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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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Question-Answering System Extracts information on Injection Drug Use from Clinical Notes

arXiv

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arXiv 2023年

作者： Mahbub, Maria Goethert, Ian Danciu, Ioana Knight, Kathryn Srinivasan, Sudarshan Tamang, Suzanne Rozenberg-Ben-Dror, Karine Solares, Hugo Martins, Susana Trafton, Jodie Begoli, Edmon Peterson, Gregory D. Cyber Resilience and Intelligence Division Oak Ridge National Laboratory Oak RidgeTN United States Information Technology Services Division Oak Ridge National Laboratory Oak RidgeTN United States Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak RidgeTN United States Department of Biomedical Informatics Vanderbilt University NashvilleTN United States Program Evaluation and Resource Center Office of Mental Health and Suicide Prevention Department of Veterans Affairs United States Department of Medicine Stanford University School of Medicine StanfordCA United States Veterans Affairs Great Lakes Health Care System WestchesterIL United States Department of Electrical Engineering and Computer Science University of Tennessee Knoxville KnoxvilleTN United States

Background: Injection drug use (IDU) is a dangerous health behavior that increases mortality and morbidity. Identifying IDU early and initiating harm reduction interventions can benefit individuals at risk. However, extracting IDU behaviors from patients’ electronic health records (EHR) is difficult because there is no International Classification of Disease (ICD) code and the only place IDU information can be indicated is unstructured free-text clinical notes. Although natural language processing can efficiently extract this information from unstructured data, there are no validated tools. Methods: To address this gap in clinical information, we design and demonstrate a question-answering (QA) framework to extract information on IDU from clinical notes. Our framework involves two main steps: (1) generating a gold-standard QA dataset and (2) developing and testing the QA model. We utilize 2323 clinical notes of 1145 patients sourced from the VA Corporate Data Warehouse to construct the gold-standard dataset for developing and evaluating the QA model. We also demonstrate the QA model’s ability to extract IDU-related information on temporally out-of-distribution data. Results: Here we show that for a strict match between gold-standard and predicted answers, the QA model achieves 51.65% F1 score. For a relaxed match between the gold-standard and predicted answers, the QA model obtains 78.03% F1 score, along with 85.38% Precision and 79.02% Recall scores. Moreover, the QA model demonstrates consistent performance when subjected to temporally out-of-distribution data. Conclusions: Our study introduces a QA framework designed to extract IDU information from clinical notes, aiming to enhance the accurate and efficient detection of people who inject drugs, extract relevant information, and ultimately facilitate informed patient care. © 2023, CC BY-NC-ND.

关键词： Data warehouses

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Evaluative Study Of Integration Of Clinic Management information System With Pcare BPJS of health Using COBIT 4.1 Approach

Evaluative Study Of Integration Of Clinic Management Informa...

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International Conference on computer science, information technology, and Electrical Engineering

作者： Ely Mulyadi Kadek Yota Ernanda Aryanto I Made Candiasa Department of Information Technology Politeknik Negeri Jember Department of Computer Science of Postgraduate Program Universitas Pendidikan Ganesha

ISBN: (纸本)9781728134376

The aim purposed of this research is to evaluate clinical management information system called (E-CLINIC) which is integrated with the Primary Care (Pcare) application provided by Indonesia sosial healt insurance organization called BPJS Kesehatan to first level of health cares facility called as FKTP. This evaluation is intended to measure the maturity level on used of two integrated applications using COBIT 4.1 framework. The integration of the Clinic's management information system with PCare is considered to be the main resource that has strategic value to be able to manage information effectively and efficiently for the achievement of organizational goals. The results achieved have been running in accordance with the organization's business objectives, this can be seen from the level of maturity that reaches level 3 (Defined), which is a condition in which the organization has formal and written standard procedures that have been socialized to all levels of management and employees to be obeyed and run in daily activities.

关键词： E-Clinic MIS PCare BPJS COBIT Maturity Management information Systems Maturity maturity level DNA Integration integration health level health clinic Daily Activities

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A Design of Crowd-Based Corruption Prevention System in Indonesia: Indonesia Corruption Map 4

A Design of Crowd-Based Corruption Prevention System in Indo...

