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Treatment Algorithm for the Resorption of Calcific Tendinitis Using Extracorporeal Shockwave Therapy: A Data Mining Study

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Orthopaedic Journal of Sports Medicine 2024年第3期12卷 23259671241231609页

作者： Chou, Wen-Yi Cheng, Jai-Hong Lien, Yu-Jui Huang, Tian-Hsiang Ho, Wen-Hsien Chou, Paul Pei-Hsi Doctoral Degree Program in Biomedical Engineering College of Medicine Kaohsiung Medical University Kaohsiung Taiwan Department of Orthopedic Surgery Kaohsiung Chang Gung Memorial Hospital Chang Gung University College of Medicine Kaohsiung Taiwan Department of Leisure and Sport Management Cheng Shiu University Kaohsiung Taiwan Center for Shockwave Medicine and Tissue Engineering Department of Medical Research Kaohsiung Chang Gung Memorial Hospital Chang Gung University College of Medicine Kaohsiung Taiwan Department of Healthcare Administration and Medical Informatics Kaohsiung Medical University Kaohsiung Taiwan Department of Computer Science and Information Engineering National Penghu University of Science and Technology Penghu Taiwan College of Professional Studies National Pingtung University of Science and Technology Pingtung Taiwan Department of Sports Medicine College of Medicine Kaohsiung Medical University Kaohsiung Taiwan Division of Sports Medicine Department of Orthopaedic Surgery Kaohsiung Medical University Hospital Kaohsiung Taiwan Department of Orthopaedics School of Medicine College of Medicine Kaohsiung Medical University Kaohsiung Taiwan Department of Orthopaedic Surgery Kaohsiung Municipal Hsiao-Kang Hospital Kaohsiung Taiwan

Background: Although evidence indicates that extracorporeal shockwave therapy (ESWT) is effective in treating calcifying shoulder tendinitis, incomplete resorption and dissatisfactory results are still reported in many cases. Data mining techniques have been applied in health care in the past decade to predict outcomes of disease and treatment. Purpose: To identify the ideal data mining technique for the prediction of ESWT-induced shoulder calcification resorption and the most accurate algorithm for use in the clinical setting. Study Design: Case-control study. Methods: Patients with painful calcified shoulder tendinitis treated by ESWT were enrolled. Seven clinical factors related to shoulder calcification were adopted as the input attributes: sex, age, side affected, symptom duration, pretreatment Constant-Murley score, and calcification size and type. The 5 data mining techniques assessed were multilayer perceptron (neural network), naïve Bayes, sequential minimal optimization, logistic regression, and the J48 decision tree classifier. Results: A total of 248 patients with calcified shoulder tendinitis were enrolled in this study. Shorter symptom duration yielded the highest gain ratio (0.374), followed by smaller calcification size (0.336) and calcification type (0.253). With the J48 decision tree method, the accuracy of 3 input attributes was 89.5% by 10-fold cross-validation, indicating satisfactory accuracy. A treatment algorithm using the J48 decision tree indicated that a symptom duration of ≤10 months was the most positive indicator of calcification resorption, followed by a calcification size of ≤10.82 mm. Conclusion: The J48 decision tree method demonstrated the highest precision and accuracy in the prediction of shoulder calcification resorption by ESWT. A symptom duration of ≤10 months or calcification size of ≤10.82 mm represented the clinical scenarios most likely to show resorption after ESWT. © The Author(s) 2024.

关键词： calcific tendinitis data mining extracorporeal shockwave therapy J48 decision tree shoulder

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What is open-access publishing and what it means for the forensic enterprise

Forensic Science International: Synergy

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Forensic science International: Synergy 2019年 1卷 290-293页

作者： Houck, M.M. Horsman, G. Sauzier, G. Bidmos, M. Forensic Studies & Justice Program University of South Florida St. Petersburg FL United States Biosciences Research Group Computer & Digital Forensics Teesside University United Kingdom Faculty of Science and Engineering Curtin University Perth Australia College of Medicine Qatar University Qatar

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Negative prognostic factors and clinical improvement prediction modeling for extracorporeal shockwave therapy in calcific shoulder tendinitis using artificial intelligence techniques

