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ویرایش: 1
نویسندگان: Arjun Panesar
سری:
ISBN (شابک) : 1484237986, 9781484237984
ناشر: Apress
سال نشر: 2019
تعداد صفحات: 389
زبان: English
فرمت فایل : PDF (درصورت درخواست کاربر به PDF، EPUB یا AZW3 تبدیل می شود)
حجم فایل: 4 مگابایت
در صورت تبدیل فایل کتاب Machine Learning and AI for Healthcare: Big Data for Improved Health Outcomes 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 یادگیری ماشینی و هوش مصنوعی برای مراقبت های بهداشتی: داده های بزرگ برای بهبود نتایج 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.
Table of Contents About the Author About the Technical Reviewer Acknowledgments Introduction Chapter 1: What Is Artificial Intelligence? A Multifaceted Discipline Examining Artificial Intelligence Reactive Machines Limited Memory—Systems That Think and Act Rationally Theory of Mind—Systems That Think Like Humans Self-Aware AI—Systems That Are Humans What Is Machine Learning? What Is Data Science? Learning from Real-Time, Big Data Applications of AI in Healthcare Prediction Diagnosis Personalized Treatment and Behavior Modification Drug Discovery Follow-Up Care Realizing the Potential of AI in Healthcare Understanding Gap Fragmented Data Appropriate Security Data Governance Bias Software Conclusion Chapter 2: Data What Is Data? Types of Data Big Data Volume Coping with Data Volume Variety The Internet of Things Legacy Data Velocity Value Veracity Validity Variability Visualization Small Data Metadata Healthcare Data—Little and Big Use Cases Predicting Waiting Times Reducing Readmissions Predictive Analytics Electronic Health Records Value-Based Care/Engagement Healthcare IoT—Real-Time Notifications, Alerts, Automation Movement Toward Evidence-Based Medicine Public Health Evolution of Data and Its Analytics Turning Data into Information: Using Big Data Descriptive Analytics Diagnostic Analytics Predictive Analytics Use Case: Realizing Personalized Care Use Case: Patient Monitoring in Real Time Prescriptive Analytics Use Case: From Digital To Pharmacology Reasoning Deduction Induction Abduction How Much Data Do I Need for My Project? Challenges of Big Data Data Growth Infrastructure Expertise Data Sources Quality of Data Security Resistance Policies and Governance Fragmentation Lack of Data Strategy Visualization Timeliness of Analysis Ethics Data and Information Governance Data Stewardship Data Quality Data Security Data Availability Data Content Master Data Management (MDM) Use Cases Deploying a Big Data Project Big Data Tools Conclusion Chapter 3: What Is Machine Learning? Basics Agent Autonomy Interface Performance Goals Utility Knowledge Environment Training Data Target Function Hypothesis Learner Hypothesis Validation Dataset Feature Feature Selection What Is Machine Learning? How Is Machine Learning Different from Traditional Software Engineering? Machine Learning Basics Supervised Learning Unsupervised Learning Semi-supervised Reinforcement Learning Data Mining Parametric and Nonparametric Algorithms How Machine Learning Algorithms Work How to Perform Machine Learning Specifying the Problem Examples Background Information Errors in the Data Preparing the Data Attribute Selection Transforming Data Aggregation Decomposition Scaling Weightings How Much Data Do I Need? Choosing the Learning Method Applying the Learning Methods Should I Code My Machine Learning Algorithm from Scratch? Training and Test Data Hold-Back Method n-fold Cross-Validation Monte-Carlo Cross-Validation Try Many Algorithms Assessing the Method and Results Algorithm Accuracy Evaluation Bias and Variance Bias Variance Performance Measures Optimization Improving Results with Better Data Should I Choose a Supervised or Unsupervised Algorithm? Ensembles Problem Distribution Implementation Problems Reporting the Results Chapter 4: Machine Learning Algorithms Defining Your Machine Learning Project Task (T) Performance (P) Experience (E) Common Libraries for Machine Learning Supervised Learning Algorithms Classification Regression Decision trees Iterative Dichotomizer 3 (ID3) C4.5 CART Ensembles Bagging Random Forest Decision Trees Boosting Gradient Boosting Adaptive Boosting Linear Regression Logistic Regression SVM Naive Bayes kNN: k-nearest neighbor Neural Networks Perceptron Artificial Neural Networks Deep Learning Feedforward Neural Network Recurrent Neural Network (RNN)—Long Short-Term Memory Convolutional Neural Network Modular Neural Network Radial Basis Neural Network Unsupervised Learning Clustering K-Means Association Apriori Dimensionality Reduction Algorithms Dimension Reduction Techniques Missing/Null Values Low Variance High Correlation Random Forest Decision Trees Backward Feature Elimination Forward Feature Construction Principal Component Analysis (PCA) Natural Language Processing (NLP) Getting Started with NLP Preprocessing: Lexical Analysis Noise Removal Lexicon Normalization Porter Stemmer Object Standardization Syntactic Analysis Dependency Parsing Part of Speech Tagging Reducing Ambiguity Identifying Features Normalization Stopword Removal Semantic analysis Techniques Used Within NLP N-grams TF IDF Vectors Latent