Applications in industry and technology Books

Book SynopsisThis heavily revised open access edition provides a thorough overview of the technologies available to assemble, manage and assess the quality of health information systems. It details a variety of scenarios in the context of both health and heath care, including where prevention and wellness are related, such as the treatment of both acute and chronic diseases. Stakeholder requirements are also described to provide perspectives for describing the architectures and management techniques associated with health information systems, enabling the reader to develop a detailed holistic overview of the subject. Health Information Systems: Technological and Management Perspectives features a detailed overview of how information systems in health care can be managed and is a vital resource for medical informatics students seeking an up-to-date text on the topic.Table of ContentsIntroduction.- Motivation And Objective Of The Book.- Life Situations.- Stakeholders’ Requirements.- Example.- Summary.- Basic Concepts And Terms.- Introduction.- Data, Information And Knowledge.- Systems And Subsystems.- Information Systems.- Health Information Systems.- Information Logistics In Health Information Systems.- Functions And Processes Of Health Care Settings.- Information Processing Tools Of Health Information Systems.- Electronic Health Records As A Part Of Health Information Systems.- Architecture And Infrastructure Of Health Information Systems.- Management Of Health Information Systems.- Modeling Information Systems.- Using The Snik Ontology Together With This Book.- Examples.- Exercises.- Summary.- Technological Perspective: Architectures, Integration And Standards.- Introduction.- Layers Of Architectures.- Integrity And Integration.- Standards For Interoperability.- Specific Health Information Systems.- Examples.- Exercises.- Summary.- Management Perspective: Tasks, Scope And Governance.- Introduction.- Strategic, Tactical And Operational Management Of Information Systems.- Tasks And Methods Of Strategic Management Of Information Systems.- It Service Management.- Data Governance.- It Governance.- Managing Specific Health Information Systems.- Examples.- Exercises.- Summary.- Quintessence: Quality.- Fulfillment Of Stakeholders’ Requirements.- Evaluation.- Summary.- Thesaurus.- Literature Cited.

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Book SynopsisThis book explores the benefits of deploying Machine Learning (ML) and Artificial Intelligence (AI) in the health care environment. The authors study different research directions that are working to serve challenges faced in building strong healthcare infrastructure with respect to the pandemic crisis. The authors take note of obstacles faced in the rush to develop and alter technologies during the Covid crisis. They study what can be learned from them and what can be leveraged efficiently. The authors aim to show how healthcare providers can use technology to exploit advances in machine learning and deep learning in their own applications. Topics include remote patient monitoring, data analysis of human behavioral patterns, and machine learning for decision making in real-time.Table of Contents1. Pandemic effect of COVID-19: Identification, Present scenario and preventive measures using Machine learning model..- 2. A Comprehensive Review of the Smart Health Records to prevent Pandemic.- 3. Automation of COVID-19 Disease Diagnosis from Radiograph.- 4. Applications of Artificial Intelligence in the attainment of Sustainable Development Goals.- 5. A Novel Model for IoT Blockchain Assurance Based Compliance to COVID Quarantine.- 6. DEEP LEARNING BASED CONVOLUTIONALNEURAL NETWORK WITH RANDOM FOREST APPROACH FOR MRI BRAIN TUMOUR SEGMENTATION .- 7. Expert systems for improving the effectiveness of remote health monitoring in Covid-19 Pandemic - A Critical Review.- 8. Artificial Intelligence-based predictive tools for Life-threatening diseases.- 9. Deep Convolutional Generative Adversarial Network for Metastatic Tissue Diagnosis in Lymph Node Section.- 10. Transformation in Health Sector during Pandemic by Photonics Devices .- 11. DIAGNOSIS OF COVID-19 FROM CT IMAGES AND RESPIRATORY SOUND SIGNALS USING DEEP LEARNING STRATEGIES.- 12. The Role of Edge Computing in Pandemic and Epidemic Situations with its Solutions.- 13. Advances and application of Artificial Intelligence and Machine learning in the field of cardiovascular diseases and its role during the Pandemic condition.- 14. Effective Health Screening and Prompt Vaccination to Counter the Spread of Covid-19 and Minimize its Adverse Effects.- 15. CROWD DENSITY ESTIMATION USING NEURAL NETWORK FOR COVID’19 AND FUTURE PANDEMICS.- 16. “Role of digital healthcare in rehabilitation during pandemic”.- 17. AN EPIDEMIC OF NEURODEGENERATIVE DISEASE ANALYSIS USING MACHINE LEARNING TECHNIQUES.- 18. Covid-19 Growth Curve Forecasting for India using Deep Learning Techniques.

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Book SynopsisThis volume LNCS 13912 constitutes the refereed proceedings of the 20th International Symposium on Web and Wireless Geographical Information Systems, W2GIS 2023, in June 12-13, 2023 in Quebec City, QC, Canada. The 9 full papers presented together with 2 short papers were carefully reviewed and selected from 14 submissions. The conference focuses on topics such as Sensors Networks and Data Steaming; Mobility and Navigation; AI for Mobility Data Analytics; Volunteered Geographic information (VGI); Network Analysis and Geovisualization.Table of ContentsKeynote.- An approach for geosensor network optimization to support decisions at multiple Scale.- Sensors Networks and Data Steaming.- Towards Integration of Spatial Context in Building Energy Demand Assessment Supported by CityGML Energy Extension.- Three-stage Framework to Estimate Pedestrian Path by Using Signaling Data and Surveillance Video.- Mobility and Navigation.- Investigating the Navigational Behavior of Wheelchair Users in Urban Environments Using Eye Movement Data.- New Approach for Accessibility Assessment of Side-Walks for Wheelchair Users Considering the Sidewalk Traffic.- AI for Mobility Data Analytics.- Mobility Data Analytics with KNOT: the KNime mObility Toolkit.- Bus Journey Time Prediction with Machine Learning: An Empirical Experience in Two Cities.- A Novel GIS-Based Machine Learning Approach for the Classification of Multi-Motorized Transportation Modes.- Volunteered Geographic information (VGI).- Cimemountainbot: A Telegram Bot to Collect Mountain Images and to Communicate Information With Mountain Guides.- A New Feature Matching Method for Matching OpenStreetMap Buildings with Those of Reference Dataset.- Network Analysis and Geovisualization.- Geovisualisation generation from semantic models: a state of the art.- A Heterogeneous Information Attentive Network for the Identification of Tourist Attraction Competitors.- Poly-GAN: Regularizing Polygons with Generative Adversarial Networks.

