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Prediction of Stroke Risk Within 7-Years Follow Up Using Machine Learning Models

Prediction of Stroke Risk Within 7-Years Follow Up Using Mac...

2023 IEEE EMBS Special Topic Conference on Data Science and engineering in Healthcare, Medicine and Biology, IEEECONF 2023

作者： Tsarapatsani, Konstantina-Helen Sakellarios, Antonis Tsakanikas, Vasilis D. Rudolf, Henrik Trampisch, Hans J. Pezoulas, Vasileios C. Matsopoulos, George K. Fotiadis, Dimitrios I. University of Ioannina Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering IoanninaGR 45110 Greece Biomedical Research Institute Greece Institute for Biostatistics and Informatics in Medicine and Ageing Research University Medical Center Rostock Germany Ruhr University Bochum Department of Medical Informatics Biometry and Epidemiology Bochum Germany National Technical University of Athens School of Electrical and Computer Engineering Athens Greece

ISBN: (纸本)9798350383386

A stroke, also known as brain attack, occurs when blood supply to your brain is interrupted. Primary prevention relies on prompt prediction of a stroke. While currently there are several clinical risk scores, machine learning (ML) models seems to be more suitable tools for accurate prediction of stroke events. Therefore, this work focuses on the prediction of stroke within 7 years follow-up in patients who have not suffered from a stroke or TIA event at baseline. LightGBM (LGBM), Extreme Grading Boosting (XGBoost), Support Vector Machine (SVM) and Decision Tree were employed in the getABI dataset, which includes 5,897 participants. The performance of models was calculated by Accuracy (ACC), Sensitivity (SENS), Specificity (SPE) and area under the receiver operating characteristic curve (AUC) of each model. According to the comparison analysis's results, LGBM has been shown to be the most trustworthy algorithm, with accuracy 68 %. Moreover, sex, age, status of peripheral artery disease (PAD), history of myocardial infarction, angina pectoris, amputation and diabetes and pulse status of different arteries can be used as a simple and cost-effective way to predict *** Relevance: A fatal medical emergency, stroke may be anticipated using artificial intelligence, and the sooner it is predicted, the more cerebrovascular disease occurrences can be avoided. © 2023 IEEE.

关键词： Forecasting

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Melon(Cucumis melo)fruit‑specific monoterpene synthase

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Molecular Horticulture 2023年第1期3卷 357-360页

作者： Kathrine H.Davidson Syamkumar Sivasankara Pillai Yukihiro Nagashima Jashbir Singh Rita Metrani Kevin MCrosby John Jifon Bhimanagouda Patil Seyednami Niyakan Xiaoning Qian Hisashi Koiwa Department of Horticultural Sciences Vegetable and Fruit Improvement CenterTexas A&M UniversityTX 77843 College StationUSA Texas A&M AgriLife Research and Extension Center 2415 E Business 83WeslacoTX 78596USA Department of Food Science and Technology Texas A&M UniversityCollege StationTX 77843USA Department of Electrical and Computer Engineering Texas A&M UniversityCollege StationTX 77843USA TEES‑AgriLife Center for Bioinformatics&Genomic Systems Engineering Texas A&M UniversityCollege StationTX 77843USA Department of Computer Science and Engineering Texas A&M UniversityCollege StationTX 77843USA Molecular and Environmental Plant Sciences Texas A&M UniversityCollege StationTX 77843USA

Muskmelon(Cucumis melo L.)is one of the important horticultural crops of the Cucurbitaceae *** production of melon fruits was approximately 27 million tons,with the united States production yielding 616,050 tons(FAO 2018).Melon is diploid(2n=24)and has an approximate genome size of 450 Mbp(Arumuganathan and Earle 1991).A high-quality reference genome of melon(DHL92 v4.0)covers 358 Mbp pseudomolecules(Castanera et al.2019).

关键词： crops yielding Melon

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Efficient multi-task learning with instance selection for biomedical NLP

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computers in Biology and Medicine 2025年 190卷 110050-110050页

作者： Bonfigli, Agnese Bacco, Luca Pecchia, Leandro Merone, Mario Dell'Orletta, Felice ItaliaNLP Lab Institute of Computational Linguistics "Antonio Zampolli" National Research Council Via Giuseppe Moruzzi 1 Pisa56124 Italy Research Unit of Intelligent Technology for Health and Wellbeing Department of Engineering Università Campus Bio-Medico di Roma Via Alvaro del Portillo 21 Rome00128 Italy Research Unit of Computer Systems and Bioinformatics Department of Engineering Università Campus Bio-Medico di Roma Via Alvaro del Portillo 21 Rome00128 Italy Fondazione Policlinico Universitario Campus Bio-Medico di Roma Via Alvaro del Portillo 200 Rome00128 Italy