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4th International Conference on information Management and technology, ICIMTech 2019

作者： Nurlina Kristin, Desi Maya Muqsith, Andi Muhammad Mursitama, Tirta Nugraha Sasmoko Makalew, Brilly Andro Information Systems Department Bina Nusantara University Jakarta11480 Indonesia Primary Teacher Education Bina Nusantara University Jakarta11480 Indonesia Mobile Application Technology Program School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9781728133331

Combatting corruption requires not only centralized and institution-based strategy, but must be met with distributive effort supported by abundance of data and technological advancement. This work tackles the issue of decentralized corruption prevention by describing the method of doing heuristic analysis based on multiple data regarding corruption. There are four main data source used for the input of the system in this work, where each data corresponds to a city in a region. All the data are processed into corresponding index, while the indexes will be further used in index interpretation and suggestion mapping process. The output of this system is a decision support system for relevant decision making related to corruption prevention, visualized using Google Map API. This system will be very powerful in the long and tedious war against corruption, when used to its maximum potential. © 2019 IEEE.

关键词： Mapping

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Integrability of Goldilocks quantum cellular automata

arXiv

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arXiv 2024年

作者： Hillberry, Logan E. Piroli, Lorenzo Vernier, Eric Halpern, Nicole Yunger Prosen, Tomaž Carr, Lincoln D. Department of Physics The University of Texas at Austin AustinTX78712 United States Dipartimento di Fisica e Astronomia Università di Bologna INFN Sezione di Bologna via Irnerio 46 BolognaI-40126 Italy Laboratoire de Probabilités Statistique et Modélisation CNRS Université Paris Cité Sorbonne Université Paris France Joint Center for Quantum Information and Computer Science NIST University of Maryland College ParkMD20742 United States Institute for Physical Science and Technology University of Maryland College ParkMD20742 United States Faculty of Mathematics and Physics University of Ljubljana Jadranska 19 LjubljanaSI-1000 Slovenia Institute of Mathematics Physics and Mechanics Jadranska 19 LjubljanaSI-1000 Slovenia Quantum Engineering Program Colorado School of Mines GoldenCO80401 United States Department of Physics Colorado School of Mines GoldenCO80401 United States Department of Applied Mathematics and Statistics Colorado School of Mines GoldenCO80401 United States

Goldilocks quantum cellular automata (QCA) have been simulated on quantum hardware and produce emergent small-world correlation networks. In Goldilocks QCA, a single-qubit unitary is applied to each qubit in a one-dimensional chain subject to a balance constraint: a qubit is updated if its neighbors are in opposite basis states. Here, we prove that a subclass of Goldilocks QCA—including the one implemented experimentally—map onto free fermions and therefore can be classically simulated efficiently. We support this claim with two independent proofs, one involving a Jordan–Wigner transformation and one mapping the integrable six-vertex model to QCA. We compute local conserved quantities of these QCA and predict experimentally measurable expectation values. These calculations can be applied to test large digital quantum computers against known solutions. In contrast, typical Goldilocks QCA have equilibration properties and quasienergy-level statistics that suggest nonintegrability. Still, the latter QCA conserve one quantity useful for error mitigation. Our work provides a parametric quantum circuit with tunable integrability properties with which to test quantum hardware. © 2024, CC BY.

关键词： Qubits

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Performance analysis of combination of fuzzy analytic hierarchy process (FAHP) algorithms with preference ranking organization method for enrichment evaluation algorithm (PROMETHEE II) in the ranking process to determine the increase in employee class

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IOP Conference Series: Materials science and Engineering 2020年第1期725卷

作者： M. Irfan Ramadhan Muhammad Zarlis Benny Benyamin Nasution Graduate Program of Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Department of Information and Technology Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Department of Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia

In this study conducted a Performance Analysis of the Combination of Fuzzy Analytic Hierarchy Process (FAHP) Algorithm with the Preference Ranking Organization Method for Enrichment Evaluation algorithm (PROMETHEE II) in the ranking process to determine the increase in employee groups. From the results of the experiment the Performance Analysis of Fuzzy Analytic Hierarchy Process (FAHP) Algorithm with the Preference Ranking Organization Method for Enrichment Evaluation algorithm (PROMETHEE II) in the ranking process to determine the increase in the employee class obtained by the average employee considered at 62.31%. Seeing the percentage value considered with the Promethee algorithm (45.33%) lower than the Fuzzy AHP algorithm (79.30%), it can be said that the Combination Fuzzy AHP algorithm with Promethee is more selective in the weighting and ranking process.

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Silica nanocone array as a template for fabricating a plasmon induced hot electron photodetector

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Photonics Research 2019年第3期7卷 294-299页

作者： ZHIQIANG YANG KANG DU FANFAN LU YANG PANG SHIJIA HUA XUETAO GAN WENDING ZHANG SOO JIN CHUA TING MEI Key Laboratory of Space Applied Physics and Chemistry Ministry of Education and Shaanxi Key Laboratory of Optical Information TechnologySchool of Science Northwestern Polytechnical University Department of Electrical and Computer Engineering National University of Singapore LEES Program Singapore-MIT Alliance for Research & Technology (SMART)

Plasmon induced hot electrons have attracted a great deal of interest as a novel route for photodetection and lightenergy harvesting. Herein, we report a hot electron photodetector in which a large array of nanocones deposited sequentially with aluminum, titanium dioxide, and gold films can be integrated functionally with nanophotonics and microelectronics. The device exhibits a strong photoelectric response at around 620 nm with a responsivity of 180 μA/W under short-circuit conditions with a significant increase under 1 V reverse bias to 360 μA/W. The increase in responsivity and a red shift in the peak value with increasing bias voltage indicate that the bias causes an increase in the hot electron tunneling effect. Our approach will be advantageous for the implementation of the proposed architecture on a vast variety of integrated optoelectronic devices.

