Medical equipment, devices and techniques Books

Book SynopsisEmerging Technologies for Healthcare begins with an IoT-based solution for the automated healthcare sector which is enhanced to provide solutions with advanced deep learning techniques. The book provides feasible solutions through various machine learning approaches and applies them to disease analysis and prediction. An example of this is employing a three-dimensional matrix approach for treating chronic kidney disease, the diagnosis and prognostication of acquired demyelinating syndrome (ADS) and autism spectrum disorder, and the detection of pneumonia. In addition, it provides healthcare solutions for post COVID-19 outbreaks through various suitable approaches, Moreover, a detailed detection mechanism is discussed which is used to devise solutions for predicting personality through handwriting recognition; and novel approaches for sentiment analysis are also discussed with sufficient data and its dimensions. This book not only covers theoretical approaches and algorTable of ContentsPreface xvii Part I: Basics of Smart Healthcare 1 1 An Overview of IoT in Health Sectors 3Sheeba P. S. 1.1 Introduction 3 1.2 Influence of IoT in Healthcare Systems 6 1.2.1 Health Monitoring 6 1.2.2 Smart Hospitals 7 1.2.3 Tracking Patients 7 1.2.4 Transparent Insurance Claims 8 1.2.5 Healthier Cities 8 1.2.6 Research in Health Sector 8 1.3 Popular IoT Healthcare Devices 9 1.3.1 Hearables 9 1.3.2 Moodables 9 1.3.3 Ingestible Sensors 9 1.3.4 Computer Vision 10 1.3.5 Charting in Healthcare 10 1.4 Benefits of IoT 10 1.4.1 Reduction in Cost 10 1.4.2 Quick Diagnosis and Improved Treatment 10 1.4.3 Management of Equipment and Medicines 11 1.4.4 Error Reduction 11 1.4.5 Data Assortment and Analysis 11 1.4.6 Tracking and Alerts 11 1.4.7 Remote Medical Assistance 11 1.5 Challenges of IoT 12 1.5.1 Privacy and Data Security 12 1.5.2 Multiple Devices and Protocols Integration 12 1.5.3 Huge Data and Accuracy 12 1.5.4 Underdeveloped 12 1.5.5 Updating the Software Regularly 12 1.5.6 Global Healthcare Regulations 13 1.5.7 Cost 13 1.6 Disadvantages of IoT 13 1.6.1 Privacy 13 1.6.2 Access by Unauthorized Persons 13 1.7 Applications of IoT 13 1.7.1 Monitoring of Patients Remotely 13 1.7.2 Management of Hospital Operations 14 1.7.3 Monitoring of Glucose 14 1.7.4 Sensor Connected Inhaler 15 1.7.5 Interoperability 15 1.7.6 Connected Contact Lens 15 1.7.7 Hearing Aid 16 1.7.8 Coagulation of Blood 16 1.7.9 Depression Detection 16 1.7.10 Detection of Cancer 17 1.7.11 Monitoring Parkinson Patient 17 1.7.12 Ingestible Sensors 18 1.7.13 Surgery by Robotic Devices 18 1.7.14 Hand Sanitizing 18 1.7.15 Efficient Drug Management 19 1.7.16 Smart Sole 19 1.7.17 Body Scanning 19 1.7.18 Medical Waste Management 20 1.7.19 Monitoring the Heart Rate 20 1.7.20 Robot Nurse 20 1.8 Global Smart Healthcare Market 21 1.9 Recent Trends and Discussions 22 1.10 Conclusion 23 References 23 2 IoT-Based Solutions for Smart Healthcare 25Pankaj Jain, Sonia F Panesar, Bableen Flora Talwar and Mahesh Kumar Sah 2.1 Introduction 26 2.1.1 Process Flow of Smart Healthcare System 26 2.1.1.1 Data Source 26 2.1.1.2 Data Acquisition 27 2.1.1.3 Data Pre-Processing 27 2.1.1.4 Data Segmentation 28 2.1.1.5 Feature Extraction 28 2.1.1.6 Data Analytics 28 2.2 IoT Smart Healthcare System 29 2.2.1 System Architecture 30 2.2.1.1 Stage 1: Perception Layer 30 2.2.1.2 Stage 2: Network Layer 32 2.2.1.3 Stage 3: Data Processing Layer 32 2.2.1.4 Stage 4: Application Layer 33 2.3 Locally and Cloud-Based IoT Architecture 33 2.3.1 System Architecture 33 2.3.1.1 Body Area Network (BAN) 34 2.3.1.2 Smart Server 34 2.3.1.3 Care Unit 35 2.4 Cloud Computing 35 2.4.1 Infrastructure as a Service (IaaS) 37 2.4.2 Platform as a Service (PaaS) 37 2.4.3 Software as a Service (SaaS) 37 2.4.4 Types of Cloud Computing 37 2.4.4.1 Public Cloud 37 2.4.4.2 Private Cloud 38 2.4.4.3 Hybrid Cloud 38 2.4.4.4 Community Cloud 38 2.5 Outbreak of Arduino Board 38 2.6 Applications of Smart Healthcare System 39 2.6.1 Disease Diagnosis and Treatment 41 2.6.2 Health Risk Monitoring 42 2.6.3 Voice Assistants 42 2.6.4 Smart Hospital 42 2.6.5 Assist in Research and Development 43 2.7 Smart Wearables and Apps 43 2.8 Deep Learning in Biomedical 44 2.8.1 Deep Learning 46 2.8.2 Deep Neural Network Architecture 47 2.8.3 Deep Learning in Bioinformatic 49 2.8.4 Deep Learning in Bioimaging 49 2.8.5 Deep Learning in Medical Imaging 50 2.8.6 Deep Learning in Human-Machine Interface 53 2.8.7 Deep Learning in Health Service Management 53 2.9 Conclusion 55 References 55 3 QLattice Environment and Feyn QGraph Models—A New Perspective Toward Deep Learning 69Vinayak Bharadi 3.1 Introduction 70 3.1.1 Machine Learning Models 70 3.2 Machine Learning Model Lifecycle 71 3.2.1 Steps in Machine Learning Lifecycle 71 3.2.1.1 Data Preparation 72 3.2.1.2 Building the Machine Learning Model 72 3.2.1.3 Model Training 72 3.2.1.4 Parameter Selection 72 3.2.1.5 Transfer Learning 73 3.2.1.6 Model Verification 73 3.2.1.7 Model Deployment 74 3.2.1.8 Monitoring 74 3.3 A Model Deployment in Keras 75 3.3.1 Pima Indian Diabetes Dataset 75 3.3.2 Multi-Layered Perceptron Implementation in Keras 76 3.3.3 Multi-Layered Perceptron Implementation With Dropout and Added Noise 77 3.4 QLattice Environment 80 3.4.1 Feyn Models 80 3.4.1.1 Semantic Types 82 3.4.1.2 Interactions 83 3.4.1.3 Generating QLattice 83 3.4.2 QLattice Workflow 83 3.4.2.1 Preparing the Data 84 3.4.2.2 Connecting to QLattice 84 3.4.2.3 Generating QGraphs 84 3.4.2.4 Fitting, Sorting, and Updating QGraphs 85 3.4.2.5 Model Evaluation 86 3.5 Using QLattice Environment and QGraph Models for COVID-19 Impact Prediction 87 References 91 4 Sensitive Healthcare Data: Privacy and Security Issues and Proposed Solutions 93Abhishek Vyas, Satheesh Abimannan and Ren-Hung Hwang 4.1 Introduction 94 4.1.1 Types of Technologies Used in Healthcare Industry 94 4.1.2 Technical Differences Between Security and Privacy 95 4.1.3 HIPAA Compliance 95 4.2 Medical Sensor Networks/Medical Internet of Things/Body Area Networks/WBANs 97 4.2.1 Security and Privacy Issues in WBANs/WMSNs/WMIOTs 101 4.3 Cloud Storage and Computing on Sensitive Healthcare Data 112 4.3.1 Security and Privacy in Cloud Computing and Storage for Sensitive Healthcare Data 114 4.4 Blockchain for Security and Privacy Enhancement in Sensitive Healthcare Data 119 4.5 Artificial Intelligence, Machine Learning, and Big Data in Healthcare and Its Efficacy in Security and Privacy of Sensitive Healthcare Data 122 4.5.1 Differential Privacy for Preserving Privacy of Big Medical Healthcare Data and for Its Analytics 124 4.6 Conclusion 124 References 125 Part II: Employment of Machine Learning in Disease Detection 129 5 Diabetes Prediction Model Based on Machine Learning 131Ayush Kumar Gupta, Sourabh Yadav, Priyanka Bhartiya and Divesh Gupta 5.1 Introduction 131 5.2 Literature Review 133 5.3 Proposed Methodology 135 5.3.1 Data Accommodation 135 5.3.1.1 Data Collection 135 5.3.1.2 Data Preparation 136 5.3.2 Model Training 138 5.3.2.1 K Nearest Neighbor Classification Technique 139 5.3.2.2 Support Vector Machine 140 5.3.2.3 Random Forest Algorithm 142 5.3.2.4 Logistic Regression 144 5.3.3 Model Evaluation 145 5.3.4 User Interaction 145 5.3.4.1 User Inputs 146 5.3.4.2 Validation Using Classifier Model 146 5.3.4.3 Truth Probability 146 5.4 System Implementation 147 5.5 Conclusion 153 References 153 6 Lung Cancer Detection Using 3D CNN Based on Deep Learning 157Siddhant Panda, Vasudha Chhetri, Vikas Kumar Jaiswal and Sourabh Yadav 6.1 Introduction 157 6.2 Literature Review 159 6.3 Proposed Methodology 161 6.3.1 Data Handling 161 6.3.1.1 Data Gathering 161 6.3.1.2 Data Pre-Processing 162 6.3.2 Data Visualization