Background: Biomedical natural language processing (NLP) increasingly relies on large language models and extensive datasets, presenting significant computational challenges. Methods: We propose Blue5, a multi-task model based on SciFive that incorporates instance selection (IS) to enable efficient, multi-task learning (MTL) on biomedical data. We adapt the E2SC-IS framework for the biomedical domain, integrating a calibrated SVM classifier to reduce computational costs. Results: Our approach achieves an average data reduction of 26.6% across the several tasks of the BLUE (Biomedical Language Understanding Evaluation) Benchmark, while maintaining performance comparable with state-of-the-art models. The multi-task SVM configuration emerges as the most effective, demonstrating the power of combining IS with MTL for biomedical NLP. As a result of the unified framework, Blue5 effectively selects the most informative instances across tasks, ensuring model generalization while efficiently handling multiple NLP tasks. Conclusion: Our work offers a practical solution to address growing computational demands, enabling more scalable and accessible applications of advanced NLP techniques in biomedical research and healthcare. © 2025 The Authors

关键词： Multi-task learning

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Innovation capabilities and their dimensions:A systematic literature review

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International Journal of Innovation Studies 2024年第3期8卷 313-333页

作者： Antonio Moreira Eurico Navaia Claudia Ribau Department of Economics ManagementIndustrial Engineering and TourismUniversity of Aveiro3810-193AveiroPortugal GOVCOPP-Research Unit on Governance Competitiveness and Public PoliciesUniversity of Aveiro3810-193AveiroPortugal INESCTEC-Institute for Systems and Computer Engineering Technology and Science4200-465PortoPortugal NECE-UBI-Research Center for Business Sciences Universidade da Beira InteriorCovilhaPortugal CICEE-Research Center in Business and Economics UALPortugal Faculdade de Ciencias Sociais e Humanidades Universidade ZambezeBeira369SofalaMozambique Instituto Superior de Contabilidade e Administragao University of Aveiro3810-193AveiroPortugal

Innovation capabilities(ICs)represent a crucial source of competitive advantage for ***,the literature on ICs is extensive,leading to a diverse understanding of their nature and measurement.A notable gap exists in delineating the dimensions constituting *** article aims to address this gap by identifying and pinpointing the various dimensions of ICs through a systematic literature review(SLR).The initial step involves identifying the diverse dimensions used in ICs,providing a distinctive insight for assessing their ***,this SLR stands out as the only comprehensive analysis of various ICs dimensions,organizing them *** 103 articles from the Web of Science and Scopus databases spanning from 2001 to 2022,the results reveal an amalgam of scales and associated approaches for IC *** study contributes to the literature by systematically identifying and analyzing the main dimensions employed by researchers to measure ***,it highlights the foundational theoretical approaches of the identified *** practical terms,the study consolidates and presents the identified dimensions and metrics in integrative tables,offering researchers and companies valuable insights into diverse innovation paths that impact performance.

关键词： Innovation capability Innovation Systematic literature review Competitiveness

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Evaluation of Quantized CNN Architectures for Land Use Classification for Onboard Cube Satellite Computing

Evaluation of Quantized CNN Architectures for Land Use Class...

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International Conference on Mechatronics (ICOM)

作者： Wynnezel Wayne Naoto P Akeboshi Robert Kerwin Billones Jerahmeel Kua Coching Adrian Jenssen Lim Pe Seth Gabriel Dy Yeung Richard Josiah Tan Ai Edwin Sybingco Elmer P Dadios Mark Angelo Purio Department of Manufacturing Engineering and Management De La Salle University Manila Philippines Intelligent Systems Laboratory Research Unit Center for Engineering and Sustainability Development Research De La Salle University Manila Philippines Department of Electronics and Computer Engineering De La Salle University Manila Philippines College of Engineering Adamson University Manila Philippines

ISBN: (数字)9798350349788

ISBN: (纸本)9798350349795

Nanosatellites have increased in popularity, but its limited communication bandwidth and computing resource constraints remain a challenge. Orbital edge computing has been explored to address this issue by utilizing neural networks onboard the satellites for land use classification. This study explores the performance of pre-trained quantized neural networks in land use classification by fine-tuning ResN et50, MobileNetV3, and GoogLeNet using the EuroSAT RGB dataset with an 80–20 split. The study evaluated the three models based on its accuracy and file size and against its non-quantized counterparts. It was found that the best model is the MobileNetV3, with an 89.04% accuracy at a 3,090 KB file size. MobileNetV3 was also found to be the best architecture for quantization with the least impact to accuracy, with a reduction of only 5.05%, and having the most significant reduction in file size of 85.71 %.

