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Stroke Prognostication in Patients Treated with Thrombolysis Using Random Forest

Open Neuroimaging Journal 2024年 17卷

作者： Yunus, Reyhan Eddy Harris, Salim Sidipratomo, Prijo Kekalih, Aria Jatmiko, Wisnu Pandelaki, Jacub Rachman, Andhika Syahrul, Syahrul Valindria, Vanya Vabrina Rachmadi, Muhammad Febrian Muzakki, Muhammad Faris Tjuatja, Andrew Wijaya, Anthony Eka Teresa, Devina Faculty of Medicine Universitas Indonesia Rumah Sakit Umum Pusat Nasional Cipto Mangunkusumo Doctoral Program in Medical Sciences Faculty of Medicine Universitas Indonesia Faculty of Computer Science Universitas Indonesia Faculty of Medicine Universitas Syiah Kuala School of Electrical Engineering and Informatics Institut Teknologi Bandung

Background: Early identification and accurate prognostication of acute ischemic stroke are crucial due to the narrow time frame for treatment and potential complications associated with thrombolysis intervention. Objectives: This pilot study in the Southeast Asian region using Indonesian data, aims to develop a novel machine learning model for predicting the clinical outcome of acute ischemic stroke patients following thrombolysis. The model seeks to aid clinicians in identifying eligible candidates for thrombolysis therapy. Methods: This retrospective study at Cipto Mangunkusumo Hospital’s medical records from 2014 to 2023 used non-contrast brain CT, clinical, and lab data to develop a Random Forest (RF) algorithm predicting Δ NIHSS (National Institutes of Health Stroke Scale) score, indicating functional outcome. The developed RF model was applied to a validation dataset, with performance evaluated. The study also compared RF with a previous Convolutional Neural Networks (CNN) algorithm. Results: This study included 145 acute ischemic stroke patients treated with thrombolysis. It demonstrated the promising feasibility of using machine learning algorithms to predict clinical outcomes in this population. Integration of CT, clinical, and laboratory data as inputs to the RF models shows the best prediction performance (Accuracy = 0.75, AUC = 0.72, F1=0.50, Precision=0.60, Sensitivity=0.43, Specificity=0.88) Conclusions: The application of machine learning shows the potential to enhance the selection process for thrombolysis intervention in treating acute ischemic stroke. Further research with larger multicenter datasets and additional imaging modalities is required to improve predictive ability. © 2024 The Author(s).

关键词： Artificial intelligence International license Machine learning Prognostication Stroke Thrombolysis

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Revolutionizing Neurological Diagnostics: Integrating 6G Technology with Deep Learning for Enhanced Detection of Multiple Sclerosis and Myelitis

Revolutionizing Neurological Diagnostics: Integrating 6G Tec...

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Telecommunications Conference (ITC-Egypt), International

作者： Rana M. NourEldeen Rahma A. Elshshtawy Omnia A. Elgohary Fatma Alzahraa A. Wahba Marco K. Attia Zeinab B. Abo elazm Mohamed G. Khattap Hend Galal Eldeen Mohamed Ali Hassan Mariam Mostafa Nayra Ibrahim Reem Ramadan Yomna Refaat Mennatallah Khaled Ali Maher Mohamed Abd Elaziz Ahmed Gamal Abdellatif Ibrahim Radiology and Medical Imaging Faculty of Applied Health Sciences Galala University Suez Egypt Biomedical Informatics Faculty of Computer Science and Engineering Galala University Suez Egypt Artificial Intelligence Science Program Faculty of Computer Science and Engineering Galala University Suez Egypt Department of Electronics and Electrical Communications Engineering Air Defense College Alexandria Egypt

ISBN: (数字)9798350351408

ISBN: (纸本)9798350351415

This paper proposes an alternative detection frame-work for multiple sclerosis (MS) and idiopathic acute transverse myelitis (ATM) within the 6G-enabled Internet of medical Things (IoMT) environment. The developed framework relies on the implementation of a deep learning technique known as Dense Convolutional Networks (DenseNets) in the 6G-enabled IoMT to enhance prediction performance. To validate the performance of DenseNets, we compared it with other deep learning techniques, including Convolutional Neural Networks (CNN) and MobileNet, using real-world datasets. The experimental results show the high performance of DenseNets in predicting MS and ATM compared to other methods, achieving an accuracy of nearly 90 %.

