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Big data in healthcare:Conceptual network structure,key challenges and opportunities

Digital Communications and Networks 2023年第4期9卷 856-868页

作者： Leonardo B.Furstenaua Pedro Leivas Michele Kremer Sott Michael S.Dohan Jose Ricardo Lopez-Robles Manuel J.Cobo Nicola Luigi Bragazzi Kim-Kwang Raymond Choo Post-Graduate Program in Industrial Engineering Federal University of Rio Grande Do SulPorto Alegre90035-190Brazil University of Santa Cruz Do Sul Av.Independencia 2293Santa Cruz Do SulRSBrazil Business School Unisinos UniversityAv.Dr.Nilo Peçanha 160091330-002Porto AlegreBrazil Faculty of Business Administration Lakehead UniversityThunder BayOntarioCanada Autonomous University of Zacatecas Av.Ramon Lopez Velarde Col.CentroZacatecas98000Mexico Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence(DaSCI)University of GranadaGranada 18071Spain York University ON M3J 1P3Canada Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249USA

Big data is a concept that deals with large or complex data sets by using data analysis tools(e.g.,data mining,machine learning)to analyze information extracted from several sources *** data has attracted wide attention from academia,for example,in supporting patients and health professionals by improving the accuracy of decision-making,diagnosis and disease *** research aimed to perform a Bibliometric Performance and Network Analysis(BPNA)supported by a Scoping Review(SR)to depict the strategic themes,thematic evolution structure,main challenges and opportunities related to the concept of big data applied in the healthcare *** this goal in mind,4857 documents from the Web of science covering the period between 2009 to June 2020 were analyzed with the support of SciMAT *** bibliometric performance showed the number of publications and citations over time,scientific productivity and the geographic distribution of publications and research *** strategic diagram yielded 20 clusters and their relative importance in terms of centrality and *** thematic evolution structure presented the most important themes and how it changes over ***,we presented the main challenges and future opportunities of big data in healthcare.

关键词： Big data Healthcare digitalization Bibliometric Strategic intelligence Co-word analysis SciMAT

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Invasive or More Direct Measurements Can Provide an Objective Early-Stopping Ceiling for Training Deep Neural Networks on Non-invasive or Less-Direct Biomedical Data

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SN computer science 2023年第2期4卷 161页

作者： Bartlett, Christopher W. Bossenbroek, Jamie Ueyama, Yukie McCallinhart, Patricia Peters, Olivia A. Santillan, Donna A. Santillan, Mark K. Trask, Aaron J. Ray, William C. Department of Pediatrics The Ohio State University College of Medicine Columbus OH United States The Steve and Cindy Rasmussen Institute for Genomic Medicine Battelle Center for Computational Biology The Abigail Wexner Research Institute at Nationwide Children’s Hospital Columbus OH United States Department of Computer Science and Engineering The Ohio State University College of Engineering Columbus OH United States Center for Cardiovascular Research The Abigail Wexner Research Institute at Nationwide Children’s Hospital Columbus OH United States Department of Obstetrics & Gynecology University of Iowa Hospitals & Clinics Iowa City IA United States The Interdisciplinary Graduate Program in Biophysics The Ohio State University Graduate School Columbus OH United States

Early stopping is an extremely common tool to minimize overfitting, which would otherwise be a cause of poor generalization of the model to novel data. However, early stopping is a heuristic that, while effective, primarily relies on ad hoc parameters and metrics. Optimizing when to stop remains a challenge. In this paper, we suggest that for some biomedical applications, a natural dichotomy of invasive/non-invasive measurements, or more generally proximal vs distal measurements of a biological system can be exploited to provide objective advice on early stopping. We discuss the conditions where invasive measurements of a biological process should provide better predictions than non-invasive measurements, or at best offer parity. Hence, if data from an invasive measurement are available locally, or from the literature, that information can be leveraged to know with high certainty whether a model of non-invasive data is overfitted. We present paired invasive/non-invasive cardiac and coronary artery measurements from two mouse strains, one of which spontaneously develops type 2 diabetes, posed as a classification problem. Examination of the various stopping rules shows that generalization is reduced with more training epochs and commonly applied stopping rules give widely different generalization error estimates. The use of an empirically derived training ceiling is demonstrated to be helpful as added information to leverage early stopping in order to reduce overfitting. © 2023, The Author(s).

关键词： Health Invasive Machine learning Model Non-invasive Overfitting

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Long term-short memory neural networks and word2vec for self-admitted technical debt detection 22

Long term-short memory neural networks and word2vec for self...