and Data Split 162 6.3.2.1 Data Visualization 162 6.3.2.2 Data Split 162 6.3.3 Model Training 163 6.3.3.1 Training Neural Network 163 6.3.3.2 Model Optimization 166 6.4 Results and Discussion 168 6.4.1 Gathering and Pre-Processing of Data 169 6.4.1.1 Gathering and Handling Data 169 6.4.1.2 Pre-Processing of Data 170 6.4.2 Data Visualization 171 6.4.2.1 Resampling 173 6.4.2.2 3D Plotting Scan 173 6.4.2.3 Lung Segmentation 173 6.4.3 Training and Testing of Data in 3D Architecture 175 6.5 Conclusion 178 References 178 7 Pneumonia Detection Using CNN and ANN Based on Deep Learning Approach 181Priyanka Bhartiya, Sourabh Yadav, Ayush Gupta and Divesh Gupta 7.1 Introduction 182 7.2 Literature Review 183 7.3 Proposed Methodology 185 7.3.1 Data Gathering 185 7.3.1.1 Data Collection 185 7.3.1.2 Data Pre-Processing 186 7.3.1.3 Data Split 186 7.3.2 Model Training 187 7.3.2.1 Training of Convolutional Neural Network 189 7.3.2.2 Training of Artificial Neural Network 191 7.3.3 Model Fitting 193 7.3.3.1 Fit Generator 193 7.3.3.2 Validation of Accuracy and Loss Plot 193 7.3.3.3 Testing and Prediction 193 7.4 System Implementation 194 7.4.1 Data Gathering, Pre-Processing, and Split 194 7.4.1.1 Data Gathering 194 7.4.1.2 Data Pre-Processing 195 7.4.1.3 Data Split 196 7.4.2 Model Building 196 7.4.3 Model Fitting 197 7.4.3.1 Fit Generator 197 7.4.3.2 Validation of Accuracy and Loss Plot 197 7.4.3.3 Testing and Prediction 198 7.5 Conclusion 199 References 199 8 Personality Prediction and Handwriting Recognition Using Machine Learning 203Vishal Patil and Harsh Mathur 8.1 Introduction to the System 204 8.1.1 Assumptions and Limitations 206 8.1.1.1 Assumptions 206 8.1.1.2 Limitations 206 8.1.2 Practical Needs 206 8.1.3 Non-Functional Needs 206 8.1.4 Specifications for Hardware 207 8.1.5 Specifications for Applications 207 8.1.6 Targets 207 8.1.7 Outcomes 207 8.2 Literature Survey 208 8.2.1 Computerized Human Behavior Identification Through Handwriting Samples 208 8.2.2 Behavior Prediction Through Handwriting Analysis 209 8.2.3 Handwriting Sample Analysis for a Finding of Personality Using Machine Learning Algorithms 209 8.2.4 Personality Detection Using Handwriting Analysis 210 8.2.5 Automatic Predict Personality Based on Structure of Handwriting 210 8.2.6 Personality Identification Through Handwriting Analysis: A Review 210 8.2.7 Text Independent Writer Identification Using Convolutional Neural Network 210 8.2.8 Writer Identification Using Machine Learning Approaches 211 8.2.9 Writer Identification from HandwrittenText Lines 211 8.3 Theory 212 8.3.1 Pre-Processing 212 8.3.2 Personality Analysis 215 8.3.3 Personality Characteristics 216 8.3.4 Writer Identification 217 8.3.5 Features Used 219 8.4 Algorithm To Be Used 220 8.5 Proposed Methodology 224 8.5.1 System Flow 225 8.6 Algorithms vs. Accuracy 226 8.6.1 Implementation 228 8.7 Experimental Results 231 8.8 Conclusion 232 8.9 Conclusion and Future Scope 232 Acknowledgment 232 References 233 9 Risk Mitigation in Children With Autism Spectrum Disorder Using Brain Source Localization 237Joy Karan Singh, Deepti Kakkar and Tanu Wadhera 9.1 Introduction 238 9.2 Risk Factors Related to Autism 239 9.2.1 Assistive Technologies for Autism 240 9.2.2 Functional Connectivity as a Biomarker for Autism 241 9.2.3 Early Intervention and Diagnosis 242 9.3 Materials and Methodology 243 9.3.1 Subjects 243 9.3.2 Methods 243 9.3.3 Data Acquisition and Processing 243 9.3.4 sLORETA as a Diagnostic Tool 244 9.4 Results and Discussion 245 9.5 Conclusion and Future Scope 247 References 247 10 Predicting Chronic Kidney Disease Using Machine Learning 251Monika Gupta and Parul Gupta 10.1 Introduction 252 10.2 Machine Learning Techniques for Prediction of Kidney Failure 253 10.2.1 Analysis and Empirical Learning 254 10.2.2 Supervised Learning 255 10.2.3 Unsupervised Learning 256 10.2.3.1 Understanding and Visualization 257 10.2.3.2 Odd Detection 257 10.2.3.3 Object Completion 258 10.2.3.4 Information Acquisition 258 10.2.3.5 Data Compression 258 10.2.3.6 Capital Market 258 10.2.4 Classification 259 10.2.4.1 Training Process 260 10.2.4.2 Testing Process 260 10.2.5 Decision Tree 261 10.2.6 Regression Analysis 263 10.2.6.1 Logistic Regression 263 10.2.6.2 Ordinal Logistic Regression 265 10.2.6.3 Estimating Parameters 266 10.2.6.4 Multivariate Regression 268 10.3 Data Sources 269 10.4 Data Analysis 272 10.5 Conclusion 274 10.6 Future Scope 274 References 274 Part III: Advanced Applications of Machine Learning in Healthcare 279 11 Behavioral Modeling Using Deep Neural Network Framework for ASD Diagnosis and Prognosis 281Tanu Wadhera, Deepti Kakkar and Rajneesh Rani 11.1 Introduction 282 11.2 Automated Diagnosis of ASD 284 11.2.1 Deep Learning 289 11.2.2 Deep Learning in ASD 290 11.2.3 Transfer Learning Approach 290 11.3 Purpose of the Chapter 292 11.4 Proposed Diagnosis System 293 11.5 Conclusion 294 References 295 12 Random Forest Application of Twitter Data Sentiment Analysis in Online Social Network Prediction 299Arnav Munshi, M. Arvindhan and Thirunavukkarasu K. 12.1 Introduction 300 12.1.1 Motivation 300 12.1.2 Domain Introduction 300 12.2 Literature Survey 302 12.3 Proposed Methodology 304 12.4 Implementation 311 12.5 Conclusion 311 References 311 13 Remedy to COVID-19: Social Distancing Analyzer 315Sourabh Yadav 13.1 Introduction 315 13.2 Literature Review 318 13.3 Proposed Methodology 321 13.3.1 Person Detection 321 13.3.1.1 Frame Creation 324 13.3.1.2 Contour Detection 325 13.3.1.3 Matching with COCO Model 326 13.3.2 Distance Calculation 326 13.3.2.1 Calculation of Centroid 326 13.3.2.2 Distance Among Adjacent Centroids 327 13.4 System Implementation 328 13.5 Conclusion 333 References 334 14 IoT-Enabled Vehicle Assistance System of Highway Resourcing for Smart Healthcare and Sustainability 337Shubham Joshi and Radha Krishna Rambola 14.1 Introduction 338 14.2 Related Work 340 14.2.1 Adoption of IoT in Vehicle to Ensure Driver Safety 341 14.2.2 IoT in Healthcare System 341 14.2.3 The Technology Used in Assistance Systems 343 14.2.3.1 Adaptive Cruise Control (ACC) 343 14.2.3.2 Lane Departure Warning 343 14.2.3.3 Parking Assistance 343 14.2.3.4 Collision Avoidance System 343 14.2.3.5 Driver Drowsiness Detection 344 14.2.3.6 Automotive Night Vision 344 14.3 Objectives, Context, and Ethical Approval 344 14.4 Technical Background 345 14.4.1 IoT With Health 345 14.4.2 Machine-to-Machine (M2M) Communication 345 14.4.3 Device-to-Device (D2D) Communication 345 14.4.4 Wireless Sensor Network 346 14.4.5 Crowdsensing 346 14.5 IoT Infrastructural Components for Vehicle Assistance System 346 14.5.1 Communication Technology 346 14.5.2 Sensor Network 347 14.5.3 Infrastructural Component 348 14.5.4 Human Health Detection by Sensors 348 14.6 IoT-Enabled Vehicle Assistance System of Highway Resourcing for Smart Healthcare and Sustainability 349 14.7 Challenges in Implementation 353 14.8 Conclusion 353 References 354 15 Aids of Machine Learning for Additively Manufactured Bone Scaffold 359Nimisha Rahul Shirbhate and Sanjay Bokade 15.1 Introduction 360 15.1.1 Bone Scaffold 360 15.1.2 Bone Grafting 362 15.1.3 Comparison Bone Grafting and Bone Scaffold 363 15.2 Research Background 364 15.3 Statement of Problem 364 15.4 Research Gap 365 15.5 Significance of Research 366 15.6 Outline of Research Methodology 366 15.6.1 Customized Design of Bone Scaffold 366 15.6.2 Manufacturing Methods and Biocompatible Material 367 15.6.2.1 Conventional Scaffold Fabrication 368 15.6.2.2 Additive Manufacturing 369 15.6.2.3 Application of Additive Manufacturing/3D Printing in Healthcare 370 15.6.2.4 Automated Process Monitoring in 3D Printing Using Supervised Machine Learning 376 15.7 Conclusion 377 References 377 Index 381

