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Prediction of futile recanalisation after endovascular treatment in acute ischaemic stroke:development and validation of a hybrid machine learning model

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Stroke & Vascular Neurology 2024年第6期9卷 631-639页

作者： Ximing Nie Jinxu Yang Xinxin Li Tianming Zhan Dongdong Liu Hongyi Yan Yufei Wei Xiran Liu Jiaping Chen Guoyang Gong Zhenzhou Wu Zhonghua Yang Miao Wen Weibin Gu Yuesong Pan Yong Jiang Xia Meng Tao Liu Jian Cheng Zixiao Li Zhongrong Miao Liping Liu Department of Neurology Capital Medical UniversityBeijingChina China National Clinical Research Center for Neurological Diseases Beijing Tiantan HospitalBeijingChina School of Computer and Communication Engineering University of Science and TechnologyBeijingChina Department of Radiology Beijing Tiantan HospitalBeijingChina Key Laboratory of Biomechanics and Mechanobiology Ministry of EducationSchool of Biological Science and Medical Engineering International Research Institute for Multidisciplinary ScienceBeihang UniversityBeijingChina Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Computer Science and EngineeringBeihang UniversityBeijingChina Department of Interventional Neuroradiology Beijing Tiantan HospitalCapital Medical UniversityBeijingChina

Background Identification of futile recanalisation following endovascular therapy(EVT)in patients with acute ischaemic stroke is both crucial and ***,we present a novel risk stratification system based on hybrid machine learning method for predicting futile *** Hybrid machine learning models were developed to address six clinical scenarios within the EVT and perioperative management *** models were trained on a prospective database using hybrid feature selection technique to predict futile recanalisation following *** optimal model was validated and compared with existing models and scoring systems in a multicentre prospective cohort to develop a hybrid machine learning-based risk stratification system for futile recanalisation *** Using a hybrid feature selection approach,we trained and tested multiple classifiers on two independent patient cohorts(n=1122)to develop a hybrid machine learning-based prediction *** model demonstrated superior discriminative ability compared with other models and scoring systems(area under the curve=0.80,95%CI 0.73 to 0.87)and was transformed into a web application(RESCUE-FR Index)that provides a risk stratification system for individual prediction(accessible online atfr-index.biomind.cn/RESCUE-FR/).Conclusions The proposed hybrid machine learning approach could be used as an individualised risk prediction model to facilitate adherence to clinical practice guidelines and shared decision-making for optimal candidate selection and prognosis assessment in patients undergoing EVT.

关键词： database prediction optimal

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SELF-Clustering Graph Transformer Approach to Model Resting State Functional Brain Activity 22

SELF-Clustering Graph Transformer Approach to Model Resting ...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Thapaliya, Bishal Akbas, Esra Sapkota, Ram Ray, Bhaskar Calhoun, Vince Liu, Jingyu Georgia State University Department of Computer Science Atlanta United States Tri-Institutional Center for Translational Research in Neuroimaging and Data Science United States School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta United States

ISBN: (纸本)9798331520526

Resting-state functional magnetic resonance imaging (rs-fMRI) offers valuable insights into the human brain's functional organization and is a powerful tool for investigating the relationship between brain function and cognitive processes as it allows for the functional organization of the brain to be captured without relying on a specific task or stimuli. In this study, we introduce a novel attention mechanism for graphs with subnetworks, named Self Clustering Graph Transformer (SCGT), designed to handle the issue of uniform node updates in graph transformers. By using static functional connectivity (FC) correlation features as input to the transformer model, SCGT effectively captures the sub-network structure of the brain by performing cluster-specific updates to the nodes unlike uniform node updates like vanilla graph transformers, further allowing us to learn and interpret the sub clusters. We validate our approach on the Adolescent Brain Cognitive Development (ABCD) dataset, comprising 7,957 participants, for the prediction of total cognitive score and gender classification. Our results demonstrate that SCGT outperforms the vanilla graph transformer method, and other recent models, offering a promising tool for modeling brain functional connectivity and interpreting the underlying subnetwork structures. © 2025 IEEE.

关键词： Brain mapping

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Intelligent Hardware Solutions for COVID -19 and Alike Diagnosis - A survey 1

Intelligent Hardware Solutions for COVID -19 and Alike Diagn...

