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 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 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 Pitfalls in the Diagnosis of Neurological Disorders written by Ambar Chakravarty and published by Jaypee Brothers Medical Publishers. This book was released on 2022-10-31 with total page 744 pages. Available in PDF, EPUB and Kindle. Book excerpt: Accurate diagnosis of neurological disorders can often be difficult due to the complex nature of the nervous system. Whilst technological advances have greatly improved diagnostic and interpretation techniques, errors can still occur, which consequently result in mistakes in therapeutic care. This book is a guide to diagnostic strategies for a multitude of common and less common neurological disorders. Scenarios are set in both a clinical and an intensive care setting. Divided into 13 sections, the text begins with an overview of general neurology. Each of the following sections examines disorders in a different part of the nervous system. Diagnostic processes are evaluated and potential areas where a clinician may make a mistake, are examined and explained in depth. The final section presents 15 authentic cases covering diagnostic challenges in critical care units, all provided by leading experts from the University of Texas Southwestern Medical Centre, USA. With contributions from internationally recognised neurologists, this comprehensive text is highly illustrated with neurological images, and many chapters feature additional editorial notes and appendices. Access to a selection of clinical videos via a QR code is also provided with this book.
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 Brain Stroke Prediction using Machine Learning Techniques A Comparative Study written by R. Balamurugan and published by GRIN Verlag. This book was released on 2023-10-05 with total page 78 pages. Available in PDF, EPUB and Kindle. Book excerpt: Scientific Study from the year 2023 in the subject Computer Science - Bioinformatics, grade: 10, VIT University (VIT), course: Computer Science, language: English, abstract: The use of machine learning for stroke prediction represents a powerful tool in enhancing patient care and reducing stroke-related mortality and disability. By focusing on key risk factors and leveraging extensive healthcare data, machine learning can substantially improve the accuracy and effectiveness of stroke prediction. This project aims to harness the potential of machine learning to better identify individuals at high risk of suffering a stroke and provide them with early, targeted interventions, ultimately saving lives and improving patient outcomes. The importance of predicting strokes cannot be overstated. Strokes are a leading cause of mortality and disability worldwide. Early detection and prevention can have a substantial impact on patient outcomes. Leveraging machine learning algorithms for stroke prediction can significantly improve the accuracy and efficacy of identifying high-risk patients. The primary objective of this project is to develop a precise stroke prediction system that can recognize high-risk patients based on a wide range of risk factors, including age, gender, medical history, lifestyle choices, and genetic factors. By creating a reliable model for stroke prediction, healthcare professionals can administer early interventions, potentially reducing stroke incidence and improving patient outcomes. The project's scope includes analyzing electronic health record (EHR) data to identify the key elements essential for stroke prediction. EHRs contain valuable information, including patient demographics, medical history, clinical findings, and other factors relevant to constructing a stroke prediction model. Machine learning for stroke prediction involves several stages. Initially, a dataset of relevant variables potentially influencing stroke occurrence is identified. This dataset may encompass demographic details, clinical information, laboratory tests, medical images, genetic data, and lifestyle factors. Subsequently, the dataset is cleaned and preprocessed to remove noise and inconsistencies. A machine learning algorithm is chosen, and the data is divided into training and testing groups. The algorithm is trained using the training data to identify patterns and relationships between variables and stroke occurrence. Once the model is trained, it is evaluated using the testing data to assess its performance.
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 Neurocritical Care An Issue of Critical Care Clinics E Book written by Lori Shutter and published by Elsevier Health Sciences. This book was released on 2022-11-06 with total page 257 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this issue of Critical Care Clinics, guest editors Drs. Lori Shutter and Deepa Malaiyandi bring their considerable expertise to the topic of Neurocritical Care, a rapidly growing specialty of complex care. Top experts in the field provide up-to-date articles on important clinical trials and evidence-based care of the critically ill patient with neurological injury. Contains 16 practice-oriented topics including current management of acute ischemic stroke; status epilepticus: a neurological emergency; neurotrauma and ICP management; neuropharmacology in the ICU; artificial intelligence and big data science in neurocritical care; and more. Provides in-depth clinical reviews on neurocritical care, offering actionable insights for clinical practice. Presents the latest information on this timely, focused topic under the leadership of experienced editors in the field. Authors synthesize and distill the latest research and practice guidelines to create clinically significant, topic-based reviews.