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Journal of Shoulder and Elbow Surgery 2025年

作者： Chou, Wen-Yi Huang, Tian-Hsiang Cheng, Jai-Hong Lien, Yu-Jui Ho, Wen-Hsien Chou, Paul Pei-Hsi Ph.D. Program in Biomedical Engineering College of Medicine Kaohsiung Medical University Kaohsiung Taiwan Department of Orthopedic Surgery Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine Kaohsiung Taiwan Department of Leisure and Sport Management Cheng Shiu University Kaohsiung Taiwan Department of Computer Science and Information Engineering National Penghu University of Science and Technology Penghu Taiwan Department of Medical Research Center for Shockwave Medicine and Tissue Engineering Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine Kaohsiung Taiwan Department of Healthcare Administration and Medical Informatics Kaohsiung Medical University Kaohsiung Taiwan Department of Medical Research Kaohsiung Medical University Hospital Kaohsiung Taiwan College of Professional Studies National Pingtung University of Science and Technology Pingtung Taiwan Precision Sports Medicine and Health Promotion Center Kaohsiung Medical University Kaohsiung Taiwan Department of Sports Medicine College of Medicine Kaohsiung Medical University Kaohsiung Taiwan Division of Sports Medicine Department of Orthopaedic Surgery Kaohsiung Medical University Hospital Kaohsiung Taiwan Department of Orthopaedics School of Medicine College of Medicine Kaohsiung Medical University Kaohsiung Taiwan

Background: The efficacy of extracorporeal shockwave therapy (ESWT) for treating shoulder calcific tendinitis can be influenced by various prognostic factors. This study aimed to identify prognostic factors associated with the failure of ESWT for symptom relief and to evaluate the predictive capability of the eXtreme Gradient Boosting (XGBoost) algorithm of artificial intelligence techniques in this context. Methods: This retrospective study enrolled patients with persistent shoulder pain attributed to calcific tendinitis who underwent ESWT after failed conservative treatment between January 1998 and December 2022. Age, sex, duration of symptoms, calcification classification and size, pre-ESWT visual analog scale (VAS), and pre-ESWT Constant-Murley score (CMS) served as potential input attributes. The difference in VAS and CMS were defined as the output attributes. The XGBoost model was used to predict treatment outcomes based on these factors. The dataset was balanced using the synthetic minority oversampling technique, and the model's performance was assessed using 10-fold cross-validation. Spearman's rank correlation coefficient analysis was adopted to explore the relationships between significant continuous input attributes and post-ESWT VAS and CMS scores. Results: A total of 296 patients with calcific tendinitis were enrolled and completed the 1-year follow-up. The findings revealed that a prolonged symptom duration (>10 months), severe pain (pre-ESWT VAS >5), and higher pre-ESWT CMS (>55) were significant prognostic factors for the failure of ESWT for symptom relief. Using these factors as inputs, the XGBoost model demonstrated high accuracy, precision, recall, and F1 score. By reducing the input attributes to age, calcification size, pre-ESWT CMS, and symptom duration, the model maintained a high prediction rate, suggesting that these factors are sufficient for effective prediction. Discussion: The present study identified significant prognostic factors asso

关键词： Artificial intelligence Basic science Study calcific tendinitis CMS Development of Predictive computer Modeling Using AI/Machine Learning ESWT shoulder VAS XGBoost

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Fast, accurate, and versatile data analysis platform for the quantification of molecular spatiotemporal signals

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Cell 2025年第10期188卷 2794-2809.e21页