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Book SynopsisThe new challenges in healthcare education require new methodological approaches and transparent integration of technology enhanced learning approaches. Technology-Enhanced Healthcare Education promotes the best practices and lessons learnt from COVID-19 and highlights the importance and impact of using information systems to increase levels of health literacy. The chapter authors cover processes such as augmented or virtual reality to allow for distraction and decreased anxiety of the patient and services such as telemedicine and tele-consultation in the follow-up of non-acute patients. These are just a few ways in which health professionals can utilise information systems and transformative technology to increase the quality of health care, levels of health literacy and, thus, increase the health outcomes of their patients. Technology-Enhanced Healthcare Education is an innovative volume for health specialists, educators, higher education medical experts, medical school students and health management professionals. It is key reading for those looking to learn more about the latest developments on active and transformative learning within health education and medical technology (MedTech).Table of ContentsChapter 1. How Digital Health Gives Clues for a Better Health Literacy Patient Experience; Cristina Vaz De Almeida Chapter 2. Forty Definitions and Metaphors for Active and Transormative Learning in Chat Gpt Times: Chat Gpt as an Active and Transformative Technology Enhanced Learning Boost in Healthcare Education; Miltiadis D. Lytras Chapter 3. Mental Health and Higher Education Institutions. Next Steps To Well - Being; Isabel Maria Abreu Rodrigues Fragoeiro Chapter 4. Digital Library in Hospital the Case of Digital Neurotic Library to Achieve Better Health Literacy of Patients and Caregivers; Berta Maria Jesus Augusto, Carlos Manuel Santos Fernandes, and Sérgio Filipe Silva Abrunheiro Chapter 5. The Importance of Therapeutic Education on Chronical Diseases: The Potential of Digital Education; Cristina Valadas and Ana Matilde Cabral Chapter 6. Health Literacy and Diabetes: Challenges and Trends; Dulce Nascimento Do Ó, Ana Rita Goes, João Filipe Raposo, and Isabel Loureiro Chapter 7. Digital Health Literacy and Young People - A Network of Mutual Influences; Patrícia Martins, Diogo Franco Santos, and Cristina Vaz De Almeida Chapter 8. Walkingpad: The Patient Experience In Peripheral Artery Disease; Ivone Fernandes Santos Silva and Susana Pedras Chapter 9. Chronic Pain And Strategies To Improve Patient Health; Raul Marques Pereira Chapter 10. Acp Model – Assertiveness, Clarity, And Positivity – The Competencies of The New Era; Cristina Vaz De Almeida Chapter 11. Patient Safety Education And Digital Technology Contributes; Ana Marinho Diniz, Susana Ramos, Karina Pecora, and José Branco Chapter 12. The Economy & The Digital: Investments To Improve The Student Experience; Eduardo Manuel de Almeida Leite and Ana Miguel Ramos Leite Chapter 13. The Flipped Classroom The Flipped Classroom in Higher Education: A Bibliometric Review; Andreia De Bem Machado, Maria José Sousa, and Helena Belchior Rocha Chapter 14. Transformative Learning as a Bold Strategy for The Vision 2030 in Saudi Arabia: Moving Higher Healthcare Education Forward; Basim S. Alsaywid, Sarah Abdulrahman Alajlan, and Miltiadis D. Lytras