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22nd International Conference on Enterprise Information Systems, ICEIS 2020

作者： Santos, Rafael Meneses Santos, Israel Meneses Rodrigues, Methanias Colaço de Mendonça Neto, Manoel Gomes Graduate Program in Computer Science Federal University of Bahia Salvador Brazil Department of Information Systems Federal University of Sergipe Itabaiana Brazil

ISBN: (纸本)9789897584237

Context: In software development, new functionalities and bug fixes are required to ensure a better user experience and to preserve software value for a longer period. Sometimes developers need to implement quick changes to meet deadlines rather than a better solution that would take longer. These easy choices, known as Technical Debt, can cause long-term negative impacts because they can bring extra effort to the team in the future. Technical debts must be managed and detected so that the team can evaluate the best way to deal with them and avoid more serious problems. One way to detect technical debts is through source code comments. Developers often insert comments in which they admit that there is a need to improve that part of the code later. This is known as Self-Admitted Technical Debt (SATD). Objective: Evaluate a Long short-term memory (LSTM) neural network model combined with Word2vec for word embedding to identify design and requirement SATDs from comments in source code. Method: We performed a controlled experiment to evaluate the quality of the model compared with two language models from literature and LSTM without word embedding in a labelled dataset. Results: The results showed that the LSTM model with Word2vec have improved in recall and f-measure. The LSTM model without word embedding achieves greater recall, but perform worse in precision and f-measure. Conclusion: Overall, we found that the LSTM model and word2vec can outperform other models. Copyright © 2020 by SCITEPRESS – science and Technology Publications, Lda. All rights reserved

关键词： Long short-term memory

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Survey and Improvement Strategies for Gene Prioritization with Large Language Models

arXiv

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arXiv 2025年

作者： Neeley, Matthew B. Qi, Guantong Wang, Guanchu Tang, Ruixiang Mao, Dongxue Liu, Chaozhong Pasupuleti, Sasidhar Yuan, Bo Xia, Fan Liu, Pengfei Liu, Zhandong Hu, Xia Jan and Dan Duncan Neurological Research Institute Texas Children’s Hospital Houston United States Graduate School of Biomedical Sciences Program in Quantitative and Computational Biosciences Baylor College of Medicine Houston United States Graduate School of Biomedical Sciences Program in Genetics and Genomics Baylor College of Medicine Houston United States Department of Computer Science Rice University Houston United States Department of Computer Science Rutgers University NJ United States Department of Pediatrics Baylor College of Medicine Houston United States Department of Molecular and Human Genetics Baylor College of Medicine Houston United States Human Genome Sequencing Center Baylor College of Medicine Houston United States Baylor Genetics Houston United States

Background Rare diseases can be difficult to diagnose due to limited patient data and broad genetic diversity. Despite the advances in variant prioritization tools, many rare disease cases remain undiagnosed. While large language models (LLMs) have performed well in medical exams, their accuracy in diagnosing rare genetic diseases has not yet been evaluated. Methods To identify causal genes for genetic diseases, we benchmarked various LLMs for gene prioritization. We employed multi-agent and Human Phenotype Ontology (HPO) classification approaches to identify patient case groups based on phenotypes categorizing them into different levels of solvability. To address LLM limitations in ranking large gene sets, we used a divide-and-conquer strategy to break down the ranking task into smaller subsets. Mini-batching inputs and limiting the number of generated tokens improved efficiency. Results In its vanilla form, GPT-4 consistently outperformed the other LLMs with an accuracy around 30%. Multi-agent and HPO classification approaches aided us in distinguishing between confidently-solved and challenging cases. In addition, we observed bias towards well-studied genes and input order sensitivity which are drawbacks of LLMs in disease-causal gene prioritization. Our divide-and-conquer strategy enhanced accuracy by overcoming positional and gene frequency biases in literature. This framework, based on benchmarking insights and novel techniques, significantly optimizes the overall process when identifying disease-causal genes for genetic diseases when compared to our baseline evaluation, and thereby better enables the development of targeted diagnostic and therapeutic interventions. Conclusions Using HPO classification and our novel multi-agent techniques, and our LLM divide-and-conquer strategy (1) highlighted the importance of accounting for differences in patient case solvability and (2) yielded improved performance in identifying causal genes for rare diseases when compare

关键词： Gene Ontology

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Chern-Protected Flatband Edge State in Metaphotonics

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Physical Review Letters 2025年第22期134卷 223806-223806页

作者： Jianfeng Chen Yidong Zheng Shuihua Yang Andrea Alù Zhi-Yuan Li Cheng-Wei Qiu Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore School of Physics and Optoelectronics South China University of Technology Guangzhou 510640 China Physics Program Graduate Center City University of New York New York New York 10016 USA Photonics Initiative Advanced Science Research Center City University of New York New York New York 10031 USA

