Download or read book Machine learning in data analysis for stroke endovascular therapy written by Benjamin Yim and published by Frontiers Media SA. This book was released on 2023-09-05 with total page 132 pages. Available in PDF, EPUB and Kindle. Book excerpt: With an estimated global incidence of 11 million patients per year, research involving ischemic stroke requires the collection and analysis of massive data sets affected by innumerable variables. Landmark studies that have historically shaped the foundation of our understanding of ischemic stroke and the development of management protocols have been derived from only a miniscule fraction of a percent of the entire population due to feasibility and capability. Machine learning provides an opportunity to capture data from an extraordinarily larger cohort size, which can be applied to training models to formulate algorithms to forecast outcomes with unparalleled accuracy and efficiency. The paradigm-shifting integration of machine learning in other industries, i.e. robotics, finance, and marketing, foreshadows its inevitable application to large population-based clinical research and practice. While prior multi-center studies have relied heavily on catalogued datasets requiring substantial manpower, the recent development of modern statistical methods can potentially expand the available quantity and quality of clinical data. In conjunction with data mining, machine learning has allowed automated extraction of clinical information from imaging, surgical videos, and electronic medical records to identify previously unseen patterns and create prediction models. Recently, it’s use in real-time detection of large vessel occlusion has streamlined health care delivery to a level of efficiency previously unmatched. The application of machine learning in ischemic stroke research – data acquisition, image evaluation, and prediction models – has the potential to reduce human error and increase reproducibility, accuracy, and precision with an unprecedented degree of power. However, one of the challenges with this integration remains the methods in which machine learning is utilized. Given the novelty of machine learning in clinical research, there remains significant variations in the application of machine learning tools and algorithms. The focus of the research topic is to provide a platform to compare the merits of various learning approaches – supervised, semi-supervised, unsupervised, self-learning – and the performances of various models.

Download or read book Machine Learning and Decision Support in Stroke written by Fabien Scalzo and published by Frontiers Media SA. This book was released on 2020-07-09 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Big data analytics to advance stroke and cerebrovascular disease A tool to bridge translational and clinical research written by Alexis Netis Simpkins and published by Frontiers Media SA. This book was released on 2023-12-26 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Machine Learning and Deep Learning in Neuroimaging Data Analysis written by Anitha S. Pillai and published by CRC Press. This book was released on 2024-02-15 with total page 133 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning (ML) and deep learning (DL) have become essential tools in healthcare. They are capable of processing enormous amounts of data to find patterns and are also adopted into methods that manage and make sense of healthcare data, either electronic healthcare records or medical imagery. This book explores how ML/DL can assist neurologists in identifying, classifying or predicting neurological problems that require neuroimaging. With the ability to model high-dimensional datasets, supervised learning algorithms can help in relating brain images to behavioral or clinical observations and unsupervised learning can uncover hidden structures/patterns in images. Bringing together artificial intelligence (AI) experts as well as medical practitioners, these chapters cover the majority of neuro problems that use neuroimaging for diagnosis, along with case studies and directions for future research.

Download or read book Artificial Intelligence in Medicine written by Niklas Lidströmer and published by Springer. This book was released on 2022-03-17 with total page 1816 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a structured and analytical guide to the use of artificial intelligence in medicine. Covering all areas within medicine, the chapters give a systemic review of the history, scientific foundations, present advances, potential trends, and future challenges of artificial intelligence within a healthcare setting. Artificial Intelligence in Medicine aims to give readers the required knowledge to apply artificial intelligence to clinical practice. The book is relevant to medical students, specialist doctors, and researchers whose work will be affected by artificial intelligence.

