This book provides applications of machine learning in healthcare systems and seeks to close the gap between engineering and medicine. It will combine the design and problem-solving skills of engineering with health sciences, in order to advance healthcare treatment. The book will include areas such as diagnosis, monitoring, and therapy.
The book will provide real-world case studies, gives a detailed exploration of applications in healthcare systems, offers multiple perspectives on a variety of disciplines, while also letting the reader know how to avoid some of the consequences of old methods with data sharing.
The book can be used as a reference for practitioners, researchers and for students at basic and intermediary levels in Computer Science, Electronics and Communications.
Автор: Damnjanovic Ivan Название: Data Analytics for Engineering and Construction Project Ris ISBN: 3030142507 ISBN-13(EAN): 9783030142506 Издательство: Springer Рейтинг: Цена: 11179.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Chapter 1: Introduction to Risk and Uncertainty. This chapter provides: a) general discussion on the types of uncertainties in projects including the examples; we cover theoretical, frequentist, belief-based epistemic, as well as agnostic viewpoints on the uncertainty; we show these viewpoints in context of typical project uncertainties and contrast them against representations of uncertainty in other engineering disciplines; b) summary on the role of knowledge and assumptions in characterizing the uncertainty; we link the discussion on uncertainty to knowledge about the underlying phenomena, the embedded assumptions, and their validity over the course of the project; c) overview on the approaches that relate the risk to the underlying uncertainty; we discuss approaches to the risk-uncertainty relationship in different disciplines, and finally d) discussion on the organizational attitude and viewpoints toward the risk and uncertainty; we cover topics such as value of u
ncertainty (is it always bad?), organizational responsibility towards risk (who should be taking risk, when, and how much?), and the contrast between the decision-theoretic vs. managerial viewpoint on the uncertainty showing the differences that govern the choice of analysis and the methods.
Chapter 2.Project Risk Management Framework. This chapter provides: a) overview of the project systems, their complexity, life-cycle and risk-based decision-making; we define project as a complex system, and its life-cycle in the context of phase-gate process where decisions are evaluated under different objectives and criteria; we emphasize the points where the uncertainty is introduced and when it is reflected in project outcomes; we particularly stress the design and construction/installation i.e. execution phases of a project as this is the key focus of this text; b) outline of the high-level guidelines in conducting risk assessment and management (such as
ISO and PMI approach), the use of "risk language" and common terms in communicating risk (such as SRA glossary of terms), and more detailed description of each step; we particularly emphasize risk identification and assessment as they are the key focus of this text; c) formal definition of risk in projects distinguishing between variability of operations, event driven risk factors, and the combination of the two; also, we discuss risks in context of low probability - high impact and low impact - high probability; we emphasize the role of assumptions and knowledge in formally developing risk statement; and finally d) classifications methods for project risks as they relate to project objectives, their inception and resolution period, relationship to project structure i.e. internal-external, technical-no technical, and other key project parameters. The chapter includes homework examples.
Chapter 3: Project Data. This chapter provides a comprehensive summary on the type and sources of project data, and the methods for data acquisition. The key underpinning of this text is that risk analysis should be driven by data in a mathematically rigorous way; so where can one find such data? This chapter covers project data as they relate to planning and execution phase of the project; more specifically, we discuss data in terms of: a) project phase and system of interest; we contrast available data during planning and estimation vs. data during monitoring and control phase of the project, as well as whether data relates to internal project system (logistics, operations, etc.) or environmental systems (weather, market trends, etc), we define data collection objectives for each of the phase and the system type; b) observed vs. judgement/simulated data, or in other words, whether data is generated by the system and recorded by the participants, or assessed by individuals using their experience, judgements, models, or just gut f
Описание: Targeted analytics to address the unique opportunities in hospitality and gaming The Analytic Hospitality Executive helps decision makers understand big data and how it can drive value in the industry.
Автор: Crane, Md, Mba Название: The Definitive Guide To Emergency D ISBN: 1498774504 ISBN-13(EAN): 9781498774505 Издательство: Taylor&Francis Рейтинг: Цена: 11023.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This revised and updated book explores the academics behind managing the complex service environment that is the ED by combining applied management science and practical experiences to create a model of how to improve operations.
