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ویرایش: 1st ed. 2022 نویسندگان: Tianhua Chen (editor), Jenny Carter (editor), Mufti Mahmud (editor), Arjab Singh Khuman (editor) سری: ISBN (شابک) : 981195271X, 9789811952715 ناشر: Springer سال نشر: 2022 تعداد صفحات: 0 زبان: English فرمت فایل : 7Z (درصورت درخواست کاربر به PDF، EPUB یا AZW3 تبدیل می شود) حجم فایل: 48 مگابایت
در صورت تبدیل فایل کتاب Artificial Intelligence in Healthcare: Recent Applications and Developments (Brain Informatics and Health) به فرمت های PDF، EPUB، AZW3، MOBI و یا DJVU می توانید به پشتیبان اطلاع دهید تا فایل مورد نظر را تبدیل نمایند.
توجه داشته باشید کتاب هوش مصنوعی در مراقبت های بهداشتی: کاربردها و پیشرفت های اخیر (انفورماتیک مغز و سلامت) نسخه زبان اصلی می باشد و کتاب ترجمه شده به فارسی نمی باشد. وبسایت اینترنشنال لایبرری ارائه دهنده کتاب های زبان اصلی می باشد و هیچ گونه کتاب ترجمه شده یا نوشته شده به فارسی را ارائه نمی دهد.
پیشرفت های اخیر در هوش مصنوعی (AI) و یادگیری ماشین شاهد
موفقیت های بسیاری در رشته های مختلف از جمله بخش مراقبت های
بهداشتی بوده است. نوآوریها در سیستمهای پزشکی هوشمند شیوه
ارائه خدمات مراقبتهای بهداشتی را متحول کرده است، از تشخیص
بالینی، توسعه درمان شخصی و داروها، کمک به نظارت بر بیمار، تا
خودکار کردن وظایف اداری و کاهش هزینههای عملیاتی. در این کتاب،
نویسندگان برنامه های کاربردی کلیدی را در حوزه کلی مراقبت های
بهداشتی ارائه می کنند، جایی که هوش مصنوعی موفقیت های چشمگیری
داشته است. از فصلهای جداگانه، طیف وسیعی از مثالها برای نشان
دادن طیف وسیعی از حوزههای کاربردی با استفاده از تکنیکهای
پیشرفته هوش مصنوعی به خوانندگان ارائه میشود که اعتبار و
تطبیقپذیری و اثربخشی رویکرد هوش مصنوعی در مراقبتهای بهداشتی و
پزشکی را اثبات میکند. . ما تصور می کنیم که این کتاب برای
افرادی که تازه با مفهوم هوش مصنوعی در مراقبت های بهداشتی آشنا
شده اند، به همان اندازه، دانشگاهیان حرفه ای اولیه که مایلند
دانش خود را در زمینه هوش مصنوعی در پزشکی بیشتر گسترش دهند، ایده
آل است. آنچه ارائه خواهد شد به هیچ وجه لیست جامعی از برنامه ها
نیست، اما قطعاً یک لیست متنوع است.
Recent advances in artificial intelligence (AI) and
machine learning have witnessed many successes in various
disciplines including the healthcare sector. Innovations in
intelligent medical systems have revolutionized the way in
which healthcare services are provided, ranging from making
clinical diagnosis, developing personalized treatment and
drugs, assisting patient monitoring, to automating
administrative tasks and reducing operational costs. In this
book, the authors present key applications in the general area
of health care, where AI has made significant
successes. From the individual chapters, the readers will
be provided with a range of examples to illustrate the wide
plethora of application domains utilizing state-of-the-art AI
techniques, proving credence to the versatility and
effectiveness of an AI approach in health care and medicine. We
envisage that this book is ideal for individuals new to the
notion of AI in health care, equally, early career academics
who wish to further expand on their knowledge in AI in
medicine. What will be presented is in no means an exhaustive
list of applications, but most definitely a varied
one.