Download or read book Improving Acute Ischemic Stroke Clinical and Imaging Outcome Classification Using Machine Learning and Deep Learning Methods written by King Chung Ho and published by . This book was released on 2019 with total page 152 pages. Available in PDF, EPUB and Kindle. Book excerpt: Stroke is the fifth leading cause of death in the United States, with approximately 795,000 new cases each year. The goal of stroke treatment is to rescue salvageable tissue by reperfusion therapy. Clinical trials have shown that intravenous tissue plasminogen activator (IV tPA) and clot retrieval devices are effective treatments for recanalizing occluded blood vessels. However, determining an optimal stroke treatment plan is not a straightforward decision because it involves different factors, such as patient risk of hemorrhage and penumbra size. The relationships between these factors and patient outcomes are not clearly understood. This dissertation attempts to overcome these challenges by developing machine learning and deep learning models for acute ischemic stroke clinical and imaging outcome classification. A novel deep learning model was first proposed using source perfusion imaging to predict voxel-wise tissue outcome. The model architecture is designed to include contralateral patches to improve the feature learning process. Second, an end-to-end machine learning approach was developed to classify stroke onset time, which is a major clinical variable in selecting patients for IV tPA treatments. The approach combines baseline descriptive features and deep features to improve stroke onset time classification using machine learning models. Third, a bi-input convolutional neural network was developed for perfusion parameter estimation. This model lays a foundation to estimate perfusion parameters using pattern recognition techniques. Finally, a machine learning model trained with a balanced data set was developed for acute stroke patient outcome prediction. Rigorous experiments and results have shown the effectiveness of these proposed methods. This dissertation describes methods that lead to better understanding of stroke imaging, which lays the foundation to offer decision-making guidance for clinicians providing acute stroke intervention treatments.
Download or read book Machine Learning Based Heart Disease Diagnosis written by Pooja Rani and published by . This book was released on 2023-03-27 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Mobile Technology for Adaptive Aging written by National Academies of Sciences, Engineering, and Medicine and published by National Academies Press. This book was released on 2020-10-25 with total page 147 pages. Available in PDF, EPUB and Kindle. Book excerpt: To explore how mobile technology can be employed to enhance the lives of older adults, the Board on Behavioral, Cognitive, and Sensory Sciences of the National Academies of Sciences, Engineering, and Medicine commissioned 6 papers, which were presented at a workshop held on December 11 and 12, 2019. These papers review research on mobile technologies and aging, and highlight promising avenues for further research.
Download or read book Improving Acute Ischemic Stroke Diagnosis Using Medical Imaging and Deep Learning Methods written by Haoyue Zhang and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Acute ischemic stroke (AIS) is a cerebrovascular disease caused by deceased blood flow in the brain. Treatment of AIS is heavily dependent on the time since stroke onset (TSS), either by clock time or tissue time. AIS treatments aim to restore blood flow in the stroke-affected area to minimize infarction. Current clinical guidelines recommend thrombolytic therapies (e.g. Intravenous(IV) or Intra-arterial (IA) tissue Plasminogen Activator (tPA) for patients presenting within 4.5 hours of TSS and Mechanical Thrombectomy (MTB) (e.g. surgical removal of the clot) for patients with TSS up to 24 hours. This research attempts to use both CT and MRI to predict the eligibility of AIS patients and their response to treatment whileaddressing several challenges in neuroimaging and AIS diagnosis in clinical settings using novel machine learning and deep learning approaches. A Self-supervised Learning approach, called intra-domain task-adaptive transfer learning, is the first proposed to predict TSS using limited training data. A hybrid transformer model that utilizes spatial neighborhood information in brain regions is proposed to predict MTB success. A pure transformer and a specifically designed Masked Image Model are developed to predict Large Vessel Occlusion (LVO). Last, a transformer-based super-resolution framework is proposed to generate synthesized thin-slice images from thick-slice images. Together, these models demonstrate the effectiveness of the attention mechanism and the usefulness of self-supervised learning for clinical deep learning applications given the limited data resources compared to natural images.
Download or read book Accelerated Path to Cures written by Josep Bassaganya-Riera and published by Springer. This book was released on 2018-04-25 with total page 88 pages. Available in PDF, EPUB and Kindle. Book excerpt: Accelerated Path to Cures provides a transformative perspective on the power of combining advanced computational technologies, modeling, bioinformatics and machine learning approaches with nonclinical and clinical experimentation to accelerate drug development. This book discusses the application of advanced modeling technologies, from target identification and validation to nonclinical studies in animals to Phase 1-3 human clinical trials and post-approval monitoring, as alternative models of drug development. As a case of successful integration of computational modeling and drug development, we discuss the development of oral small molecule therapeutics for inflammatory bowel disease, from the application of docking studies to screening new chemical entities to the development of next-generation in silico human clinical trials from large-scale clinical data. Additionally, this book illustrates how modeling techniques, machine learning, and informatics can be utilized effectively at each stage of drug development to advance the progress towards predictive, preventive, personalized, precision medicine, and thus provide a successful framework for Path to Cures.
