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Author: Hua Xu (S. M.)
Publisher:
ISBN:
Category :
Languages : en
Pages : 80
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Book Description
This thesis helps find limits within which automated methods can support and surpass the capabilities of medical professionals and the limits beyond which these methods are not yet adequate. This will inform later exploration about (a) what improvements in data collection, interpretation, and visualization will enhance technology's capacity and (b) what changes clinicians can make to improve their decision making-augmented or not. This thesis includes (a) describing clinical decisions, informed by literature and clinical case studies and (b) reviewing current capabilities of machine methods. This led to (c) a test experiment-how to use data about a particular condition (e.g. in-hospital mortality rate prediction) from a particular source (the MIMIC III data base). The results will help define current limits on augmenting clinical decisions and establish direction for future work including more demanding experiments. Artificial Intelligence (AI) includes Machine Learning (ML), Natural Language Processing (NLP), Computer Vision, Speech Recognition, and Robotics. As an important branch of Al, ML builds statistical models to learn from sample data, known as "training", identifies patterns, and makes predictions based on new data, known as "inference." In this way, ML is useful in rationalizing and predicting in uncertain environments, with minimum human interventions. Decision making is central to the healthcare practice, with many decisions made under conditions of uncertainty. Clinicians must integrate a huge variety of data while pressured to decrease diagnostic uncertainties and risks to patients. Deciding what information to gather, which test to order, how to interpret and integrate this information to draw diagnostic conclusions, and which treatments to give are essential. In typical situations, clinicians evaluate patient symptoms and potential disease patterns, confirmed by a variety of tests, and they initiate treatments based on their experience and customary practice. This is complicated when multiple illnesses coexist, the illness may be rare, the information may be conflicting, or prior interventions may affect the presenting symptoms.
Author: Hua Xu (S. M.)
Publisher:
ISBN:
Category :
Languages : en
Pages : 80
Get Book Here
Book Description
This thesis helps find limits within which automated methods can support and surpass the capabilities of medical professionals and the limits beyond which these methods are not yet adequate. This will inform later exploration about (a) what improvements in data collection, interpretation, and visualization will enhance technology's capacity and (b) what changes clinicians can make to improve their decision making-augmented or not. This thesis includes (a) describing clinical decisions, informed by literature and clinical case studies and (b) reviewing current capabilities of machine methods. This led to (c) a test experiment-how to use data about a particular condition (e.g. in-hospital mortality rate prediction) from a particular source (the MIMIC III data base). The results will help define current limits on augmenting clinical decisions and establish direction for future work including more demanding experiments. Artificial Intelligence (AI) includes Machine Learning (ML), Natural Language Processing (NLP), Computer Vision, Speech Recognition, and Robotics. As an important branch of Al, ML builds statistical models to learn from sample data, known as "training", identifies patterns, and makes predictions based on new data, known as "inference." In this way, ML is useful in rationalizing and predicting in uncertain environments, with minimum human interventions. Decision making is central to the healthcare practice, with many decisions made under conditions of uncertainty. Clinicians must integrate a huge variety of data while pressured to decrease diagnostic uncertainties and risks to patients. Deciding what information to gather, which test to order, how to interpret and integrate this information to draw diagnostic conclusions, and which treatments to give are essential. In typical situations, clinicians evaluate patient symptoms and potential disease patterns, confirmed by a variety of tests, and they initiate treatments based on their experience and customary practice. This is complicated when multiple illnesses coexist, the illness may be rare, the information may be conflicting, or prior interventions may affect the presenting symptoms.
Author: Paul Cerrato
Publisher: Taylor & Francis
ISBN: 1000055558
Category : Business & Economics
Languages : en
Pages : 164
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Book Description
This book takes an in-depth look at the emerging technologies that are transforming the way clinicians manage patients, while at the same time emphasizing that the best practitioners use both artificial and human intelligence to make decisions. AI and machine learning are explored at length, with plain clinical English explanations of convolutional neural networks, back propagation, and digital image analysis. Real-world examples of how these tools are being employed are also discussed, including their value in diagnosing diabetic retinopathy, melanoma, breast cancer, cancer metastasis, and colorectal cancer, as well as in managing severe sepsis. With all the enthusiasm about AI and machine learning, it was also necessary to outline some of criticisms, obstacles, and limitations of these new tools. Among the criticisms discussed: the relative lack of hard scientific evidence supporting some of the latest algorithms and the so-called black box problem. A chapter on data analytics takes a deep dive into new ways to conduct subgroup analysis and how it’s forcing healthcare executives to rethink the way they apply the results of large clinical trials to everyday medical practice. This re-evaluation is slowly affecting the way diabetes, heart disease, hypertension, and cancer are treated. The research discussed also suggests that data analytics will impact emergency medicine, medication management, and healthcare costs. An examination of the diagnostic reasoning process itself looks at how diagnostic errors are measured, what technological and cognitive errors are to blame, and what solutions are most likely to improve the process. It explores Type 1 and Type 2 reasoning methods; cognitive mistakes like availability bias, affective bias, and anchoring; and potential solutions such as the Human Diagnosis Project. Finally, the book explores the role of systems biology and precision medicine in clinical decision support and provides several case studies of how next generation AI is transforming patient care.
