer awareness, cancer prevention and cancer management comprised up to 40% of influencers. Researchers (24%) and physicians (14%) were also found to be influential participants;the extent of influence varying by each cancer, being as high as 40% research influence for colorectal cancer. Notably, scientific organizations (JAMA, CDC_cancer, AACR) played a key role in conversations about colorectal cancer whereas patient-focused organizations played a greater influencing role in conversations about prostate cancer and skin cancer. This study shows that Twitter data can be a valuable source of cancer surveillance data, and has potential to influence policies, strategies, and research directions around each cancer. © 2022 International Medical informatics Association (IMIA) and IOS Press.

关键词： Diseases

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A Concept for Mining Transitive Sequential Patterns from Pancreatic Cancer Patient Journeys

A Concept for Mining Transitive Sequential Patterns from Pan...

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69th Annual Meeting of the German Association of Medical informatics, Biometry and Epidemiology, GMDS 2024

作者： Hügel, Jonas Schneider, Jan Janosch Tran Ortega, Daniel Jentsch, Ella Maria Rheinländer, Sophia Beyer, Nils Hendrik Estiri, Hossein Ammer-Hermenau, Christoph Hessmann, Elisabeth König, Alexander Otto Ellenrieder, Volker Sax, Ulrich University Medical Center Göttingen Department of Medical Informatics Germany University of Göttingen Campus Institute Data Science Germany Clinical Augmented Intelligence Group Massachusetts General Hospital BostonMA United States Harvard Medical School BostonMA United States University Medical Center Göttingen Clinic for Gastroenterology Gastrointestinal Oncology and Endocrinology Germany

ISBN: (纸本)9781643685366

Pancreatic cancer, renowned for its aggressive nature and poor prognosis, necessitates the optimization of treatment strategies. The sequence of procedures in clinical trials is critical, such as evaluating the potential benefits of preoperative chemo-radio-therapy for pancreatic cancer. Nevertheless, we might not be aware of other temporal sequences which have an effect on therapy response or the general outcome. Extracting transitive sequential patterns from patients' medical trajectories allows researchers to identify temporal characteristics for complex diseases. We illustrate how such sequential patterns can be discovered and might be utilized in pancreatic cancer research as well as patient care. © 2024 The Authors.

关键词： Diseases

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A comprehensive imputation-based evaluation of tag SNP selection strategies 13

A comprehensive imputation-based evaluation of tag SNP selec...

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13th International Conference Knowledge and Systems Engineering, KSE 2021

作者： Nguyen, Dat Thanh Dinh, Hieu Quang Vu, Giang Minh Nguyen, Duong Thuy Vo, Nam Sy Center for Biomedical Informatics Vingroup Big Data Institute Hanoi Viet Nam Massachusetts Institute of Technology CambridgeMA United States Center for Biomedical Informatics Vingroup Big Data Institute Institute of Genome Research Vietnam Academy of Science and Technology Hanoi Viet Nam VinUniversity Center for Biomedical Informatics Vingroup Big Data Institute College of Engineering and Computer Science Hanoi Viet Nam

ISBN: (纸本)9781665499750

Regardless of the rapid development of sequencing technology, single nucleotide polymorphism (SNP) array has been widely used for many large-scale genomic studies due to its cost-effectiveness. Recently, in parallel with the advancement in imputation strategies, several genotyping platforms for various species have been developed. Despite the importance of imputation accuracy in SNP array design, to the best of our knowledge, there are no systematic studies for evaluating tag SNP selection methods based on this metric. In this paper, using the leave-one-out cross-validation approach on the 1000 genome high-coverage dataset, we comprehensively evaluated four well-known tag SNP selection algorithms based on imputation accuracy. Our results showed that although all widely used methods for SNP array design can provide reasonable imputation accuracy, pairwise linkage disequilibrium based tag SNP selection algorithm achieves the best performance. Our pipelines for running evaluated algorithms and leave-one-out cross-validation are available for public use at https://***/datngu/TagSNP_evaluation. © 2021 IEEE.

关键词： Cost effectiveness

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Towards Robust Local Key Estimation with a Musically Inspired Neural Network

Towards Robust Local Key Estimation with a Musically Inspire...

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European Signal Processing Conference (EUSIPCO)

作者： Yiwei Ding Christof Weiβ Music Informatics Group Georgia Institute of Technology Atlanta GA USA Center for Artifical Intelligence and Data Science University of Würzburg Würzburg Germany

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

Local key estimation from music audio recordings is a challenging task. Due to its complexity and inherent ambiguity, machine-learning methods often overfit to specific pieces and their annotations, therefore lacking robustness and generalizability. Based on a previous case study on the Schubert Winterreise dataset, this paper aims to build a robust local key estimation methods. To this end, we propose a novel neural network architecture (OctaveNet), which is inspired by the musical relationship of frequency bins in the constant-Q transform (CQT) and the ability of recurrent layers to process sequential data. OctaveNet rearranges the CQT spectrogram in two different ways, processes each of the branches with convolutional and recurrent layers, and finally fuses the two feature maps to predict the local key. Our results show that, while having fewer parameters, OctaveNet achieves a substantial improvement over previous methods, especially for unseen songs, which indicates its stronger generalizability.