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Book SynopsisThis book aims to serve as a playbook and a guide for the creation of a safer healthcare system in the contemporary healthcare ecosystem. It meets this goal through examinations of clinical case studies that illustrate core principles of patient safety, coverage of a broad range of medical errors including medication errors, and solutions to reducing medical errors that are widely applicable in many settings. Throughout the book, the chapters offer viewpoints from healthcare leaders, accomplished practitioners, and experts in patient safety. In addition to highlighting important concepts in patient safety, the book also provides a vision of patient safety in the subsequent decade. Furthermore, it will describe what changes need to “fall into place” between now and the next 10-15 years to have that future realized. The book presents and analyzes a number of cases to illustrate the most common types of medical errors and to help readers learn the key clinical, organizational, and systems issues in patient safety. Patient Safety, 2nd edition, is an invaluable text for all physicians, healthcare workers, policymakers, and residents who are working towards a more equitable and effective healthcare system. Table of ContentsSection I: IntroductionChapter 1 - Patient safety methodologiesChapter 2 - Measuring patient safety Section II: Concepts Chapter 3 - Patient identificationChapter 4 - Teamwork and communication Chapter 5 - Transitions of Care and CommunicationsChapter 6 - Graduate medical education and patient safetyChapter 7 – Information Technology and patient safetyChapter 8–Clinical ethics and patient safetyChapter 9 – Health Equity: A key aspect of patient safety Chapter 10 – High ReliabilitySection III: ExamplesChapter 11 -Medication errorChapter 12 - Medication reconciliation error Chapter 13 - Retained surgical itemsChapter 14 - Wrong-site surgeryChapter 15 - Transfusion-related hazardsChapter 16 - Hospital-acquired infections Chapter 17–Hospital fallsChapter 18- Pressure ulcersChapter 19 - Diagnostic errorSection IV: Special considerations Chapter 20–Patient safety in pediatricsChapter 21 - Patient safety in radiology Chapter 22- Patient safety in anesthesiaChapter 23 - Patient safety in behavioral healthChapter 24–Patient safety in outpatient careChapter 25 - Patient safety in critical careChapter 26: Patient safety in long-term care and nursing homesChapter 27: Patient safety in emergency departmentSection V: Organizational issues Chapter 28–Error disclosureChapter 29–The culture of safetyChapter 30 - Second victim

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Book SynopsisThis book is a beginner's guide to AutomationML Edition 2, written for students, engineers, lecturers, developers and those interested. In guides through the basics of AutomationML Edition 2, CAEX and the AutomationML Editor. AutomationML stands for digitisation of engineering data and engineering workflows. AutomationML achieves both human readability and machine-readability. It is a method for converting data into digital information, and it supports the special needs of iterative engineering data exchange. AutomationML is in the hot spot of the digitisation of automation engineering data. It enables the modelling and transport of engineering data in a vendor neutral and machine-readable models, a valuable source of digital innovation. Machine readable engineering data makes the data accessible and interpretable by software, enabling a plethora of opportunities. This book carefully introduces AutomationML, its goals, values and innovations. It teaches the architecture of AutomationML and explains the language elements with a multitude of examples and step-by-step instructions. Additional material to the book and more information about AutomationML on the website: https://www.automationml.org/about-automationml/publications/amlbook/

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Book SynopsisArtificial intelligence advancements, machine intelligence innovations, and semantic web developments together make up semantic intelligence technologies. The edited book integrates artifi cial intelligence, machine learning, IoT, blockchain, and natural language processing with semantic web technologies. This book also aims to offer real-life solutions to the pressing issues currently being faced by semantic web technologies.

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Book SynopsisMachine Learning under Resource Constraints addresses novel machine learning algorithms that are challenged by high-throughput data, by high dimensions, or by complex structures of the data in three volumes. Resource constraints are given by the relation between the demands for processing the data and the capacity of the computing machinery. The resources are runtime, memory, communication, and energy. Hence, modern computer architectures play a significant role. Novel machine learning algorithms are optimized with regard to minimal resource consumption. Moreover, learned predictions are executed on diverse architectures to save resources. It provides a comprehensive overview of the novel approaches to machine learning research that consider resource constraints, as well as the application of the described methods in various domains of science and engineering. Volume 2 covers machine learning for knowledge discovery in particle and astroparticle physics. Their instruments, e.g., particle detectors or telescopes, gather petabytes of data. Here, machine learning is necessary not only to process the vast amounts of data and to detect the relevant examples efficiently, but also as part of the knowledge discovery process itself. The physical knowledge is encoded in simulations that are used to train the machine learning models. At the same time, the interpretation of the learned models serves to expand the physical knowledge. This results in a cycle of theory enhancement supported by machine learning.

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Book SynopsisThe book presents a theoretical and technical background for applying MAS (Multi Agent Systems) in Architecture, Engineering and Construction. It focuses in the early design stage and makes use of domain specific data which relate to different design domains (structural, environmental, architectural design) to inform the agent behaviors. The proposed framework is applicable especially to design problems which traditionally require the close collaboration of engineers and architects.　

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Book SynopsisMachine learning (ML) is the fastest growing field in computer science, and Health Informatics (HI) is amongst the greatest application challenges, providing future benefits in improved medical diagnoses, disease analyses, and pharmaceutical development. However, successful ML for HI needs a concerted effort, fostering integrative research between experts ranging from diverse disciplines from data science to visualization. Tackling complex challenges needs both disciplinary excellence and cross-disciplinary networking without any boundaries. Following the HCI-KDD approach, in combining the best of two worlds, it is aimed to support human intelligence with machine intelligence. This state-of-the-art survey is an output of the international HCI-KDD expert network and features 22 carefully selected and peer-reviewed chapters on hot topics in machine learning for health informatics; they discuss open problems and future challenges in order to stimulate further research and international progress in this field.Table of ContentsMachine Learning for Health Informatics.- Bagging Soft Decision Trees.- Grammars for Discrete Dynamics.- Empowering Bridging Term Discovery for Cross-domain Literature Mining in the TextFlows Platform.- Visualisation of Integrated Patient-Centric Data as Pathways: Enhancing Electronic Medical Records in Clinical Practice.- Deep learning trends for focal brain pathology segmentation in MRI.- Differentiation between Normal and Epileptic EEG using K-Nearest-Neighbors Technique.- Survey on Feature Extraction and Applications of Biosignals.- Argumentation for knowledge representation, conflict resolution, defeasible inference and its integration with machine learning.- Machine Learning and Data mining Methods for Managing Parkinson’s Disease.- Challenges of Medical Text and Image Processing: Machine Learning Approaches.- Visual Intelligent Decision Support Systems in the medical field: design and evaluation.

