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 Handbook of Decision Support Systems for Neurological Disorders written by Hemanth D. Jude and published by Academic Press. This book was released on 2021-03-30 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: Handbook of Decision Support Systems for Neurological Disorders provides readers with complete coverage of advanced computer-aided diagnosis systems for neurological disorders. While computer-aided decision support systems for different medical imaging modalities are available, this is the first book to solely concentrate on decision support systems for neurological disorders. Due to the increase in the prevalence of diseases such as Alzheimer, Parkinson’s and Dementia, this book will have significant importance in the medical field. Topics discussed include recent computational approaches, different types of neurological disorders, deep convolution neural networks, generative adversarial networks, auto encoders, recurrent neural networks, and modified/hybrid artificial neural networks. Includes applications of computer intelligence and decision support systems for the diagnosis and analysis of a variety of neurological disorders Presents in-depth, technical coverage of computer-aided systems for tumor image classification, Alzheimer’s disease detection, dementia detection using deep belief neural networks, and morphological approaches for stroke detection Covers disease diagnosis for cerebral palsy using auto-encoder approaches, contrast enhancement for performance enhanced diagnosis systems, autism detection using fuzzy logic systems, and autism detection using generative adversarial networks Written by engineers to help engineers, computer scientists, researchers and clinicians understand the technology and applications of decision support systems for neurological disorders
Download or read book Leveraging Clinical Imaging and Machine Learning Algorithms to Characterize Acute Ischemic Stroke Patients for Treatment Decision Making written by Jennifer Polson and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: For patients diagnosed with acute ischemic stroke, treatments such as thrombolysis and thrombectomy aim to restore blood flow to areas experiencing ischemia. These treatments have vastly improved outcomes, but it is currently unknown why some patients experience unsuccessful reperfusion or hemorrhagic complications. Taking advantage of recent advances in deep learning vision transformers, we developed algorithms for classification and prediction tasks regarding a patient's potential response to therapies using imaging taken at hospital admission. These models achieved higher generalization performance when identifying patients within the treatment window and those that will achieve successful recanalization. Our results illustrate that magnetic resonance (MR) and computed tomography (CT) imaging contains signal that can predict successful treatment response and that deep learning models can localize to salient regions within imaging without requiring time-intensive manual segmentation.
Download or read book Human Centered Machine Learning for Healthcare written by Vishwajith Ramesh and published by . This book was released on 2020 with total page 145 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning (ML) in healthcare has enabled the automatic detection of diseases from medical images or sensors with high accuracy, often outperforming domain experts. Unfortunately, there is a large variance between how such diagnostic aids perform in research settings and in the real-world. This is due to challenges unique to healthcare that, if unaddressed, limit the usefulness of ML-based software when deployed in hospitals. For example, in human subjects research and clinical trials, subject recruitment and data acquisition are involved processes for both patients and healthcare providers; there are several regulatory and cybersecurity requirements to satisfy to ensure that patient care is not compromised in the pursuit of big data. Without abundant data to train ML models, it can be difficult to elicit good performance that also generalizes well on unseen data in clinical practice. Moreover, ML tools in hospitals cannot function independently but must integrate with existing workflows. There are ethical considerations with respect to how these tools influence the decision making of clinicians and whether they encourage an over-reliance on predictions. In this dissertation, we discuss these and other concerns in the context of three focus areas: stroke, respiratory disease, and Parkinson's disease. We present machine and deep learning pipelines for weakness detection in stroke patients from video, respiratory disease classification from audio of coughs, and gait assessment in Parkinson's disease with body sensors. In our efforts, we were cognizant of the technical and human challenges of healthcare. We developed models that not only performed well but also could be trained and rigorously evaluated in a data-conscious way. Our ML solutions ranged from simple leave-one-out approaches to data augmentation with generative adversarial nets. Lastly, we show how ML can more effectively aid medical diagnosis when paired with human-centered design. We describe a clinical decision support system for acute stroke, focusing on the development of an intuitive user interface that balances neurologist assessments with the symptom predictions of our models. This dissertation details novel, human-centered ML techniques for disease diagnosis in neurology and pulmonology, highlighting several lessons learned to benefit the field of machine learning in healthcare at large.
Download or read book Artificial Intelligence in Healthcare written by Adam Bohr and published by Academic Press. This book was released on 2020-06-21 with total page 385 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. Highlights different data techniques in healthcare data analysis, including machine learning and data mining Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks Includes applications and case studies across all areas of AI in healthcare data
Download or read book Decision Support in Clinical Practice for Stroke Clinician Experiences and Expectations written by Andrew Bivard and published by Frontiers Media SA. This book was released on 2021-07-01 with total page 165 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book A Systematic Survey of Computer Aided Diagnosis in Medicine Past and Present Developments written by Juri Yanase and published by Infinite Study. This book was released on with total page 51 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computer-aided diagnosis (CAD) in medicine is the result of a large amount of effort expended in the interface of medicine and computer science. As some CAD systems in medicine try to emulate the diagnostic decision-making process of medical experts, they can be considered as expert systems in medicine.