Preface Contents AI in Healthcare: Malignant or Benign? 1 Introduction 2 Prevention 2.1 AI as a Tool to Change the World 2.2 Governance, Laws and Ethics 2.3 Touchless Control Preventative Healthcare in COVID 2.4 Personalised Medicine: The Bridge Between Prevention and Diagnosis 3 Diagnosis 3.1 History 3.2 Fuzzy Diagnosis 3.3 NLP and Diagnostic Chatbots 3.4 Medical Imaging 3.5 Mental Health 3.6 Problems with AI Diagnosis 4 Care 4.1 The Problem with Current Care 4.2 Mental Health Chat Bots 4.3 Companionship for the Elderly 4.4 Sex Bots 4.5 Summary and Problems 5 Cure 5.1 Discovery 5.2 Prosthetics, Implants and Exoskeletons 6 Conclusion References Process Mining in Healthcare: Challenges and Promising Directions 1 Introduction 2 Process Mining for Healthcare 2.1 Process Discovery in Healthcare 2.2 Conformance Checking in Healthcare 2.3 Process Enhancement in Healthcare 3 Challenges and Promising Directions 3.1 Data and Data Sources 3.2 Scheduling and Planning 3.3 Patients\' Privacy and Multicentric Clinical Studies 3.4 Explainability and Understandability 4 Conclusion References Computational Intelligence in Drug Discovery for Non-small Cell Lung Cancer 1 Introduction 1.1 In Silico Methods of Drug Discovery 2 Methodology and Experimentation 2.1 Data Collection 2.2 Data Cleaning 2.3 Feature Selection 2.4 Application of Machine Learning 2.5 Network Inference 2.6 Application of Deep Learning 2.7 KnowledgeFlow 3 Results and Discussion 3.1 Traditional Machine Learning for TCGA and GEO Data 3.2 Network Interactions 3.3 Performance with Deep Learning 4 Conclusion References AI for Brain Disorders The Emerging Role of AI in Dementia Research and Healthcare 1 Introduction to Dementia Research and Healthcare 2 Genetics 2.1 How Do We Determine the Biological Effect of Genetic Variants? 2.2 How Do We Get from Genetic Epidemiology and -Omics to Practical Applications? 3 Experimental Medicine 3.1 What Makes a Good Experimental Model? 3.2 How Can We Make Best Use of Multimodal Data? 3.3 How Can We Translate Insights from Experimental Models to Human Disease Biology? 4 Drug Discovery and Trials Optimisation 5 Neuroimaging 6 Prevention 7 A Global Initiative for AI Applied to Dementia Research and Healthcare 8 Conclusions References Effective Diagnosis of Parkinson’s Disease Using Machine Learning Techniques 1 Introduction 2 Related Works 3 Experimental Pipeline and Setup 4 Experimental Results and Discussion 5 Conclusion References Brain Networks in Autism Spectrum Disorder, Epilepsy and Their Relationship: A Machine Learning Approach 1 Introduction 2 Mapping Eeg Time-Series to Complex Network 3 Material and Method 3.1 Dataset 3.2 Network Metrics and Analysis 3.3 Classification and Performance Evaluation of Metrics 4 Results 4.1 Brain Network Topology 4.2 Statistical Analysis of Network Metrics 4.3 Assessment With Standardized Data 5 Discussion 6 Conclusion References AI in Mental Health Computational Intelligence in Depression Detection 1 Introduction 2 Literature Review 3 Depression Detection 4 Computational Intelligence in Depression Detection 4.1 Depression Detection from Social Media Data 4.2 Depression Detection from Image/Video Data 4.3 Depression Detection from Bio Signal 4.4 Depression Detection from Smartphone Data 5 Challenges and Research Direction 5.1 Gaps in the Literature 5.2 Future Research Scopes 6 Conclusion References Investigating Mental Wellbeing in the Technology Workplace Using Machine