关键词： Training Accuracy Quantization (signal) Microcontrollers Computational modeling Neural networks Hardware

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Multi-stage intermediate fusion for multimodal learning to classify non-small cell lung cancer subtypes from CT and PET

arXiv

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arXiv 2025年

作者： Aksu, Fatih Gelardi, Fabrizia Chiti, Arturo Soda, Paolo Department of Biomedical Sciences Humanitas University Milan Italy Università Vita-Salute San Raffaele Milan Italy IRCCS Ospedale San Raffaele Milan Italy Unit of Computer Systems and Bioinformatics Department of Engineering Università Campus Bio-Medico di Roma Rome Italy Department of Diagnostics and Intervention Radiation Physics Umeå University Umeå Sweden

Accurate classification of histological subtypes of non-small cell lung cancer (NSCLC) is essential in the era of precision medicine, yet current invasive techniques are not always feasible and may lead to clinical complications. This study presents a multi-stage intermediate fusion approach to classify NSCLC subtypes from CT and PET images. Our method integrates the two modalities at different stages of feature extraction, using voxel-wise fusion to exploit complementary information across varying abstraction levels while preserving spatial correlations. We compare our method against unimodal approaches using only CT or PET images to demonstrate the benefits of modality fusion, and further benchmark it against early and late fusion techniques to highlight the advantages of intermediate fusion during feature extraction. Additionally, we compare our model with the only existing intermediate fusion method for histological subtype classification using PET/CT images. Our results demonstrate that the proposed method outperforms all alternatives across key metrics, with an accuracy and AUC equal to 0.724 and 0.681, respectively. This non-invasive approach has the potential to significantly improve diagnostic accuracy, facilitate more informed treatment decisions, and advance personalized care in lung cancer management. © 2025, CC BY-NC-ND.

关键词： Lung cancer

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Large-Scale Radiomics Analysis for Prostate Cancer Detection Harnessing Machine and Deep Learning Models 22

Large-Scale Radiomics Analysis for Prostate Cancer Detection...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Mylona, Eugenia Zaridis, Dimitrios I. Kalantzopoulos, Charalampos N. Pezoulas, Vasileios C. Tachos, Nikolaos S. Matsopoulos, George K. Regge, Daniele Papanikolaou, Nikolaos Tsiknakis, Manolis Marias, Kostas Fotiadis, Dimitrios I. Biomedical Research Institute Forth IoanninaGR 45110 Greece University of Ioannina Unit of Medical Technology Intelligent Information Systems Greece School of Electrical & Computer Engineering National Technical University of Athens Biomedical Engineering Laboratory Athens15780 Greece Candiolo Cancer Institute FPO-IRCCS Department of Radiology Strada Provinciale Candiolo Turin10060 Italy Champalimaud Foundation Computational Clinical Imaging Group Portugal Institute of Computer Science Forth Computational Biomedicine Laboratory HeraklionGR 70013 Greece Hellenic Mediterranean University Department of Electrical and Computer Engineering HeraklionGR 71004 Greece

ISBN: (纸本)9798331520526

Prostate cancer (PCa) is the second most common cancer in men, making early detection crucial for effective treatment. This study explores the efficacy of machine learning (ML) and deep learning (DL) models in detecting PCa lesions using radiomics features derived from multiparametric MRI data. Leveraging a dataset of 7621 retrospective and 2132 prospective cases from 12 clinical centers across Europe, we compared the performance of various ML (LASSO, Random Forest, SVM, XGBoost) and DL models (CNN, MLP, Transformer, CNN&MLP). Radiomic features were extracted and refined through a robust feature selection process. Models' performance was evaluated through nested cross-validation (CV) and externally. The CNN&MLP model achieved the highest ROC AUC (0.86) and PR AUC (0.90) in CV, while LASSO showed consistent generalization in external validation. Our findings indicate that DL models excel in high-dimensional data analysis but face challenges in generalizing to external datasets, highlighting the importance of external validation for clinical applicability. © 2025 IEEE.

