Explainable Artificial Intelligence and Process Mining Applications for Healthcare
Author: Jose M. Juarez
Publisher: Springer Nature
Published:
Total Pages: 140
ISBN-13: 3031543033
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Process Mining in Healthcare
Author: Ronny S. Mans
Publisher: Springer
Published: 2015-03-12
Total Pages: 99
ISBN-13: 3319160710
DOWNLOAD EBOOKWhat are the possibilities for process mining in hospitals? In this book the authors provide an answer to this question by presenting a healthcare reference model that outlines all the different classes of data that are potentially available for process mining in healthcare and the relationships between them. Subsequently, based on this reference model, they explain the application opportunities for process mining in this domain and discuss the various kinds of analyses that can be performed. They focus on organizational healthcare processes rather than medical treatment processes. The combination of event data and process mining techniques allows them to analyze the operational processes within a hospital based on facts, thus providing a solid basis for managing and improving processes within hospitals. To this end, they also explicitly elaborate on data quality issues that are relevant for the data aspects of the healthcare reference model. This book mainly targets advanced professionals involved in areas related to business process management, business intelligence, data mining, and business process redesign for healthcare systems as well as graduate students specializing in healthcare information systems and process analysis.
Advances in Smart Healthcare Paradigms and Applications
Author: Halina Kwaśnicka
Publisher: Springer Nature
Published: 2023-08-16
Total Pages: 230
ISBN-13: 3031373065
DOWNLOAD EBOOKThis book is dedicated to showcase research and innovation in smart healthcare systems and technologies led by women scientists, researchers, and practitioners. With the advent of artificial intelligence (AI) and related technologies, the healthcare sector has undergone tremendous changes in practice and management in recent years. On par to men, women have made significant contributions to tackle a variety of healthcare problems, creating smarter paradigms to provide effective and efficient solutions for patients and stakeholders. The book presents a small collection of contributions by outstanding women in STEM (Science, Technology, Engineering and Mathematics) education, focusing on the healthcare domain. The selected articles allow readers to comprehend current advances in AI and other methods for undertaking healthcare challenges. It is envisaged that the inspiring work by prominent women scientists, researchers, and practitioners reported in this book offers a beacon to propel women in pursuing STEM education and advancing the healthcare sector for the benefits of humankind.
Convergence of Blockchain and Explainable Artificial Intelligence
Author: Akansha Singh
Publisher: CRC Press
Published: 2024-10-30
Total Pages: 180
ISBN-13: 8770046395
DOWNLOAD EBOOKExplainable AI (XAI) is an upcoming research field in the domain of machine learning. This book aims to provide a detailed description of the topics related to XAI and Blockchain. These two technologies can benefit each other, and the research outcomes will benefit society in multiple ways. Existing AI systems make decisions in a black box manner. Explainable AI delineates how an AI system arrived at a particular decision. It inspects the steps and models that are responsible for making a particular decision. It is an upcoming trend that aims at providing explanations to the AI decisions. Blockchain is emerging as an effective technique for XAI. It enables accessibility to digital ledgers amongst the various AI agents. The AI agents collaborate using consensus and decisions are saved on Blocks. These blocks can be traced back but cannot be changed. Thus, the combination of AI with blockchain provides transparency and visibility to all AI decisions. BlockXAI is also being widely used for improving data security and intelligence. The decisions made are consensus based and decentralized leading to highly efficient AI systems. This book also covers topics that present the convergence of Blockchain with explainable AI and will provide researchers, academics, and industry experts with a complete guide to BlockXAI.
Interactive Process Mining in Healthcare
Author: Carlos Fernandez-Llatas
Publisher: Springer Nature
Published: 2020-10-28
Total Pages: 310
ISBN-13: 3030539938
DOWNLOAD EBOOKThis book provides a practically applicable guide to the methodologies and technologies for the application of interactive process mining paradigm. Case studies are presented where this paradigm has been successfully applied in emergency medicine, surgery processes, human behavior modelling, strokes and outpatients’ services, enabling the reader to develop a deep understanding of how to apply process mining technologies in healthcare to support them in inferring new knowledge from past actions, and providing accurate and personalized knowledge to improve their future clinical decision-making. Interactive Process Mining in Healthcare comprehensively covers how machine learning algorithms can be utilized to create real scientific evidence to improve daily healthcare protocols, and is a valuable resource for a variety of health professionals seeking to develop new methods to improve their clinical decision-making.