Semantic Analysis Cosine Similarity Naïve Bayesian Classifier Genetic Algorithms Best Practices and Considerations Good Data Management Establish a Performance Baseline Spend Time Cleaning Your Data Training Time Choosing an Appropriate Model Choosing Appropriate Variables Redundancy Overfitting Productivity Understandability Accuracy Impact of False Negatives Linearity Parameters Ensembles Use Case: Type 2 Diabetes Chapter 5: Evaluating Learning for Intelligence Model Development and Workflow Why Are There Two Approaches to Evaluating a Model? Evaluation Metrics Classification Accuracy Confusion matrix Per-class accuracy Logarithmic loss Area Under the Curve (AUC) Precision, recall, specificity, and F-measure Regression RMSE Percentiles of errors Skewed Datasets, Anomalies, and Rare Data Parameters and Hyperparameters Tuning Hyperparameters Hyperparameter Tuning Algorithms Grid Search Random Search Multivariate Testing Which Metric Should I Use for Evaluation? Correlation Does Not Equal Causation What Amount of Change Counts as Real Change? Types of Tests, Statistical Power, and Effect Size Checking the Distribution of Your Metric Determining the Appropriate p Value How Many Observations Are Required? How Long to Run a Multivariate Test? Data Variance Spotting Distribution Drift Keep a Note of Model Changes Chapter 6: Ethics of Intelligence What Is Ethics? What Is Data Science Ethics? Data Ethics Informed Consent Freedom of Choice Should a Person’s Data Consent Ever Be Overturned? Public Understanding Who Owns the Data? What Can the Data Be Used For? Privacy: Who Can See My Data? How Will Data Affect the Future? Prioritizing Treatments Determining New Treatments and Management Pathways More real-world evidence Enhancements in Pharmacology Optimizing Pathways Through Connectivity—Is There a Limit? Security Ethics of Artificial Intelligence and Machine Learning Machine Bias Data Bias Human Bias Intelligence Bias Bias Correction Is Bias a Bad Thing? Prediction Ethics Explaining Predictions Protecting Against Mistakes Validity Preventing Algorithms from Becoming Immoral Unintended Consequences How Does Humanity Stay in Control of a Complex and Intelligent System? Intelligence Health Intelligence Who Is Liable? First-Time Problems Defining Fairness How Do Machines Affect Our Behavior and Interaction Humanity Behavior and Addictions Economy and Employment Affecting the future Playing God Overhype and Scaremongering Stakeholder Buy-In and Alignment Policy, Law, and Regulation Data and Information Governance Is There Such a Thing as Too Much Policy? Global standards and schemas Do We Need to Treat AI with Humanity? Employing Data Ethics Within Your Organization Ethical Code Ethical Framework Considerations Collect the Minimal Amount of Data Identify and Scrub Sensitive Data Compliance with Applicable Laws and Regulations A Hippocratic Oath for Data Scientists Auditing Your Frameworks Chapter 7: Future of Healthcare Shifting from Volume to Value Evidence-Based Medicine Personalized Medicine Vision of the Future Connected Medicine Disease and Condition Management Virtual Assistants Remote Monitoring Medication Adherence Accessible Diagnostic Tests Smart Implantables Digital Health and Therapeutics Education Incentivized Wellness AI Mining Records Conversational AI Making Better Doctors Optimization Diagnosing disease Making and rationalizing decisions Drug discovery 3-D printing Personalized prosthetics Bioprinting and tissue engineering Pharmacology and devices Education Gene therapy Virtual and Augmented Reality Virtual Reality Augmented Reality Merged Reality Pain Management Physical Therapy Cognitive Rehabilitation Nursing and Delivery of Medicine Virtual Appointments and Classrooms Blockchain Verifying the Supply Chain Incentivized Wellness Patient Record Access Robots Robot-Assisted Surgery Exoskeletons Inpatient Care Companions Drones Smart Places Smart Homes Smart Hospitals Reductionism Innovation vs. Deliberation Chapter 8: Case Studies Case Study Selection Conclusion Case Study: AI for Imaging of Diabetic Foot Concerns and Prioritization of Referral for Improvements in Morbidity and Mortality Background Cognitive Vision Project Aims Challenges Conclusions Case Study: Outcomes of a Digitally Delivered, Low-Carbohydrate, Type 2 Diabetes Self-Management Program: 1-Year Results of a Single-Arm Longitudinal Study Background Objectives Methods Results Observations Conclusions Case Study: Delivering A Scalable and Engaging Digital Therapy for Epilepsy Background Implementing the Evidence Base Sensor-Driven Digital Program Research Project Impact Preliminary Analysis Case Study: Improving Learning Outcomes For Junior Doctors Through the Novel Use of Augmented and Virtual Reality Background Aims Project Description Conclusions Case Study: Big Data, Big Impact, Big Ethics: Diagnosing Disease Risk from Patient Data Background Platform Services Medication Adherence, Efficacy and Burden Community Forum AI prioritization of patient interactions Real-World Evidence Ethical Implications of Predictive Analytics Integration of the IoT Conclusions Technical Glossary References Index