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Book SynopsisThis text provides an examination of the monitoring of cardiac rhythms. After introductory chapters on the physiology of the cardiovascular system, and the difference between clinical and technical observations, it describes and discusses all forms of cardiac monitoring, including ECG, blood gases, acid-base balance, haemodynamic and central venous pressure.Table of ContentsEssential Anatomy and Physiology of The Cardiovascular System. Cardiovascular Monitoring - Clinical or Technical. Electrocardiographic monitoring. Slow Heart Rhythms. Fast Heart Rhythms. Ambulatory Monitoring of cardiac Rhythm. An Introduction to Haemodynamic Monitoring. Monitoring The central Venous and Arterial Blood Pressure. Haemodynamic Monitoring With pulmonary Artery Catheters. Monitoring Blood Gases and Acid-base balance.

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Book SynopsisThis book presents a collection of accounts by internationally renowed experts on current techniques in the instrumental investigation of speech and disorders of speech.Table of ContentsRecording and Displaying Speech, Marcel A.a. Tatham et al. Spectrogrpahy, Alvirda Farmer. Electromyography, Michel Gentil and Walter H. Moore. Aerometry, James Anthony and Nigel Hewlett. Electrolaryngography, Evelyn abberton and Adrianfrocin. Electropalatography, William J. Hardcastle and Fiona Gibbon. Imaging Techniques, Martin J. Ball and Berthold Groene. Auditory phonetic Techniques, Chris Code. Time-variated Speech, Linda Riensche et al. A pc-based Experimentation, Assessment and Treatment Technique, Wolfram Ziegler et al.

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Book SynopsisThis book presents the parameters of Mastery Learning (ML), an especially stringent variety of competency-based education that guides students to acquire essential knowledge and skill, measured rigorously against a minimum passing standard (MPS). As both a scholarly resource and a teaching tool, this is a “how to” book that serves as a resource for a wide variety of health professions educators.A seminal source of information and practical advice about ML, this book divided into five parts: Clinical Education in the Health Professions, The Mastery Learning Model, Mastery Learning in Action, Transfer of Training from Mastery Learning and The Road Ahead. Complete with high-quality images and tables, chapters take an in-depth look into ML principles and practices across the health professions. Specific educational content instructs readers on how to build and present ML curricula, evaluate short and long-run results, conduct learner debriefing and give powerful feedback, set learner achievement standards, and prepare faculty for new educational roles.An invaluable addition to the Comprehensive Healthcare Simulation Series, Mastery Learning in Health Professions Education is written and edited by leaders in the field for practicing clinicians in a variety of health professions.Table of ContentsDedicationTable of ContentsContributing AuthorsForeword (S. Barry Issenberg, MD)PrefacePART ONEClinical Education in the Health ProfessionsChapter 1. Clinical Education: Origins, Problems, and Uneven OutcomesWilliam C. McGaghie, PhD, Jeffrey H. Barsuk, MD, MS, and Diane B. Wayne, MDPART TWOThe Mastery Learning ModelChapter 2. Mastery Learning Model: Origins, Description, Evidence that it Works in Health Professions EducationWilliam C. McGaghie, PhDChapter 3. Developing a Mastery Learning CurriculumJeffrey H. Barsuk, MD, MS, Elaine R. Cohen, MEd, Diane B. Wayne, MD,William C. McGaghie, PhD, and David Salzman, MD, MEdChapter 4. Evaluating Mastery Learning OutcomesCelia O’Brien, PhD and Mark Adler, MD, MAChapter 5. Standard Setting for Mastery LearningDiane B. Wayne, MD and Elaine R. Cohen, MEdChapter 6. Implementing and Managing a Mastery Learning ProgramElaine R. Cohen, MEd and Diane B. Wayne, MDChapter 7. Feedback and Debriefing for Mastery Learning : Faculty as Coaches and MentorsWalter Eppich, MD, MEd and Mark Adler, MD, MAPART THREETransfer of Training from Mastery LearningChapter 8. Translational Outcomes from Mastery LearningWilliam C. McGaghie, PhD, Jeffrey H. Barsuk, MD, MS, Diane B. Wayne, MD,and Elaine R. Cohen, MEdChapter 9. Dissemination and Maintenance of Mastery Learning Educational ProgramsWilliam C. McGaghie, PhD, Jeffrey H. Barsuk, MD, MS, Elaine R. Cohen, MEd,and Diane B. Wayne, MDPART FOURChallenges and OpportunitiesChapter 10. Faculty Development for Mastery LearningWalter Eppich, MD, MEd and David Salzman, MD, MEdChapter 11. A New Horizon: Mastery Learning, Continuing Professional Education (CPE) and Maintenance of Certification (MOC)Clara Schroedl, MD, Brian McMahon, MD, MMSc and William C. McGaghie, PhDPART FIVEMastery Learning in ActionChapter 12. Mastery Learning of Clinical Communication SkillsGordon Wood, MD and Julia Vermylen, MDChapter 13. Mastery Learning of Team SkillsAashish Didwania, MD, et. alChapter 14. Mastery Learning of Surgical SkillsEric Hungness, MD, Ezra Teitelbaum, MD, Ben Schwab, MD,and Katherine Barsness, MDChapter 15. Mastery Learning of Bedside Procedural SkillsJeffrey H. Barsuk, MD, MS, Anessa Mikolajczak BSN, RN, and Stephanie Seburn, BSN, RNChapter 16. Mastery Learning of Medical EmergenciesWalter Eppich, MD, Mark Adler, MD, Marcelo Malakooti, MD, and Mary McBride, MDChapter 17. Readiness for Residency: Mastery Learning of Essential Clinical SkillsDavid Salzman, MD, MEd, Timothy W. Caprio, MD, Kathryn K. Hufmeyer, MD,and Nabil M. Issa, MDPART SIXThe Road AheadChapter 18: Mastery Learning, Entrustable Professional Activities, and Postgraduate Milestones Eric Holmboe, MD, David Salzman, MD, MEd, Joshua Goldstein, MD, and William C. McGaghie, PhDChapter 19.: Mastery Learning: Research Opportunities and ChallengesWilliam C. McGaghie, PhD, Jeffrey H. Barsuk, MD MS, Diane B. Wayne,, MDGlossaryName IndexSubject IndexChapter features: Tables, Figures, Examples, Cases, Reprinted Material (with permission)Chapter organization: Introduction (including chapter structure), sections (in order), summary

