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Manual of cardiovascular medicine
Cardiovascular medicine has experienced an unforeseen and impressive development over the last fifty years, particularly recently, as new diagnostic innovative medications have been developed, as well as interventional and surgical procedures to treat patients with cardiac disease. Thus, the number of cardiovascular diagnoses, the number of diagnostic modalities, as well as the number of treatment options has expanded enormously and made cardiovascular medicine one of the biggest specialties in medicine. This cardiovascular manual focuses on diagnostic algorithms and therapeutic recommendations according to European Guidelines. It encompasses all aspects of cardiovascular medicine from hypertension to transplantation; from imaging to intervention; and from pharmacotherapy to surgical procedures.
Machine learning for neurodegenerative disorders : advancements and applications
Explores the application of machine learning to the understanding, early diagnosis, and management of neurodegenerative disorders. With a specific focus on its role in ongoing clinical trials, the book covers essential topics such as data collection, pre-processing, feature extraction, model development, and validation techniques. It delves into the applications of neuroimaging techniques like magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET) in the diagnosis and understanding of neurodegenerative disorders. Additionally, the book examines various machine-learning algorithms employed for biomarker discovery in neurodegenerative disorders. It highlights the role of neuroinformatics and big data analysis in advancing the understanding and management of neurodegenerative disorders. Furthermore, the book reviews future prospects and presents the ethical considerations and regulatory challenges associated with implementing machine learning approaches in the diagnosis, treatment, and prevention of neurodegenerative disorders.
Learning Surgery : The Surgery Clerkship Manual
Provides a ready reference to those in third and fourth year residencies. Essential algorithms and case presentations meet with clerkship learning objectives as outlined by the Association of Surgical Education in their ASE Manual. Two sections include Introduction to Clinical Surgery in the Surgical Clerkship Setting and Management of Surgical Diseases During the Clerkship. Chapters include: Stroke, Hypertension, Abdominal Masses, Head Injuries, and Burns. Written by leading clinicians and educators, both surgery residents and medical students will find LEARNING SURGERY indispensible in their rotations and clerkships. Surgeons who train residents will also find the text a valuable ajunct to their teaching.
Common Surgical Diseases : An Algorithmic Approach to Problem Solving
Common Surgical Diseases: An Algorithmic Approach to Problem Solving, provides surgical residents and house staff with a current, concise and algorithmic approach to frequently encountered clinical challenges.
Borrelia burgdorferi : Methods and protocols
Covers the latest advancements and techniques used to understand the fastidious bacterium, Borrelia burgdorferi, and its significance in infectious disorders by combining both conventional and cutting-edge approaches. This book covers diverse topics, including direct detection, diagnostic methods, immune response analysis, alternative model systems, advanced proteomics, social media analysis, and clinical research. It also discusses unconventional wet lab research such as content analysis, the use of ChatGPT, clinical algorithms for chronic Lyme, establishment of a pregnancy Lyme disease biobank, and investigates Lyme in pregnant women. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls.
Bioinformatics drug discovery
Quantitative tools are becoming increasingly important in order to understand complex cascade of signal transduction events, pathways or biochemical reactions. The book showcases how computational techniques and algorithms are applied to biological data analysis, interpretation, and modelling. It covers applications in drug design and discovery, immune systems, phylogenetic analysis and protein structures.
Bioinformatics and the cell : Modern computational approaches in genomics, proteomics and transcriptomics
"Xuhua Xia’s Bioinformatics and the Cell is a welcome addition to the bourgeoning field of bioinformatics text books. Xia stakes out a too-often neglected middle ground in bioinformatics by presenting a work that emphasizes methods’ biological utility without eschewing algorithmic formalism. Readers will find a well-rounded presentation of bioinformatics techniques employed in genomics, transcriptomics and proteomics – unified throughout by the common theme of molecular evolution."
Best of five MCQs for the endocrinology and diabetes SCE
Including questions based on the latest National Institute for Health & Care Excellence (NICE), Joint British Diabetes Societies (JBDS) & Endocrine Society clinical practice guidelines. Includes 20 more questions on diabetes to reflect the increased weighing given to this subject in the SCE exam. New tables and algorithms have been added to provide specialty trainees useful information relevant to clinical practice
Artificial intelligence based cancer nanomedicine : Diagnostics, therapeutics and bioethics
Nanomedicine is evolving with novel drug formulations devised for multifunctional approaches towards diagnostics ad therapeutics. Nanomedicine-based drug therapy is normally explored at a fixed dose. The drug action is time-dependent, dose-dependent and patient-specific. To overcome challenges of nanomedicine testing, artificial intelligence (AI) serves as a helping tool for optimizing the drug and dose parameters. Real time conversions between these two features enables upgradation of patient data acquisition and improved design of nanomaterials. In this scenario, AI-based pattern analysis and algorithms models can greatly improve accuracy of diagnostics and therapeutics.