关键词： Convolution Fuses Neural networks Estimation Music Transforms Machine learning Robustness Multiple signal classification Spectrogram

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The Time-Varying Impact of COVID-19 on the Acute Kidney Disorders:A Historical Matched Cohort Study and Mendelian Randomization Analysis

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Health data science 2024年第1期4卷 215-225页

作者： Chunyang Li Chao Zhang Jie Chen Yilong Chen Zhiye Ying Yao Hu Huan Song Ping Fu Xiaoxi Zeng Division of Nephrology West China Biomedical Big Data CenterWest China HospitalSichuan UniversityChengdu 610041China Med-X Center for Informatics Sichuan UniversityChengdu 610065China Department of Core Laboratory Sichuan Provincial Peopleʼs HospitalUniversity of Electronic Science and Technology of ChinaChengdu 610054China Centre of Public Health Sciences Faculty of MedicineUniversity of IcelandReykjavíkIceland

Background:This study aimed to explore the time-varying impact of COVID-19 on acute kidney disorders,including acute kidney injury and other acute kidney ***:From the UK Biobank,10,121 participants with COVID-19 were matched with up to 3 historically unexposed controls by age,sex,Townsend deprivation index,and the status of hospitalization or receiving critical *** investigated the association between COVID-19 and incidence of acute kidney disorders,within the ffrst 4 weeks after infection,using conditional and time-varying Cox proportional hazard *** addition,one-sample Mendelian randomization,utilizing the polygenic risk score for COVID-19 as an instrumental variable,was conducted to explore the potential causality of the ***:In the matched cohort study,we observed a signiffcant association between COVID-19 and acute kidney disorders predominantly within the ffrst 3 *** impact of COVID-19 was time dependent,peaking in the second week(hazard ratio,12.77;95%conffdence interval,5.93 to 27.70)and decreasing by the fourth week(hazard ratio,2.28;95%conffdence interval,0.75 to 6.93).In subgroup analyses,only moderate to severe COVID-19 cases were associated with acute worsening of renal function in a time-dependent ***-sample Mendelian randomization analyses further showed that COVID-19 might exert a“short-term”causal effect on the risk of acute kidney disorders,primarily conffned to the ffrst week after ***:The risk of acute kidney disorders following COVID-19 demonstrates a time-varying *** effects were observed only in patients with moderate or severe but not mild COVID-19.

关键词： Kidney matched utilizing

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Analysis of General Event Types and Contributing Factors in Patient Safety System data

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Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care 2023年第1期12卷 103 - 107页

作者： Azade Tabaie Allan Fong Center of Biostatistics Informatics and Data Science MedStar Health Research Institute Washington DC USANational Center for Human Factors in Healthcare MedStar Health Research Institute Washington DC USA

Patient safety event reporting systems (PSRS) aim to identify safety hazards by encouraging hospital staff to report adverse events, errors, and potential errors in the healthcare system. PSRS collect different information regarding a safety event such as contributing factors and general event types. Users select contributing factors and general event types of a safety incident from predefined lists; therefore, most frequent general event types and contributing factors are commonly recorded together. Contributing factors are often related to the leading cause of an unsafe condition; therefore, identifying contributing factors can help with detecting and mitigating safety issues. In this study, we identified the general event types and contributing factors which were frequently recorded together. We calculated point-wise mutual information (PMI) between each pair to find the association between a contributing factor and a general event type. Our analysis demonstrated strong association between general event type and contributing factors which were most frequently recorded together. Fall and lost/impaired balance (PMI=15.12), skin/tissue and friction/shear (PMI=14.32), and blood bank and patient/sample incorrectly identified (PMI=13.78) were the three pairs of general event type and contributing factors which presented strong association.