Описание: This book aims to stay one step beyond the innovations of information and communication technologies and smart healthcare management and provides an overview of the risks smart healthcare management could help to alleviate, and those risks it would create or amplify. Inclusive discussions of the core of smart healthcare services in the perspective of system engineering are enclosed, such as smart healthcare definition, data information knowledge service, and intelligent hospital management. Summaries of technological and theoretical innovations spanning each step of the modern healthcare system are included, from health screening, clinical diagnosis, cancer screening, to in-hospital mortality monitoring, minimally invasive surgeries, and medical data storages. Analytics of risks reduced and induced by these innovations are provided, with potential solutions to such risks in healthcare management discussed. This book seeks to provide demonstrative examples of incidence capable innovations of healthcare technologies, which, while greatly enhancing abilities of healthcare workers and institutions, could pose risks to patients and sometimes even greater threats to the integrity of the healthcare system. The style of the book is intended to be demonstrative but most suited for researchers and graduate students, explaining the methodology behind healthcare innovations, with some citations and some deep scholarly reference.
Описание: Features statistical and operational research methods and tools being used to improve the healthcare industry With a focus on cutting–edge approaches to the quickly growing field of healthcare, Healthcare Analytics: From Data to Knowledge to Healthcare Improvement provides an integrated and comprehensive treatment on recent research advancements in data–driven healthcare analytics in an effort to provide more personalized and efficient healthcare services. Emphasizing data and healthcare analytics from an operational management and statistical perspective, the book details how analytical methods and tools can be utilized to enhance care quality and operational efficiency. Organized into two main sections, Part One features biomedical and health informatics and specifically addresses the analytics of genomic and proteomic data; physiological signals from patient monitoring systems; data uncertainty in clinical laboratory tests; predictive modeling; disease modeling for sepsis; and the design of cyber infrastructures for early prediction of epidemic events. Part Two focuses on healthcare delivery systems, including system advances for transforming clinic workflow and patient care; macro analysis of patient flow distribution; intensive care units; primary care; demand and resource allocation; mathematical models for predicting patient readmission and postoperative outcome; physician–patient interactions; insurance claims; and the role of social media in healthcare. Healthcare Analytics: From Data to Knowledge to Healthcare Improvement also features: Contributions from well–known international experts who shed light on new approaches in this growing area Discussions on contemporary methods and techniques to address the handling of rich and large–scale healthcare data as well as the overall optimization of healthcare system operations Numerous real–world examples and case studies that emphasize the vast potential of statistical and operational research tools and techniques to address the big data environment within the healthcare industry Plentiful applications that showcase the various analytical methods and tools that can be applied to successful predictive modeling The book is an ideal reference for academics and practitioners in operations research, management science, applied mathematics, statistics, business, industrial and systems engineering, healthcare systems, and economics. Healthcare Analytics: From Data to Knowledge to Healthcare Improvement is also appropriate for graduate–level courses typically offered within operations research, industrial engineering, business, and public health departments. Hui Yang, PhD, is Associate Professor in the Harold and Inge Marcus Department of Industrial and Manufacturing Engineering at The Pennsylvania State University. His research interests include sensor–based modeling and analysis of complex systems for process monitoring/control; system diagnostics/prognostics; quality improvement; and performance optimization with special focus on nonlinear stochastic dynamics and the resulting chaotic, recurrence, self–organizing behaviors. Eva K. Lee, PhD, is Professor in the H. Milton Stewart School of Industrial and Systems Engineering at the Georgia Institute of Technology, Director of the Center for Operations Research in Medicine and HealthCare, and Distinguished Scholar in Health System, Health Systems Institute at both Emory University School of Medicine and Georgia Institute of Technology. Her research interests include health risk prediction; early disease prediction and diagnosis; optimal treatment strategies and drug delivery; healthcare outcome analysis and treatment prediction; public health and medical preparedness; large–scale healthcare/medical decision analysis and quality improvement; clinical translational science; and business intelligence and organization transformation.
Chapter 1: Introduction to Risk and Uncertainty. This chapter provides: a) general discussion on the types of uncertainties in projects including the examples; we cover theoretical, frequentist, belief-based epistemic, as well as agnostic viewpoints on the uncertainty; we show these viewpoints in context of typical project uncertainties and contrast them against representations of uncertainty in other engineering disciplines; b) summary on the role of knowledge and assumptions in characterizing the uncertainty; we link the discussion on uncertainty to knowledge about the underlying phenomena, the embedded assumptions, and their validity over the course of the project; c) overview on the approaches that relate the risk to the underlying uncertainty; we discuss approaches to the risk-uncertainty relationship in different disciplines, and finally d) discussion on the organizational attitude and viewpoints toward the risk and uncertainty; we cover topics such as value of u
ncertainty (is it always bad?), organizational responsibility towards risk (who should be taking risk, when, and how much?), and the contrast between the decision-theoretic vs. managerial viewpoint on the uncertainty showing the differences that govern the choice of analysis and the methods.