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Book SynopsisThink - Plan - BuyThis is the first complete guide for creating a sustainable healthcare simulation program/center. It is created by experts in the field that are on the front lines of this emerging field. It is intended and written with all professions in mind, and should be a resource for all, be they healthcare educators, administrators, or executives. It covers specific elements of the entire process, from concept to execution, and identifies key decision points along the way. The book is organized around fundamental considerations in center and program development including governance, ​structural/facility and curricular design, business planning, to establishing policies and procedures. Chapters analyze the fundamental aspects of planning, such as budgeting, revenue streams, and philanthropy as well as the inclusion of educational resources within such programs.Comprehensive Healthcare Simulation: Program and Center Development is an invaluable addition to the Comprehensive Healthcare Simulation series, and features an authoritative authorship of experts in the field. Table of ContentsSection I: Business, Implementation, and Sustainability.- Chapter 1. Governance and Organizational Structure.- Chapter 2. Importance of Executive Buy-in.- Chapter 3. Establishing your Vision, Mission, and Strategy.- Chapter 4. Basic Business Planning .- Chapter 5. Implementation, Phases, and Project Management.- Chapter 6. Funding Sources and Fee Structures.- Chapter 7. Effective Budgeting: Putting it Together.- Section II: Facility Design, Supporting Equipment, and Technology.- Chapter 8. Facility Design Part I - Process Considerations.- Chapter 9. Facility Design Part II - Structure Considerations.- Chapter 10. Equipment Selection, Acquisition, and Maintenance.- Chapter 11. Simulation Audio Visual and Learning Management System Considerations.- Section III. Curriculum and Faculty Development.- Chapter 12. Curriculum Integration and Development.- Chapter 13. Faculty and Staff Development.- Section IV. Administration and Operations.- Chapter 14. Simulation Operations: An Overview.- Chapter 15. Effective Staffing, Recruitment, and HR Management.- Chapter 16. Policies and Procedures: Key Considerations.- Chapter 17. Evaluation, Metrics, and Measuring ROI/VOI.- Section V. Marketing & Partnerships.- Chapter 18. Communication, Marketing Plan, and Strategy.- Chapter 19. Collaborative Partnerships: Local, Regional, National, and Global.

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Book SynopsisThis book reviews the convergence technologies like cloud computing, artificial intelligence (AI) and Internet of Things (IoT) in healthcare and how they can help all stakeholders in the healthcare sector. The book is a proficient guide on the relationship between AI, IoT and healthcare and gives examples into how IoT is changing all aspects of the healthcare industry. Topics include remote patient monitoring, the telemedicine ecosystem, pattern imaging analytics using AI, disease identification and diagnosis using AI, robotic surgery, prediction of epidemic outbreaks, and more. The contributors include applications and case studies across all areas of computational intelligence in healthcare data. The authors also include workflow in IoT-enabled healthcare technologies and explore privacy and security issues in healthcare-based IoT.Table of ContentsIntroduction.- Remote Patient Monitoring Using IoT, Cloud Computing and AI.- The Internet of M-Health Things (m-IoT).- Healthcare Data Storage Options Using Cloud.- Cloud-based telemedicine ecosystem and adoption of AI.- The benefits and risks of Integrating IoT, AI in cloud services for healthcare.- Pattern Imaging Analytics using Artificial Intelligence techniques.- Identifying Diseases and Diagnosis using Artificial Intelligence.- Robotic Surgery.- Personalized Treatment with the help of IoT Artificial Intelligence and cloud.- Predicting Epidemic Outbreaks using IoT Artificial Intelligence and Cloud.- Crowd sourced Data Collection.- Maintaining Healthcare Records using Cloud storage.- Privacy and Security issues in health care based IoT.- IoT Healthcare Applications.- Applications of AI, IoT and Cloud computing in battling COVID-19.- Intelligent Health Informatics for Handling the COVID-19 Situation.- Conclusion.

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Book SynopsisThis extensively revised new edition comprehensively reviews the rise of clinical research informatics (CRI). Clinical Research Informatics presents a detailed review of using informatics in the continually evolving clinical research environment.

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Book SynopsisChapter 1. Digital Health in Allergology: State of the Art.- Chapter 2. Digital Health: General concepts & terminology.- Chapter 3. Legal and regulatory challenges for digital health technologies.- Chapter 4. Digital Health for patients with Pollen Allergy: the Pollen/Hustenblume experience.- Chapter 5. Patients as Sensors, Automated Pollen Stations, & Artificial Intelligence: a Technological Revolution in Allergy Care.- Chapter 6. Digital Health for Pollen Allergy and Allergen Immunotherapy: the AllergyMonitor Experience.- Chapter 7. Clinical Decision Support Systems for Allergic Diseases.- Chapter 8. Databanks and expert systems in allergomics: scientific and clinical implications.- Chapter 9. Digital Health and Asthma.- Chapter 10. Digital Health to manage the pre-school child with wheeze.- Chapter 11. Digital Health and Chronic Urticaria.- Chapter 12. Digital Health and Atopic Dermatitis.- Chapter 13. Machine Learning for the management of Allergies and Asthma in childhood.- Chapter 14. Digital Health and drug allergies.- Chapter 15. Smart devices and digital therapeutics for asthma care regulatory challenges.- Chapter 16. Wearables and Sensor Technology for Allergy Care.- Chapter 17. Unveiling the Potential of Social Media in Allergology: From Patient Education and Support to Advocacy and AI Integration.- Chapter 18. A Practical Guide to Social Media in Allergic Disease Management: Background, Tools, Tips and Tactics.- Chapter 19. The landscape of digital health approaches to allergy in Australia (Digital Allergology in Australia).- Chapter 20. Digital Allergology in Germany.- Chapter 21. Digital Allergology in Spain.

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Book SynopsisThis book is intended for professionals who must deal with telemedicine having little or no knowledge of matrix algebra and matrix calculus, and little or no knowledge of higher mathematics with special reference to differential equations: no other books cover these topics starting from scratch.

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Book Synopsis

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Book SynopsisThis book presents a comprehensive description of the basic concepts of soft matter mechanics and of the nano- and microscale biomedical methods that allow characterizing the mechanical properties of cells and tissues.

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Book SynopsisThe updated edition of the first of three volumes on Medical Physics focuses even more on body systems related to physical principles such as body mechanics, energy balance, and action potentials. Thanks to numerous newly incorporated didactic features, the introductory text into the broad fi eld of medical physics is easy to understand and supports self-study. New: highlighted boxes emphasize special topics; math boxes explain more advanced mathematical issues; each chapter concludes with a summary of the key concepts, questions, a self-assessment of the acquired competence, and exercises. The appendix contains answers to questions and solutions to exercises.

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Book SynopsisThe updated edition of the second of three volumes on Medical Physics presents modern physical methods for medical diagnostics. It provides a solid background on imaging techniques that use non-ionizing probes (ultrasound, endoscopy including CLE and OCT, MRI) and imaging techniques that use ionizing radiation (X-ray radiography, CT, SPECT, PET). Radiation sources, interactions of radiation with matter and radiation protection for x-rays, -rays, protons and neutrons are presented. Some of these topics are also relevant to the therapeutic applications presented in Volume 3. NEW: highlighted boxes emphasize specifi c topics; math boxes explain more advanced mathematical issues; each chapter concludes with a summary of the key concepts, questions, a self-assessment of the acquired competence and exercises. The appendix provides answers to questions and solutions to exercises.