Learning Techniques 1 Introduction 2 Materials and Methods 2.1 The Data 2.2 Data Preprocessing 2.3 Flowchart 3 Experimentation and Discussion 3.1 Cluster Analysis 3.2 Visualisation 3.3 Building a Predictive Model Using Artificial Neural Networks 4 Conclusion References Computational Intelligence in Detection and Support of Autism Spectrum Disorder 1 Introduction 2 Computational Intelligence 2.1 Neural Networks 2.2 Fuzzy Logic 2.3 Evolutionary Computation 3 Computational Intelligence in Autism Detection 3.1 Datasets and Methods 4 Computational Intelligence in Autism Management 4.1 Apps and Platforms for Supporting People with Autism 5 Challenges and Future Research 6 Conclusion References AI for COVID-19 A Case Study of Using Machine Learning Techniques for COVID-19 Diagnosis 1 Introduction 2 Literature Review 3 Experimentation and Discussion 3.1 Data Preprocessing 3.2 Data Sampling 3.3 Model Selection 3.4 Hyperparameters Optimization 3.5 Evaluation 4 Conclusion References A Fuzzy Logic Approach to a Hybrid Lexicon-Based Sentiment Analysis Detection Tool Using Healthcare Covid-19 News Articles 1 Introduction 2 What is Sentiment Analysis? 3 Consensus Sentiment Analysis of News Articles Using a Fuzzy Inference System 4 Data Pre-processing and Sentiment Analysis 5 Data Transformation Process 6 Sentiment Analysis Algorithms 7 Fuzzification 8 Designing the Fuzzification Interface? 9 Functionality Block 10 Rule-Base 11 Membership Functions 12 Multi-inference System 13 Defuzzification 14 Cogs 15 Boa 16 Mom 17 Post Processing 18 Conclusion References AI for Cardivascular Diseases Using Fuzzy Logic to Diagnose Blood Pressure 1 Introduction 2 Literature Review 3 System Overview 3.1 Approach to the Problem 3.2 System Description 4 System Testing 4.1 Data Collection for Testing 5 Critical Analysis 6 Conclusion References An AI-Based Approach to Identifying High Impact Comorbidities in Public Health Management of Diseases 1 Introduction 2 Background 2.1 Non-random Comorbidities 2.2 Coronary Heart Disease (CHD) in the UK 2.3 The “Small Changes” Philosophy 2.4 The Market Target (mt) Model 3 Case Study: Using the mt Model to Support Decision-Making in the Public Health Management of CHD in the UK 3.1 Overview 3.2 Results and Discussion 3.3 Deaths by CHD 3.4 Prevalence Versus Deaths 4 Concluding Remarks 4.1 Limitations References Risk Detection of Heart Disease 1 Introduction 2 Related Work 3 System Overview 4 System Design and Configuration 4.1 System Inputs 4.2 System Output 5 System Performance 6 Critical Evaluation 7 Conclusion References AI for Diabetes A Case Study of Diabetes Diagnosis Using a Neuro-Fuzzy System 1 Introduction 2 Literature Review 3 Methods 4 Experimentation 5 Conclusion References A Fuzzy Logic Risk Assessment System for Type 2 Diabetes 1 Introduction 2 Related Research 2.1 What is Fuzzy Logic, and How Does It Work? 2.2 How Can Fuzzy Logic Be Applied to the Healthcare Field? 2.3 How Does Fuzzy Logic Improve This System? 3 Final System Overview 3.1 Description 3.2 Patient/User Risk Inference Subsystem 3.3 Doctor Risk Inference Subsystem (Optional) 3.4 Total Risk Inference Subsystem (Requires Other Subsystems) 4 Design Evolution and Evaluation 4.1 Initial System 4.2 Test Data and Data Generation 4.3 Test and Test Results Summary 4.4 Final System 5 Reflection 5.1 Testing 5.2 Effects of Different Risk Factors 5.3 System Performance and Possible Improvements 6 Conclusion References