Machine learning in data analysis for stroke/endovascular therapy
Title | Machine learning in data analysis for stroke/endovascular therapy PDF eBook |
Author | Benjamin Yim |
Publisher | Frontiers Media SA |
Total Pages | 132 |
Release | 2023-09-05 |
Genre | Medical |
ISBN | 2832531873 |
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.
Machine Learning and Decision Support in Stroke
Title | Machine Learning and Decision Support in Stroke PDF eBook |
Author | Fabien Scalzo |
Publisher | Frontiers Media SA |
Total Pages | 162 |
Release | 2020-07-09 |
Genre | |
ISBN | 2889638464 |
Big data analytics to advance stroke and cerebrovascular disease: A tool to bridge translational and clinical research
Title | Big data analytics to advance stroke and cerebrovascular disease: A tool to bridge translational and clinical research PDF eBook |
Author | Alexis Netis Simpkins |
Publisher | Frontiers Media SA |
Total Pages | 320 |
Release | 2023-12-26 |
Genre | Medical |
ISBN | 2832539084 |
Machine Learning and Deep Learning in Neuroimaging Data Analysis
Title | Machine Learning and Deep Learning in Neuroimaging Data Analysis PDF eBook |
Author | Anitha S. Pillai |
Publisher | CRC Press |
Total Pages | 133 |
Release | 2024-02-15 |
Genre | Computers |
ISBN | 1003815545 |
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.
Artificial Intelligence in Medicine
Title | Artificial Intelligence in Medicine PDF eBook |
Author | Niklas Lidströmer |
Publisher | Springer |
Total Pages | 1816 |
Release | 2022-03-17 |
Genre | Medical |
ISBN | 9783030645724 |
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.
Machine learning and data science in heart failure and stroke
Title | Machine learning and data science in heart failure and stroke PDF eBook |
Author | Leonardo Roever |
Publisher | Frontiers Media SA |
Total Pages | 126 |
Release | 2023-09-07 |
Genre | Medical |
ISBN | 2832533388 |
The application of artificial intelligence in interventional neuroradiology
Title | The application of artificial intelligence in interventional neuroradiology PDF eBook |
Author | Yuhua Jiang |
Publisher | Frontiers Media SA |
Total Pages | 94 |
Release | 2023-07-03 |
Genre | Medical |
ISBN | 2832528597 |