Data Driven Clinical Decision Making Using Deep Learning In Imaging
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Data Driven Clinical Decision Making Using Deep Learning In Imaging
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Author : M. F. Mridha
language : en
Publisher: Springer Nature
Release Date :
Data Driven Clinical Decision Making Using Deep Learning In Imaging written by M. F. Mridha and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
Deep Learning For Medical Applications With Unique Data
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Author : Deepak Gupta
language : en
Publisher: Academic Press
Release Date : 2022-02-15
Deep Learning For Medical Applications With Unique Data written by Deepak Gupta and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-15 with Science categories.
Deep Learning for Medical Applications with Unique Data informs readers about the most recent deep learning-based medical applications in which only unique data gathered in real cases are used. The book provides examples of how deep learning can be used in different problem areas and frameworks in both clinical and research settings, including medical image analysis, medical image registration, time series analysis, medical data synthesis, drug discovery, and pre-processing operations. The volume discusses not only positive findings, but also negative ones obtained by deep learning techniques, including the use of newly developed deep learning techniques rarely reported in the existing literature. The book excludes research works with ready data sets and includes only unique data use to better understand the state of deep learning in real-world cases, along with the feedback and user experiences from physicians and medical staff for applied deep learning-based solutions. Other applications presented in the book include hybrid solutions with deep learning support, disease diagnosis with deep learning focusing on rare diseases and cancer, patient care and treatment, genomics research, as well as research on robotics and autonomous systems. Introduces deep learning, demonstrating concepts for a wide variety of medical applications using unique data, excluding research with ready datasets Encompasses a wide variety of biomedical applications, including unsupervised learning, natural language processing, pattern recognition, image and video processing and disease diagnosis Provides a robust set of methods that will help readers appropriately and judiciously use the most suitable deep learning techniques for their applications
Reinventing Clinical Decision Support
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Author : Paul Cerrato
language : en
Publisher: Taylor & Francis
Release Date : 2020-01-06
Reinventing Clinical Decision Support written by Paul Cerrato and has been published by Taylor & Francis this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-01-06 with Business & Economics categories.
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.
Multimodal Learning For Clinical Decision Support And Clinical Image Based Procedures
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Author : Tanveer Syeda-Mahmood
language : en
Publisher: Springer Nature
Release Date : 2020-10-03
Multimodal Learning For Clinical Decision Support And Clinical Image Based Procedures written by Tanveer Syeda-Mahmood and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-03 with Computers categories.
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.
The Combination Of Data Driven Machine Learning Approaches And Prior Knowledge For Robust Medical Image Processing And Analysis
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Author : Jinming Duan
language : en
Publisher: Frontiers Media SA
Release Date : 2024-06-11
The Combination Of Data Driven Machine Learning Approaches And Prior Knowledge For Robust Medical Image Processing And Analysis written by Jinming Duan and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-06-11 with Medical categories.
With the availability of big image datasets and state-of-the-art computing hardware, data-driven machine learning approaches, particularly deep learning, have been used in numerous medical image (CT-scans, MRI, PET, SPECT, etc..) computing tasks, ranging from image reconstruction, super-resolution, segmentation, registration all the way to disease classification and survival prediction. However, training such high-precision approaches often require large amounts of data to be collected and labelled and high-capacity graphics processing units (GPUs) installed, which are resource intensive and hence not always practical. Other hurdles such as the generalization ability to unseen new data and difficulty to interpret and explain can prevent their deployment to those clinical applications which deem such abilities imperative.
Deep Learning In Personalized Healthcare And Decision Support
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Author : Harish Garg
language : en
Publisher: Elsevier
Release Date : 2023-07-20
Deep Learning In Personalized Healthcare And Decision Support written by Harish Garg and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-20 with Computers categories.
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
Deep Learning Techniques For Biomedical And Health Informatics
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Author : Basant Agarwal
language : en
Publisher: Academic Press
Release Date : 2020-01-14
Deep Learning Techniques For Biomedical And Health Informatics written by Basant Agarwal and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-01-14 with Science categories.
