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A Cross-institutional Evaluation on Breast Cancer Phenotyping NLP Algorithms on Electronic Health Records

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arXiv 2023年

作者： Zhou, Sicheng Wang, Nan Wang, Liwei Sun, Ju Blaes, Anne Liu, Hongfang Zhang, Rui MS Institute for Health Informatics University of Minnesota MinneapolisMN United States School of Statistics University of Minnesota MinneapolisMN United States Department of AI and Informatics Research Mayo Clinic RochesterMN United States Department of Computer Science & Engineering University of Minnesota MinneapolisMN United States Department of Medicine University of Minnesota MinneapolisMN United States Division of Computational Health Sciences Department of Surgery University of Minnesota MinneapolisMN United States

Objective: The generalizability of clinical large language models is usually ignored during the model development process. This study evaluated the generalizability of BERT-based clinical NLP models across different clinical settings through a breast cancer phenotype extraction task. Materials and Methods: Two clinical corpora of breast cancer patients were collected from the electronic health records from the University of Minnesota and the Mayo Clinic, and annotated following the same guideline. We developed three types of NLP models (i.e., conditional random field, bi-directional long short-term memory and CancerBERT) to extract cancer phenotypes from clinical texts. The models were evaluated for their generalizability on different test sets with different learning strategies (model transfer vs. locally trained). The entity coverage score was assessed with their association with the model performances. Results: We manually annotated 200 and 161 clinical documents at UMN and MC, respectively. The corpora of the two institutes were found to have higher similarity between the target entities than the overall corpora. The CancerBERT models obtained the best performances among the independent test sets from two clinical institutes and the permutation test set. The CancerBERT model developed in one institute and further fine-tuned in another institute achieved reasonable performance compared to the model developed on local data (micro-F1: 0.925 vs 0.932). Conclusions: The results indicate the CancerBERT model has the best learning ability and generalizability among the three types of clinical NLP models. The generalizability of the models was found to be correlated with the similarity of the target entities between the corpora. © 2023, CC BY.

关键词： Natural language processing systems

Middle East and North African Health informatics Association (MENAHIA): Technological initiatives for 'One Health'

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Yearbook of Medical informatics 2023年第1期32卷 320-329页

作者： Abd-Alrazaq, Alaa Al-Shorbaji, Najeeb Lakhdari, Kheira Elbokl, Ahmed Farouk, Hani Wahba, Hoda Elgasser, Naema Mohell, Rania Emara, Tamer Ayatollahi, Haleh Kalhori, Sharareh R. Niakan Rabiei, Reza Tara, Mahmood Akhu-Zaheya, Laila Al-Qutob, Raeda Alrazak, Sadam Alabed Al-Jafar, Eiman Alhuwail, Dari Ghazal, Hassan Dehbi, Zineb El Otmani Al Idrissi, Najib Fihri, Ouafaa Fassi Belyamani, Lahcen Shahzad, Tariq Ahmed, Zakiuddin Ahmad, Arfan Shah, Zubair Hamra, Eman Abu Taweel, Adel Nashwan, Abdulqadir J. Househ, Mowafa Hamdi, Mounir Al-Thani, Dena Alam, Tanvir Alshammari, Abdulwahhab Alnafrani, Sana Alali, Haitham AI Center for Precision Health Weill Cornell Medicine-Qatarc Doha Qatar EHealth Development Association Amman Jordan Abou Bekr Belkaid University of Tlemcen Chetouane Algeria Ain Shams University Virtual Hospital Ain Shams University Cairo Egypt Ain Shams University Cairo Egypt World Health Organization Cairo Egypt Alexandria Regional Blood Transfusion Center Alexandria University Alexandria Egypt Health Management and Economics Research Center Health Management Research Institute Iran University of Medical Sciences Tehran Iran Department of Health Information Management School of Health Management and Information Sciences Iran University of Medical Sciences Tehran Iran Department of Health Information Management School of Allied Medical Sciences Tehran University of Medical Sciences Tehran Iran Peter L. Reichertz Institute for Medical Informatics (PLRI) Technical University of Braunschweig Hannover Medical School Braunschweig Germany Department of Health Information Technology and Management School of Allied Medical Sciences Shahid Beheshti University of Medical Sciencesc Tehran Iran Department of Medical Informatics Faculty of Medicine Mashhad University of Medical Sciencesc Mashhad Iran School of Nursing Jordan University of Science and Technology Jordan Jordan Centre for Disease Control and Prevention Amman Jordan Faculty of Applied Science and Engineering University of Toronto Toronto Canada Kuwait Health Informatics Association Kuwait Information Science Department College of Life Sciences Kuwait University Kuwait Health Informatics Unit Dasman Diabetes Institute Kuwait Mohammed VI University of Health Sciences Casablanca Morocco National Center for Scientific and Technical Research (CNRST) Rabat Morocco Hassan II Agronomic and Veterinary Institute Rabat Morocco COMSATS University Islamabad Sahiwal Campus Islamabad Pakistan Digital Care Karachi Pakistan College of Science and Engineering Hamad Bin Khalifa University Doha Qatar Al Quds Unive