Download or read book SUPERVISED MACHINE LEARNING METHOD FOR CLASSIFYING STROKE AND NON STROKE PATIENTS FROM MEDICAL RECORDS PROOF OF CONCEPT written by John Ly and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Background: Diagnosis of patients as stroke and non-stroke can be difficult in the Emergency Department. This step can help by ambulance officers and Emergency physicians to expedite care of stroke patients for time-critical therapy such as recombinant tissue plasminogen activator (TPA) and endovascular clot retrieval (ECR). In this study, a supervised machine learning approach is implemented to compare models for classifying stroke and non-stroke patients using their ambulance assessment notes. Method: Ambulance records of patients admitted to the Monash Medical Centre Stroke Unit over a 3-month period were collected, labelled and pre-processed to prepare the text for analysis. The data were split into training and testing subsets. Models for text classification were subsequently built using a variety of machine learning tools: Random Forest, Support Vector Machine (SVM), Generalised Linear Model (GLM) and Nau00efve Bayes. Accuracy of the models were compared by running the testing data subset through each model. Results:The data contained ambulance notes of 303 patients of which 8.46% were diagnosed as u2018non-strokeu2019. The positive class used in this analysis was u2018non-strokeu2019. Random Forest was the overall best performing model with the highest ROC and specificity, followed by GLM, SVM and Nau00efve Bayes respectively (figure 1). Variable importance was plotted for the Random Forest model which showed terms u2018motoru2019, u2018fatigueu2019 and u2018perceptionu2019 made the largest contribution to the model (figure 2). Conclusion:This analysis demonstrates the use of machine learning methods for supervised text classification for prediction of stroke from non-stroke patients using their initial encounter ambulance assessment notes. This model can be further developed with larger sets of data for implementation in the clinical setting where it can assist with prediction of stroke code outcomes.

Download or read book Machine learning and data science in heart failure and stroke written by Leonardo Roever and published by Frontiers Media SA. This book was released on 2023-09-07 with total page 126 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Omics Based Approaches in Stroke Research written by Shubham Misra and published by Frontiers Media SA. This book was released on 2024-08-23 with total page 110 pages. Available in PDF, EPUB and Kindle. Book excerpt: Omics-based approaches have emerged as powerful tools in stroke research, revolutionizing our understanding of the underlying molecular mechanisms and potential therapeutic targets. These approaches encompass various disciplines such as genomics, transcriptomics, proteomics, metabolomics, radiomics, and epigenomics, enabling comprehensive analysis of biological and imaging markers and their interactions. Through genomics, researchers can identify genetic variants associated with stroke susceptibility, offering insights into individual risk factors and personalized medicine. Transcriptomics allows the investigation of gene expression patterns, highlighting key molecular pathways involved in stroke pathology and providing potential targets for intervention. Proteomics aids in the identification and quantification of proteins associated with stroke, aiding in the discovery of novel biomarkers and therapeutic targets. Metabolomics explores the metabolites involved in stroke pathophysiology, shedding light on metabolic alterations and potential therapeutic strategies. Radiomics involves the extraction and analysis of a multitude of quantitative features from medical imaging data, such as CT or MRI scans serving as potential imaging biomarkers, contributing to risk stratification and the identification of novel insights into stroke pathophysiology. Finally, epigenomics investigates modifications in gene expression without changing the DNA sequence, uncovering epigenetic mechanisms underlying stroke susceptibility and recovery. By integrating and analyzing data from these omics platforms, researchers can gain a comprehensive understanding of stroke pathogenesis, paving the way for the development of innovative diagnostic tools and effective therapeutic interventions.

Download or read book The application of artificial intelligence in interventional neuroradiology written by Yuhua Jiang and published by Frontiers Media SA. This book was released on 2023-07-03 with total page 94 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Application of Machine Learning Algorithm on Binary Classification Model for Stroke Treatment Eligibility written by Joon Ho Han and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In Canada, stroke is the leading cause of adult disability and the third leading cause of death. Ischemic stroke is the most common type, making up approximately 85% of all stroke patients. Endovascular treatment (EVT) is effective for severe ischemic stroke patients. Unfortunately, EVT requires specialized equipment and personnel, which limits its availability. There are several clinical and imaging factors that are critical in determining eligibility for EVT. Furthermore, in stroke, minutes matter as the brain dies quickly after onset, making EVT treatment's effectiveness highly time dependent. For this reason, timely across to EVT is critical. This study is to create a binary classification model to predict the EVT eligibility of stroke patients and discover attributes of the patient information that help to make efficient decision on transfer EVT eligible patient. Following algorithms applied to dataset: Logistic Regression, Decision Tree, Random Forest, and Support Vector Machine.