关键词： Deep learning

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A Deep Learning Approach for Virtual Contrast Enhancement in Contrast Enhanced Spectral Mammography

arXiv

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arXiv 2023年

作者： Rofena, Aurora Guarrasi, Valerio Sarli, Marina Piccolo, Claudia Lucia Sammarra, Matteo Zobel, Bruno Beomonte Soda, Paolo Unit of Computer Systems & Bioinformatics Department of Engineering University Campus Bio-Medico Rome Italy Department of Radiology Fondazione Policlinico Campus Bio-Medico Rome Italy Department of Radiology University Campus Bio-Medico Rome Italy Department of Radiation Sciences Radiation Physics Biomedical Engineering Umeå University Sweden

Contrast Enhanced Spectral Mammography (CESM) is a dual-energy mammographic imaging technique that first needs intravenously administration of an iodinated contrast medium;then, it collects both a low-energy image, comparable to standard mammography, and a high-energy image. The two scans are then combined to get a recombined image showing contrast enhancement. Despite CESM diagnostic advantages for breast cancer diagnosis, the use of contrast medium can cause side effects, and CESM also beams patients with a higher radiation dose compared to standard mammography. To address these limitations this work proposes to use deep generative models for virtual contrast enhancement on CESM, aiming to make the CESM contrast-free as well as to reduce the radiation dose. Our deep networks, consisting of an autoencoder and two Generative Adversarial Networks, the Pix2Pix, and the CycleGAN, generate synthetic recombined images solely from low-energy images. We perform an extensive quantitative and qualitative analysis of the model’s performance, also exploiting radiologists’ assessments, on a novel CESM dataset that includes 1138 images that, as a further contribution of this work, we make publicly available. The results show that CycleGAN is the most promising deep network to generate synthetic recombined images, highlighting the potential of artificial intelligence techniques for virtual contrast enhancement in this field. © 2023, CC BY-NC-ND.

关键词： Generative adversarial networks

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Fundus Image Quality Assessment and Enhancement: a Systematic Review

arXiv

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arXiv 2025年

作者： Li, Heng Li, Haojin Ou, Mingyang Yu, Xiangyang Zhang, Xiaoqing Niu, Ke Fu, Huazhu Liu, Jiang Research Institute of Trustworthy Autonomous Systems SUSTech Shenzhen China Department of Computer Science and Engineering SUSTech Shenzhen China Center for High Performance Computing and Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Computer School Beijing Information Science and Technology University Beijing China Singapore

As an affordable and convenient eye scan, fundus photography holds the potential for preventing vision impairment, especially in resource-limited regions. However, fundus image degradation is common under intricate imaging environments, impacting following diagnosis and treatment. Consequently, image quality assessment (IQA) and enhancement (IQE) are essential for ensuring the clinical value and reliability of fundus images. While existing reviews offer some overview of this field, a comprehensive analysis of the interplay between IQA and IQE, along with their clinical deployment challenges, is lacking. This paper addresses this gap by providing a thorough review of fundus IQA and IQE algorithms, research advancements, and practical applications. We outline the fundamentals of the fundus photography imaging system and the associated interferences, and then systematically summarize the paradigms in fundus IQA and IQE. Furthermore, we discuss the practical challenges and solutions in deploying IQA and IQE, as well as offer insights into potential future research directions. Copyright © 2025, The Authors. All rights reserved.

关键词： Photography

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Hybrid PET/MRI Analysis in the Juvenile Huntington Disease Evaluation

Hybrid PET/MRI Analysis in the Juvenile Huntington Disease E...

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2023 IEEE International Workshop on Biomedical Applications, Technologies and Sensors, BATS 2023

作者： Caligiuri, Maria Eugenia Vizza, Patrizia Squitieri, Ferdinando Cascini, Giuseppe Lucio Sabatini, Umberto Veltri, Pierangelo Magna Graecia University Department of Surgical and Medical Sciences Catanzaro Italy Irccs Casa Sollievo della Sofferenza/CSS-Mendel Huntington and Rare Diseases Unit San Giovanni Rotondo Italy Italian League for Research on Huntington Lirh Foundation Rome Italy Magna Graecia University Department of Experimental and Clinical Medicine Catanzaro Italy University of Calabria Department of Computer Science Modeling Electronics and Systems Engineering Rende Italy

ISBN: (纸本)9798350343472

Hybrid PET/MRI is an emerging approach to simultaneously investigate the connection of intrinsic metabolic with functional brain changes. In this work, we propose an image analysis workflow for longitudinal PET/MRI study in Juvenile-onset Huntington's Disease(JoHD) aiming to evaluate progressive disease-related volumetric, functional, and metabolic alterations. This examination/methodology could be very useful in the characterization of the rapidly worsening course of this degenerative pathology. Also, our main contribution lays in the multimodal hybrid PET/MRI data-fusion approach to identify features as potential markers of JoHD progression, and to support future trials in test drugs and treatments. © 2023 IEEE.

关键词： Metabolism

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