关键词： 6G mobile communication Deep learning Multiple sclerosis Accuracy Internet of medical Things Telecommunications Convolutional neural networks

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Enhancing Acute Ischemic Stroke Diagnosis Using IoMT and Deep Learning Technologies

Enhancing Acute Ischemic Stroke Diagnosis Using IoMT and Dee...

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Telecommunications Conference (ITC-Egypt), International

作者： Mohamed M. El-Sheikh Aya Bayoumy Nada M. Ahmed Amany Mohamed Eldeeb Fatma I. Rabea Amira A. Nawar Mohamed G. Khattap Hend Galal Eldeen Mohamed Ali Hassan Mariam Mostafa Nayra Ibrahim Reem Ramadan Yomna Refaat Mennatallah Khaled Ali Maher Mohamed Abd Elaziz Ahmed Gamal Abdellatif Ibrahim Radiology and Medical Imaging Faculty of Applied Health Sciences Galala University Suez Egypt Biomedical Informatics Faculty of Computer Science and Engineering Galala University Suez Egypt Artificial Intelligence Science Program Faculty of Computer Science and Engineering Galala University Suez Egypt Department of Electronics and Electrical Communications Engineering Air Defense College Alexandria Egypt

ISBN: (数字)9798350351408

ISBN: (纸本)9798350351415

This paper introduces an alternative technique for diagnosing Acute Ischemic Stroke within the IoMT environment. In the proposed approach, the collected data is transmitted to a cloud-based center where the technique utilizes EfficientNet, a deep learning model, designed to extract features from MRI images thereby enhancing the detection of acute ischemic infarctions. The performance of EfficientNet is compared against two other models, CNN and MobileNet, demonstrating its superior efficacy through metrics such as accuracy, precision, recall, and F1-score, which stand at 92.31%, 92.28%, 92.33%, and 92.30%, respectively.

关键词： Deep learning Accuracy Magnetic resonance imaging Soft sensors medical services Feature extraction Regulation

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COWID: an efficient cloud-based genomics workflow for scalable identification of SARS-COV-2

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Briefings in Bioinformatics 2023年第5期24卷 bbad280-bbad280页

作者： Lim, Hendrick Gao-Min Fann, Yang C Lee, Yuan-Chii Gladys Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei 11031 Taiwan Department of Medical Research Tzu Chi Hospital Indonesia Pantai Indah Kapuk Greater Jakarta 14470 Indonesia It and Bioinformatics Program Division of Intramural National Institute of Neurological Disorders and Stroke National Institutes of Health Bethesda 20892 MD United States

Implementing a specific cloud resource to analyze extensive genomic data on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) poses a challenge when resources are limited. To overcome this, we repurposed a cloud platform initially designed for use in research on cancer genomics (https://***) to enable its use in research on SARS-CoV-2 to build Cloud Workflow for Viral and Variant Identification (COWID). COWID is a workflow based on the Common Workflow Language that realizes the full potential of sequencing technology for use in reliable SARS-CoV-2 identification and leverages cloud computing to achieve efficient parallelization. COWID outperformed other contemporary methods for identification by offering scalable identification and reliable variant findings with no false-positive results. COWID typically processed each sample of raw sequencing data within 5 min at a cost of only US$0.01. The COWID source code is publicly available (https://***/hendrick0403/COWID) and can be accessed on any computer with Internet access. COWID is designed to be user-friendly;it can be implemented without prior programming knowledge. Therefore, COWID is a time-efficient tool that can be used during a pandemic. © 2023 Published by Oxford University Press.

关键词： cloud repurposing cloud workflow COVID-19 Illumina sequencing parallel computation SARS-CoV-2

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Deep Learning-based Fall Detection Algorithm Using Ensemble Model of Coarse-fine CNN and GRU Networks

arXiv

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arXiv 2023年

作者： Liu, Chien-Pin Li, Ju-Hsuan Chu, En-Ping Hsieh, Chia-Yeh Liu, Kai-Chun Chan, Chia-Tai Tsao, Yu Department of Biomedical Engineering National Yang Ming Chiao Tung University Taipei112 Taiwan Bachelor's Program in Medical Informatics and Innovative Applications Fu Jen Catholic University New Taipei242062 Taiwan Research Center for Information Technology Innovation Academia Sinica Taipei115 Taiwan