Download or read book MACHINE LEARNING ALLOWS EASY PREDICTOR RANKING IN CLINICAL STROKE MODELS written by Jochen Fiebach and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Clinically applicable prediction models gain importance in the field of stroke. Next to their performance, information about the most important predictors is crucial. In the past, classical linear classification models were used. However, the interpretation of their coefficients for predictor ranking is unreliable. Modern machine learning methods such as boosted-decision-trees allow for high performance as well as easy to interpret and reliable predictor rankings. We tested this new method in comparison with linear classification methods in a model of stroke outcome prediction.
Download or read book Interpretable Machine Learning Methods for Stroke Prediction written by Rebecca Zhang (S.M.) and published by . This book was released on 2019 with total page 75 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning has long been touted as the next big tool, revolutionizing scientific endeavors as well as impacting industries like retail and finance. Naturally, there is much interest in the potential of next improving healthcare. However, using traditional machine learning approaches in this domain has many difficulties, chief among which is the issue of interpretability. We focus on the medical condition of stroke, a particularly desirable problem to target because it is one of the most prevalent and yet preventable conditions affecting Americans today. In this thesis, we apply novel interpretable prediction techniques to the problem of predicting stroke presence, location, acuity, and mortality risk for patient populations at two different hospital systems. We show that using an interpretable, optimal tree-based approach is roughly as effective if not better than black-box approaches. Using the clinical learnings from these studies, we explore new interpretable methodologies designed with medical applications and their unique challenges in mind. We present a novel regression algorithm to predict outcomes when the population is comprised of notably different subpopulations, and demonstrate that this gives comparable performance with improved interpretability. Finally, we explore new natural language processing techniques for machine learning from text. We propose an alternate end-to- end framework for going from unprocessed textual data to predictions, with an interpretable linguistics-based approach to model words. Altogether, this work demonstrates the promise that new parsimonious, interpretable algorithms have in the domain of stroke and broader healthcare problems.
Download or read book Neural Information Processing written by Derong Liu and published by Springer. This book was released on 2017-10-27 with total page 912 pages. Available in PDF, EPUB and Kindle. Book excerpt: The six volume set LNCS 10634, LNCS 10635, LNCS 10636, LNCS 10637, LNCS 10638, and LNCS 10639 constitues the proceedings of the 24rd International Conference on Neural Information Processing, ICONIP 2017, held in Guangzhou, China, in November 2017. The 563 full papers presented were carefully reviewed and selected from 856 submissions. The 6 volumes are organized in topical sections on Machine Learning, Reinforcement Learning, Big Data Analysis, Deep Learning, Brain-Computer Interface, Computational Finance, Computer Vision, Neurodynamics, Sensory Perception and Decision Making, Computational Intelligence, Neural Data Analysis, Biomedical Engineering, Emotion and Bayesian Networks, Data Mining, Time-Series Analysis, Social Networks, Bioinformatics, Information Security and Social Cognition, Robotics and Control, Pattern Recognition, Neuromorphic Hardware and Speech Processing.
Download or read book New Interpretable Machine Learning Techniques and an Application to Stroke Prediction in Atrial Fibrillation Patients written by Hongyu Yang (Ph. D.) and published by . This book was released on 2019 with total page 125 pages. Available in PDF, EPUB and Kindle. Book excerpt: Building interpretable and accurate models are attracting more and more interest in the machine learning community. In this thesis, we developed an interpretable machine learning algorithm called SBRL and we built an interpretable and statistically more accurate model for predicting strokes for patients in atrial fabrication (AF) who have not had a prior history of stroke and who are not taking anticoagulants. The first part of the thesis presents an interpretable machine learning algorithm that can be used as an alternative algorithm to the decision tree algorithm. Our algorithm builds an optimized rules list model from data by maximizing the posterior probability of a natural hierarchical generative model. It has the form of chained IF-THEN clauses which is simple for a human to follow and derive its prediction by hand. We developed two theoretical bounds for the algorithm. One for the length of the optimal rules list model; and the other for the upper bounds of the posterior probability of the optimized rules list given its prefixes. We thoroughly tested our algorithm against other interpretable and non-interpretable machine learning algorithms across multiple public datasets, in terms of interpretability, computational speed, and accuracy. Our algorithm strikes a balance among these metrics. The second part of the thesis presents how we used the ATRIA2-CVRN study cohort to build a stroke prediction model that is as simple as but statistically significantly more accurate than the stroke models in wide use, such as the CHA2DS2-VASc and ATRIA scores, for patients in AF who are not taking anticoagulants. We focused on the more challenging problem of primary prevention. We assessed the strengths of predictors and identified informative predictors not used in existing stroke models. We created a univariate stroke model using the most informative predictor age and achieved statistically significantly better performance than CHA2DS2-VASc and similar performance as ATRIA. We used various machine learning models to test the limit of the information that can be extracted from the data. We built a linear model with optimized integer coefficients using RiskSLIM. We used SBRL to generate simple-yet-accurate representations for high-risk patients who should be recommended anticoagulants.