MENAHIA (Middle East and North African Health informatics Association) is the International Medical informatics Association chapter dedicated to the Middle East and North Africa region. This region is rapidly growing in terms of the use of health informatics or what has been recently coined digital health. Human health is highly affected by the health of the environment, animal health, food, nutrition, climate change, and many other factors that are beyond the biological or genetic structure of human beings. The impact of animal health and the health of the environment on people's health is an old phenomenon but recent reemerging and appearance of diseases have clearly demonstrated the link between these. The Novel Coronavirus disease (COVID-19) that almost all of us have been suffering from is an example of this. A number of countries in the region have already shown the depth and the work that they do to integrate the concept of 'One Health' in the public health surveillance system as they have described the work that has been done to capture data from databases other than those dealing with human beings. The examples that were provided to monitor the health of animals, agriculture, environmental health, climate change, and man-made and natural disasters are just examples of what countries have been registering in their databases and informing the health authorities of these changes and emerging trends. © The Author(s) 2023.

关键词： 'One Health' 'One Health' informatics Algeria Artificial Intelligence Climate Change Digital Health Egypt Health informatics Health Information Technologies International Medical informatics Association (IMIA) Iran Jordan Kuwait MENAHIA Middle East and North Africa Morocco Pakistan Palestine Qatar Saudi Arabia United Arab Emirates Zoonitic Diseases

Hierarchical Blockchain Radio Access Networks: Architecture, Modelling, and Performance Assessment

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arXiv 2024年

作者： Kouvakis, Vasileios Trevlakis, Stylianos E. Boulogeorgos, Alexandros-Apostolos A. Liu, Hongwu Khalid, Waqas Tsiftsis, Theodoros Dobre, Octavia A. Department of Research and Development InnoCube P.C. 17is Noemvriou 79 Thessaloniki55534 Greece Department of Electrical and Computer Engineering University of Western Macedonia ZEP Area Kozani50100 Greece School of Information Science and Electrical Engineering Shandong Jiaotong University Jinan250357 China Institute of Industrial Technology Korea University Sejong30019 Korea Republic of Department of Informatics & Telecommunications University of Thessaly Lamia35100 Greece Faculty of Engineering and Applied Science Memorial University St. John’sNLA1B 3X9 Canada

Demands for secure, ubiquitous, and always-available connectivity have been identified as the pillar design parameters of the next generation radio access networks (RANs). Motivated by this, the current contribution introduces a network architecture that leverages blockchain technologies to augment security in RANs, while enabling dynamic coverage expansion through the use of intermediate commercial or private wireless nodes. To assess the efficiency and limitations of the architecture, we employ Markov chain theory in order to extract a theoretical model with increased engineering insights. Building upon this model, we quantify the latency as well as the security capabilities in terms of probability of successful attack, for three scenarios, namely fixed topology fronthaul network, advanced coverage expansion and advanced mobile node connectivity, which reveal the scalability of the blockchain-RAN architecture. © 2024, CC BY.