Two-dimensional Dirac semimetals feature flatband edge states but a zero Chern number, while Chern insulators support one-way edge states associated with a nonzero Chern number. Here, we demonstrate a two-dimensional photonic crystal combining the response of a semimetal and of a Chern insulator, termed a Chern semimetal. This photonic semimetal is characterized by a nonzero Chern number, simultaneously hosting both a flatband edge state and a one-way edge state. We experimentally realize such a photonic Chern semimetal using a gyromagnetic photonic crystal with programmable magnetic bias, tailoring its second-neighbor coupling within each sublattice. We also verify the ability to control over the light speed while preserving topological protection. Our findings open new avenues toward exploring topological phases and their applications across fermionic and bosonic systems, providing an exciting platform for nanophotonic applications and a playground for topological physics discoveries.

关键词： Nanophotonics

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More Efficient Identifiability Verification in ODE Models by Reducing Non-Identifiability

arXiv

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arXiv 2022年

作者： Ilmer, Ilia Ovchinnikov, Alexey Soto, Pedro Pogudin, Gleb Program in Computer Science Graduate Center CUNY New YorkNY United States Program in Mathematics CUNY Queens College New YorkNY United States LIX CNRS École Polytechnique Institute Polytechnique de Paris Paris France

Structural global parameter identifiability indicates whether one can determine a parameter’s value from given inputs and outputs in the absence of noise. If a given model has parameters for which there may be infinitely many values, such parameters are called non-identifiable. We present a procedure for accelerating a global identifiability query by eliminating algebraically independent non-identifiable parameters. Our proposed approach significantly improves performance across different computer algebra frameworks. © 2022, CC BY-SA.

关键词： Algebra

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Explainable machine learning models for estimating daily dissolved oxygen concentration of the Tualatin River

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Engineering Applications of Computational Fluid Mechanics 2024年第1期18卷

作者： Li, Shuguang Qasem, Sultan Noman Band, Shahab S. Ameri, Rasoul Pai, Hao-Ting Mehdizadeh, Saeid School of Computer Science and Technology Shandong Technology and Business University Yantai China Computer Science Department College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University (IMSIU) Riyadh Saudi Arabia Computer Science Department Faculty of Applied Science Taiz University Taiz Yemen Future Technology Research Center National Yunlin University of Science and Technology Douliu Taiwan Department of Information Management International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Douliu Taiwan Department of Information Management National Yunlin University of Science and Technology Douliu Taiwan Bachelor Program of Big Data Applications in Business National Pingtung University Pingtung Taiwan Water Engineering Department Urmia University Urmia Iran

Monitoring the quality of river water is of fundamental importance and needs to be taken into consideration when it comes to the research into the hydrological field. In this context, the concentration of the dissolved oxygen (DO) is one of the most significant indicators of the quality of river water. The current study aimed to estimate the minimum, maximum, and mean DO concentrations (DO min, DO max, DO mean) at a gauging station located on Tualatin River, United States. To that end, four machine learning models, such as support vector regression (SVR), multi-layer perceptron (MLP), random forest (RF), and gradient boosting (GB) were established. Root mean square error (RMSE), mean absolute error (MAE), coefficient of correlation (R), and Nash-Sutcliffe efficiency (NSE) metrics were employed to better assess the accuracies of these models. The modeling results demonstrated that the SVR and MLP surpassed the RF and GB models. Despite this, the SVR was concluded to be the best-performing method when used to estimate the DO min, DO max, and DO mean. The best error statistics in the testing phase were related to the SVR model with full (four) inputs to estimate DO mean concentration (RMSE = 0.663 mg/l, MAE = 0.508 mg/l, R = 0.945, NSE = 0.875). Finally, the explainability of the superior models (i.e. SVR models) was conducted using SHapley Additive exPlanations (SHAP) for the first time to estimate DO concentration. In fact, evaluating the explainability of machine learning models can provide useful information about the impact of each of the input estimators used in the procedure of models development. It was concluded that the specific conductance (SC) and followed by water temperature (WT) could provide the most contributions for estimating the DO min, DO max, and DO mean concentrations. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： dissolved oxygen concentration estimation Explainable machine learning SHapley additive explanations

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The Influence of Business Strategy, Leadership Styles and Business Process on Quality of Accounting Information 21

The Influence of Business Strategy, Leadership Styles and Bu...