Applied Bioinformatics : An Introduction
In this book, anyone who can operate a PC, standard software and the Internet will learn to understand the biological basis of bioinformatics of the existence as well as the source and availability of bioinformatics software how to apply these tools and interpret results with confidence.This is aided by introductory chapters to important aspects of bioinformatics, detailed bioinformatics exercises, including solutions and a glossary of definitions and terminology relating to bioinformatics.
Acute coronary syndrome : Multidisciplinary and pathway-based approach
Acute coronary syndrome (ACS) affects millions of patients annually and requires immediate diagnosis and therapy. This practical algorithm-based handbook addresses the diagnosis and treatment of these patients and is designed for the medical personnel involved in the triage and management of ACS patients.
Mathematical Methods in Computer Science : Essays in Memory of Thomas Beth
This Festschrift volume contains the proceedings of the conference Mathematical Methods in Computer Science, MMICS 2008, which was held during December 17-19, 2008, in Karlsruhe, Germany, in memory of Thomas Beth.The themes of the conference reflected the many interests of Thomas Beth. Although, these interests might seem diverse, mathematical methods and especially algebra as a language constituted the common denominator of all of his scientific achievements.
Markov Models for Pattern Recognition : From Theory to Applications
Describes the underlying theoretical concepts - covering Hidden Markov models and Markov chain models - and presents the techniques and algorithmic solutions essential to creating real world applications. The actual use of Markov models in their three main application areas - namely speech recognition, handwriting recognition, and biological sequence analysis - is presented with examples of successful systems.
Machine-learning-assisted intelligent processing and optimization of complex systems
Focuses on the most recent developments in intelligent optimization methods and their applications in various test cases. The reprint covers various topics, including distributed multiagent modeling, metaheuristic algorithms, multisource data fusion, mobile computing and mobile sensing, machine learning-based intelligent processing for modeling complex manufacturing systems, and data-driven intelligent modeling
Machine Learning Refined : Foundations, Algorithms, and Applications
Provides students with the fundamental knowledge and practical tools needed to conduct research and build data-driven products. The authors prioritize geometric intuition and algorithmic thinking, and include detail on all the essential mathematical prerequisites, to offer a fresh and accessible way to learn. Practical applications are emphasized, with examples from disciplines including computer vision, natural language processing, economics, neuroscience, recommender systems, physics, and biology.
Machine learning in healthcare : Fundamentals and recent applications
Discusses how to build various ML algorithms and how they can be applied to improve healthcare systems. Healthcare applications of AI are innumerable: medical data analysis, early detection and diagnosis of disease, providing objective-based evidence to reduce human errors, curtailing inter- and intra-observer errors, risk identification and interventions for healthcare management, real-time health monitoring, assisting clinicians and patients for selecting appropriate medications, and evaluating drug responses. Extensive demonstrations and discussion on the various principles of machine learning and its application in healthcare is provided, along with solved examples and exercises.
Machine Learning in Computer Vision
The goal of this book is to address the use of several important machine learning techniques into computer vision applications. An innovative combination of computer vision and machine learning techniques has the promise of advancing the field of computer vision, which contributes to better understanding of complex real-world applications. The effective usage of machine learning technology in real-world computer vision problems requires understanding the domain of application, abstraction of a learning problem from a given computer vision task, and the selection of appropriate representations for the learnable (input) and learned (internal) entities of the system. In this book, we address all these important aspects from a new perspective: that the key element in the current computer revolution is the use of machine learning to capture the variations in visual appearance, rather than having the designer of the model accomplish this. As a bonus, models learned from large datasets are likely to be more robust and more realistic than the brittle all-design models.
Machine Learning for Multimedia Content Analysis
Challenges in complexity and variability of multimedia data have led to revolutions in machine learning techniques. Multimedia data, such as digital images, audio streams and motion video programs, exhibit richer structures than simple, isolated data items. A number of pixels in a digital image collectively conveys certain visual content to viewers. A TV video program consists of both audio and image streams that unfold the underlying story. To recognize the visual content of a digital image, or to understand the underlying story of a video program, we may need to label sets of pixels or groups of image and audio frames jointly.
Machine Learning for Cyber Physical Systems : Selected papers from the International Conference ML4CPS 2020
Presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It contains selected papers from the fifth international Conference ML4CPS – Machine Learning for Cyber Physical Systems, which was held in Berlin, March 12-13, 2020.
Machine Learning for Computer Scientists and Data Analysts: From an Applied Perspective
Describes traditional as well as advanced machine learning algorithms / Enables students to learn which algorithm is most appropriate for the data being handled / Includes numerous, practical case-studies; implementation codes in Python available for readers