Artificial Intelligence in Medicine
Author: Jose M. Juarez
Publisher: Springer Nature
Published: 2023-06-04
Total Pages: 398
ISBN-13: 3031343441
DOWNLOAD EBOOKThis book constitutes the refereed proceedings of the 21st International Conference on Artificial Intelligence in Medicine, AIME 2023, held in Portoroz, Slovenia, in June12–15, 2023. The 23 full papers and 21 short papers presented together with 3 demonstration papers were selected from 108 submissions. The papers are grouped in topical sections on: machine learning and deep learning; explainability and transfer learning; natural language processing; image analysis and signal analysis; data analysis and statistical models; knowledge representation and decision support.
Explainable Machine Learning for Multimedia Based Healthcare Applications
Author: M. Shamim Hossain
Publisher: Springer Nature
Published:
Total Pages: 240
ISBN-13: 3031380363
DOWNLOAD EBOOKExplainable Artificial Intelligence for Smart Cities
Author: Mohamed Lahby
Publisher: CRC Press
Published: 2021-11-09
Total Pages: 361
ISBN-13: 1000472361
DOWNLOAD EBOOKThanks to rapid technological developments in terms of Computational Intelligence, smart tools have been playing active roles in daily life. It is clear that the 21st century has brought about many advantages in using high-level computation and communication solutions to deal with real-world problems; however, more technologies bring more changes to society. In this sense, the concept of smart cities has been a widely discussed topic in terms of society and Artificial Intelligence-oriented research efforts. The rise of smart cities is a transformation of both community and technology use habits, and there are many different research orientations to shape a better future. The objective of this book is to focus on Explainable Artificial Intelligence (XAI) in smart city development. As recently designed, advanced smart systems require intense use of complex computational solutions (i.e., Deep Learning, Big Data, IoT architectures), the mechanisms of these systems become ‘black-box’ to users. As this means that there is no clear clue about what is going on within these systems, anxieties regarding ensuring trustworthy tools also rise. In recent years, attempts have been made to solve this issue with the additional use of XAI methods to improve transparency levels. This book provides a timely, global reference source about cutting-edge research efforts to ensure the XAI factor in smart city-oriented developments. The book includes both positive and negative outcomes, as well as future insights and the societal and technical aspects of XAI-based smart city research efforts. This book contains nineteen contributions beginning with a presentation of the background of XAI techniques and sustainable smart-city applications. It then continues with chapters discussing XAI for Smart Healthcare, Smart Education, Smart Transportation, Smart Environment, Smart Urbanization and Governance, and Cyber Security for Smart Cities.
Explainable Artificial Intelligence for Biomedical and Healthcare Applications
Author: Aditya Khamparia
Publisher: CRC Press
Published: 2024-10-09
Total Pages: 303
ISBN-13: 1040126375
DOWNLOAD EBOOKThis reference text helps us understand how the concepts of explainable artificial intelligence (XAI) are used in the medical and healthcare sectors. The text discusses medical robotic systems using XAI and physical devices having autonomous behaviors for medical operations. It explores the usage of XAI for analyzing different types of unique data sets for medical image analysis, medical image registration, medical data synthesis, and information discovery. It covers important topics including XAI for biometric security, genomics, and medical disease diagnosis. This book: • Provides an excellent foundation for the core concepts and principles of explainable AI in biomedical and healthcare applications. • Covers explainable AI for robotics and autonomous systems. • Discusses usage of explainable AI in medical image analysis, medical image registration, and medical data synthesis. • Examines biometrics security-assisted applications and their integration using explainable AI. The text will be useful for graduate students, professionals, and academic researchers in diverse areas such as electrical engineering, electronics and communication engineering, biomedical engineering, and computer science.
Explainable AI: Interpreting, Explaining and Visualizing Deep Learning
Author: Wojciech Samek
Publisher: Springer Nature
Published: 2019-09-10
Total Pages: 435
ISBN-13: 3030289540
DOWNLOAD EBOOKThe development of “intelligent” systems that can take decisions and perform autonomously might lead to faster and more consistent decisions. A limiting factor for a broader adoption of AI technology is the inherent risks that come with giving up human control and oversight to “intelligent” machines. For sensitive tasks involving critical infrastructures and affecting human well-being or health, it is crucial to limit the possibility of improper, non-robust and unsafe decisions and actions. Before deploying an AI system, we see a strong need to validate its behavior, and thus establish guarantees that it will continue to perform as expected when deployed in a real-world environment. In pursuit of that objective, ways for humans to verify the agreement between the AI decision structure and their own ground-truth knowledge have been explored. Explainable AI (XAI) has developed as a subfield of AI, focused on exposing complex AI models to humans in a systematic and interpretable manner. The 22 chapters included in this book provide a timely snapshot of algorithms, theory, and applications of interpretable and explainable AI and AI techniques that have been proposed recently reflecting the current discourse in this field and providing directions of future development. The book is organized in six parts: towards AI transparency; methods for interpreting AI systems; explaining the decisions of AI systems; evaluating interpretability and explanations; applications of explainable AI; and software for explainable AI.