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Book SynopsisThis book is dedicated to the discussion of several biomedical applications of the mechanical phenotyping of cells and tissues to specific disease models. The topical chapters on mechanics in disease are preceded by chapters describing cell and tissue structure and their relationship with the biomechanical properties, as well as by the description of dedicated sample preparation methods for the nano- and microscale mechanical measurements.

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Book SynopsisThe updated edition of the third of three vollumes on Medical Physics presents modern physical methods for medical therapy with a focus on tumor treatment. It provides background information on radiation biology, radiation response of tissues, and linear energy transfer through radiation. Therapies with external radiation sources (x-rays, protons, neutrons) as well as internal radiation sources (brachytherapy) are discussed in detail. Other chapters deal with the use of lasers and nanoparticles in modern medicine. This volume closes with a short chapter on medical statistics. NEW: highlighted boxes emphasize specifi c topics; math boxes explain more advanced mathematical issues; each chapter concludes with a summary of the key concepts, questions, exercises, and a self-assessment of the acquired competence. The appendix provides answers to questions and solutions to exercises.

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Book SynopsisThe book offers an introduction to all the informatics concepts that are represented on the Clinical Informatics Board Examination The core and direction of this book is to mirror the model of clinical informatics which is used by the American Board of Preventive Medicine to create their exam. Unlike any other text on the market, the book includes simulated exam questions, to help the reader asses his knowledge and focus his study.Clinical Informatics Board Review and Self Assessment is a thorough practical assistant to refine the reader's knowledge regarding this youngest and possibly broadest fields of medicine.Table of ContentsPart I Fundamentals 1.1 Clinical Informatics 1.2 The Health System Part II Clinical Decision Making and Care Process Improvement 2.1 Clinical Decision Support 2.2 Evidence-Based Patient Care 2.3 Clinical Workflow Analysis, Process Redesign, and Quality Improvement Part III Health Information Systems 3.1 Information Technology Systems 3.2 Human Factors Engineering 3.3 Health Information Systems and Applications 3.4 Clinical Data Standards 3.5 Information System Lifecycle Part IV Leading and Managing Change 4.1 Leadership Models, Processes, and Practices 4.2 Effective Interdisciplinary Teams 4.3 Effective Communications 4.4 Project Management 4.5 Strategic and Financial Planning for Clinical Information Systems 4.6 Change Management Numerical Methods Questions Answers Index

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Book SynopsisThis unique collection synthesizes insights and evidence from innovators in consumer informatics and highlights the technical, behavioral, social, and policy issues driving digital health today and in the foreseeable future. Consumer Informatics and Digital Health presents the fundamentals of mobile health, reviews the evidence for consumer technology as a driver of health behavior change, and examines user experience and real-world technology design challenges and successes. Additionally, it identifies key considerations for successfully engaging consumers in their own care, considers the ethics of using personal health information in research, and outlines implications for health system redesign. The editors’ integrative systems approach heralds a future of technological advances tempered by best practices drawn from today’s critical policy goals of patient engagement, community health promotion, and health equity. Here’s the inside view of consumer health informatics and key digital fields that students and professionals will find inspiring, informative, and thought-provoking. Included among the topics:• Healthcare social media for consumer informatics• Understanding usability, accessibility, and human-centered design principles• Understanding the fundamentals of design for motivation and behavior change• Digital tools for parents: innovations in pediatric urgent care• Behavioral medicine and informatics in the cancer community• Content strategy: writing for health consumers on the web• Open science and the future of data analytics• Digital approaches to engage consumers in value-based purchasingConsumer Informatics and Digital Health takes an expansive view of the fields influencing consumer informatics and offers practical case-based guidance for a broad range of audiences, including students, educators, researchers, journalists, and policymakers interested in biomedical informatics, mobile health, information science, and population health. It has as much to offer readers in clinical fields such as medicine, nursing, and psychology as it does to those engaged in digital pursuits.Table of ContentsForewordSusanna Fox, Chief Technology Officer, US Department of Healthand Human Services Paul Tang, CMIO, Palo Alto Medical FoundationSECTION I: Domains of Consumer Informatics and Mobile Health 1. Promoting Consumer Engagement in Health and Health Care Margo Edmunds, VP for Evidence Generation and Translation, AcademyHealthThe opening chapter will define consumer informatics, describe the history of different branches of the field (e.g., virtual visits/telehealth, mobile health, and the e-patient movement), and provide a framework for evaluating and disseminating evidence about ways to improve the consumer experience and change health behaviors. 2. Increasing Consumer Engagement Through Social Media, Crowd-sourcing, and BeyondMandi Bishop, Health Plan Analytics Innovation Practice Lead, Dell This chapter will review the state of the art and new directions for online tools to engage consumers in health and health care, focusing on social media platforms, consumer-facing web portals, and emerging technologies from the quantified self movement.3. The Global Connected Health EcosystemJody Ranck, Gigaom ResearchThis chapter will review the development, state of the art, and new directions for mobile devices in global health. 4. Bridging the Gaps: New Approaches to Address Population Health Ned Calonge, President and CEO, The Colorado Trust This chapter will describe innovations in the use of online and mobile technologies to promote community health and health equity with diverse populations. SECTION II: A New Environment for Development and Design5. User-Centered Design Christopher Hass, SVP, Experience Design, Mad*PowThis chapter will lay out the fundamentals of user experience (UX) and user-centered design for the Web, including accessibility for people with disabilities. Case examples will be provided. 6. Usability and Utility Testing Christopher Hass, SVP, Experience Design, Mad*PowBuilding on Chapter 5, this chapter will provide practical guidance on applying the principles of user-centered design to ensure the quality of consumer-facing products and services. 7. Motivational Design and Persuasive Technology for Consumer LearningDustin Ditommaso, SVP of Behavior Change Design, Mad*Pow Motivational design is an approach to online learning that can be used to increase consumer engagement with content. Persuasive technology uses behavioral science to design systems that promote consumer behavior change similar to personal health coaching, and taking into account both health and technology literacy. This chapter will provide practical advice on using these approaches in the development of health content for consumers.8. New Directions and Techniques for "Big Data": The Promises and Risks John Mattison, CMIO and Assistant Medical Director, Kaiser Permanente Southern California (by interview)As used in this chapter, big data refers to the storage and analysis of sets of data, including health data, that are too large to process using typical software and analytics processes. As a source for discovery and breakthroughs in health care treatment, big data holds promise but it also presents risks and uncertainties about consumer privacy and security. This chapter will separate out the reality and hype and provide a technical reference for the chapters in Section IV on privacy and security. SECTION III: Content Development and Consumer-Generated Data9. Addressing Information Overload: Knowledge Management to Improve Person-Centered Care Erin Holve, Senior Director, AcademyHealthWith the explosion of consumer-relevant and consumer-generated health data, this chapter will describe ways to help consumers make the best use of information from a wide variety of sources. 10. Content Strategy: Writing for Health Consumers on the Web Carolyn Petersen, Senior Editor, mayoclinic.orgSeven out of ten visits to the Web are part of consumer searches for health information, but the quality, appropriateness, and accessibility of the information varies widely across different sites. This chapter will review evidence on the value of web information in consumer-driven health care and describe best practices for developing consumer-friendly web content. 11. Visualization for Comprehension, Engagement, and Healthcare ImprovementSuzanne Bakken, Professor of Biomedical Informatics and Alumni Professor, Columbia University School of Nursing This chapter will describe innovative methods to provide consumer-friendly content through visual means. It will be based on the author's work with electronic health records and self-reported information from residents of low-income communities in Northern Manhattan, New York City. 12. Consumer-Generated Data and Health Information ExchangePaul Fu, Jr., CMIO Harbor-UCLA Medical Center As the nation pursues the goal of population health, it is important for personal health information and consumer-generated data to be standardized, structured, and aggregated for health planning purposes by local health agencies. This chapter will address some of the important technical and governance issues involved in integrating individual health data into health information exchanges at the local and regional levels. 13. Engaging Consumers in Culturally Diverse CommunitiesJoy Davis, Kaiser Permanente Institute for Health Policy This chapter will provide examples of emerging best practices in culturally competent consumer engagement in a variety of clinical and community settings. 14. Aging in PlaceCharlotte Weaver, Board Director, VitalSims, LLCMany adults prefer to remain in their own homes as they get older. Technology-enabled supports for older adults and their caregivers are becoming increasingly important with the growing number of people managing chronic health conditions through self-care and home-based care. This chapter will review the key technologies, challenges, and opportunities for remotely generated patient data to improve the health of older adults. SECTION IV: POLICY AND REGULATORY ISSUES15. How Standards and Interoperability Support ConsumersKevin Larsen, Medical Director of Meaningful Use, Office of the National Coordinator for Health IT, HHSIncluding consumers in the free flow of health information is a policy priority, but it will require significant changes in culture and infrastructure. This chapter will provide a consumer-friendly primer on how federal requirements for meaningful use of health data are evolving to improve consumer access to personal health information. 16. The Role of HIPAA in Consumer Health Informatics Ann Waldo, Wittie Letsche & Waldo LLP and Doug Peddicord, Washington Health Strategies GroupThe Privacy Rule that governs health information transactions has not kept pace with consumer technology and the Web, and many providers believe HIPAA restricts data sharing with consumers and patients. This chapter provides a legal perspective on how HIPAA should be rewritten to allow the free flow of health information to consumers. 17. Building Consumer Trust Through Information SecurityBradley Malin, Health Information Privacy Laboratory, Vanderbilt University, and Fred Trotter, Co-author, Hacking Healthcare (by interview) Some consumers, such as those in the quantified self movement, openly share their personal health data, while others want to maintain personal privacy or are concerned about data breaches. This chapter provides some innovative perspectives on privacy technology that enables trust. 18. Using Consent to Improve Informedness John Wilbanks, Director of Consent to Research (CtR) Project at Sage Bionetworks (by interview)This chapter will address ethical issues in consumer informatics and online content, including quality assurance, privacy, and security concerns; consent and governance; and the important role consumer information plays in shared decision-making. If privacy concerns have slowed the progress of research, new approaches, such as a pool of freely available health and genomic data, could accelerate innovation. This chapter will describe the range of ethical perspectives on data sharing and propose a new approach.19. Consumer Expectations About PrivacyShane Harris, Senior Intelligence and National Security Correspondent, The Daily Beast Health care systems have lagged behind other industry sectors in developing secure information-sharing strategies. This chapter will describe the information technology infrastructure and implications for consumers to protect their personal information. Section V: Conclusions20. Back to the Future: Emerging Technology, Social, and Political Trends Affecting Consumer Informatics Margo Edmunds, Chris Hass, and Erin HolveThis final chapter will summarize technology, social, regulatory, and other trends and issues raised in the book, including the role of collaboration and citizen science for health delivery system reform and population health.