Falls are the public health issue for the elderly all over the world since the fall-induced injuries are associated with a large amount of healthcare cost. Falls can cause serious injuries, even leading to death if the elderly suffers a "long-lie." Hence, a reliable fall detection (FD) system is required to provide an emergency alarm for first aid. Due to the advances in wearable device technology and artificial intelligence, some fall detection systems have been developed using machine learning and deep learning methods to analyze the signal collected from accelerometer and gyroscopes. In order to achieve better fall detection performance, an ensemble model that combines a coarse-fine convolutional neural network and gated recurrent unit is proposed in this study. The parallel structure design used in this model restores the different grains of spatial characteristics and capture temporal dependencies for feature representation. This study applies the FallAllD public dataset to validate the reliability of the proposed model, which achieves a recall, precision, and F-score of 92.54%, 96.13%, and 94.26%, respectively. The results demonstrate the reliability of the proposed ensemble model in discriminating falls from daily living activities and its superior performance compared to the state-of-the-art convolutional neural network long short-term memory (CNN-LSTM) for FD. Copyright © 2023, The Authors. All rights reserved.

关键词： Fall detection

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Wearable-based Frozen Shoulder Rehabilitation Exercise Recognition using Machine Learning Approaches

Wearable-based Frozen Shoulder Rehabilitation Exercise Recog...

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IEEE International Workshop on medical Measurement and Applications (MEMEA)

作者： Chien-Pin Liu Chih-Chun Lai Kai-Chun Liu Chia-Yeh Hsieh Chia-Tai Chan Department of Biomedical Enginnering National Yang Ming Chiao Tung University Taipei City Taiwan Research Center for Information Technology Innovation Academia Sinica Taipei City Taiwan Bachelor’s Program in Medical Informatics and Innovative Applications Fu Jen Catholic University New Taipei City Taiwan

Frozen shoulder is a disease that causes shoulder pain and stiffness. It limits the range of movement of the shoulder and has a great impact on the quality of daily life. One of the common treatment methods is to do frozen shoulder rehabilitation exercises. However, patients often fail to follow the instructions of the physical therapists during the program of home-based rehabilitation. Furthermore, clinical professionals are unavailable to track and monitor the home-based rehabilitation exercise performance of patients. To support clinical monitoring, we develop a wearable-based frozen shoulder rehabilitation exercise recognizer using different machine learning models and deep learning models. The proposed methods can automatically identify movement/silence segments from continuous signals and classify types of frozen shoulder rehabilitation exercises. Besides, we propose a finite state machine and fragmentation revision mechanism for error correction. Twenty subjects are invited to perform six types of rehabilitation exercises. The proposed methods achieve the best result of 95.6% accuracy, 95.83% F-score for the identification of movement/silence and 95.58% accuracy, 95.49% F-score for classification of exercise type, respectively. The results demonstrate the feasibility of the proposed method to automatically monitor the frozen shoulder rehabilitation exercise, which has the potential to provide objective, continuous and quantitative information for telerehabilitation.

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The role of input imaging combination and ADC threshold on segmentation of acute ischemic stroke lesion using U-Net

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European Radiology 2023年第9期33卷 6157-6167页

作者： Li, Ya-Hui Lin, Shao-Chieh Chung, Hsiao-Wen Chang, Chia-Ching Peng, Hsu-Hsia Huang, Teng-Yi Shen, Wu-Chung Tsai, Chon-Haw Lo, Yu-Chien Lee, Tung-Yang Juan, Cheng-Hsuan Juan, Cheng-En Chang, Hing-Chiu Liu, Yi-Jui Juan, Chun-Jung Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan Department of Medical Imaging China Medical University Hsinchu Hospital No. 199 Sec. 1 Xinglong Rd. Zhubei City Hsinchu County 302 Hsinchu Taiwan Ph.D. Program in Electrical and Communication Engineering Feng Chia University Taichung Taiwan Department of Electrical Engineering National Taiwan University Taipei Taiwan Department of Management Science National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Biomedical Engineering and Environmental Sciences National Tsing Hua University Hsinchu Taiwan Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Radiology School of Medicine College of Medicine China Medical University Taichung Taiwan Department of Medical Imaging Medical University Hospital Taichung Taiwan Department of Neurology China Medical University Hospital Taichung Taiwan Cheng Ching Hospital Taichung Taiwan Master’s Program of Biomedical Informatics and Biomedical Engineering Feng Chia University Taichung Taiwan Department of Automatic Control Engineering Feng Chia University No. 100 Wenhwa Rd. Seatwen Taichung 40724 Taiwan Department of Biomedical Engineering The Chinese University of Hong Kong ERB1112 11/F William M.W. Mong Engineering Building N.T Shatin Hong Kong Multi-Scale Medical Robotics Center The Chinese University of Hong Kong N.T Shatin Hong Kong Department of Biomedical Engineering National Defense Medical Center Taipei Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan

Background: To evaluate the effect of the weighting of input imaging combo and ADC threshold on the performance of the U-Net and to find an optimized input imaging combo and ADC threshold in segmenting acute ischemic stroke (AIS) lesion. Methods: This study retrospectively enrolled a total of 212 patients having AIS. Four combos, including ADC-ADC-ADC (AAA), DWI-ADC-ADC (DAA), DWI-DWI-ADC (DDA), and DWI-DWI-DWI (DDD), were used as input images, respectively. Three ADC thresholds including 0.6, 0.8 and 1.8 × 10^–3 mm²/s were applied. Dice similarity coefficient (DSC) was used to evaluate the segmentation performance of U-Nets. Nonparametric Kruskal–Wallis test with Tukey–Kramer post-hoc tests were used for comparison. A p <.05 was considered statistically significant. Results: The DSC significantly varied among different combos of images and different ADC thresholds. Hybrid U-Nets outperformed uniform U-Nets at ADC thresholds of 0.6 × 10^–3 mm²/s and 0.8 × 10^–3 mm²/s (p <.001). The U-Net with imaging combo of DDD had segmentation performance similar to hybrid U-Nets at an ADC threshold of 1.8 × 10^–3 mm²/s (p =.062 to 1). The U-Net using the imaging combo of DAA at the ADC threshold of 0.6 × 10^–3 mm²/s achieved the highest DSC in the segmentation of AIS lesion. Conclusions: The segmentation performance of U-Net for AIS varies among the input imaging combos and ADC thresholds. The U-Net is optimized by choosing the imaging combo of DAA at an ADC threshold of 0.6 × 10^–3 mm²/s in segmentating AIS lesion with highest DSC. Key Points: • Segmentation performance of U-Net for AIS differs among input imaging combos. • Segmentation performance of U-Net for AIS differs among ADC thresholds. • U-Net is optimized using DAA with ADC = 0.6 × 10^–3mm²/s. © 2023, The Author(s), under exclusive licence to European Society of Radiology.

关键词： Deep Learning Diffusion Magnetic Resonance Imaging Ischemic Stroke Neural Networks, Computer Retrospective Study

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Deep Learning-based Fall Detection Algorithm Using Ensemble Model of Coarse-fine CNN and GRU Networks

Deep Learning-based Fall Detection Algorithm Using Ensemble ...

引用

IEEE International Workshop on medical Measurement and Applications (MEMEA)

作者： Chien-Pin Liu Ju-Hsuan Li En-Ping Chu Chia-Yeh Hsieh Kai-Chun Liu Chia-Tai Chan Yu Tsao Department of Biomedical Engineering National Yang Ming Chiao Tung University Taipei City Taiwan Bachelor’s Program in Medical Informatics and Innovative Applications Fu Jen Catholic University New Taipei City Taiwan Research Center for Information Technology Innovation Academia Sinica Taipei City Taiwan

Falls are the public health issue for the elderly all over the world since the fall-induced injuries are associated with a large amount of healthcare cost. Falls can cause serious injuries, even leading to death if the elderly suffers a “long-lie.” Hence, a reliable fall detection (FD) system is required to provide an emergency alarm for first aid. Due to the advances in wearable device technology and artificial intelligence, some fall detection systems have been developed using machine learning and deep learning methods to analyze the signal collected from accelerometer and gyroscopes. In order to achieve better fall detection performance, an ensemble model that combines a coarse-fine convolutional neural network and gated recurrent unit is proposed in this study. The parallel structure design used in this model restores the different grains of spatial characteristics and capture temporal dependencies for feature representation. This study applies the FallAllD public dataset to validate the reliability of the proposed model, which achieves a recall, precision, and F-score of 92.54%, 96.13%, and 94.26%, respectively. The results demonstrate the reliability of the proposed ensemble model in discriminating falls from daily living activities and its superior performance compared to the state-of-the-art convolutional neural network long short-term memory (CNN-LSTM) for FD.