关键词： Silica nanocone array plasmon induced hot electron photodetector

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The Effect of Several Kernel Functions to Financial Transaction Anomaly Detection Performance using One-Class SVM

The Effect of Several Kernel Functions to Financial Transact...

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International Congress on Applied information technology

作者： Yaya Heryadi Dandalina Doctor of Computer Science Program Bina Nusantara University Jakarta Indonesia Master of Information and Technology Program Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9781728158624

ISBN: (纸本)9781728158631

Anomalies in data become ubiquitous and often unavoidable. Despite it might be caused by various errors during the data collection or transportation, some anomalies potentially give important information such as indication of a new underlying process. The aim of anomaly detection task is to determine all such instances in the input dataset in a data-driven fashion. In the past decade, the proliferation of anomaly data in many domains have raised research interest resulted in a plethora of anomaly detection methods A vast number of previous study reports suggested that there is no single model will achieve the best performance for every dataset. This paper presents empiric results on the effect of several kernel functions to performance of One-Class Support Vector Machine (OC-SVM) as an anomaly detector model. The proposed model is tested using financial transactions of microfinance service dataset from an Indonesian Bank. The empiric results showed that OC-SVM model with Sigmoid and RBF kernels achieve the best statistically significant value of training, validation, and testing accuracies than the OC-SVM model with no-kernel, linear kernel and polynomial (degree 3,4,5,6) kernels.

关键词： Kernel Anomaly detection Training Testing Support vector machines Task analysis Feature extraction

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An intelligent applied Fuzzy Logic to prediction the Parts per Million (PPM) as hydroponic nutrition on the based Internet of Things (IoT)

An intelligent applied Fuzzy Logic to prediction the Parts p...

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International Conference on Informatics and Computing (ICIC)

作者： Purnawarman Musa Herik Sugeru Haya Fadhila Mufza Faculty of Computer Science and Information Technology Universitas Gunadarma Indonesia Faculty of Industrial Technology Study Program of Agrotechnology Universitas Gunadarma Indonesia

Hydroponic plants require regular monitoring of plants that grow well and can even cause plants to die. Because too late in providing nutrition or getting too little or too little food or routine monitoring in the hydroponic system. The innovation proposed in the agricultural sector that we suggest is the process of monitoring hydroponic plants in real-time. This study aims to predict the excess or lack of nutrient content based on the results of routine monitoring and has the analytical ability to determine the prediction of the value of Parts per Million (PPM) to find the nutritional value of plant media. Measurement of nutritional value using Electrical Conductivity (EC) and the potential of Hydrogen (pH) in hydroponic plant media. In this research, using the NFT hydroponic system as the application of fuzzy logic with the Mamdani method to determine the predictions of PPM values in providing nutrients at plants to adjusting the nutritional at each plant (e.g., for water spinach plants are need 1050 until 1400 PPM). The implemented is method Internet of Things (IoT) to remote sensing on measure using a smartphone as the representation of the measurement results on the hydroponic system.

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Analysis of classification and Naïve Bayes algorithm k-nearest neighbor in data mining

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IOP Conference Series: Materials science and Engineering 2020年第1期725卷

作者： Lotar Mateus Sinaga Sawaluddin Saib Suwilo Graduate Program of Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia

Naïve Bayes is a prediction method that contains a simple probabilistic that is based on the application of the Bayes theorem (Bayes rule) with the assumption that the dependence is strong. K-Nearest Neighbor (K-NN) is a group of instance-based learning, K-NN is also a lazy learning technique by searching groups of k objects in training data that are closest (similar) to objects on new data or testing data. Classification is a technique in Data mining to form a model from a predetermined data set. Data mining techniques are the choices that can be overcome in solving this problem. The results of the two different classification algorithms result in the discovery of better and more efficient algorithms for future use. It is recommended to use different datasets to analyze comparisons of naïve bayes and K-NN algorithms. the writer formulates the problem so that the research becomes more directed. The formulation of the problem in this study is to find the value of accuracy in the Naïve Bayes and KNN algorithms in classifying data.

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