Machine Learning and AI for Healthcare : Big Data for Improved Health Outcomes /: Big Data for Improved Health Outcomes. ([2019])

• Record Type: Book
• Further Information: Note: Arjun Panesar.
• Authors: Panesar, Arjun
• Contents: Intro; 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 HealthcareUnderstanding 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 MedicinePublic 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 StrategyVisualization; 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?
• Publisher Details: Berkeley, CA : Apress
• Publication Date: 2019
• Extent: 1 online resource (389 pages)
• Subjects: 610.285/63; Artificial intelligence -- Medical applications; Machine learning; Artificial intelligence -- Medical applications; Machine learning; Electronic books
• Languages: English
• ISBNs: 9781484237991; 1484237994
• Related ISBNs: 9781484237984
• Notes: Note: Includes bibliographical references and index.; Note: Description based on online resource; title from digital title page (viewed on March 19, 2019).
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.