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1. |
Record Nr. |
UNINA9910956906203321 |
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Autore |
Frumkin Norman |
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Titolo |
Guide to economic indicators / / Norman Frumkin |
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Pubbl/distr/stampa |
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Armonk, N.Y., : M.E. Sharpe, c2006 |
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ISBN |
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1-315-70391-2 |
1-317-46928-3 |
1-280-91269-3 |
9786610912698 |
0-7656-2147-9 |
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Edizione |
[4th ed.] |
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Descrizione fisica |
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1 online resource (304 p.) |
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Disciplina |
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Soggetti |
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Economic indicators - United States |
Business cycles - United States |
United States Economic conditions Statistics |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references (p. 267-270) and indexes. |
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Nota di contenuto |
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Contents; Tables, Formulas, and Figure; Preface; 1 Average Weekly Earnings; 2 Average Weekly Hours; 3 Balance of Payments; 4 Balance of Trade; 5 Bank Loans: Commercial and Industrial; 6 Bankruptcies: Personal; 7 Capacity Utilization; 8 Consumer Attitude Indexes; 9 Consumer Credit; 10 Consumer Credit Delinquency; 11 Consumer Price Index; 12 Corporate Profits; 13 Distribution of Income; 14 Distribution of Wealth; 15 Employment; 16 Employment Cost Index; 17 Farm Parity Ratio; 18 GDP Price Measures; 19 Government Economic Transactions: Expenditures, Receipts, Surplus/Deficit |
20 Gross Domestic Product21 Help-Wanted Advertising Index; 22 Home Sales: New and Existing Houses; 23 House Prices: New and Existing Houses; 24 Housing Affordability Index; 25 Housing Starts; 26 Housing Vacancy Rate; 27 Import and Export Price Indexes; 28 Industrial Production Index; 29 Interest Rates; 30 International Investment Position of the United States; 31 Inventory-Sales Ratios; 32 Job Gains and Losses; 33 Job Openings and Labor Turnove r; 34 Leading, Coincident, and Lagging Indexes; 35 Manufacturers' Orders; 36 Mortgage Loan Applications; 37 Mortgage Delinquency and |
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Foreclosure |
38 Non-Manufacturing Business Activity Index39 PMI; 40 Personal Income and Saving; 41 Poverty; 42 Producer Price Indexes; 43 Productivity: Business Sector; 44 Retail Sales; 45 Selected Services Revenue; 46 Stock Market Price Aggregates and Dividend Yields; 47 Unemployment; 48 Unit Labor Costs: Business Sector; 49 Value of the Dollar; Appendix: Note on Sampling and Nonsampling Errors in Statistical Surveys; Index; About the Author |
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Sommario/riassunto |
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Provides quick and easy access to reliable data on the major statistical measures of the U.S. economy. Equally useful for students, general readers, economists, analysts, journalists, and investors, this guide provides concise, jargon-free explanations of the meaning, use, and availability of more than 70 macroeconomic indicators. |
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2. |
Record Nr. |
UNINA9911054510903321 |
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Autore |
Kumar Abhishek |
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Titolo |
Artificial Intelligence and Machine Learning in Neurology, 2 Volume Set |
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Pubbl/distr/stampa |
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Newark : , : John Wiley & Sons, Incorporated, , 2026 |
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©2026 |
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ISBN |
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1-394-38912-4 |
1-394-38911-6 |
1-394-38913-2 |
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Edizione |
[1st ed.] |
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Descrizione fisica |
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1 online resource (846 pages) |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Cover -- Volume One -- Series Page -- Title Page -- Copyright Page -- Contents -- Brief Contents of Volume 2 -- Preface -- Chapter 1 Ethical Frameworks for AI-Driven Healthcare: Genetic and Epidemiological Perspectives on Ethical AI Frameworks -- 1.1 Introduction -- 1.1.1 The Role of AI in Healthcare -- 1.1.2 Importance of Ethical Frameworks -- 1.2 Ethical Considerations in AI-Driven Healthcare -- 1.2.1 Medical |