Chapter 2.Project Risk Management Framework. This chapter provides: a) overview of the project systems, their complexity, life-cycle and risk-based decision-making; we define project as a complex system, and its life-cycle in the context of phase-gate process where decisions are evaluated under different objectives and criteria; we emphasize the points where the uncertainty is introduced and when it is reflected in project outcomes; we particularly stress the design and construction/installation i.e. execution phases of a project as this is the key focus of this text; b) outline of the high-level guidelines in conducting risk assessment and management (such as
ISO and PMI approach), the use of "risk language" and common terms in communicating risk (such as SRA glossary of terms), and more detailed description of each step; we particularly emphasize risk identification and assessment as they are the key focus of this text; c) formal definition of risk in projects distinguishing between variability of operations, event driven risk factors, and the combination of the two; also, we discuss risks in context of low probability - high impact and low impact - high probability; we emphasize the role of assumptions and knowledge in formally developing risk statement; and finally d) classifications methods for project risks as they relate to project objectives, their inception and resolution period, relationship to project structure i.e. internal-external, technical-no technical, and other key project parameters. The chapter includes homework examples.
Chapter 3: Project Data. This chapter provides a comprehensive summary on the type and sources of project data, and the methods for data acquisition. The key underpinning of this text is that risk analysis should be driven by data in a mathematically rigorous way; so where can one find such data? This chapter covers project data as they relate to planning and execution phase of the project; more specifically, we discuss data in terms of: a) project phase and system of interest; we contrast available data during planning and estimation vs. data during monitoring and control phase of the project, as well as whether data relates to internal project system (logistics, operations, etc.) or environmental systems (weather, market trends, etc), we define data collection objectives for each of the phase and the system type; b) observed vs. judgement/simulated data, or in other words, whether data is generated by the system and recorded by the participants, or assessed by individuals using their experience, judgements, models, or just gut f
Автор: Paola De Vincentiis; Francesca Culasso; Stefano A. Название: The Future of Risk Management, Volume I ISBN: 3030145476 ISBN-13(EAN): 9783030145477 Издательство: Springer Рейтинг: Цена: 22359.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: With contributions presented during the Second International Risk Management Conference, this first volume addresses important areas of risk management from a variety of angles and perspectives. The book will cover three separate tracks, including: legal issues in risk management, risk management in the public sector and in healthcare, and environmental risk management, and will be of interest to academic researchers and students in risk management, banking, and finance.
The Covid 19 pandemic has created chaos in the business world and forced leaders to rethink their operational status quo. Balancing the physical and virtual spaces of the global digital economy wherein economic, commercial, and professional transactions are enabled by information and communication technologies has called for additional support from data-driven technologies like smart analytics and artificial intelligence. Opportunities created within digital economies to leverage technologies to execute tasks better, faster, and often differently have found the desired prominence in the recent past. Though the benefits outweigh the risks, the challenges in digitalised economies are as sophisticated as the solutions they offer.
Contemporary Studies in Economic and Financial Analysis publishes a series of current and relevant themed volumes within the fields of economics and finance. Both disciplinary and interdisciplinary studies are welcome.
Автор: Marr, Bernard (advanced Performance Institute, Buckinghamshire, Uk) Название: Big data ISBN: 1118965833 ISBN-13(EAN): 9781118965832 Издательство: Wiley Рейтинг: Цена: 2058.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
The Covid 19 pandemic has created chaos in the business world and forced leaders to rethink their operational status quo. Balancing the physical and virtual spaces of the global digital economy wherein economic, commercial, and professional transactions are enabled by information and communication technologies has called for additional support from data-driven technologies like smart analytics and artificial intelligence. Opportunities created within digital economies to leverage technologies to execute tasks better, faster, and often differently have found the desired prominence in the recent past. Though the benefits outweigh the risks, the challenges in digitalised economies are as sophisticated as the solutions they offer.
Smart Analytics, Artificial Intelligence and Sustainable Performance Management in a Global Digitalized Economy presents various viewpoints on topics like artificial intelligence, blockchain technology, digitalisation in various sectors, technology issues like cybersecurity and financial inclusion, and technology-enabled banking issues like money laundering. The theme of sustainability forms the core of the book.
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