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1st International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2023

作者： Salama, Ramiz Al-Turjman, Fadi Aeri, Manisha Yadav, Satya Prakash Ai and Robotics Institute Research Center for Ai and IoT Near East University Nicosia Department of Computer Engineering Mersin10 Turkey Ai and Robotics Institute Near East University Artificial Intelligence Engineering Dept. Mersin Nicosia10 Turkey University of Kyrenia Research Center for Ai and IoT Faculty of Engineering Kyrenia Mersin10 Turkey Graphic Era Hill University Computer Science and Engineering Dehradun India Department of Computer Science and Engineering Greater Noida201306 India

ISBN: (纸本)9798350338027

Globally, 210 nations have been affected by the 2019 Novel Coronavirus (COVID-19), which has been classified as a pandemic. The modern health system, as well as the economic, educational, and social sides of society, have all been severely impacted. While the rate of transmission keeps increasing, several cooperative strategies between stakeholders to create cutting-edge methods of screening and detecting COVID-19 instances among people at a comparable rate have been noticed. Also, the importance of computational models connected to the technologies of the fourth industrial revolution in accomplishing the desired feat has been emphasized. Unfortunately, there is a gap in the precision of COVID-19 case detection, prediction, and contact tracking. For patients with COVID-19 in isolation units, teleultrasound (TUS), particularly with the support of fifth generation (5G) wireless transmission technology, can offer rapid monitoring, quick clinical progress assessment, and assistance with guiding interventional procedures. Also, it helps conserve medical resources like equipment and supplies while lowering the risk of infection among medical personnel. The review of computer models presented in this work can be used to improve the effectiveness of COVID-19 pandemic case detection and prediction. We concentrate on adoptable big data, ai, and nature-inspired computing solutions for the current pandemic. According to the review, models inspired by nature have shown strong performance in feature selection for medical problems. In pandemic-related cases like COVID-19, contact tracing using big data analytics should also be investigated. © 2023 IEEE.

关键词： COVID-19

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How Graph Neural Networks Learn: Lessons from Training Dynamics 41

How Graph Neural Networks Learn: Lessons from Training Dynam...

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41st International Conference on Machine Learning, ICML 2024

作者： Yang, Chenxiao Wu, Qitian Wipf, David Sun, Ruoyu Yan, Junchi School of Artificial Intelligence Department of Computer Science and Engineering MoE Lab of AI Shanghai Jiao Tong University China Amazon Web Services United States School of Data Science The Chinese University of Hong Kong Shenzhen China Shenzhen International Center for Industrial and Applied Mathematics Shenzhen Research Institute of Big Data China

A long-standing goal in deep learning has been to characterize the learning behavior of black-box models in a more interpretable manner. For graph neural networks (GNNs), considerable advances have been made in formalizing what functions they can represent, but whether GNNs will learn desired functions during the optimization process remains less clear. To fill this gap, we study their training dynamics in function space. In particular, we find that the gradient descent optimization of GNNs implicitly leverages the graph structure to update the learned function, as can be quantified by a phenomenon which we call kernel-graph alignment. We provide theoretical explanations for the emergence of this phenomenon in the overparameterized regime and empirically validate it on real-world GNNs. This finding offers new interpretable insights into when and why the learned GNN functions generalize, highlighting their limitations in heterophilic graphs. Practically, we propose a parameter-free algorithm that directly uses a sparse matrix (i.e. graph adjacency) to update the learned function. We demonstrate that this embarrassingly simple approach can be as effective as GNNs while being orders-of-magnitude faster. Copyright 2024 by the author(s)

关键词： Adversarial machine learning

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Prognostic Phenotype Clustering of Uterine Sarcoma Patients

Prognostic Phenotype Clustering of Uterine Sarcoma Patients

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2024 IEEE International Conference on E-Health Networking, application and Services, HealthCom 2024

作者： Zhang, Tuao Yuan, Suzhen Zhou, Xue Wang, Wenwen Zhu, Xin Graduate School of Computer Science and Engineering The University of Aizu Aizuwakamatsu Japan Department of Obstetrics and Gynecology Tongji Hospital Huazhong University of Science and Technology Wuhan China Department of AI Technology Development M&D Data Science Center Institute of Science Tokyo Tokyo Japan

ISBN: (纸本)9798350350548

Uterine sarcoma is a rare but highly aggressive malignancy with poor prognosis. In this study, prognostic phenotypes corresponding to the end point of mortality were explored by unsupervised machine learning (ML) on training (n = 1985) and validation (n = 498) datasets. The data were extracted from patients with uterine sarcoma in the Surveillance, Epidemiology, and End Results (SEER) database. A decision-making process of phenotype identification was performed and the findings were compared with existing research. Three phenotypes associated with stratified levels of mortality risks (high, intermediate and low) were identified. Kaplan-Meier survival analysis revealed significant differences among these phenotypes (pairwise comparison p © 2024 IEEE.