作者： Mi, Xuelong Chen, Alex Bo-Yuan Duarte, Daniela Carey, Erin Taylor, Charlotte R. Braaker, Philipp N. Bright, Mark Almeida, Rafael G. Lim, Jing-Xuan Ruetten, Virginia M.S. Wang, Yizhi Wang, Mengfan Zhang, Weizhan Zheng, Wei Reitman, Michael E. Huang, Yongkang Wang, Xiaoyu Li, Lei Deng, HanFei Shi, Song-Hai Poskanzer, Kira E. Lyons, David A. Nimmerjahn, Axel Ahrens, Misha B. Yu, Guoqiang Bradley Department of Electrical and Computer Engineering Virginia Polytechnic Institute and State University Arlington 22203 VA United States Janelia Research Campus Howard Hughes Medical Institute Ashburn 20147 VA United States Department of Molecular and Cellular Biology Harvard University Cambridge 02138 MA United States Graduate Program in Neuroscience Harvard Medical School Boston 02115 MA United States Waitt Advanced Biophotonics Center The Salk Institute for Biological Studies La Jolla 92037 CA United States Department of Biochemistry & Biophysics University of California San Francisco San Francisco CA United States Neuroscience Graduate Program University of California San Francisco San Francisco CA United States Centre for Discovery Brain Sciences University of Edinburgh Edinburgh BioQuarter Edinburgh EH16 4SB United Kingdom Gatsby Computational Neuroscience Unit UCL London W1T 4JG United Kingdom Kavli Institute for Fundamental Neuroscience San Francisco CA United States Department of Automation Tsinghua University Beijing 100084 China IDG/McGovern Institute for Brain Research Tsinghua University Beijing 100084 China New Cornerstone Science Laboratory Tsinghua-Peking Center for Life Sciences Beijing Frontier Research Center for Biological Structure School of Life Sciences Tsinghua University Beijing 100084 China State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science Institutes of Brain Science Fudan University Shanghai 200032 China Beijing National Research Center for Information Science and Technology Beijing 100084 China

Optical recording of intricate molecular dynamics is becoming an indispensable technique for biological studies, accelerated by the development of new or improved biosensors and microscopy technology. This creates major computational challenges to extract and quantify biologically meaningful spatiotemporal patterns embedded within complex and rich data sources, many of which cannot be captured with existing methods. Here, we introduce activity quantification and analysis (AQuA2), a fast, accurate, and versatile data analysis platform built upon advanced machine-learning techniques. It decomposes complex live-imaging-based datasets into elementary signaling events, allowing accurate and unbiased quantification of molecular activities and identification of consensus functional units. We demonstrate applications across a wide range of biosensors, cell types, organs, animal models, microscopy techniques, and imaging approaches. As exemplar findings, we show how AQuA2 identified drug-dependent interactions between neurons and astroglia, as well as distinct sensorimotor signal propagation patterns in the mouse spinal cord. © 2025 The Author(s)

关键词： AQuA2 astrocytes cell interaction analysis functional units glial cells image analysis machine learning molecular spatiotemporal signals neurons time-lapse imaging

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Relational database reconstruction from SQL to Entity Relational Diagrams

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Journal of Physics: Conference Series 2020年第1期1566卷

作者： Juanda Hakim Lubis Elviawaty Muisa Zamzami Faculty of Computer Science and Information Technology Doctoral Program of Computer Science Studies Universitas Sumatera Utara Faculty of Computer Science and Information Technology Universitas Sumatera Utara

The absence of requirement documents or incompatibility of the requirement documents with existing software can be a problem in developing or maintaining software. Without knowing things that become requirements can cause software as a product that fails because it does not meet customer desires. One of the main problems in developing or maintaining software is that developers do not know the entity relational schema of the software. So developers will prefer to rebuild from scratch. This paper discusses database reconstruction from SQL to entity relational diagrams by identifying the primary and foreign keys of SQL sampling.

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Interpretable bias mitigation for textual data: Reducing gender bias in patient notes while maintaining classification performance

arXiv

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arXiv 2021年

作者： Minot, Joshua R. Cheney, Nicholas Maier, Marc Elbers, Danne C. Danforth, Christopher M. Dodds, Peter Sheridan Computational Story Lab University of Vermont BurlingtonVT05401 United States Vermont Complex Systems Center University of Vermont BurlingtonVT05401 United States Department of Computer Science University of Vermont BurlingtonVT05401 United States Neurobotics Lab University of Vermont BurlingtonVT05401 United States MassMutual Data Science AmherstMA01002 United States VA Cooperative Studies Program VA Boston Healthcare System BostonMA02130 United States Department of Mathematics and Statistics University of Vermont BurlingtonVT05401 United States