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Spectra of Cardinality Queries over Description Logic Knowledge Bases 39

Spectra of Cardinality Queries over Description Logic Knowle...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Manière, Quentin Przybyłko, Marcin Department of Computer Science Leipzig University Germany Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) Dresden Germany Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) Leipzig Germany Institute of Informatics University of Warsaw Poland

ISBN: (纸本)157735897X

Recent works have explored the use of counting queries coupled with Description Logic ontologies. The answer to such a query in a model of a knowledge base is either an integer or ∞, and its spectrum is the set of its answers over all models. While it is unclear how to compute and manipulate such a set in general, we identify a class of counting queries whose spectra can be effectively represented. Focusing on atomic counting queries, we pinpoint the possible shapes of a spectrum over ALCIF ontologies: they are essentially the subsets of N ∪ {∞} closed under addition. For most sublogics of ALCIF, we show that possible spectra enjoy simpler shapes, being Jm, ∞K or variations thereof. To obtain our results, we refine constructions used for finite model reasoning and notably rely on a cycle-reversion technique for the Horn fragment of ALCIF. We also study the data complexity of computing the proposed effective representation and establish the FP^NP[log]-completeness of this task under several settings. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Expert systems

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Using annotation for computerized support for fast skimming of cardiology electronic health record notes

Using annotation for computerized support for fast skimming ...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Dehkordi, Mahshad Koohi H. Einstein, Andrew J. Zhou, Shuxin Elhanan, Gai Perl, Yehoshua Keloth, Vipina K. Geller, James Liu, Hao Njit Department of Computer Science NewarkNJ United States Irving Medical Center Cardiology Division Dept. of Medicine New YorkNY United States Desert Research Institute Renown Institute for Health Innovation RenoNV United States Yale School of Medicine Biomedical Informatics and Data Science New HavenCT United States Njit Department of Data Science NewarkNJ United States Montclair State University Department of Computer Science MontclairNJ United States

ISBN: (纸本)9798350337488

Under the circumstances prevalent in current healthcare, medical professionals such as physicians and nurses need to read large numbers of Electronic Health Record (EHR) notes. This demand fosters a situation in which providers typically do not read a whole note but quickly skim it, to capture its essential content. Consequently, by fast skimming, one may miss a critical medical fact. Annotation highlights important content in EHR notes, and enables healthcare professionals to perform fast skimming, thereby minimizing the risk of missing critical information, which is detrimental to patient care. We designed the Cardiology Interface Terminology (CIT) for the purpose of annotation of cardiology EHRs. We emphasize that by annotation we refer to highlighting important information in the EHR notes for enabling fast skimming rather than just recognizing names of diseases, drugs, etc. from Reference Terminologies as is usually done by existing Named Entity Recognition (NER) systems. The CIT design starts with the cardiology components of SNOMED CT. It is enhanced by mining phrases from cardiology EHRs, as potential CIT concepts, which are of higher granularity than SNOMED concepts. Machine learning (ML), the state of the art technique for mining concepts from EHRs, requires training data. However, there is no training data for designing CIT. In the first stage, we introduce an innovative semi-automatic method for mining concepts from EHRs, to replace costly manual mining. The only manual portion is the review of the automatically mined phrases, before their insertion as CIT concepts. The effectiveness of annotation of cardiology EHRs with CIT was evaluated utilizing proper metrics, and compared to annotation with SNOMED CT. In a future second stage, ML mining techniques will be used for enhancing CIT with extra concepts from EHRs, utilizing the concepts added in the first stage as training data. This work focuses on a novel semi-automated method to design the Cardiology I

关键词： annotation cardiology EHR fast skimming

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Analyzing Reward Dynamics and Decentralization in Ethereum 2.0: An Advanced data Engineering Workflow and Comprehensive datasets for Proof-of-Stake Incentives

arXiv

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arXiv 2024年

作者： Yan, Tao Li, Shengnan Kraner, Benjamin Zhang, Luyao Tessone, Claudio J. Blockchain áDistributed Ledger Technologies Group Department of Informatics UZH Blockchain Center University of Zurich Zurich8057 Switzerland Data Science Research Center and Social Science Division Duke Kunshan University Suzhou215316 China

Ethereum 2.0, as the preeminent smart contract blockchain platform, guarantees the precise execution of applications without third-party intervention. At its core, this system leverages the Proof-of-Stake (PoS) consensus mechanism, which utilizes a stochastic process to select validators for block proposal and validation, consequently rewarding them for their contributions. However, the implementation of blockchain technology often diverges from its central tenet of decentralized consensus, presenting significant analytical challenges. Our study collects consensus reward data from the Ethereum Beacon chain and conducts a comprehensive analysis of reward distribution and evolution, categorizing them into attestation, proposer and sync committee rewards. To evaluate the degree of decentralization in PoS Ethereum, we apply several inequality indices, including the Shannon entropy, the Gini Index, the Nakamoto Coefficient, and the Herfindahl-Hirschman Index (HHI). Our comprehensive dataset is publicly available on Harvard dataverse, and our analytical methodologies are accessible via GitHub, promoting open-access research. Additionally, we provide insights on utilizing our data for future investigations focused on assessing, augmenting, and refining the decentralization, security, and efficiency of blockchain systems. Copyright © 2024, The Authors. All rights reserved.

关键词： Ethereum

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