关键词： Markov chains

Accuracy Enhancement in Refractive Index Sensing via Full-Spectrum Machine Learning Modeling

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arXiv 2025年

作者： Aalizadeh, Majid Raut, Chinmay Afshar, Morteza Azmoudeh Tabartehfarahani, Ali Fan, Xudong Department of Biomedical Engineering University of Michigan Ann ArborMI48109 United States Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109 United States University of Michigan Ann ArborMI48109 United States Max Harry Weil Institute for Critical Care Research and Innovation University of Michigan Ann ArborMI48109 United States Department of Computational Medicine and Bioinformatics University of Michigan Ann ArborMI48109 United States Informatics Institute Istanbul Technical University Istanbul34485 Turkey

We present a full-spectrum machine learning framework for refractive index sensing using simulated absorption spectra from meta-grating structures composed of titanium or silicon nanorods under TE and TM polarizations. Linear regression was applied to 80 principal components extracted from each spectrum, and model performance was assessed using five-fold cross-validation, simulating real-world biosensing scenarios where unknown patient samples are predicted based on standard calibration data. Titanium-based structures, dominated by broadband intensity changes, yielded the lowest mean squared errors and the highest accuracy improvements—up to a 6065-fold reduction compared to the best single-feature model. In contrast, silicon-based structures, governed by narrow resonances, showed more modest gains due to spectral nonlinearity that limits the effectiveness of global linear models. We also show that even the best single-wavelength predictor is identified through data-driven analysis, not visual selection, highlighting the value of automated feature preselection. These findings demonstrate that spectral shape plays a key role in modeling performance and that full-spectrum linear approaches are especially effective for intensity-modulated index sensors. Copyright © 2025, The Authors. All rights reserved.

关键词： Linear regression

Indoor Positioning and Navigation Using Bluetooth Low Energy and Cloud Service in Healthcare Perspective 2nd

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Indoor Positioning and Navigation Using Bluetooth Low Energ...

2nd International Conference on Applied Intelligence and informatics, AII 2022

作者： Mizan, K. Shayekh Ebne Kaiser, M. Shamim Mamun, Shamim Al Biswas, Milon Zenia, Nusrat Zerin Mahmud, Mufti Adamov, Abzetdin Institute of Information Technology Jahangirnagar University Dhaka Savar1342 Bangladesh Lab Wazed Miah Science Research Centre Jahangirnagar University Dhaka Savar1342 Bangladesh Department of Computer Science and Engineering Bangladesh University of Business and Technology Dhaka Mirpur-021216 Bangladesh Department of Electrical and Software Engineering University of Calgary Calgary Canada Medical Technologies Innovation Facility Nottingham Trent University Clifton NottinghamNG11 8NS United Kingdom ADA University Baku Azerbaijan

ISBN: (纸本)9783031248009

People are able to explore unfamiliar surroundings with more ease due to navigation devices. Users can now incorporate these systems into handheld devices as a result of recent technological advancements that have increased the popularity and number of people using navigation systems. Due to poor reception of Global Positioning System (GPS) signals and a non-line of sight with orbiting satellites, it is more difficult to navigate within a building using GPS signals. Tracking and navigation within a structure can be accomplished by a handheld device (such as a smartphone or wearable) through the use of a wireless interface such as Bluetooth Low Energy (BLE). This type of technology can be used to monitor and guide patients with neurological illnesses, such as Alzheimer’s disease (AD), within the hospital premises. This study describes a system for indoor navigation based on wireless sensors, a mobile health application (mHealth app), and Bluetooth beacons. The study goes into great detail about how the mHealth app interacts with the cloud-based architecture. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Bluetooth