Medical systems in general, and patient treatment decisions and outcomes in particular, are affected by bias based on gender and other demographic elements. As language models are increasingly applied to medicine, there is a growing interest in building algorithmic fairness into processes impacting patient care. Much of the work addressing this question has focused on biases encoded in language models—statistical estimates of the relationships between concepts derived from distant reading of corpora. Building on this work, we investigate how word choices made by healthcare practitioners and language models interact with regards to bias. We identify and remove gendered language from two clinical-note datasets and describe a new debiasing procedure using BERT-based gender classifiers. We show minimal degradation in health condition classification tasks for low- to medium-levels of bias removal via data augmentation. Finally, we compare the bias semantically encoded in the language models with the bias empirically observed in health records. This work outlines an interpretable approach for using data augmentation to identify and reduce the potential for bias in natural language processing pipelines. Copyright © 2021, The Authors. All rights reserved.

关键词： Natural language processing systems

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Quantum-inspired classical sublinear-time algorithm for solving low-rank semidefinite programming via sampling approaches

arXiv

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arXiv 2019年

作者： Chia, Nai-Hui Li, Tongyang Lin, Han-Hsuan Wang, Chunhao Department of Computer Science University of Texas Austin United States Department of Computer Science Institute for Advanced Computer Studies Joint Center for Quantum Information and Computer Science University of Maryland U.S. Department of Energy Office of Science Office of Advanced Scientific Computing Research Quantum Algorithms Teams program

Semidefinite programming (SDP) is a central topic in mathematical optimization with extensive studies on its efficient solvers. Recently, quantum algorithms with superpolynomial speedups for solving SDPs have been proposed assuming access to its constraint matrices in quantum superposition. Mutually inspired by both classical and quantum SDP solvers, in this paper we present a sublinear classical algorithm for solving low-rank SDPs which is asymptotically as good as existing quantum algorithms. Specifically, given an SDP with m constraint matrices, each of dimension n and rank poly(log n), our algorithm gives a succinct description and any entry of the solution matrix in time O(m · poly(log n, 1/Ε)) given access to a sample-based low-overhead data structure of the constraint matrices, where Ε is the precision of the solution. In addition, we apply our algorithm to a quantum state learning task as an application. Technically, our approach aligns with both the SDP solvers based on the matrix multiplicative weight (MMW) framework and the recent studies of quantum-inspired machine learning algorithms. The cost of solving SDPs by MMW mainly comes from the exponentiation of Hermitian matrices, and we propose two new technical ingredients (compared to previous sample-based algorithms) for this task that may be of independent interest: • Weighted sampling: assuming sampling access to each individual constraint matrix A1,..., Aτ, we propose a procedure that gives a good approximation of A = A1 + ··· + Aτ. • Symmetric approximation: we propose a sampling procedure that gives low-rank spectral decomposition of a Hermitian matrix A. This improves upon previous sampling procedures that only give low-rank singular value decompositions, losing the signs of eigenvalues. Copyright © 2019, The Authors. All rights reserved.

关键词： Singular value decomposition

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SYNAPTIC SPINE HEAD MORPHODYNAMICS FROM GRAPH GRAMMAR RULES FOR ACTIN DYNAMICS

arXiv

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arXiv 2025年

作者： Hur, Matthew Bartol, Thomas Rangamani, Padmini Sejnowski, Terrence Mjolsness, Eric Program in Mathematical Computational and Systems Biology Univeristy of California Irvine IrvineCA United States Computational Neurobiology Laboratory Salk Institute for Biological Studies La Jolla CA United States Departments of Pharmacology and Mechanical and Aerospace Engineering University of California San Diego La Jolla CA United States Deparment of Neurobiology University of California San Diego La Jolla CA United States Departments of Computer Science and Mathematics Univeristy of California Irvine IrvineCA United States