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Book SynopsisDo interventions improve health outcomes? This volume provides a model and road map to answer clinical questions related to intervention effectiveness research, quality improvement, and program evaluations. It offers clear and simple guidance for all phases of a clinical inquiry projects from planning through dissemination and communication of results and findings. The book emphasizes the value and importance of leveraging existing data to advance research, practice, and quality improvement efforts. Intervention and Effectiveness Research is a practical guide for organizing and navigating the intersections of research and practice. Structure, process and outcome worksheets for every step are provided together with examples from diverse settings and populations to lead readers through the process of implementing their own projects. The author guides readers through the process of designing, implementing, and evaluating projects. This book is intended for teachers of DNP and PhD programs in nursing and other disciplines, their students, and healthcare leaders who need to leverage data to demonstrate care quality and outcomes.Trade Review“This is a unique book, which can be used by students and novices. It can be useful for DNP students as they develop their end-of-program projects and for nurses as they begin to work on quality improvement and program evaluation projects. There is no comparable book in the field. This would be a great addition to the library of teachers and managers to share with their students and nurses.” (Michalene A. King, Doody's Book Reviews, November, 2017)Table of ContentsBackground: Why we need this bookChapter 1. Intervention Effectiveness Research History and State of the ScienceChapter 2. Project/Program Evaluation History and State of the Science 2.1 Worksheet to compare research and evaluation for a projectChapter 3. Methods for Interventions Effectiveness Research and Program EvaluationChapter 4. Existing Data: EHR data and administrative dataChapter 5. New Data: Observational Tools and SurveysChapter 6. Interviews: Professionals and Consumers6.1 Worksheet to plan methods for a projectChapter 7. Instruments: Baseline and follow up assessmentsChapter 8. Instruments: Intervention descriptionsChapter 9. Instruments: Problem classification9.1 Worksheet to describe baseline and final assessments 9.2 Worksheet to describe interventionsChapter 10. Ethical reviewChapter 11. ImplementationChapter 12. AnalysisChapter 13. Publication

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Book SynopsisComputational Drug Discovery A comprehensive resource that explains a wide array of computational technologies and methods driving innovation in drug discovery Computational Drug Discovery: Methods and Applications (2 volume set) covers a wide range of cutting-edge computational technologies and computational chemistry methods that are transforming drug discovery. The book delves into recent advances, particularly focusing on artificial intelligence (AI) and its application for protein structure prediction, AI-enabled virtual screening, and generative modeling for compound design. Additionally, it covers key technological advancements in computing such as quantum and cloud computing that are driving innovations in drug discovery. Furthermore, dedicated chapters that addresses the recent trends in the field of computer aided drug design, including ultra-large-scale virtual screening for hit identification, computational strategies for designing new therapeutic modalities like PROTACs and covalent inhibitors that target residues beyond cysteine are also presented. To offer the most up-to-date information on computational methods utilized in Computational Drug Discovery, it covers chapters highlighting the use of molecular dynamics and other related methods, application of QM and QM/MM methods in computational drug design, and techniques for navigating and visualizing the chemical space, as well as leveraging big data to drive drug discovery efforts. The book is thoughtfully organized into eight thematic sections, each focusing on a specific computational method or technology applied to drug discovery. Authored by renowned experts from academia, pharmaceutical industry, and major drug discovery software providers, it offers an overview of the latest advances in computational drug discovery. Key topics covered in the book include: Application of molecular dynamics simulations and related approaches in drug discovery The application of QM, hybrid approaches such as QM/MM, and fragment molecular orbital framework for understanding protein-ligand interactions Adoption of artificial intelligence in pre-clinical drug discovery, encompassing protein structure prediction, generative modeling for de novo design, and virtual screening. Techniques for navigating and visualizing the chemical space, along with harnessing big data to drive drug discovery efforts. Methods for performing ultra-large-scale virtual screening for hit identification. Computational strategies for designing new therapeutic models, including PROTACs and molecular glues. In silico ADMET approaches for predicting a variety of pharmacokinetic and physicochemical endpoints. The role of computing technologies like quantum computing and cloud computing in accelerating drug discovery This book will provide readers an overview of the latest advancements in Computational Drug Discovery and serve as a valuable resource for professionals engaged in drug discovery.Table of ContentsVolume 1 Preface xv Acknowledgments xix About the Editors xxi Part I Molecular Dynamics and Related Methods in Drug Discovery 1 1 Binding Free Energy Calculations in Drug Discovery 3Anitade Ruiter and Chris Oostenbrink 2 Gaussian Accelerated Molecular Dynamics in Drug Discovery 21Hung N. Do, Jinan Wang, Keya Joshi, Kushal Koirala, and Yinglong Miao 3 MD Simulations for Drug-Target(Un)binding Kinetics 45Steffen Wolf 4 Solvation Thermodynamics and its Applications in Drug Discovery 65Kuzhanthaivelan Saravanan and Ramesh K. Sistla 5 Site-Identification by Ligand Competitive Saturation as a Paradigm of Co-solvent MD Methods 83Asuka A. Orr and Alexander D. MacKerell Jr. Part II Quantum Mechanics Application for Drug Discovery 119 6 QM/MM for Structure-Based Drug Design: Techniques and Applications 121Marc W. van der Kamp and Jaida Begum 7 Recent Advances in Practical Quantum Mechanics and Mixed-QM/MM-Driven X-Ray Crystallography and Cryogenic Electron Microscopy (Cryo-EM) and Their Impact on Structure-Based Drug Discovery 157Oleg Borbulevych and Lance M. Westerhoff 8 Quantum-Chemical Analyses of Interactions for Biochemical Applications 183Dmitri G. Fedorov Part III Artificial Intelligence in Pre-clinical Drug Discovery 211 9 The Role of Computer-Aided Drug Design in Drug Discovery 213Stormvander Voort, Andreas Bender, and Bart A. Westerman 10 AI-Based Protein Structure Predictions and Their Implications in Drug Discovery 227Tahsin F. Kellici, Dimitar Hristozov, and Inaki Morao 11 Deep Learning for the Structure-Based Binding Free Energy Prediction of Small Molecule Ligands 255Venkatesh Mysore, Nilkanth Patel, and Adegoke Ojewole 12 Using Artificial Intelligence for de novo Drug Design and Retrosynthesis 275Rohit Arora, Nicolas Brosse, Clarisse Descamps, Nicolas Devaux, Nicolas Do Huu, Philippe Gendreau, Yann Gaston-Mathé, Maud Parrot, Quentin Perron, and Hamza Tajmouati 13 Reliability and Applicability Assessment for Machine Learning Models 299Fabio Urbina and Sean Ekins Volume 2 Preface xv Acknowledgments xix About the Editors xxi Part IV Chemical Space and Knowledge-Based Drug Discovery 315 14 Enumerable Libraries and Accessible Chemical Space in Drug Discovery 317Tim Knehans, Nicholas A. Boyles, and Pieter H. Bos 15 Navigating Chemical Space 337Akos Tarcsay, András Volford, Jonathan Buttrick, Jan-Constantin Christopherson, Máte Erdos, and Zoltán B. Szabó 16 Visualization, Exploration, and Screening of Chemical Space in Drug Discovery 365José J. Naveja, Fernanda I. Saldívar-González, Diana L. Prado-Romero, Angel J.Ruiz-Moreno, Marco Velasco-Velázquez, Ramón Alain Miranda-Quintana, and José L. Medina-Franco 17 SAR Knowledge Bases for Driving Drug Discovery 395Nishanth Kandepedu, Anil Kumar Manchala, and Norman Azoulay 18 Cambridge Structural Database (CSD)–Drug Discovery Through Data Mining & Knowledge-Based Tools 419Francesca Stanzione, Rupesh Chikhale, and Laura Friggeri Part V Structure-Based Virtual Screening Using Docking 441 19 Structure-Based Ultra-Large Virtual Screenings 443Christoph Gorgulla 20 Community Benchmarking Exercises for Docking and Scoring 471Bharti Devi, Anurag TK Baidya, and Rajnish Kumar PartVI In Silico ADMET Modeling 495 21 Advances in the Application of In Silico ADMET Models–An Industry Perspective 497Wenyi Wang, Fjodor Melnikov, Joe Napoli, and Prashant Desai Part VII Computational Approaches for New Therapeutic Modalities 537 22 Modeling the Structures of Ternary Complexes Mediated by Molecular Glues 539Michael L. Drummond 23 Free Energy Calculations in Covalent Drug Design 561Levente M. Mihalovits, György G. Ferenczy, and György M. Keseru Part VIII Computing Technologies Driving Drug Discovery 579 24 Orion A Cloud-Native Molecular Design Platform 581Jesper Sorensen, Caitlin C. Bannan, Gaetano Calabrò, Varsha Jain, Grigory Ovanesyan, Addison Smith, She Zhang, Christopher I. Bayly, Tom A. Darden, Matthew T. Geballe, David N. LeBard, Mark McGann, Joseph B. Moon, Hari S. Muddana, Andrew Shewmaker, Jharrod LaFon, Robert W. Tolbert, A. Geoffrey Skillman, and Anthony Nicholls 25 Cloud-Native Rendering Platform and GPUs Aid Drug Discovery 617Mark Ross, Michael Drummond, Lance Westerhoff, Xavier Barbeu, Essam Metwally, Sasha Banks-Louie, Kevin Jorissen, Anup Ojah, and Ruzhu Chen 26 The Quantum Computing Paradigm 627Thomas Ehmer, Gopal Karemore, and Hans Melo Index 679