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Ethics -- 1.2.2 Challenges in Applying Ethical Principles to AI -- 1.3 Genetic Perspectives on Ethical AI Frameworks -- 1.3.1 Personalized Medicine and AI -- 1.3.2 Data Protection of Genetics Information -- 1.3.3 Informed Consent and Genetic Testing -- 1.3.4 Equity in Access to Genetic Treatments -- 1.4 Ethical Frameworks for AI in Genetics -- 1.4.1 Principles of Autonomy, Beneficence, and Justice -- 1.4.2 Ensuring Patient Control Over Genetic Data -- 1.4.3 Utilizing AI-Derived Knowledge for the Patient's Treatment -- 1.4.4 Fair Access to Available Services for Genetic Examinations and Interventions -- 1.5 Epidemiological Perspectives on Ethical AI Frameworks -- 1.5.1 AI and Disease Surveillance -- 1.5.2 Public Health Interventions and AI -- 1.5.3 Information Confidentiality Applying to Consent in Epidemiology Designs -- 1.5.4 Bias in Algorithmic Decision-Making -- 1.6 Ethical Frameworks for AI in Epidemiology -- 1.6.1 Principles of Transparency, Accountability, and Fairness -- 1.6.2 Responsible Use of AI-Powered Insights -- 1.6.3 Fair Division of AI Advantages Across Social Groups -- 1.7 Conclusion -- References -- Chapter 2 Ethical Challenges and Guidelines for AI Deployment in Healthcare: Urological and Gastroenterological Perspectives on Ethical AI Deployment -- 2.1 Introduction -- 2.1.1 Summary of AI in Medicine -- 2.1.2 Importance of Ethical Considerations in AI Deployment -- 2.2 Ethical Principles in AI Deployment. |
2.2.1 Principles of Beneficence, Nonmaleficence, Autonomy, and Justice -- 2.2.2 Explainability and Transparency in AI Algorithms -- 2.2.3 The Importance of Accountability and Responsibility in AI Decision-Making -- 2.3 Challenges in AI Deployment in Urology and Gastroenterology -- 2.3.1 Data Privacy and Security Concerns -- 2.3.2 Bias and Fairness in AI Algorithms -- 2.3.3 Clinical Integration and Acceptance of AI Technologies -- 2.4 Guidelines for Ethical AI Deployment in Urology and Gastroenterology -- 2.4.1 Data Governance and Management -- 2.4.2 Safeguarding Patient Consent and Sensitive Information -- 2.4.3 Addressing Bias within AI Algorithms -- 2.4.4 Clinical Validation and Evaluation of AI Technologies -- 2.5 Case Studies -- 2.5.1 Application of AI Technology in Urology with Regard to Chronic Prostate Cancer -- 2.5.2 The Role of AI in Gastroenterology, with Relation to Diagnosing Other Digestive Tract Ailments -- 2.6 Future Directions and Recommendations -- 2.6.1 Progress of AI Ethics and Regulation -- 2.6.2 Collaboration Between Stakeholders for Ethical AI Deployment -- 2.6.3 Continuous Monitoring and Evaluation of AI Technologies -- 2.7 Conclusion -- References -- Chapter 3 Bias Mitigation and Fairness in AI Healthcare Applications: Addressing Bias and Equity in AI-Driven Healthcare Solutions -- 3.1 Introduction -- 3.1.1 AI in Healthcare -- 3.1.2 Bias Mitigation and Fairness -- 3.2 Bias in AI Healthcare Applications -- 3.2.1 Sources of Bias in AI Algorithms -- 3.2.2 Impact of Bias on Healthcare Equity -- 3.3 Strategies for Bias Mitigation in AI Healthcare -- 3.3.1 Diverse and Representative Training Data -- 3.3.2 Designing Algorithms in a Clear Manner -- 3.3.3 Auditing and Measuring for Fairness -- 3.4 Promoting Equity in AI Healthcare -- 3.4.1 Accessibility of AI Technologies -- 3.4.2 Addressing Unique Needs of Marginalized Communities. |
3.4.3 Designing for Inclusivity -- 3.5 Case Studies and Examples -- 3.5.1 Real-World Examples of Bias in Healthcare AI -- 3.5.2 Effective Approaches for Equity Promotion and Bias Mitigation -- 3.6 Future Directions and Challenges -- 3.6.1 Emerging Trends in Bias Mitigation -- 3.6.2 Ethical and Legal Considerations -- 3.7 Conclusion -- References -- Chapter 4 Regulatory Compliance and Data Governance in AI-Driven Healthcare: Legal and Regulatory Considerations for AI-Driven Healthcare Solutions -- 4.1 Introduction -- 4.1.1 An Overview |
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of Healthcare Options Based on AI -- 4.1.1.1 Benefits of AI-Driven Healthcare Solutions -- 4.1.1.2 AI-Driven Solutions in Healthcare- Implementation Challenges -- 4.1.2 Importance of Compliance with Regulations and Governance of Data -- 4.2 Legal and Regulatory Frameworks -- 4.2.1 Health Insurance Portability and Accountability Act -- 4.2.2 General Data Protection Regulation -- 4.2.3 National Laws and Guidelines -- 4.3 Data Governance in AI-Driven Healthcare -- 4.3.1 Establishing Data Governance Frameworks -- 4.3.2 Ensuring Data Quality, Security, and Privacy -- 4.3.3 Ethical Considerations and Best Practices -- 4.4 Regulatory Compliance Challenges -- 4.4.1 Sensitive Patient Data -- 4.4.1.1 Challenges of Regulatory Compliance -- 4.4.1.2 Techniques for Resolving Compliance Issues-Regulations -- 4.4.2 Owning Data: The Legal Maze -- 4.4.3 Assigning Responsibility for AI Deficiencies and Defaults -- 4.5 Ethical Guidelines for AI in Healthcare -- 4.5.1 European Commission's Ethical Guidelines for Trustworthy AI -- 4.5.2 Ethical Considerations in AI Algorithm Design and Deployment -- 4.6 Case Studies -- 4.6.1 Successful Implementation of Data Governance Frameworks -- 4.6.2 Challenges Faced and Lessons Learned -- 4.7 Future Trends and Considerations -- 4.7.1 Emerging Regulatory Trends in AI-Driven Healthcare. |