There is a morphodynamic component to synaptic learning by which changes in dendritic (postsynaptic) spine head size are associated with the strengthening or weakening of the synaptic connection between two neurons, in response to the temporal correlation of local presynaptic and postsynaptic signals. These morphological factors are in turn sculpted by the dynamics of the actin cytoskeleton. In this paper, we use Dynamical Graph Grammars (DGGs) implemented within a computer algebra system to model how networks of actin filaments can dynamically grow or shrink, reshaping the spine head. Dynamical Graph Grammars (DGGs) provide a well-defined way to accommodate dynamically changing system structure such as active cytoskeleton represented using dynamic graphs, within nonequilibrium statistical physics under the master equation. We show that DGGs can also incorporate biophysical forces between graph-connected objects at a finer time scale, with specialized DGG kinetic rules obeying biophysical constraints of Galilean invariance, conservation of momentum, and dissipation of conserved global energy. We use graph-local energy functions for cytoskeleton networks interacting with membranes, and derive DGG rules from the specialization of dissipative stochastic dynamics - separated into dissipative and thermal noise rule types - to a mutually exclusive and exhaustive collection of graph-local neighborhood types for the rule left hand sides. The dissipative rules comprise a stochastic version of gradient descent dynamics. The thermal noise rules use a Gaussian approximation of each position coordinate to sample jitter-like displacements. For the spine head model we designed and implemented DGG grammar mathematical sub-models including actin network growth, non-equilibrium statistical mechanics, and filament-membrane mechanical interaction to regulate the re-writing of graph objects. We simulate emergent biophysics of simplified networks of actin polymers and their interactions

关键词： Stochastic models

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Distributional property testing in a quantum world

arXiv

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arXiv 2019年

作者： Gilyén, András Liy, Tongyang QuSoft CWI University of Amsterdam Netherlands Department of Computer Science Institute for Advanced Computer Studies Joint Center for Quantum Information and Computer Science University of Maryland U.S. Department of Energy Office of Science Office of Advanced Scientific Computing Research Quantum Algorithms Teams Program

A fundamental problem in statistics and learning theory is to test properties of distributions. We show that quantum computers can solve such problems with significant speed-ups. In particular, we give fast quantum algorithms for testing closeness between unknown distributions, testing independence between two distributions, and estimating the Shannon / von Neumann entropy of distributions. The distributions can be either classical or quantum, however our quantum algorithms require coherent quantum access to a process preparing the samples. Our results build on the recent technique of quantum singular value transformation, combined with more standard tricks such as divide-and-conquer. The presented approach is a natural fit for distributional property testing both in the classical and the quantum case, demonstrating the first speed-ups for testing properties of density operators that can be accessed coherently rather than only via sampling;for classical distributions our algorithms significantly improve the precision dependence of some earlier results. Copyright © 2019, The Authors. All rights reserved.

关键词： Quantum theory

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Alert and Response of Earthquake and Tsunami for Community Based Disaster Risk Reduction

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Journal of Physics: Conference Series 2021年第1期1811卷

作者： Rr M I Retno Susilorini Andre Kurniawan Pamudji Ridwan Sanjaya Amrizarois Ismail Ardhito Hayyu Amasto Department of Infrastructure and Environmental Engineering Faculty of Environmental Science and Technology Soegijapranata Catholic University Semarang Master Program of Environmental and Urban Studies Faculty of Environmental Science and Technology Soegijapranata Catholic University Semarang Department of Information System Faculty of Computer Science Soegijapranata Catholic University Semarang

Indonesia had been suffered by earthquake and tsunami for many centuries. Since many lives have taken by tsunami strikes, alert and response system are very important as important part of Disaster Risk Reduction, especially in area with high population density. This research conducted in Kangkung fishing village in Bandar Lampung coastal area which is an earthquake and tsunami prone area. The aim of the research is to study alert and response of Community Based Disaster Risk Reduction. The research conducted by qualitative method and purposive-descriptive approach. There were 53 respondents which participated in the survey and interview. It is reported that 'Smart Resilient City' model suitable for community needs of alert and response of earthquake and tsunami disaster. This research meets conclusion that 'Smart Resilient City' model may become a great solution in Community Based Disaster Risk Reduction to cope with the hazard of earthquake and tsunami comprehensively and sustainably.

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