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Book SynopsisTable of Contents1. Einleitung und Fragestellung.- 2. Allgemeines.- 2.1 Typische Beispiele aus der Literatur.- 2.1.1 University Group Diabetes Program (UGDP)-Studie.- 2.1.2 Coronary Drug Project (CDP) — Studie.- 2.1.3 Clofibrat Studie.- 2.2 Generelle Probleme.- 2.2.1 Inhalt von Zwischenauswertungen.- 2.2.2 Gründe für einen vorzeitigen Studienabbruch.- 2.2.3 Allgemeine methodische statistische Aspekte bei Zwischenauswertungen und Studienabbruch.- 3. Verfahrensübersicht.- 3.1 Offene Sequentialpläne.- 3.2 Geschlossene Sequentialpläne.- 3.3 Gruppensequentielle Pläne.- 3.4 Testverfahren für Lebensdauerdaten.- 3.5 Multivariate Methoden und multiple Vergleiche.- 3.6 Sonstige Verfahren.- 4. Gemischte Strategien.- 4.1 Formale Ableitung.- 4.2 Praktische Berechnung.- 4.3 Ergebnisse.- 4.4 Vergleiche mit den bisherigen Verfahren.- 5. Verfahrensvergleiche bei Lebensdauerdaten durch Simulation.- 5.1 Simulationsmodell.- 5.2 Simulation und Ergebnisse.- 6. Schlußfolgerungen.- 7. Zusammenfassung.- 8. Literatur.- 9. Anhang.

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Book SynopsisValuation is a hot topic among life sciences professionals. There is no clear understanding on how to use the different valuation approaches and how to determine input parameters. Some do not value at all, arguing that it is not possible to get realistic and objective numbers out of it. Some claim it to be an art. In the following chapters we will provide the user with a concise val- tion manual, providing transparency and practical insight for all dealing with valuation in life sciences: project and portfolio managers, licensing executives, business developers, technology transfer managers, entrep- neurs, investors, and analysts. The purpose of the book is to explain how to apply discounted cash flow and real options valuation to life sciences p- jects, i.e. to license contracts, patents, and firms. We explain the fun- mentals and the pitfalls with case studies so that the reader is capable of performing the valuations on his own and repeat the theory in the exercises and case studies. The book is structured in five parts: In the first part, the introduction, we discuss the role of the players in the life sciences industry and their p- ticular interests. We describe why valuation is important to them, where they need it, and the current problems to it. The second part deals with the input parameters required for valuation in life sciences, i.e. success rates, costs, peak sales, and timelines.Trade ReviewFrom the reviews:"‘Valuation in Life Sciences: A Practical Guide’ leads readers step by step through the theory of life sciences valuation. … it is an important read for biotech investors. … It is the first book of its kind that combines industry data and valuation theory together with practical advice. It includes valuation techniques and tools that allow investors to pick undervalued biotech stocks." (GEN – genetic engineering news, February, 2007)"Valuation in Life Sciences: A Practical Guide opens up the black box and describes, step by step, a relatively simple procedure for quantitative valuation in life sciences. The book guides the reader through the recommended procedure of quantitative valuation … . This book is recommended to those who would like to acquire a profound understanding of quantitative valuation and use a simple spreadsheet approach of their own cases." (Rudolf Gygax, Nature Biotechnology, Vol. 25 (9), 2007)"This book provides some interesting examples – for business development and licensing executives – of where ROV may be used. … For the general reader and business development executive there is a good explanation of ROV and its applications in life science … . The book may also be of interest to biotechnology company business development executives and university technology transfer officers … . this book is essential reading as it not only provides the mathematical theory but also the applications in different types of deals." (Roger Davies, Business Development & Licensing Journal, Issue 4, 2007)From the reviews of the third edition:“The content and discussion contained in ‘Valuation in Life Sciences’ is excellent. The Swiss authors provide a practical guide to what mathematical tools gives meaningful valuations … . Recommended for anyone planning to be involved in the development of a biotech venture.” (C. Mathews, Amazon, December, 2010)“The authors always keep the necessary rigour to make the book a valuable reference to professionals such as business developers, investment bankers, or analysts. … it an excellent textbook for students as well. … ‘Valuation in Life Sciences’ addresses also the financial beginner with guided and easy-to-understand examples in Excel. I highly recommend the book to everyone in financial biotech.” (Amazon, November, 2009)“This book is an excellent overview of business development valuation and great for those who want to catch up on some techniques and tools. It gives an accurate picture of BD in pharma. For those in the business … it remains a very valuable tool.” (J. Serres, Amazon, March, 2009)Table of ContentsBasics of Valuation.- Valuation in Life Sciences.- Exercises.- Case Studies.

