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AI-BASED CLINICAL DECISION SUPPORT SYSTEMS USING MULTIMODAL HEALTHCARE DATA

Author: Veena Mayya

Publisher: Veena Mayya

Published: 2023-07-05

Total Pages: 0

ISBN-13: 9788196431549

Healthcare analytics is a branch of data science that examines underlying patterns in healthcare data in order to identify ways in which clinical care can be improved - in terms of patient care, cost optimization, and hospital management. Towards this end, Clinical Decision Support Systems (CDSS) have received extensive research attention over the years. CDSS are intended to influence clinical decision making during patient care. CDSS can be defined as "a link between health observations and health-related knowledge that influences treatment choices by clinicians for improved healthcare delivery".A CDSS is intended to aid physicians and other health care professionals with clinical decision-making tasks based on automated analysis of patient data and other sources of information. CDSS is an evolving system with the potential for wide applicability to improve patient outcomes and healthcare resource utilization. Recent breakthroughs in healthcare analytics have seen an emerging trend in the application of artificial intelligence approaches to assist essential applications such as disease prediction, disease code assignment, disease phenotyping, and disease-related lesion segmentation. Despite the significant benefits offered by CDSSs, there are several issues that need to be overcome to achieve their full potential. There is substantial scope for improvement in terms of patient data modelling methodologies and prediction models, particularly for unstructured clinical data. This thesis discusses several approaches for developing decision support systems towards patient-centric predictive analytics on large multimodal healthcare data. Clinical data in the form of unstructured text, which is rich in patientspecific information sources, has largely remained unexplored and could be potentially used to facilitate effective CDSS development. Effective code assignment for patient clinical records in a hospital plays a significant role in the process of standardizing medical records, mainly for streamlining clinical care delivery, billing, and managing insurance claims. The current practice employed is manual coding, usually carried out by trained medical coders, making the process subjective, error-prone, inexact, and time-consuming.

Reinventing Clinical Decision Support

Author: Paul Cerrato

Publisher: Taylor & Francis

Published: 2020-01-06

Total Pages: 164

ISBN-13: 1000055558

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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.

Diverse Perspectives and State-of-the-Art Approaches to the Utilization of Data-Driven Clinical Decision Support Systems

Author: Connolly, Thomas M.

Publisher: IGI Global

Published: 2022-11-11

Total Pages: 406

ISBN-13: 1668450941

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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.

Multimodal Learning for Clinical Decision Support and Clinical Image-Based Procedures

Author: Tanveer Syeda-Mahmood

Publisher: Springer Nature

Published: 2020-10-03

Total Pages: 147

ISBN-13: 3030609464

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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.

Clinical Decision Support System

Author: Fouad Sabry

Publisher: One Billion Knowledgeable

Published: 2023-07-06

Total Pages: 138

ISBN-13:

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What Is Clinical Decision Support System A clinical decision support system, often known as a CDSS, is a type of health information technology that offers physicians, staff members, patients, and other individuals access to knowledge and information that is personal to them in order to improve health and health care. The Clinical Decision Support System (CDSS) is comprised of several different applications that improve clinical workflow decision-making. These tools include computerized alerts and reminders to care providers and patients, clinical guidelines, condition-specific order sets, focused patient data reports and summaries, documentation templates, diagnostic support, and contextually appropriate reference information, as well as a variety of other tools. A working definition of "health evidence" has been offered by Robert Hayward of the Centre. It reads as follows: "Clinical decision support systems link health observations with health knowledge to influence health choices by clinicians for improved health care." CDSSs comprise a prominent topic in artificial intelligence in medicine. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Clinical decision support system Chapter 2: Gello Expression Language Chapter 3: International Health Terminology Standards Development Organisation Chapter 4: Medical algorithm Chapter 5: Health informatics Chapter 6: Personal Health Information Protection Act Chapter 7: Treatment decision support Chapter 8: Artificial intelligence in healthcare Chapter 9: Health information technology Chapter 10: Applications of artificial intelligence (II) Answering the public top questions about clinical decision support system. (III) Real world examples for the usage of clinical decision support system in many fields. (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of clinical decision support system' technologies. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of clinical decision support system.