Microwave signal processing using an analog quantum reservoir computer

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arXiv 2023年

作者： Senanian, Alen Prabhu, Sridhar Kremenetski, Vladimir Roy, Saswata Cao, Yingkang Kline, Jeremy Onodera, Tatsuhiro Wright, Logan G. Wu, Xiaodi Fatemi, Valla McMahon, Peter L. Department of Physics Cornell University NY United States School of Applied and Engineering Physics Cornell University NY United States Department of Computer Science University of Maryland MD United States Joint Center for Quantum Information and Computer Science University of Maryland MD United States NTT Physics and Informatics Laboratories NTT Research Inc. CA United States Kavli Institute Cornell for Nanoscale Science Cornell University NY United States

Quantum reservoir computing (QRC) has been proposed as a paradigm for performing machine learning with quantum processors where the training is efficient in the number of required runs of the quantum processor and takes place in the classical domain, avoiding the issue of barren plateaus in parameterized-circuit quantum neural networks. It is natural to consider using a quantum processor based on superconducting circuits to classify microwave signals that are analog—continuous in time. However, while theoretical proposals of analog QRC exist, to date QRC has been implemented using circuit-model quantum systems—imposing a discretization of the incoming signal in time, with each time point input by executing a gate operation. In this paper we show how a quantum superconducting circuit comprising an oscillator coupled to a qubit can be used as an analog quantum reservoir for a variety of classification tasks, achieving high accuracy on all of them. Our quantum system was operated without artificially discretizing the input data, directly taking in microwave signals. Our work does not attempt to address the question of whether QRCs could provide a quantum computational advantage in classifying pre-recorded classical signals. However, beyond illustrating that sophisticated tasks can be performed with a modest-size quantum system and inexpensive training, our work opens up the possibility of achieving a different kind of advantage than a purely computational advantage: superconducting circuits can act as extremely sensitive detectors of microwave photons;our work demonstrates processing of ultra-low-power microwave signals in our superconducting circuit, and by combining sensitive detection with QRC processing within the same system, one could achieve a quantum sensing-computational advantage, i.e., an advantage in the overall analysis of microwave signals comprising just a few photons. © 2023, CC BY.

关键词： Photons

Creating Resilient Digital Asset Management Frameworks in Financial Operations Using Blockchain technology

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Creating Resilient Digital Asset Management Frameworks in Fi...

Trends in Quantum Computing and Emerging Business Technologies (TQCEBT), International Conference on

作者： B. T. Geetha Kafila Kafila S Tulasi Ram Dr. Amar Prabhakar Narkhede Ahmad Y. A. Bani Ahmad Mohit Tiwari Department of ECE Saveetha School of Engineering SIMATS Tamil Nadu India School of Business SR University Warangal Telangana School of Management Studies Chaitanya Bharathi Institute of Technology Hyderabad ISMS Sankalp Business School Vadgon Pune Department of Accounting and Finance Faculty of Business Middle East University Amman Jordan Applied Science Research Center Applied Science Private University Jordan Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering Delhi

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

Using blockchain technology to build strong digital asset management tools for financial operations is a revolutionary way to bring financial systems up to date. Blockchain technology is changing the way money works by making transfers decentralised, open, and safe. Digital Asset Management (DAM) systems help businesses easily store and share digital assets like cryptocurrencies and blockchain-based assets. Using blockchain tools like smart contracts and tokenization makes financial deals safer and more trustworthy by using cryptographic encryption and ledger systems that can't be changed. Streamlining processes and payment makes financial operations more efficient. But problems like scaling and following the rules must be dealt with. Blockchain technology has many uses in finance, such as cross-border payments, tokenizing assets, and smart contracts. It is also changing how financial services are regulated. This study uses secondary data from academic research papers, peer-reviewed journals, and industry reports to look the use of blockchain technology to financial transactions, learn about DAM basics, look into blockchain tools in financial services, and judge how blockchain is used in financial transactions. This study helps us understand and use strong digital asset management systems in financial transactions that use blockchain technology by doing a thorough analysis.

关键词： Quantum computing Dams Smart contracts Transportation Tokenization Blockchains Asset management

Generative Adversarial Networks for Heterogeneous Unsupervised Domain Adaptation Detection

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Generative Adversarial Networks for Heterogeneous Unsupervis...