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 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 Fundamentals of Clinical Data Science written by Pieter Kubben and published by Springer. This book was released on 2018-12-21 with total page 219 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book comprehensively covers the fundamentals of clinical data science, focusing on data collection, modelling and clinical applications. Topics covered in the first section on data collection include: data sources, data at scale (big data), data stewardship (FAIR data) and related privacy concerns. Aspects of predictive modelling using techniques such as classification, regression or clustering, and prediction model validation will be covered in the second section. The third section covers aspects of (mobile) clinical decision support systems, operational excellence and value-based healthcare. Fundamentals of Clinical Data Science is an essential resource for healthcare professionals and IT consultants intending to develop and refine their skills in personalized medicine, using solutions based on large datasets from electronic health records or telemonitoring programmes. The book’s promise is “no math, no code”and will explain the topics in a style that is optimized for a healthcare audience.
Download or read book Precision Medicine in Stroke written by Ana Catarina Fonseca and published by Springer. This book was released on 2022-05-06 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive coverage of the state of the art in precision medicine in stroke. It starts by explaining and giving general information about precision medicine. Current applications in different strokes types (ischemic, haemorrhagic) are presented from diagnosis to treatment. In addition, ongoing research in the field (early stroke diagnosis and estimation of prognosis) is extensively discussed. The final part provides an in-depth discussion of how different interdisciplinary areas like artificial intelligence, molecular biology and genetics are contributing to this area. Precision Medicine in Stroke provides a practical approach to each chapter, reinforcing clinical applications and presenting clinical cases. This book is intended for all clinicians that interact with stroke patients (neurologists, internal medicine doctors, general practitioners, neurosurgeons), students and basic researchers.
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 Deep Learning in Personalized Healthcare and Decision Support written by Harish Garg and published by Elsevier. This book was released on 2023-07-20 with total page 402 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning in Personalized Healthcare and Decision Support discusses the potential of deep learning technologies in the healthcare sector. The book covers the application of deep learning tools and techniques in diverse areas of healthcare, such as medical image classification, telemedicine, clinical decision support system, clinical trials, electronic health records, precision medication, Parkinson disease detection, genomics, and drug discovery. In addition, it discusses the use of DL for fraud detection and internet of things. This is a valuable resource for researchers, graduate students and healthcare professionals who are interested in learning more about deep learning applied to the healthcare sector. Although there is an increasing interest by clinicians and healthcare workers, they still lack enough knowledge to efficiently choose and make use of technologies currently available. This book fills that knowledge gap by bringing together experts from technology and clinical fields to cover the topics in depth. Discusses the application of deep learning in several areas of healthcare, including clinical trials, telemedicine and health records management Brings together experts in the intersection of deep learning, medicine, healthcare and programming to cover topics in an interdisciplinary way Uncovers the stakes and possibilities involved in realizing personalized healthcare services through efficient and effective deep learning technologies
Download or read book Data Driven Clinical Decision Making Using Deep Learning in Imaging written by M. F. Mridha and published by Springer Nature. This book was released on with total page 277 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Telemedicine in the ICU written by Matthew A. Koenig and published by Springer. This book was released on 2019-04-25 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: This text provides a concise, yet comprehensive overview of telemedicine in the ICU. The first part of the book reviews common issues faced by practitioners and hospital administrators in implementing and managing tele-ICU programs, including the merits of different staffing models, the challenges of building homegrown programs versus contracting for services, and the impact of state laws and payer policies on reimbursement for tele-ICU services. The second part of the book presents the current state of evidence for and against ICU telemedicine, based on clinical trials, before-and-after implementation studies, and observational data. The third part dives deeper into specific use cases for telemedicine in the ICU, including telestroke, pediatric and cardiac intensive care, and early treatment of declining patients with sepsis. Written by experts in the field, Telemedicine in the ICU is a practical guide for intensive care physicians and hospital administrators that provides all the information necessary in building and maintaining a successful tele-ICU program.
Download or read book Multimodal Learning for Clinical Decision Support and Clinical Image Based Procedures written by Tanveer Syeda-Mahmood and published by Springer Nature. This book was released on 2020-10-03 with total page 147 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed joint proceedings of the 10th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2020, and the 9th International Workshop on Clinical Image-Based Procedures, CLIP 2020, held in conjunction with the 23rd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2020, in Lima, Peru, in October 2020. The workshops were held virtually due to the COVID-19 pandemic. The 4 full papers presented at ML-CDS 2020 and the 9 full papers presented at CLIP 2020 were carefully reviewed and selected from numerous submissions to ML-CDS and 10 submissions to CLIP. The ML-CDS papers discuss machine learning on multimodal data sets for clinical decision support and treatment planning. The CLIP workshops provides a forum for work centered on specific clinical applications, including techniques and procedures based on comprehensive clinical image and other data.
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.