Download or read book Machine Learning in Action Stroke Diagnosis and Outcome Prediction written by Ramin Zand and published by Frontiers Media SA. This book was released on 2022-08-18 with total page 121 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Medical Image Understanding and Analysis written by Bartłomiej W. Papież and published by Springer Nature. This book was released on 2020-07-08 with total page 452 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 24th Conference on Medical Image Understanding and Analysis, MIUA 2020, held in July 2020. Due to COVID-19 pandemic the conference was held virtually. The 29 full papers and 5 short papers presented were carefully reviewed and selected from 70 submissions. They were organized according to following topical sections: ​image segmentation; image registration, reconstruction and enhancement; radiomics, predictive models, and quantitative imaging biomarkers; ocular imaging analysis; biomedical simulation and modelling.

Download or read book Intelligent Diagnosis with Adversarial Machine Learning in Multimodal Biomedical Brain Images written by Yuhui Zheng and published by Frontiers Media SA. This book was released on 2021-09-23 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book The Combination of Data Driven Machine Learning Approaches and Prior Knowledge for Robust Medical Image Processing and Analysis written by Jinming Duan and published by Frontiers Media SA. This book was released on 2024-06-11 with total page 165 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the availability of big image datasets and state-of-the-art computing hardware, data-driven machine learning approaches, particularly deep learning, have been used in numerous medical image (CT-scans, MRI, PET, SPECT, etc..) computing tasks, ranging from image reconstruction, super-resolution, segmentation, registration all the way to disease classification and survival prediction. However, training such high-precision approaches often require large amounts of data to be collected and labelled and high-capacity graphics processing units (GPUs) installed, which are resource intensive and hence not always practical. Other hurdles such as the generalization ability to unseen new data and difficulty to interpret and explain can prevent their deployment to those clinical applications which deem such abilities imperative.

Download or read book Machine Learning Assisted Diagnosis and Treatment of Endocrine Related Diseases written by Qiuming Yao and published by Frontiers Media SA. This book was released on 2023-12-27 with total page 171 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Acute Stroke Nursing written by Jane Williams and published by John Wiley & Sons. This book was released on 2013-05-07 with total page 367 pages. Available in PDF, EPUB and Kindle. Book excerpt: Stroke is a medical emergency that requires immediate medical attention. With active and efficient nursing management in the initial hours after stroke onset and throughout subsequent care, effective recovery and rehabilitation is increased. Acute Stroke Nursing provides an evidence-based, practical text facilitating the provision of optimal stroke care during the primary prevention, acute and continuing care phases. This timely and comprehensive text is structured to follow the acute stroke pathway experienced by patients. It explores the causes, symptoms and effects of stroke, and provides guidance on issues such as nutrition, continence, positioning, mobility and carer support. The text also considers rehabilitation, discharge planning, palliative care and the role of the nurse within the multi-professional team. Acute Stroke Nursing is the definitive reference on acute stroke for all nurses and healthcare professionals wishing to extend their knowledge of stroke nursing. Evidence-based and practical in style, with case studies and practice examples throughout Edited and authored by recognised stroke nursing experts, clinicians and leaders in the field of nursing practice, research and education The first text to explore stroke management from UK and international perspectives, and with a nursing focus

Download or read book Bio inspired Neurocomputing written by Akash Kumar Bhoi and published by Springer Nature. This book was released on 2020-07-21 with total page 427 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers the latest technological advances in neuro-computational intelligence in biological processes where the primary focus is on biologically inspired neuro-computational techniques. The theoretical and practical aspects of biomedical neural computing, brain-inspired computing, bio-computational models, artificial intelligence (AI) and machine learning (ML) approaches in biomedical data analytics are covered along with their qualitative and quantitative features. The contents cover numerous computational applications, methodologies and emerging challenges in the field of bio-soft computing and bio-signal processing. The authors have taken meticulous care in describing the fundamental concepts, identifying the research gap and highlighting the problems with the strategical computational approaches to address the ongoing challenges in bio-inspired models and algorithms. Given the range of topics covered, this book can be a valuable resource for students, researchers as well as practitioners interested in the rapidly evolving field of neurocomputing and biomedical data analytics.