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Book SynopsisTaxonomien sind Modelle, um unübersichtliche Domänen zu analysieren, Komplexität zu reduzieren sowie Unterschiede und Ähnlichkeiten von Objekten zu erkennen. Ferner helfen sie, Wissen zu ordnen und so Wissen zu vergrößern. Da täglich neues Wissen entsteht, ist eine nutzungsorientierte Entwicklung von Taxonomien für die Wissenschaft und die Praxis von fundamentaler Bedeutung. Taxonomien sind unerlässlich für die Erforschung, Beschreibung und Strukturierung von Domänen. Trotz ihrer besonderen Bedeutung gibt es für viele Wissenschaften nur wenige Handlungsanleitungen zur Entwicklung von Taxonomien.In diesem Buch werden die Forschungsergebnisse der Dissertation mit dem Titel „Entwicklung einer Taxonomie für Patientenportale für Health Information Manager“ präsentiert. Aufgrund der detaillierten Darstellung der Entwicklungsschritte und der eingesetzten Methoden kann diese Dissertationsforschung als Anleitung zur Entwicklung von Taxonomien dienen. Dabei ist die Evaluierung für die Nützlichkeit einer Taxonomie maßgeblich. Mit dem „TED Taxonomy-Evaluation-Delphi Approach“ entwickelte der Autor eine neue Methode zur Evaluierung von Taxonomien, welche hier ausführlich beschrieben wird.Table of ContentsEinleitung.- Grundlagen.- Methodologie.- Methodik.- Ergebnisse.- Diskussion.- Schlussfolgerung.- Literaturverzeichnis.