关键词： Contrastive Learning

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Semantic Template-based Convolutional Neural Network for Text Classification

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ACM Transactions on Asian and Low-Resource Language Information Processing 2023年第11期22卷 1-21页

作者： Chang, Yung-Chun Ng, Siu Hin Chen, Jung-Peng Liang, Yu-Chi Hsu, Wen-Lian Graduate Institute of Data Science Taipei Medical University Taipei Taiwan Clinical Big Data Research Center Taipei Medical University Hospital Taipei Taiwan Snhcc Tigp Academia Sinica National Cheng Chi University Taipei Taiwan Institute of Information Systems and Applications National Tsing Hua University Hsinchu Taiwan Department of Computer Science and Information Engineering College of Information and Electrical Engineering Asia University Taichung Taiwan Pervasive Ai Research Labs Ministry of Science and Technology Hsinchu City Taiwan

We propose a semantic template-based distributed representation for the convolutional neural network called Semantic Template-based Convolutional Neural Network (STCNN) for text categorization that imitates the perceptual behavior of human comprehension. STCNN is a highly automatic approach that learns semantic templates that characterize a domain from raw text and recognizes categories of documents using a semantic-infused convolutional neural network that allows a template to be partially matched through a statistical scoring system. Our experiment results show that STCNN effectively classifies documents in about 140,000 Chinese news articles into predefined categories by capturing the most prominent and expressive patterns and achieves the best performance among all compared methods for Chinese topic classification. Finally, the same knowledge can be directly used to perform a semantic analysis task. © 2023 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Semantics

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ConvNeXt Model for Breast Cancer Image Classification 6

ConvNeXt Model for Breast Cancer Image Classification

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6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Setiawan, Devin Karnyoto, Andrea Stevens Intan, Indo Pardamean, Bens Department of Electrical Engineering and Computer Science University of Kansas Lawrence United States Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Department of Informatics Engineering Universitas Dipa Makassar Makassar Indonesia Computer Science Department BINUS Graduate Program Computer Science Program Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798331539603

Breast cancer is an occurrence of cancer that attacks breast tissue and is the most common cancer among women worldwide, affecting one in eight women. In this modern world, breast cancer image classification simplifies the process of analyzing, providing objective and accurate results. By leveraging machine learning algorithms and computer vision techniques, we developed breast cancer detection. The dataset is histopathology dataset from BreakHis and UNHAS Hospital. We chose the ConvNeXt-Tiny model then modified the classifier head as the proposed method. Before the dataset is processed by the model, we augment the images by applying random horizontal and vertical flips, adjustments to brightness, contrast, saturation, and hue using color jitter. The augmentation process simulates real-world variance and enhances the model's ability to generalize to unseen data. Our proposed model gained better performance (accuracy, F1-Score) results compared two other techniques to VGG16 and SVM. According to our experiments, the F1-Score for the ConvNeXt-Tiny model with classifier head modification is higher at 0.9516, than the gain for VGG16 at 0.9292, and the gain for the SVM at 0.83. © 2024 IEEE.

关键词： Lung cancer

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A Bioinformatics Analysis and Homology 3D Modeling of Dihydroxy-orotate-dehydrogenase of Plasmodium ovale

A Bioinformatics Analysis and Homology 3D Modeling of Dihydr...

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2023 International Conference on Informatics, Multimedia, Cyber and Information Systems, ICIMCIS 2023

作者： Nirwantono, Rudi Assidqi, Khoirunnisa Hidayat, Alam Ahmad Pebrianto Trinugroho, Joko Pardamean, Bens Bina Nusantara University Faculty of Engineering Bioinformatics and Data Science Research Center Biotechnology Department Jakarta11480 Indonesia Bina Nusantara University Faculty of Engineering Food Biotechnology Research Center Biotechnology Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Bioinformatics and Data Science Research Center Mathematics Departement Jakarta11480 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program - Computer Science Program Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350307306