Deep Learning Techniques for Biomedical and Health Informatics provides readers with the state-of-the-art in deep learning-based methods for biomedical and health informatics. The book covers not only the best-performing methods, it also presents implementation methods. The book includes all the prerequisite methodologies in each chapter so that new researchers and practitioners will find it very useful. Chapters go from basic methodology to advanced methods, including detailed descriptions of proposed approaches and comprehensive critical discussions on experimental results and how they are applied to Biomedical Engineering, Electronic Health Records, and medical image processing. Examines a wide range of Deep Learning applications for Biomedical Engineering and Health Informatics, including Deep Learning for drug discovery, clinical decision support systems, disease diagnosis, prediction and monitoring Discusses Deep Learning applied to Electronic Health Records (EHR), including health data structures and management, deep patient similarity learning, natural language processing, and how to improve clinical decision-making Provides detailed coverage of Deep Learning for medical image processing, including optimizing medical big data, brain image analysis, brain tumor segmentation in MRI imaging, and the future of biomedical image analysis
Medical Image Processing Using Ai
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Author : Priyanka Sharma
language : en
Publisher: Academic Guru Publishing House
Release Date : 2024-04-19
Medical Image Processing Using Ai written by Priyanka Sharma and has been published by Academic Guru Publishing House this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-19 with Study Aids categories.
"Medical Image Processing Using AI" provides a thorough review of the current breakthroughs, methodologies, and applications in medical imaging with artificial intelligence (AI). This book by leading medical imaging and AI researchers delves into the junction of these two fields, giving readers the insights and practical expertise to navigate AI-driven medical image analysis. The book covers medical imaging and AI's fundamental principles and methods, including image acquisition, preprocessing, feature extraction, and machine learning algorithms for medical image processing. Readers master the key principles and strategies needed to use AI in medical imaging via simple explanations and examples. As readers progress through the chapters, they are introduced to a diverse array of clinical applications and use cases where AI has made significant inroads, revolutionizing diagnostic workflows, treatment planning, and patient care across various medical specialties. Real-world case studies and examples illustrate how AI algorithms are being deployed in radiology, pathology, oncology, cardiology, and other fields to enhance diagnostic accuracy, improve treatment outcomes, and optimize clinical decision-making. Moreover, the book explores the ethical considerations, challenges, and future directions shaping the landscape of AI-driven medical image processing. From data privacy and algorithmic bias to regulatory frameworks and clinical integration, readers gain insight into the broader implications of AI in healthcare and the importance of responsible and equitable deployment of AI technologies.
Machine Learning Analytics For Data Driven Decision Support In Healthcare
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Author : Andrew Thomas Ward
language : en
Publisher:
Release Date : 2020
Machine Learning Analytics For Data Driven Decision Support In Healthcare written by Andrew Thomas Ward and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
Machine learning has the potential to revolutionize the field of healthcare. With the increasing availability of electronic healthcare data, machine learning algorithms and techniques are able to offer novel data-driven insights in the form of descriptive, predictive, and prescriptive analytics. Research efforts in machine learning-driven clinical decision support systems have demonstrated performance comparable to, or surpassing, that of doctors across a wide range of disciplines. However, very few of these solutions are implemented and used. This may be due to the solution being too specialized, too difficult to operationalize, or both. My research in machine learning for clinical decision support has focused on delivering broadly applicable and clinically actionable predictions for heart disease and opioid use and misuse. As some of the leading causes of death in the US and worldwide, these are important public health concerns. A less-explored facet of decision support in healthcare lies on operational delivery of care: improving hospital efficiency, modeling patient admissions and discharges, and preventing medical errors. While these research topics are not as popular as their clinical counterparts, the potential for real-world improvement through the study of these issues is far greater in the near-term. In this dissertation, I present novel contributions spanning both the clinical and operational delivery of care. I focus on four lines of data-driven research which have the potential to deliver widespread impact: heart disease prediction, opioid use prediction in pediatric patients, medical error reduction, and hospital discharge planning and resource allocation.
Deep Learning For Medical Decision Support Systems
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Author : Utku Kose
language : en
Publisher: Springer Nature
Release Date : 2020-06-17
Deep Learning For Medical Decision Support Systems written by Utku Kose and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-17 with Technology & Engineering categories.
This book explores various applications of deep learning-oriented diagnosis leading to decision support, while also outlining the future face of medical decision support systems. Artificial intelligence has now become a ubiquitous aspect of modern life, and especially machine learning enjoysgreat popularity, since it offers techniques that are capable of learning from samples to solve newly encountered cases. Today, a recent form of machine learning, deep learning, is being widely used with large, complex quantities of data, because today’s problems require detailed analyses of more data. This is critical, especially in fields such as medicine. Accordingly, the objective of this book is to provide the essentials of and highlight recent applications of deep learning architectures for medical decision support systems. The target audience includes scientists, experts, MSc and PhD students, postdocs, and any readers interested in the subjectsdiscussed. The book canbe used as a reference work to support courses on artificial intelligence, machine/deep learning, medical and biomedicaleducation.