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Book SynopsisIn April of 1991, 425 partICIpants from 18 countries met in Hamamatsu in Japan for the 6th International Symposium on Computing in Anesthesia and Intensive Care (lSCAIC). The meeting was one of the most spectacular academic and fruitful in the history of ISCAIC. We had four days of fascinating presentations and discussions covering many areas of technology in Anesthesia and intensive care. New technologies were presented and old technology reexamined. The measures of success of the meeting were the excellent research material in oral and poster presentations, and state of the art reviews of the latest issues by distinguished worldwide key speakers. It must be sure that the meeting was most effective to promote and disseminate up-to-date information in these fields across the participating countries. The aim of this book is to record the exciting achievements of the meeting and extend them further among our colleagues. We hope the readers of this book will share the same excitation as well as the latest information in this speciality. Finally we would like to extend our deepest gratitude to all participants and others for the contribution to the compilation of this book. Kazuyuki Ikeda, M.D.Table of ContentsPatient Safety: Minimum Requirements and Advanced Techniques in Monitoring.- Minimum Monitoring Requirements (USA).- Oxygen Monitoring in Respiratory Gas.- Anesthetic Gas Monitoring.- Intraoperative Echocardiography.- CNS Monitoring.- Patient Monitoring — The European View.- Artificial Intelligence.- Artificial Neural Networks in Medical Monitoring: A Primer for Physicians.- The Use of Model and Artificial Intelligence Techniques in Patient Monitoring.- Expert System.- A Computational Architecture Using Procedural Reasoning for Decision Support in Anesthesiology.- Critical Care Decision Support Systems.- Intelligént Alarm System for Anesthesia (IASA).- A Knowledge Based Alarmer for the Postoperative Care of Cardiac Patients.- Medical Expert Systems with Physiological Models for Hemodynamical Monitoring.- Information Interpretation in a Real-Time Knowledge-Based Respiratory Monitoring System.- Computer Assisted Diagnostic Procedures in the Pain Clinic Using an Expert System.- Simulation and Modeling.- ASC: Educational Anesthesia Simulator with Expert System.- Digital Computer Simulation of Cardiovascular System in Bleeding Patient for Clinical Management.- Gas Man Simulates Correct Alveolar Plateaus and Quantifies Overpressure for Desired Alveolar Tension.- Real Time Joint of Pharmacokinetic Simulation and Monitoring in Inhalational Agents.- A 24-Hour Prediction Model of Blood Pressure Employing Endocrine System and Autonomic Nervous System.- A Simulation Analysis for Optimal Plasma Potassium Concentration during Cardiopulmonary Extracorporeal Bypass.- Eucapnic Hyperpnea Facilitates Recovery from Inhalational Anesthesia.- Anaesthetic Agent Uptake and Distribution.- A Theoretical Analysis of Optimal Control of Cardiovascular System.- Education.- A Revised Computer Program for Respiratory Care and Blood Gases.- Use of Pharmacologic Models for Teaching Anesthesiology Residents.- Automated Control.- Computer Controlled Infusion Systems.- Computer Controlled Continuous Infusion.- A Computer-Aided Controller of Fluid Infusion Rate in Post- Operative Management of Open Heart Surgery.- Computer Aided Patient (CAP) Care System.- Fluid Control Based on Fuzzy Control Algorithm.- A Simulation and a Control System of Neuromuscular Blockade.- Evaluation of a Computer Aided Propofol Infusion System.- Computerised Alfentanil Infusion in Postoperative Analgesia.- Assessment of the Value of Computerized Propofol Infusion.- Development of a Computer Controlled Infusion System.- Isocapnic Clamping with Feedback and Feed-Forward Control.- The Intelligent Control System of Physiological System Regulation.- Performance Evaluation of a Closed-Loop Sodium Nitroprusside Delivery Device during Hypotensive Anesthesia in Mongrel Dogs.- Adaptive Control of Arterial Pressure: A Supervisor can Improve Safety and Efficacy.- A Computer Control System of Applying Anesthesia Using Fuzzy Logic for Medical Operation (Categorization of anaesthesiologists thinking represented by artificial intelligence and comparison of each category).- Automatic Adjustment of Minute Volume by Carbon Dioxide Excretion with Servocontrol System.- Patient Data Management System.- Patient Data Management.- Anesthesia Records, Displays, and Alarms.- PDP System/PC an Efficient Combination for Data Management in the Intensive Care Unit.- Data Management in the Operating Room and Intensive Care.- Computerized Monitoring and Recording System in Anesthesia and Intensive Care Medicine Using an Engineering Work Station.- Computerized Intraoperative Monitoring System at Montefiore University Hospital.- Microcomputer Management of Anesthetized Patients’ Information.- Laboratory Data Management in Our ICU.- A Computerized Graphic Monitoring System for the Management of Open Heart Surgery.- The Anesthesia Information Console: An Integrated Information System for Anesthetic Care.- Perioperative Data Management System — An Initial Report on the Intraoperative Patient Data Management System.- Automated Analog and Digital Data Logging System in Intensive Care Unit.- Clinical Process Models for Intensive Care.- Ohmeda Arkivel Patient Information Management System — An Operating Room Based Database Analysis Network.- Network.- LAN in the Automatic Collection of Postoperative Monitoring Data.- Multiple Media for Monitoring Respired Air during Anesthesia.- On-Line Data Integration System in Kanazawa Medical University Hospital.- Intraoperative Transmission of Digital Data Using Intelligent Radio Modems.- Use of an Inter-Bed Local Area Network System in Operating Room.- A System of Computer Integration of Patient Monitors in Operating Rooms.- Development of Operation System.- On-line Multi-Channel Audio-Video Signal Transmission by Optical Fiber and its Clinical Use.- Wireless Data Communication: Example of an Application for the Operating Room.- Remote Monitoring of Home Ventilation Through the Personal Paging System.- Data Base.- Anesthetic Concerns in Uncommon Diseases — A New Database.- Data-Base in Our Department Using REXAS as a Sort of LAN.- An Application of the Optical Drive System for Keeping Anesthesia Records.- An Ultrafiche Storage of Anesthesia Case, Computer Display and Search by a Scanner.- Management of Anesthesia Record Using Hyper-Text System.- Data Record.- Monitor; a Low Cost, Versatile Acquisition Program.- Time Keeper and History Maker in an Anesthesia Practice.- Clinical Usefulness of Fiberoptic D-C Coupling EKG Monitoring System.- Registration of Data during Narcosis for Scientific Evaluation.- Registration and Documentation of All Data Relevant for Narcosis.- Continuous Digital Values Recording in Hemodynamic Research.- An Application of a Data Transfer Program (Lotus Measure™) with a Pulse Oximeter.- A Personal Computerized Anesthesia Recording System — With Multiplexer and Serial Interfaced Monitors.- Automated Record Keeping.- Information Management Systems, Especially the Automatic Anesthetic Record Keeper.- The Influence of the Automatic Anesthetic Recording on Surgeon- Anesthetist Relationship.- Automatic Recording in Clinical Anaesthesia: Three Years Experience.- What do We Write on Anesthesia Records? — Analysis of Descriptive Information.- Automated Data Collection in a Simulated Respiratory Circuit.- Development of a Computer-Assisted Monitoring System for the ICU Patient Management.- Anesthesia Machine.- Anesthesia Machine Design.- Systems Engineering.- A Turning Point to Systems Engineering.- People and Machine Interface.- Anesthesia Record Keeping by Voice Recognition System.- Bar Code System Applied to Operation Center of University Hospital.- Medical Data Collection — The Human Interface Bar Code Entry, Hand Held Terminals.- Visibility of the Luminous Type Numerical Display Devices Installed the Medical Diagnostic Instruments Influenced by the Irradiation Illuminance.- A Compact TV Screen, a New Monitoring Device of Operative Field.- Quality Assurance.- Minimum Anaesthetic Peroperative Audit Dataset.- Computerized Quality Assurance Assessment of Obstetric Anesthesia Care: New Outcome Indicators.- Quality Assurance in Anesthesia Practice from the View Point of Personal Workload with Variability.- Patient Monitoring (Circulation).- Validity of Continuous Cardiac Output Measured by a Doppler Pulmonary Artery Catheter Versus Thermodilution, and Effect of Distal Angle on the Variance Between Methods.- Noninvasive Continuous Blood Pressure Measurement with the Cortronic APM 770™.- Intraoperative Myocardial Ischemia Detected by Multiple ECG Leads.- Continuous Thermographic Determination of Myocardial Ischemic Area in Dogs.- New Approach to Central Venous Pressure Monitoring.- Pressure Pulse Transmission Ratio (PPTR) of Inferior Vena Cava and the Blood Volume Status.- Online Analysis of Cardiovascular Control Factors by Power Spectral Analysis on the Heart Rate Variability during Anesthesia.- Continuous Evaluating System of Cardiac Output in Patients with Intra-Aortic Balloon Pumping.- Continuous Pulse Contour Cardiac Output during Major Abdominal Vascular Surgery.- Calculation of Pulmonary Capillary Pressure from Pulmonary Artery and Venous Wedge Pressures in Children.- Clinical Application of CCOM — Report of Post Op. Bleeding Case.- Continuous Cardiac Output Monitoring System Applying Fick’s Principle.- Continuous, On-Line, Real-Time Spectral Analysis of Heart Rate Variations during Anesthesia.- Patient Monitoring (Respiration).- End-Tidal PCO2: A Clinical Noninvasive Cardiac Output Monitor.- Ventilator Work Ratio: A Guide to the Adequacy of Weaning.- Measurement of Respiratory Work in the Breath by Breath Technique on the Continuous Respiratory Monitoring System in ICU.- Measurement of Lung Mechanics in Mechanically Ventilated Infants.- Respiratory Loop Analysis Using the Respiratory Inductance Plethysmography in Infants and Children.- Patient Monitoring (Oxygen metabolism).- Clinical Evaluation of Continuous Metabolic Monitoring in ICU.- Continuous Monitoring of Oxygen Delivery & Consumption.- The Precision of Oxygen Consumption and Delivery in a Computer Integrated Fick Based Monitoring System.- Simultaneous Measurements of SvO2 and SjO2 during Major Cardiac Surgeries.- Non-Invasive Monitoring of the Total Body Oxygen Uptake and Carbon Dioxide Production during Anesthesia.- Patient Monitoring (Neuromuscular junction).- A System for On-Line Analysis of Neuromuscular Blockade during Anesthesia by Use of a Personal Computer.- Patient Monitoring (Coagulation).- Problems Associated with Coagulation Monitoring during Cardiovascular Surgery.- Use of the Coagulation Monitor 512 for Reversal of Heparin-Induced Anticoagulation and the Effect of Fresh Frozen Plasma.- Patient Monitoring (Body temperature).- Evaluation of a New, Improved Deep Body Thermometry System.- Transesophageal Echocardiography.- Another Application of Transesophageal Echocardiography — Aortic Annulus Measurement.- Verification of the Tip Position of Intra-Aortic Balloon Pump Catheter by Perioperative Transesophageal Echocardiography.- Implications of Transesophageal Echocardiography as a Monitoring Device during Cardiac Anesthesia.- Pulse Oximetry.- Pulse Oximetry.- Comparison of Hypoxemia during One Lung Anesthesia with or without Pulse Oximeter.- An Effect of Unstable Hemoglobin Köln on Oximetry.- The Influence of Hematocrit and Blood Flow Conditions on Pulse Oximetry Accuracy.- Electroencephalography.- The Efficacy of Aperiodic Analysis of the Electroencephalogram (EEG) during Anesthesia.- The Lifescan™ EEG Monitor for Detection of Cerebral Ischemia in Carotid Endarterectomy: Case Reports, Prospective Study of Comparison Between Lifescan™ and Somatosensory Evoked Potential (SEP).- Intra-Operative EEG Monitoring: Monitoring of Background Rhythm and Paroxysmal Activity.- Evoked Potential.- Knowledge-Based Automatic Flash Evoked Potential Recognition System.- Automated Flash Visual Evoked Potential Monitoring: Comparison with Intracranial Pressure.- Gastrointestinal Tissue pH Monitoring.- New Objetives in the Resuscitation of the Critically III.- Studies on Prediction and Prevention of Stress Ulcer Using Tonometry, Reflectance Spectrophotometry and Oxygenated Perfluorochemicals.- Near Infrared Spectroscopy.- Experimental Study of Noninvasive Tissue Oxygenation Monitoring with Near-Infrared Spectroscopy.- Noninvasive Monitoring of Tissue Oxygenation by Near Infrared Spectroscopy.- Gas Analyzer.- A Case Study of Characterization and Continuous Monitoring of Trace Odorants in the Shinshu University School of Medicine Hospital ICU Room Air.- Simultaneous Measurement of 5 Anesthetic and 3 Respiratory Gases with Quadrupole Mass Spectrometer and Simple Technique of Calibration.- Miscellaneous.- A Request for Native English Speakers from Non-Natives.- Development and Application of Apple II Microcomputer System in Anesthesia.- Intraoperative Assessment of the Corrective Surgery of Tetralogy of Fallot.- Human Factors Affecting the Accuracy of Hand-Written Arterial Pressure Record.- An Assessment of Frequency Characteristics of the Fluid-Filled Catheter-Manometer System.- Multiple Monitors of Hemodynamics and Oxygenation during High Dose Thiopental.- A Ventilator to Create a Ventilatory Pattern Which Harmonizes with a Patient.- Estimation for Maximum Dosage of Lidocaine in Interpleural Block.- The Application of SALS (Statistical Analysis with the Least Square) to Michaelis-Menten Pharmacokinetics of Phenytoin.- Comments.- Author Index.