The infection of Plasmodium vivax is relatively less virulent than the deathliest Plasmodium falciparum. However, it still can lead to a fatal case and often induces recurring malaria due to dormant parasites in the liver. Thus, the research to study the drug to treat Plasmodium vivax is essential, where the enzyme dihydroorotate dehydrogenase (DHODH) has recently become a new drug target. However, the drug-enzyme interaction study has only recently been conducted in Plasmodium falciparum (pfDHODH) due to the lack of the 3D structure of enzyme DHODH from Plasmodium vivax that is crucial for the study. Therefore, this study aimed to perform the modelling study of Plasmodium vivax DHODH (PvDHODH) to create a 3D structure as a basis for drug-protein interaction study in upcoming studies. Sequence pvDHODH (Accession: SCO68359.1) was used in homology modelling using Modeller 10.2 with the crystal structure of DHODH from DHODH (Accession: 7KZY) as a template. Overall, the model generated from the homology modelling was considered a good model, and the 3D structure was close to the native state according to several parameters, including DOPE score, GA341 score, QMEAN4 value, 3D-1D score, ERRAT2 score, and ProSA score. Ramachandran plot also revealed that almost all amino acids were distributed in the desirable and allowed area (99.1%), and only 0.9% were in generously allowed regions. Superimposition of the model with the template also indicated that the model has an almost similar structure and amino acids positioning, including in the binding pocket and active site. Therefore, the model can be used for downstream analysis in drug-protein interaction studies. Nevertheless, some improvements can still be performed to upgrade the quality of the model by deleting unaligned residues on the C-terminal and realigning the residues at the doubting site. © 2023 IEEE.

关键词： Amino acids

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Imitation Learning from Purified Demonstrations 41

Imitation Learning from Purified Demonstrations

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41st International Conference on Machine Learning, ICML 2024

作者： Wang, Yunke Dong, Minjing Zhao, Yukun Du, Bo Xu, Chang School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Wuhan Institute of Data Intelligence Wuhan University China Department of Computer Science City University of Hong Kong Hong Kong School of Computer Science Faculty of Engineering The University of Sydney Australia

Imitation learning has emerged as a promising approach for addressing sequential decision-making problems, with the assumption that expert demonstrations are optimal. However, in real-world scenarios, most demonstrations are often imperfect, leading to challenges in the effectiveness of imitation learning. While existing research has focused on optimizing with imperfect demonstrations, the training typically requires a certain proportion of optimal demonstrations to guarantee performance. To tackle these problems, we propose to purify the potential noises in imperfect demonstrations first, and subsequently conduct imitation learning from these purified demonstrations. Motivated by the success of diffusion model, we introduce a two-step purification via diffusion process. In the first step, we apply a forward diffusion process to smooth potential noises in imperfect demonstrations by introducing additional noise. Subsequently, a reverse generative process is utilized to recover the optimal demonstration from the diffused ones. We provide theoretical evidence supporting our approach, demonstrating that the distance between the purified and optimal demonstration can be bounded. Empirical results on MuJoCo and RoboSuite demonstrate the effectiveness of our method from different aspects. Copyright 2024 by the author(s)

关键词： Contrastive Learning

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Benefits of Internet of Things (IoT) applications in Health care - An Overview 1

Benefits of Internet of Things (IoT) Applications in Health ...

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1st International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2023

作者： Salama, Ramiz Al-Turjman, Fadi Chaudhary, Preeti Yadav, Satya Prakash Ai and Robotics Institute Research Center for Ai and IoT Near East University Nicosia Department of Computer Engineering Mersin10 Turkey Ai and Robotics Institute Near East University Nicosia Artificial Intelligence Engineering Dept. Mersin10 Turkey University of Kyrenia Research Center for Ai and IoT Faculty OfEngineering Kyrenia Mersin10 Turkey Graphic Era Hill University Computer Science and Engineering Dehradun India Department of Computer Science and Engineering Greater Noida201306 India

ISBN: (纸本)9798350338027

In a nutshell, the IoT refers to the idea of integrating high-tech technology in the form of "smart"gadgets and sensors into a single setting to gather data. There are still numerous areas in which machines cannot compete favorably with people, but data collection and analysis are undeniably two of their strongest points. The Internet of Things makes it possible to fully or partially automation data, it involves related activities, which is obviously very important for the healthcare industry. What exactly is the Internet connected medical things, and how exactly might it make a significant impact on the sector? By having to apply the concept of the internet of things (IoT) to the field of medicine, several significant advancements have become possible, including the ability to automatically control the thermostat of containers while transmitting inoculations and antibiotics, the accurate tracking of the ailments of a lot of illnesses without a need for additional meetings with a general practitioner, the correct and most useful use of meds, and a great deal more. All of this is realizable because of the capability of MedTech gadgets to carry out continuous monitoring system. The potential applications of internet of things technology in healthcare are very interesting. Let's review the ways in which this technology. © 2023 IEEE.

关键词： Medical applications

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