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Book SynopsisAndreas Neff zeigt die exzellenten diagnostischen und therapeutischen Optionen der Endoskopie, die für den MKG-Chirurgen bei der komplexen Anatomie der Mund-Kiefer-Gesichtsregion, speziell der Kieferhöhlen und der Nasengänge, des Kiefergelenks, aber auch der Speicheldrüsen heute unverzichtbar ist. Minimalinvasive Darstellung und Zugänge gemäß State of the Art erfordern neben chirurgischem Know-how auch ein spezielles technisches Equipment. Während die Endoskopie in der MKG-Chirurgie meist speziellen Indikationen vorbehalten ist, hat sie insbesondere in der HNO inzwischen einen Großteil der traditionellen direkten bzw. invasiv-offenen Verfahren ersetzt. Auch in der MKG-Chirurgie wird es analog zu ihren Nachbardisziplinen zunehmend zu Paradigmenwechseln in der Behandlungsstrategie kommen, denen sich die MKG-Chirurgie stellen muss, um sich auch in Zukunft auf Augenhöhe weiterentwickeln zu können.Table of ContentsArthroskopie des Kiefergelenks und Chirurgie des Sinus maxillaris.- Endoskopische Speicheldrüsenchirurgie und dentoalvelären Chirurgie.- Endoskopie in der Traumatologie und plastisch-ästhetischen Chirurgie.

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Book SynopsisDie Informationstechnologien haben, wie in alle Lebensbereiche, längst auch Einzug in Medizin und Gesundheitsversorgung gehalten:- digitale Systeme erstellen Diagnosen, errechnen Krankheitsrisiken und geben individuelle Therapieempfehlungen,- neue Technologien eröffnen ungeahnte Behandlungsmöglichkeiten,- aus der Analyse großer Datenmengen ergeben sich ein tieferes Verständnis der Entstehung und der Verläufe von Krankheiten und neue Therapieideen. In diesem Spannungsfeld verändern sich auch die Erwartungen, die Patient und Arzt aneinander stellen, und ihr jeweiliges Rollenverständnis. In sieben anschaulich und einprägsam geschriebenen Kapiteln stellt der Autor die Hauptfelder der Digitalisierung in der Medizin dar, sowohl die Chancen, die sie für viele Patienten beinhalten, als auch die Gefahren, die sie für die Bewahrung der Privatsphäre und die am Patienten orientierte ärztliche Behandlung mit sich bringen können – eine gut lesbare Einführung für alle, die sich für die Entwicklung der Medizin und des Gesundheitswesens interessieren.Table of ContentsKünstliche Intelligenz.- Diagnostik und Krankheitsprognosen.- Neue therapeutische Möglichkeiten.- "Big Data" und die Folgen.- Unterstützende Technologien.- Die zukünftige Rolle des Arztes.- Wo geht die digitale Reise hin?.

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Book Synopsis.- Biomedical data processing and model application..- VSDQ: A Comprehensive Vaccine Stance Detection Quadruple Dataset for Analyzing Vaccine Discussions on Social Media..- Predicting Patients' Physician Selection Behavior Based on Multimodal Data: A Comparison of Feature Engineering and End-to-End Approaches..- Automatic Pancreatitis CT Image Segmentation Model Based on UNet and SE Module..- Utilizing Retrieval-Augmented Generation for Open-Domain Question Answering in Healthcare..- Self-supervised learning driven doctor recommendation model: combining communication ability and professional competence..- Prompt-Aware Large Language Model for Sleep Stages Classification..- Research on Classification Methods for Public Health Question Based on Model Ensemble and Voting Mechanisms..- A 3D MRI brain image segmentation and reconstruction system based on Augmented Reality Technology..- Analysis of Hospitalization Data before, during and after the COVID19 Epidemic using Short Time Series Clustering..- Look, Imitate and Refine: A Hierarchical Multimodel Retrieval Augmented Vision-Language Model for Radiology Report Generation..- CTGLM: A Vision-Language Model for Automated Chinese Chest CT Report Generation..- Pattern matching of positive and negative DNA sequences with general gaps and One-off constraints..- Mental health and disease prediction..- Survey of Suicidal Tendency Recognition Based on Social Media..- Global trends in the application of extended reality technology to autism research: Based on the Web of Bibliometric Analysis of Science (1999.1.1-2023.3.4)..- A Review of Machine Learning-based Assessment of Depression..- Research Progress on Psychological Health Assessment of College Students Based on Eye Movement, EEG, and Facial Expression Recognition.