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Book SynopsisThe debate over eHealth is alive as never before. Supporters suggest that it will result in dramatic innovations in healthcare, including a giant leap towards patient-centered care, new opportunities to improve effectiveness, and enhanced wellness and quality of life. In addition, the growing market value of investments in health IT suggests that eHealth can offer at least a partial cure for the current economic stagnation. Detractors counter these arguments by claiming that eHealth has already failed: the UK Department of Health has shut down the NHS National Program for IT, Google has discontinued its Health flagship, and doubts have arisen over privacy safeguards for both patients and medical professionals. This book briefly explains why caregivers, professionals, technicians, patients, politicians, and others should all consider themselves stakeholders in eHealth. It offers myth-busting responses to some ill-considered arguments from both sides of the trench, in the process allowing a fresh look at eHealth. In addition, it describes how the technical failures of previous eHealth systems can be avoided, examines the legal basis of eHealth, and discusses associated ethical issues.​ Table of Contents1 Introduction: The debate over eHealth.- 2 Definitions of eHealth.- 3 An introduction to the technological basis of eHealth.- 4 eHealth and me: The implications of the Net for health care relationships.- 5 Legally eHealth.- 6 EU support to eHealth and cost-benefits.- 7 No (e)Health without (e)Research.- 8 eHealth policy.- 9 The high-tech face of eHealth.- 10 The data-driven revolution of healthcare.- 11 eEducation and eHealth: a call for action.- 12 Conclusions.

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Book SynopsisThis book covers applications for hybrid artificial intelligence (AI) and Internet of Things (IoT) for integrated approach and problem solving in the areas of radiology, drug interactions, creation of new drugs, imaging, electronic health records, disease diagnosis, telehealth, and mobility-related problems in healthcare. The book discusses the convergence of AI and the hybrid approaches in healthcare which optimizes the possible solutions and better treatment. Internet of Things (IoT) in healthcare is the next-gen technologies which automate the healthcare facility by mobility solutions are discussed in detail. It also discusses hybrid AI with bio-inspired techniques, genetic algorithm, neuro-fuzzy algorithms, and soft computing approaches which significantly improves the prediction of critical cardiovascular abnormalities and other healthcare solutions to the ongoing challenging research.Table of ContentsChapter 1. Hybrid Cloud/Fog Environment for Healthcare: An Exploratory Study, Opportunities, Challenges, and Future Prospects.- Chapter 2. Hybrid Intelligent System for Medical Diagnosis in Healthcare.- Chapter 3. Remote Patient Monitoring Using IoT, Cloud Computing and AI.- Chapter 4. An Analytical Study of the Role of M-IoT in Healthcare Domain.- Chapter 5. Hybrid AI and IoT Approaches Used in Health Care for Patients Diagnosis.- Chapter 6. RADIoT: The unifying framework for IoT, Radiomics and Deep Learning Modelling.- Chapter 7. Hybrid Artificial Intelligence and IoT in Healthcare for cardiovascular patient in decision making system.- Chapter 8. A Smart Assistive System for Visually Impaired to in-form acquaintance using Image Processing (ML) supported by IoT.- Chapter 9. Internet of things in healthcare: A survey.- Chapter 10. Disease Diagnosis System for IoT-Based Wearable Body Sensors with Machine Learning Algorithm.- Chapter 11. Integration of Machine Learning and IoT in Healthcare Domain.- Chapter 12. Managing Interstitial Lung Diseases with Computer Aided Visualization.- Chapter 13. Use of Machine Learning Algorithms to Identify Sleep Phases Starting from ECG Signals.- Chapter 14. Emerging Technologies for Pandemic and its Impact.- Chapter 15. Impact of Artificial Intelligence in Healthcare: A Study.

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Book SynopsisThis book focuses on contemporary technologies and research in computational intelligence that has reached the practical level and is now accessible in preclinical and clinical settings. This book's principal objective is to thoroughly understand significant technological breakthroughs and research results in predictive modeling in healthcare imaging and data analysis. Machine learning and deep learning could be used to fully automate the diagnosis and prognosis of patients in medical fields. The healthcare industry's emphasis has evolved from a clinical-centric to a patient-centric model. However, it is still facing several technical, computational, and ethical challenges. Big data analytics in health care is becoming a revolution in technical as well as societal well-being viewpoints. Moreover, in this age of big data, there is increased access to massive amounts of regularly gathered data from the healthcare industry that has necessitated the development of predictive models and automated solutions for the early identification of critical and chronic illnesses. The book contains high-quality, original work that will assist readers in realizing novel applications and contexts for deep learning architectures and algorithms, making it an indispensable reference guide for academic researchers, professionals, industrial software engineers, and innovative model developers in healthcare industry.Table of Contents​Segmentation of White Blood Cells in Acute Myeloid Leukaemia Microscopic Images: The Current Challenges and Future Solutions.- Computer Vision Based Prognostic Modeling of COVID-19 from Medical Imaging.- Skin Lesion Classification From Dermoscopic Images with Deep Residual Network based Fused Pigmented Deep Feature Extraction and Entropy Based Best Features Selection Approach.- Computer Vision Technologies for COVID-19 Prediction, Diagnosis and Prevention.- Health monitoring methods in heart diseases based on data mining approach, a directional survey.- Machine learning based brain diseases diagnosing in electroencephalogram signals, Alzheimer and Parkinson's.- Skin Lesion Detection Using Recent Machine Learning Approaches.- Improving monitoring and controling parameters for Alzheimer's patients based on IoT.- A Novel Method for Lung Segmentation of Chest with Convolutional Neural Network.- Leukemia Detection Using Machine and Deep Learning Through Microscopic Images-A Review.