4.7.2 Possible Effects of New Technologies on Fulfilling Regulatory Obligations -- 4.8 Conclusion -- References -- Chapter 5 Ensuring Responsible Data Use in Healthcare AI Applications: Radiological and Surgical Approaches to Responsible AI Data Usage -- 5.1 Introduction -- 5.1.1 Overview of Healthcare AI Applications -- 5.1.2 Significance on Responsible Use of Data -- 5.2 Responsible Data Use in Radiological AI Applications -- 5.2.1 Role of AI in Radiological Imaging -- 5.2.2 Data Privacy and Anonymization -- 5.2.3 Consent Management for AI Data Usage -- 5.2.4 Strategies for Addressing Bias -- 5.2.5 Transparency and Monitoring in AI Algorithms -- 5.3 Responsible Data Use in Surgical AI Applications -- 5.3.1 Utilizing AI in the Preoperative Planning and Surgical Decision-Making Process -- 5.3.2 Data Security and Patient Privacy in Surgical AI -- 5.3.3 Data Security: Encryption and Protection Measures -- 5.3.4 Methods of Ensuring Secure Transmission of Data -- 5.3.5 Use of AI in Interpretable Algorithms for Surgery -- 5.4 Multidisciplinary Approaches to Responsible AI Data Usage -- 5.4.1 Collaboration Between Radiologists, Surgeons, and Data Scientists -- 5.4.2 Ethical Considerations in AI Development -- 5.4.3 Compliance with Regulatory Frameworks -- 5.5 Case Studies and Best Practices -- 5.5.1 Successful Implementations of Responsible Data Use in Healthcare AI -- 5.5.2 Recap of the Case Real-Life Use Studies -- 5.6 Radiological and Surgical Approaches to Responsible AI Data Usage -- 5.6.1 Radiological Approaches -- 5.6.2 Surgical Approaches -- 5.6.3 Collaboration and Compliance -- 5.7 Conclusion -- References -- Chapter 6 Implementing Secure Health Data Exchange with Blockchain: Orthopedic and Ophthalmological Insights into Secure Health Data Exchange -- 6.1 Introduction -- 6.1.1 Overview of Health Data Exchange. |
6.1.2 Importance of Security in Health Data Exchange -- 6.1.3 Role of Blockchain Technology in Secure Health Data Exchange -- 6.2 Orthopedic Insights into Secure Health Data Exchange -- 6.2.1 Orthopedic Data Exchange Obstacles -- 6.2.2 Implementing Blockchain in the Sharing of Orthopedic Data -- 6.2.3 Case Studies and Stories of Success -- 6.3 Ophthalmological Insights into Secure Health Data Exchange -- 6.3.1 Challenges in Sharing Ophthalmological Information -- 6.3.2 Using Blockchain Technology for Data Sharing in Ophthalmology -- 6.3.3 Case Studies and Success Stories -- 6.4 Blockchain Technology for Health Data Exchange -- 6.4.1 |
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Understanding Blockchain Technology -- 6.4.2 Advantages and Disadvantages of Blockchain Technology with Respect to Heath Data Exchange -- 6.4.2.1 Advantages of Applying Blockchain Technology in Health Data Exchange -- 6.4.2.2 Limitations of Blockchain in Health Data Exchange -- 6.5 Regulatory and Legal Considerations -- 6.5.1 HIPAA Compliance and Health Data Security -- 6.5.2 GDPR and Protection of Sensitive Health Information -- 6.5.3 Legal Implications of Blockchain in Health Data Exchange -- 6.6 Future Trends and Challenges -- 6.6.1 New Developments in Technological Health Data Exchange -- 6.6.2 Challenges and Opportunities in Implementing Blockchain -- 6.6.3 Future Directions for Secure Health Data Exchange -- 6.7 Conclusion -- References -- Chapter 7 Securing Clinical Trial Data with Decentralized Technologies and Exploring Blockchain Applications in Modern Healthcare Management -- 7.1 Introduction -- 7.2 Related Work -- 7.3 Overview of Blockchain Technology -- 7.4 Methodology -- 7.5 Blockchain Applications in Clinical Trial Data Management -- 7.6 Decentralized Technologies in Healthcare Management -- 7.7 Results and Discussion -- 7.8 Conclusion -- References. |
Chapter 8 Blockchain-Enabled Healthcare Ecosystems: Scalability, Security, and Interoperability. |
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Sommario/riassunto |
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Unlock the future of brain health with this indispensable guide, which offers a comprehensive exploration of how artificial intelligence and machine learning are revolutionizing the diagnosis, treatment, and management of complex neurological disorders. |
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