Author: Adam Bohr
Publisher: Academic Press
ISBN: 0128184396
Category : Computers
Languages : en
Pages : 385
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Book Description
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
Author: Ilker Ozsahin
Publisher: Bentham Science Publishers
ISBN: 168108872X
Category : Computers
Languages : en
Pages : 316
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Book Description
This book provides an ideal foundation for readers to understand the application of artificial intelligence (AI) and machine learning (ML) techniques to expert systems in the healthcare sector. It starts with an introduction to the topic and presents chapters which progressively explain decision-making theory that helps solve problems which have multiple criteria that can affect the outcome of a decision. Key aspects of the subject such as machine learning in healthcare, prediction techniques, mathematical models and classification of healthcare problems are included along with chapters which delve in to advanced topics on data science (deep-learning, artificial neural networks, etc.) and practical examples (influenza epidemiology and retinoblastoma treatment analysis). Key Features: - Introduces readers to the basics of AI and ML in expert systems for healthcare - Focuses on a problem solving approach to the topic - Provides information on relevant decision-making theory and data science used in the healthcare industry - Includes practical applications of AI and ML for advanced readers - Includes bibliographic references for further reading The reference is an accessible source of knowledge on multi-criteria decision-support systems in healthcare for medical consultants, healthcare policy makers, researchers in the field of medical biotechnology, oncology and pharmaceutical research and development.
Author: M. F. Mridha
Publisher: Springer
ISBN: 9789819739653
Category : Computers
Languages : en
Pages : 0
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Book Description
This book explores cutting-edge medical imaging advancements and their applications in clinical decision-making. The book contains various topics, methodologies, and applications, providing readers with a comprehensive understanding of the field's current state and prospects. It begins with exploring domain adaptation in medical imaging and evaluating the effectiveness of transfer learning to overcome challenges associated with limited labeled data. The subsequent chapters delve into specific applications, such as improving kidney lesion classification in CT scans, elevating breast cancer research through attention-based U-Net architecture for segmentation and classifying brain MRI images for neurological disorders. Furthermore, the book addresses the development of multimodal machine learning models for brain tumor prognosis, the identification of unique dermatological signatures using deep transfer learning, and the utilization of generative adversarial networks to enhance breast cancer detection systems by augmenting mammogram images. Additionally, the authors present a privacy-preserving approach for breast cancer risk prediction using federated learning, ensuring the confidentiality and security of sensitive patient data. This book brings together a global network of experts from various corners of the world, reflecting the truly international nature of its research.
Author: Mihaela Pop
Publisher: Springer
ISBN: 3319594486
Category : Computers
Languages : en
Pages : 524
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Book Description
This book constitutes the refereed proceedings of the 9th International Conference on Functional Imaging and Modeling of the Heart, held in Toronto, ON, Canada, in June 2017. The 48 revised full papers were carefully reviewed and selected from 63 submissions. The focus of the papers is on following topics: novel imaging and analysis methods for myocardial tissue characterization and remodeling; advanced cardiac image analysis tools for diagnostic and interventions; electrophysiology: mapping and biophysical modeling; biomechanics and flow: modeling and tissue property measurements.
Author: Connolly, Thomas M.
Publisher: IGI Global
ISBN: 1668450941
Category : Business & Economics
Languages : en
Pages : 406
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Book Description
The medical domain is home to many critical challenges that stand to be overcome with the use of data-driven clinical decision support systems (CDSS), and there is a growing set of examples of automated diagnosis, prognosis, drug design, and testing. However, the current state of AI in medicine has been summarized as “high on promise and relatively low on data and proof.” If such problems can be addressed, a data-driven approach will be very important to the future of CDSSs as it simplifies the knowledge acquisition and maintenance process, a process that is time-consuming and requires considerable human effort. Diverse Perspectives and State-of-the-Art Approaches to the Utilization of Data-Driven Clinical Decision Support Systems critically reflects on the challenges that data-driven CDSSs must address to become mainstream healthcare systems rather than a small set of exemplars of what might be possible. It further identifies evidence-based, successful data-driven CDSSs. Covering topics such as automated planning, diagnostic systems, and explainable artificial intelligence, this premier reference source is an excellent resource for medical professionals, healthcare administrators, IT managers, pharmacists, students and faculty of higher education, librarians, researchers, and academicians.
Author: Tyler John Loftus
Publisher: Frontiers Media SA
ISBN: 2832533256
Category : Medical
Languages : en
Pages : 121
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Book Description
Author: Joy Higgs
Publisher: Elsevier Health Sciences
ISBN: 0750688858
Category : Medical
Languages : en
Pages : 518
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Book Description
Clinical reasoning is the foundation of professional clinical practice. Totally revised and updated, this book continues to provide the essential text on the theoretical basis of clinical reasoning in the health professions and examines strategies for assisting learners, scholars and clinicians develop their reasoning expertise. key chapters revised and updated nature of clinical reasoning sections have been expanded increase in emphasis on collaborative reasoning core model of clinical reasoning has been revised and updated
Author: Vishal Jain
Publisher: CRC Press
ISBN: 1000846520
Category : Technology & Engineering
Languages : en
Pages : 311
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Book Description
Health care today is known to suffer from siloed and fragmented data, delayed clinical communications, and disparate workflow tools due to the lack of interoperability caused by vendor-locked health care systems, lack of trust among data holders, and security/privacy concerns regarding data sharing. The health information industry is ready for big leaps and bounds in terms of growth and advancement. This book is an attempt to unveil the hidden potential of the enormous amount of health information and technology. Throughout this book, we attempt to combine numerous compelling views, guidelines, and frameworks to enable personalized health care service options through the successful application of deep learning frameworks. The progress of the health-care sector will be incremental as it learns from associations between data over time through the application of suitable AI, deep net frameworks, and patterns. The major challenge health care is facing is the effective and accurate learning of unstructured clinical data through the application of precise algorithms. Incorrect input data leading to erroneous outputs with false positives is intolerable in healthcare as patients’ lives are at stake. This book is written with the intent to uncover the stakes and possibilities involved in realizing personalized health-care services through efficient and effective deep learning algorithms. The specific focus of this book will be on the application of deep learning in any area of health care, including clinical trials, telemedicine, health records management, etc.