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Book SynopsisThis edited book provides information on emerging fields of next-generation healthcare informatics with a special emphasis on emerging developments and applications of artificial intelligence, deep learning techniques, computational intelligence methods, Internet of medical things (IoMT), optimization techniques, decision making, nanomedicine, and cloud computing. The book provides a conceptual framework and roadmap for decision-makers for this transformation. The chapters involved in this book cover challenges and opportunities for diabetic retinopathy detection based on deep learning applications, deep learning accelerators in IoT and IoMT, health data analysis, deep reinforcement-based conversational AI agent in healthcare systems, examination of health data performance, multisource data in intelligent medicine, application of genetic algorithms in health care, mental disorder, digital healthcare system with big data analytics, encryption methods in healthcare data security, computation and cognitive bias in healthcare intelligence and pharmacogenomics, guided imagery therapy, cancer detection and prediction techniques, medical image processing for coronavirus, and imbalance learning in health care.Table of ContentsChapter 1. Methods for the recognition of multisource data in intelligent medicine: A review and next generation trends.- Chapter 2. Deep Learning in Healthcare: Challenges and Opportunities.- Chapter 3. Examination of Health Data Performance Depending on the Creative Use of Optimization Methods and Machine Learning Algorithms.- Chapter 4. Effect of computation and cognitive bias in healthcare intelligence and pharmacogenomics.- Chapter 5. Application of Genetic Algorithms in Healthcare: A Review.- Chapter 6. Decision-Making in Healthcare Nano-informatics.- Chapter 7. A Succinct Analytical Study of the Usability of Encryption Methods in Healthcare Data Security.- Chapter 8. IoMT in healthcare industry – Concepts and Applications.- Chapter 9. The Effect of Heuristic Methods Towards Performance of Health Data Analysis.- Chapter 10. AI for Stress Diagnosis at Home Environment.- Chapter 11. Contemporary Technologies to Combat Pandemics and Epidemics.- Chapter 12. Deep Learning for Diabetic Retinopathy Detection: Challenges and Opportunities.- Chapter 13. Deep Reinforcement Based Conversational AI Agent in Healthcare System.- Chapter 14. Deep Learning Empowered Fight against COVID-19: A Survey.- Chapter 15. Application of GAN in Guided Imagery Therapy.- Chapter 16. Digital Transformation in Healthcare Industry: A Survey.- Chapter 17. Application of Deep Learning in Mental Disorder: Challenges and Opportunities.

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Book SynopsisThis book presents the proceedings of the KES International Conferences on Innovation in Medicine and Healthcare (KES-InMed-23), held in Rome, Italy, on June 14–16, 2023. Covering a number of key areas, including digital IT architecture in healthcare; advanced ICT for medicine and healthcare; biomedical engineering, trends, research and technologies; and healthcare support systems, this book is a valuable resource for researchers, managers, industrialists and anyone wishing to gain an overview of the latest research in these fields.Table of ContentsDesigning deep learning architectures with neuroevolution. Study case: fetal morphology scan.- Optimized Deployment Planning for Elderly Welfare Facilities Based on Network Theory ~A Case Study of Ibaraki Prefecture's Day Care Welfare Facilities for the Elderly.- Online Parent Counselling in Speech and Language Therapy - the View of the Professionals.- Modelling Competing Risk for Stroke Survival Data.- Artificial Intelligence Chatbots and Conversational Agents - an Overview of Clinical Studies in Health Care.- Challenges and Solutions for Artificial Intelligence Adoption in Healthcare – A Literature Review.- An Explainable Deep Network Framework with Case-based Reasoning Strategies for Survival Analysis in Oncology.- Healthcare under Society 5.0: A Systematic Literature Review of Applications and Case Studies.- Strategic Risk Management for Low-Code Development Platforms with Enterprise Architecture Approach: Case of Global Pharmaceutical Enterprise.- Optimized Deployment Planning for Elderly Welfare Facilities based on Network Theory ~A Case Study of Ibaraki Prefecture's Day Care Welfare Facilities for the Elderly.- Characterizing Groups of Patients with Depression using Clustering Techniques.

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Book SynopsisThis book constitutes the refereed proceedings of the 12th International Conference on Health Information Science, HIS 2023, held in Melbourne, VIC, Australia, during October 23–24, 2023.The 20 full papers and 9 short papers included in this book were carefully reviewed and selected from 54 submissions. They were organized in topical sections as follows: Depression & Mental Health, Data Security, Privacy & Healthcare Systems, Neurological & Cognitive Disease Studies, COVID-19 Impact Studies, Advanced Medical Data & AI Techniques, Predictive Analysis & Disease Recognition, Medical Imaging & Dataset Exploration, Elderly Care and Knowledge Systems.Table of ContentsDepression & Mental Health.- Detection of depression and its likelihood in children and adolescents: Evidence from a 15-years study.- A combined attribute extraction method for detecting Postpartum Depression using Social Media.- Network Analysis of Relationships and Change Patterns in Depression and Multiple Chronic Diseases based on the China Health and Retirement Longitudinal Study.- Exploring Etiology of Nonsuicidal Self-Injury by Using Knowledge Graph Approach.- A Question and Answering System for Mental Health of the ElderlyBased on BiLSTM-CRF Model and Knowledge Graph.- Data Security, Privacy & Healthcare Systems.- Australia's Notifiable Data Breach scheme: An analysis of risk management findings for healthcare.- Analysis and Protection of Public Medical Dataset: from Privacy Perspective.- Enhancing Health Information Systems Security: An Ontology Model Approach.- Developing a Risk Management Framework for E-health Care Delivery.- Neurological & Cognitive Disease Studies.- Knowledge-Based Nonlinear to Linear Dataset Transformation for Chronic Illness Classification.- A Robust Approach for Parkinson Disease Detection from Voice Signal.- Analysis on Association between Vascular Risk Factors and Lifestyle Factors with the risk of Dementia/Alzheimer's Disease using Medical Ontologies.- COVID-19 Impact Studies.- Unveiling the Pandemic's Impact: A Dataset for Probing COVID-19's Effects on E-Learning Activities and Academic Performance.- Understanding the influence of multiple factors on the spread of Omicron variant strains via the Multivariate Regression Method.- Analyzing the Impact of COVID-19 on Education: A Comparative Study Based on TOEFL Test Results.- Advanced Medical Data & AI Techniques.- BiblioEngine: An AI-empowered Platform for Disease Genetic Knowledge Mining.- Enhancing Clustering Performance in Sepsis Time Series Data Using Gravity Field.- Multi-modal Medical Data Exploration Based on Data Lake.- Multi-model Transfer Learning and Genotypic Analysis for Seizure Type Classification.- Requirement survey in Thai clinician for designing digital solution of pain assessment.- Predictive Analysis & Disease Recognition.- A Comprehensive Approach for Enhancing Motor Imagery EEG Classification in BCI's.- Image Recognition of Chicken Diseases Based on Improved Residual Networks.- An Adaptive Feature Fusion Network for Alzheimer's Disease Prediction.- A Review on Predicting Drug Target Interactions Based on Machine Learning.- KNN-Based Patient Network and Ensemble Machine Learning for Disease Prediction.- Medical Imaging & Dataset Exploration.- Optimizing the Size of Peritumoral Region for Assessing Non-Small Cell Lung Cancer Heterogeneity Using Radiomics.- Multi-dimensional Complex Query Optimization for Disease-specific Data Exploration Based on Data Lake.- Analyzing Health Risks Resulting from Unplanned Land Use Plan and Structure: A Case Study in Historic Old Dhaka.- Elderly Care & Knowledge Systems.- Home Self-medication Question-Answering System for the Elderly Based on Seq2Seq Model and Knowledge Graph Technology.- Constructing Multi-Constrained Cognitive Diagnostic Test: An Improved Ant Colony Optimization Algorithm.- Enhancing Patient Safety in Pharmacotherapy.

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