Clinical Decision Support Systems

Author: Eta S. Berner

Publisher: Springer Science & Business Media

Published: 2007-04-03

Total Pages: 278

ISBN-13: 0387383190

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This is a resource book on clinical decision support systems for informatics specialists, a textbook for teachers or students in health informatics and a comprehensive introduction for clinicians. It has become obvious that, in addition to physicians, other health professionals have need of decision support. Therefore, the issues raised in this book apply to a broad range of clinicians. The book includes chapters written by internationally recognized experts on the design, evaluation and application of these systems, who examine the impact of computer-based diagnostic tools both from the practitioner’s perspective and that of the patient.

Deep Learning for Medical Decision Support Systems

Author: Utku Kose

Publisher: Springer Nature

Published: 2020-06-17

Total Pages: 185

ISBN-13: 981156325X

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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.

Clinical Decision Support and Beyond

Author: Robert Greenes

Publisher: Academic Press

Published: 2023-02-10

Total Pages: 880

ISBN-13: 0323995772

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Clinical Decision Support and Beyond: Progress and Opportunities in Knowledge-Enhanced Health and Healthcare, now in its third edition, discusses the underpinnings of effective, reliable, and easy-to-use clinical decision support systems at the point of care as a productive way of managing the flood of data, knowledge, and misinformation when providing patient care. Incorporating CDS into electronic health record systems has been underway for decades; however its complexities, costs, and user resistance have lagged its potential. Thus it is of utmost importance to understand the process in detail, to take full advantage of its capabilities. The book expands and updates the content of the previous edition, and discusses topics such as integration of CDS into workflow, context-driven anticipation of needs for CDS, new forms of CDS derived from data analytics, precision medicine, population health, integration of personal monitoring, and patient-facing CDS. In addition, it discusses population health management, public health CDS and CDS to help reduce health disparities. It is a valuable resource for clinicians, practitioners, students and members of medical and biomedical fields who are interested to learn more about the potential of clinical decision support to improve health and wellness and the quality of health care. Presents an overview and details of the current state of the art and usefulness of clinical decision support, and how to utilize these capabilities Explores the technological underpinnings for developing, managing, and sharing knowledge resources and deploying them as CDS or for other uses Discusses the current drivers and opportunities that are expanding the prospects for use of knowledge to enhance health and healthcare

Advanced Computational Intelligence Paradigms in Healthcare 5

Author: Sheryl Brahnam

Publisher: Springer

Published: 2010-10-14

Total Pages: 235

ISBN-13: 3642160956

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This book is a continuation of the previous volumes of our series on Advanced Computational Intelligence Paradigms in Healthcare. The recent advances in computational intelligence paradigms have highlighted the need of intelligent systems in healthcare. This volume provides the reader a glimpse of the current state of the art in intelligent support system design in the field of healthcare. The book reports a sample of recent advances in: • Clinical Decision Support Systems • Rehabilitation Decision Support Systems • Technology Acceptance in Medical Decision Support Systems The book is directed to the researchers, professors, practitioner and students interested to design and develop intelligent decision support systems.

Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Support

Author: Kenji Suzuki

Publisher: Springer Nature

Published: 2019-10-24

Total Pages: 93

ISBN-13: 3030338509

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This book constitutes the refereed joint proceedings of the Second International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2019, and the 9th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2019, held in conjunction with the 22nd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2019, in Shenzhen, China, in October 2019. The 7 full papers presented at iMIMIC 2019 and the 3 full papers presented at ML-CDS 2019 were carefully reviewed and selected from 10 submissions to iMIMIC and numerous submissions to ML-CDS. The iMIMIC papers focus on introducing the challenges and opportunities related to the topic of interpretability of machine learning systems in the context of medical imaging and computer assisted intervention. The ML-CDS papers discuss machine learning on multimodal data sets for clinical decision support and treatment planning.