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Book Synopsis.- Syndrome Differentiation Thought in Traditional Chinese Medicine..- Overview of the evaluation task for syndrome differentiation thought in traditional Chinese medicine in CHIP2024..- Traditional Chinese Medicine Case Analysis System for High-Level Semantic Abstraction: Optimized with Prompt and RAG..- A TCM Syndrome Differentiation Thinking Method Based on Chain of Thought and Knowledge Retrieval Augmentation..- Fine-Tuning Large Language Models for Syndrome Differentiation in Traditional Chinese Medicine..- Iterative Retrieval Augmentation for Syndrome Differentiation via Large Language Models..- Lymphoma Information Extraction and Automatic Coding..- Benchmark for Lymphoma Information Extraction and Automated Coding..- Overview of the Lymphoma Information Extraction and Automatic Coding Evaluation Task in CHIP 2024..- Automatic ICD Code Generation for Lymphoma Using Large Language Models..- Lymphoma Tumor Coding and Information Extraction: A Comparative Analysis of Large Language Model-based Methods..- Leveraging Chain of Thought for Automated Medical Coding of Lymphoma Cases..- Harnessing Retrieval-Augmented LLMs for Training-Free Tumor Coding Classification..- Hierarchical Information Extraction and Classification of Lymphoma Tumor Codes Based On LLM..- Typical Case Diagnosis Consistenc..- Benchmark of the Typical Case Diagnosis Consistency Evaluation Task in CHIP2024..- Overview of the Typical Case Diagnosis Consistency Evaluation Task in CHIP2024..- The Diagnosis of Typical Medical Cases through Optimized Fine-Tuning of Large Language Models..- Utilizing Large Language Models Enhanced by Chain-of-Thought for the Diagnosis of Typical Medical Cases..- Assessing Diagnostic Consistency in Clinical Cases: A Fine-Tuned LLM Voting and GPT Error Correction Framework..- Typical Medical Case Diagnosis with Voting and Answer Discrimination using Fine-tuned LLM..- Reliable Typical Case Diagnosis via Optimized Retrieval-Augmented Generation Techniques.

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Book SynopsisIntelligent Technological Developments In Health Sciences And Practical Research Challenges.- Some Aspects Of Applying The Empirical Mode Decomposition Technique To Processing Biomedical Signals.- An Automated Framework For The Segmentation Of Skin Lesions Using Deep Learning.

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Table of ContentsPart 1. Structure and properties of orthodontic biomaterials 1. Structure/property relationships in orthodontic alloys 2. Structure/property relationships in orthodontic polymers 3. Structure/property relationships in orthodontic ceramics Part 2. Orthodontic alloys: properties and clinical effects 4. Orthodontic brackets 5. In vitro friction assessment in orthodontics 6. Nickel-titanium products in daily orthodontic practice 7. Material properties and effects on mechanotherapy 8. Adverse effects from orthodontic alloys Part 3. Bonding to enamel with orthodontic adhesives 9. Composite resin polymerization and relevant parameters 10. Frequent handling mistakes during bonding 11. Bond strength and its limited role in assessing efficacy 12. Clinical evidence of bond failure and associated factors 13. Bisphenol A and orthodontic materials 14. Enamel alterations due to orthodontic treatment 15. Bonding of fixed lingual retainers in orthodontics 16. Bonding with glass-ionomer cements and resin-modified glass ionomer cements 17. Future of bonding Part 4. Orthodontic aligners 18. Aligners for orthodontic applications

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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Book SynopsisLearn how to architect ultra low power robust electronics with applications to implantable and non-invasive medicine, and how bio-inspired principles from cell biology and neurobiology can revolutionize low power electronics design, with this all-inclusive guide. Efficient, sustainable energy harvesting is also covered, and a wealth of practical examples and case studies are included.Trade Review'… an excellent overview of ten key fundamental principles related to ultra low power circuit and system design. Examples of many practical, experimental micro-power systems in cardiac, neural,and other medical-electronics applications make the text highly useful. Practitioners in this field will gain insight from his system-level analysis, which is presented at a level deeper than that found in most texts. In fact, the focus on systems thinking and connections made to a diverse set of problems - natural and man-made, from medical implants, to cells, to low-power cars - truly sets this book apart.' Tim Denison, Medtronic Fellow'This truly interdisciplinary book is about much more than circuits. It contains the most comprehensive and deep treatment I have seen of the interplay and parallels between biology and circuits, and of how one discipline can inform the other. The comparisons between analog, digital, and biological implementations are fundamental and highly valuable. The breadth of the book is unique, ranging from feedback and antennas to battery chemistry.' Yannis Tsividis, Columbia University'Sarpeshkar's focus on modeling cells as analog rather than digital circuits offers a new approach that will expand the frontiers of synthetic biology. Rahul has nicely laid a foundation that many of us in synthetic biology will be able to build on.' James Collins, Boston UniversityTable of ContentsPart I. Foundations: Ten chapters lay a foundation in device physics, noise, and feedback systems including nano scales in a highly original fashion, emphasizing intuitive thinking. This foundation is important in designing and analyzing ultra-low-power systems in both electronics and biology; Part II. Low-Power Analog and Biomedical Circuits: Five chapters present building-block circuits that are useful for ultra-low-power biomedical electronics and analog electronic systems in general; Part III. Low-Power RF and Energy-Harvesting Circuits for Biomedical Systems: Three chapters provide an in-depth description of energy-efficient power and data radio-frequency (RF) links that are fundamental to biomedical systems; Part IV. Biomedical Electronic Systems: Two chapters provide an in-depth look at ultra-low-power implantable electronics and ultra-low-power noninvasive electronics for biomedical applications, respectively. Case studies for cochlear implants for the deaf, brain implants for the blind and paralyzed, wearable cardiac devices, and biomolecular sensing are provided; Part V. Principles for Ultra-Low-Power Analog and Digital Design: Two chapters discuss principles for ultra-low-power digital design and ultra-low-power analog and mixed-signal design, respectively. The chapters identify ten fundamental principles that are common in both biology and electronics, analog and digital design; Part VI. Bio-Inspired Systems: A chapter on neuromorphic electronics discusses electronics inspired by neurobiology followed by a chapter that discusses a novel form of electronics termed Cytomorphic Electronics, electronics inspired by cell biology. These chapters discuss applications of bio-inspired systems to engineering and medicine, deep connections between chemistry and electronics, and provide a unifying viewpoint of ultra-low-power design in biology and in electronics; Part VII. Energy Sources: A chapter on batteries and electrochemistry discusses how batteries work from a unique circuit viewpoint. The last chapter discusses energy harvesting in biomedical systems at portable scales (vibration and body heat) and at larger scales (low-power cars and solar cells). Principles of low-power design are shown to extend from small scales in electronics to larger scales and to non-electrical systems. This book reveals the deep connections between energy use and energy generation, vital for sustainable energy systems of the future.

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