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ASHP Foundation Pharmacy Forecast 2023: Strategic Planning Guidance for Pharmacy Departments in Hospitals and Health Systems

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American journal of health-system pharmacy : AJHP : official journal of the American Society of Health-System Pharmacists 2023年第2期80卷 10-35页

作者： DiPiro, Joseph T. Nesbit, Todd W. Reuland, Charles Cunningham, Francesca E. Schweitzer, Pamela Chisholm-Burns, Marie A. Martinez, Leyner Shane, Rita Scott, Christopher M. Nelson, Scott D. Mize, Dara L E Van Devender, Elva Angelique Oyen, Lance Professor of Pharmacy Associate Vice President for Health Sciences - Faculty Affairs Virginia Commonwealth University Richmond VA United States Vice President for Pharmacy Services Johns Hopkins Health System and Chief Pharmacy Officer Johns Hopkins Hospital Baltimore MD United States Executive Vice President and Chief Operating Officer Johns Hopkins Hospital Baltimore MD United States Director VA Center for Medication Safety Associate Chief Consultant Department of Veterans Affairs Hines IL United States Assistant Surgeon General and Chief Pharmacist Officer (Ret) US Public Health Service Commissioned Corps Windsor Mill MD United States Executive Vice President and Provost Oregon Health & Science University Portland OR USA Director of Pharmacy Services Baptist Hospital of Miami | Baptist Health South Florida Miami FL United States Vice President and Chief Pharmacy Officer and Professor of Medicine Cedars-Sinai Medical Center Los Angeles CA United States Eskenazi Health Indianapolis IN United States Program Director MS Applied Clinical Informatics (MS-ACI) Department of Biomedical Informatics Vanderbilt University Medical Center Nashville TN United States Department of Biomedical Vanderbilt University Medical Center Nashville TN United States Department of Medicine Division of Diabetes Endocrinology and Metabolism Vanderbilt University Medical Center Nashville TN United States Regional Program Manager Clinical Pharmacy Education Providence Health & Services Oregon Region Portland OR USA Chief Pharmacy Officer Mayo Clinic Rochester MN United States

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Vaccine credential technology principles

arXiv

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arXiv 2021年

作者： Siddarth, Divya Hart, Vi Cantrell, Bethan Yasuda, Kristina Mandel, Josh Easterbrook, Karen Microsoft United States Microsoft Research United States Computational Health Informatics Program Boston Children's Hospital United States Department of Biomedical Informatics Harvard Medical School United States

The historically rapid development of effective COVID-19 vaccines has policymakers facing evergreen public health questions regarding vaccination records and verification. Governments and institutions around the world are already taking action on digital vaccine certificates, including guidance and recommendations from the European Commission,[1] the WHO,[5] and the Biden Administration. These could be encouraging efforts: an effective system for vaccine certificates could potentially be part of a safe return to work, travel, and daily life, and a secure technological implementation could improve on existing systems to prioritize privacy, streamline access, and build for necessary interoperability across countries and contexts. However, vaccine credentials are not without potential harms, and, particularly given major inequities in vaccine access and rollout, there are valid concerns that they may be used in ineffective or exclusionary ways that exacerbate inequality, allow for discrimination, violate privacy, and assume consent. While the present moment calls for urgency, we must also acknowledge that choices made in the vaccine credentialing rollout for COVID-19 are likely to have long-term implications, and must be made with care. In this paper, we outline potential implementation and ethical concerns that may arise from tech-enabled vaccine credentialing programs now and in the future, and discuss the technological tradeoffs implicated in these concerns. We suggest a set of principles that, if adopted, may mitigate these concerns, forestall preventable harms, and point the way forward;the paper is structured as a deep dive into each of these principles: 1. Access and Equity. We commit to working to understand and design for those who may not want or be able to participate in the vaccine credentialing process, including those who may not want to or be able to access vaccines. 2. Choice and Consent. We commit to maintaining active oversight to ensure that individu

关键词： Vaccines

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