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Proceedings of the 2021 5th International Conference on Software and e-Business

作者： Meiryani Meiryani Zaidi Mat Daud Jajat Sudrajat Tommy Andrian Suryadiputra Liawatimena Accounting Department Faculty of Economics and Communication Bina Nusantara University Indonesia BINUS Entrepreneuship Center Management DepartementBINUS Business School Undergraduate Program Bina Nusantara University Indonesia Accounting Department Faculty of Economics and Communication Bina Nusantara University Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Indonesia

ISBN: (纸本)9781450385831

This study aims to determine the effect of business strategy on the quality of accounting information, to determine the effect of leadership style on the quality of accounting information and to determine the effect of business processes on the quality of accounting information. This study uses primary data from a population of State-Owned Enterprises throughout Indonesia with a total sample of 61 state-owned enterprises, and is analyzed using a simultaneous equation estimation model or SEM (Structural Equation Modeling), the sample determination is done by purposive sampling. The results showed that (1) leadership style and business processes affect the quality of accounting information systems (2) business strategy has no effect on the quality of accounting information systems on accounting information systems at State-Owned Enterprises in Indonesia.

关键词： Business Strategy Leadership Style Business Process Accounting Information Systems Accounting Information Quality

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cytoNet: Spatiotemporal network analysis of cell communities (vol 18, e1009846, 2022)

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PLOS COMPUTATIONAL BIOLOGY 2022年第11期18卷 e1009846页

作者： Mahadevan, A. S. Long, B. L. Hu, C. W. Ryan, D. T. Grandel, N. E. Department of Bioengineering University of Pennsylvania Philadelphia Pennsylvania United States of America. Department of Bioengineering Rice University Houston Texas United States of America. Systems Synthetic and Physical Biology Program Rice University Houston Texas United States of America. Department of Biomedical Engineering University of Texas at San Antonio San Antonio Texas United States of America. Biophysics Graduate Program University of California Berkeley California United States of America. Department of Oral & Maxillofacial Surgery University of Texas Health Science Center at San Antonio San Antonio Texas United States of America. Department of Electrical and Computer Engineering Rice University Houston Texas United States of America.

We introduce cytoNet, a cloud-based tool to characterize cell populations from microscopy images. cytoNet quantifies spatial topology and functional relationships in cell communities using principles of network science. Capturing multicellular dynamics through graph features, cytoNet also evaluates the effect of cell-cell interactions on individual cell phenotypes. We demonstrate cytoNet's capabilities in four case studies: 1) characterizing the temporal dynamics of neural progenitor cell communities during neural differentiation, 2) identifying communities of pain-sensing neurons in vivo, 3) capturing the effect of cell community on endothelial cell morphology, and 4) investigating the effect of laminin α4 on perivascular niches in adipose tissue. The analytical framework introduced here can be used to study the dynamics of complex cell communities in a quantitative manner, leading to a deeper understanding of environmental effects on cellular behavior. The versatile, cloud-based format of cytoNet makes the image analysis framework accessible to researchers across domains.

关键词： Calcium imaging Fluorescence imaging Growth factors Centrality Cell cycle and cell division Endothelial cells Network analysis Stem cells

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The Role of Financial Technology for Development of Micro, Small and Medium Enterprises (Msmes) in Indonesia 11

The Role of Financial Technology for Development of Micro, S...

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11th International Conference on Communication and Network Security, ICCNS 2021

作者： Meiryani, Meiryani Hanna Uli Pakpahan, Noviyanti Wahyuningtias, Dianka Mad Daud, Zaidi Liawatimena, Suryadiputra Accounting Department Faculty of Economics and Communication Bina Nusantara University Jakarta11480 Indonesia Hotel Management Department Faculty of Economics and Communication Bina Nusantara University Jakarta11480 Indonesia Accounting Department Faculty of Economics and Business Universiti Putra Malaysia Malaysia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9781450386425

The rapid development has made financial technology/financial technology's innovations more diverse according to the needs and financial problems that arise. With the existence of financial technology (financial technology) it can also make the financial service process faster, more effective and easier. Because of the development in the field of financial technology with the various solutions offered, this study aims to examine the role of financial technology for the development of MSMEs (Micro and Medium Enterprises), this research also includes aspects of the role of financial inclusion for the development of MSMEs to be able to see how much public knowledge is about financial service products and risk knowledge. The case study in this research is MSMEs in the Tangerang area using quantitative methods. The results of research using multiple linear regression analysis found that the role of financial financial technology for the development of MSMEs has a significant positive effect. Meanwhile, financial inclusion for the development of MSMEs has a positive and insignificant effect on the dependent variable. This study concludes that the use of financial technology for MSME businesses, as well as the high reference to financial products, are important factors in encouraging the development of MSMEs. © 2021 ACM.

关键词： Linear regression

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