IEEE International Conference on Intelligent engineering Systems (INES)

作者： Homayoun Safarpour Motealegh Mahalegi Amirfarhad Farhadi György Molnár Enikő Nagy Institute of Mechatronics and Vehicle Engineering Óbuda University Budapest Hungary Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran Kandó Kálmán Faculty of Electrical Engineering Óbuda University Budapest Hungary János Csere Apáczai Faculty Széchenyi University Győr Hungary Antal Bejczy Center for Intelligent Robotics John von Neumann Faculty of Informatics Óbuda University Budapest Hungary

ISBN: (数字)9798350367591

ISBN: (纸本)9798350367607

Domain adaptation, a subset of transfer learning, involves generating examples from two related but differently distributed source and target domains. This paper introduces an incremental adversarial learning method for unsupervised domain adaptation, where the source data is labeled, and the target data remains unlabeled. We employ a discriminative adversarial strategy as the primary technique for unsupervised domain adaptation to minimize the significant distributional variance between the source and target domains. Traditional domain adaptation methods typically train the source domain to align with a fixed target domain distribution. However, in many real-world scenarios, the target domain's distribution continuously evolves, leading to a lack of generalizability in the trained model for subsequent domains. To tackle this issue, we suggest using a continuous sequential domain adaptation method. This involves including instances from several related target domains in the training process in a sequential and continuous manner. This process ensures that all receiving domains are correctly aligned with their respective sources, and to maintain sequence continuity, the source domain is incrementally updated by incorporating the most reliable data from the current target domain. This enhancement aims to improve the network's training for forthcoming domain data. Leveraging the effectiveness of deep neural networks, our method utilizes deep GANs for domain adaptation. The approach enhances domain matching by augmenting the source with selected target data and introduces a novel strategy for continuous sequential domain matching. This involves a progressive update of source domain samples with reliable data from the current target domain, facilitating seamless adaptation to successive domain shifts. The proposed method demonstrated a high classification accuracy, achieving an impressive 99% and 95% in different settings. Furthermore, it outperforms previous studies rega

关键词： Training Adaptation models Machine learning algorithms Accuracy Transfer learning Clustering algorithms Sensitivity and specificity

Multiregion Multiscale Image Segmentation with Anisotropic Diffusion 25th

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Multiregion Multiscale Image Segmentation with Anisotropic D...

25th International Conference on Pattern Recognition Workshops, ICPR 2020

作者： Prasath, V. B. Surya Thanh, Dang Ngoc Hoang Hai, Nguyen Hoang Dvoenko, Sergey Department of Electrical Engineering and Computer Science University of Cincinnati CincinnatiOH45221 United States Division of Biomedical Informatics Cincinnati Children’s Hospital Medical Center CincinnatiOH45229 United States Department of Biomedical Informatics College of Medicine University of Cincinnati CincinnatiOH45267 United States Department of Pediatrics University of Cincinnati College of Medicine CincinnatiOH45257 United States Department of Information Technology School of Business Information Technology University of Economics Ho Chi Minh City Ho Chi Minh City Viet Nam Department of Computer Science Vietnam-Korea University of Information and Communication Technology – The University of Danang Danang Viet Nam Department of Information Security Institute of Applied Mathematics and Computer Science Tula State University Tula city Russia

ISBN: (纸本)9783030688202

We present a multiregion image segmentation approach which utilizes multiscale anisotropic diffusion based scale spaces. By combining powerful edge preserving anisotropic diffusion smoothing with isolevel set linking and merging, we obtain coherent segments which are tracked across multiple scales. A hierarchical tree representation of the given input image with progressively simplified regions is used with intra-scale splitting and inter-scale merging for obtaining multiregion segmentations. Experimental results on natural and medical images indicate that multiregion, multiscale image segmentation (MMIS) approach obtains coherent segmentation results. © 2021, Springer Nature Switzerland AG.

关键词： Diffusion