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Biblioteca

MARC Lista (tabellare)
1: Epidemiologia generale ed applicata / Bruno Angelillo ... \et al.!
1: Epidemiologia generale ed applicata / Bruno Angelillo ... \et al.!
Pubbl/distr/stampa Milano \etc.!, : Masson, 1993
Descrizione fisica XV, 385 p. : ill. ; 27 cm.
Disciplina 614
Soggetto topico Igiene pubblica - Epidemologia
Epidemiologia
ISBN 8821421805
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione ita
Record Nr. UNICAS-BVE0045977
Milano \etc.!, : Masson, 1993
Materiale a stampa
Lo trovi qui: Univ. di Cassino e del Lazio Meridionale
Opac: Controlla la disponibilità qui
2000 Years of Pandemics : Past, Present, and Future / / by Claudia Ferreira, Marie-Françoise J. Doursout, Joselito S. Balingit
2000 Years of Pandemics : Past, Present, and Future / / by Claudia Ferreira, Marie-Françoise J. Doursout, Joselito S. Balingit
Autore Ferreira Claudia <1955->
Edizione [1st ed. 2023.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023
Descrizione fisica 1 online resource (416 pages)
Disciplina 306.461
362.1969
Soggetto topico Internal medicine
Public health
Epidemiology
Emergency medical services
Family medicine
Internal Medicine
Public Health
Emergency Services
General Practice and Family Medicine
Epidèmies
Epidemiologia
Salut pública
Història
Soggetto genere / forma Llibres electrònics
ISBN 3-031-10035-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Introduction. Part-1: Pandemic in the past -- First 1000 Years -- Black Death -- Outbreaks in the new World -- Syphilis, Cholera, and Yellow Fever -- Part-2: Pandemics In The Present -- The World Since 1900: Background to Pandemics in the Present -- Influenza Pandemics -- Coronavirus Pandemics -- Human Immunodeficiency Virus (HIV) -- Vaccines -- Zoonoses -- The Next Pandemic -- The Next Pandemic: Hemorrhagic Fevers -- Part-3:The Next Pandemic: Bioterrorism -- The Next Pandemic: Climate Change -- The Next Pandemic: Antibiotic Resistance -- The Next Pandemic: Challenges and Hopes.
Record Nr. UNINA-9910659484503321
Ferreira Claudia <1955->  
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
AIDS : la sfida antropologica / Francoise Heritier-Auge ; a cura di Francesco Maiello ; nota introduttiva di Luigi M. Lombardi Satriani
AIDS : la sfida antropologica / Francoise Heritier-Auge ; a cura di Francesco Maiello ; nota introduttiva di Luigi M. Lombardi Satriani
Autore Héritier-Augé, Francoise
Pubbl/distr/stampa Roma : Ei, [c1993]
Descrizione fisica 103 p. ; 20 cm.
Altri autori (Persone) Maiello, Francesco
Collana Ventunesimo secolo ; 1
Soggetto topico Epidemiologia
Sindrome da immunodeficienza acquisita
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione ita
Record Nr. UNISALENTO-991001734569707536
Héritier-Augé, Francoise  
Roma : Ei, [c1993]
Materiale a stampa
Lo trovi qui: Univ. del Salento
Opac: Controlla la disponibilità qui
Applying quantitative bias analysis to epidemiologic data / / Matthew P. Fox, Richard F. MacLehose, and Timothy L. Lash
Applying quantitative bias analysis to epidemiologic data / / Matthew P. Fox, Richard F. MacLehose, and Timothy L. Lash
Autore Lash Timothy L.
Edizione [Second edition.]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (475 pages)
Disciplina 614.4072
Collana Statistics for Biology and Health
Soggetto topico Epidemiology - Research
Social sciences - Methodology
Epidemiologia
Estadística matemàtica
Soggetto genere / forma Llibres electrònics
ISBN 3-030-82673-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Acknowledgments -- Contents -- Chapter 1: Introduction, Objectives, and an Alternative -- Introduction: Biases in Health Research -- Statistical Inference in Public Health Research -- The Treatment of Uncertainty in Nonrandomized Research -- When Bias Analysis Will Be Most Useful -- Judgments Under Uncertainty -- The Dual-Process Model of Cognition -- Anchoring and Adjustment -- Overconfidence -- Failure to Account for the Base-Rate -- Conclusion -- References -- Chapter 2: A Guide to Implementing Quantitative Bias Analysis -- Introduction -- Reducing Error -- Reducing Error by Design -- Reducing Error in the Analysis -- Quantifying Error -- Evaluating the Potential Value of Quantitative Bias Analysis? -- Planning for Bias Analysis -- Creating a Data Collection Plan for Bias Analysis -- Creating an Analytic Plan for a Bias Analysis -- Type of Data: Record-Level Versus Summary -- Type of Bias Analysis -- Order of Bias Analysis Adjustments -- Bias Analysis Techniques -- Simple Bias Analysis -- Multidimensional Bias Analysis -- Probabilistic Bias Analysis -- Multiple Bias Modeling -- Direct Bias Modeling and Missing Data Methods -- Bayesian Bias Analysis -- Assigning Values and Distributions to Bias Parameters -- Directed Acyclic Graphs -- Conclusion -- References -- Chapter 3: Data Sources for Bias Analysis -- Bias Parameters -- Internal Data Sources -- Selection Bias -- Uncontrolled Confounding -- Information Bias -- Design of Internal Validation Studies -- Limitations of Internal Validation Studies -- External Data Sources -- Selection Bias -- Unmeasured Confounder -- Information Bias -- Expert Opinion -- Summary -- References -- Chapter 4: Selection Bias -- Introduction -- Definitions and Terms -- Conceptual -- Depicting Selection Bias Using Causal Graphs -- Design Considerations -- Bias Analysis.
Motivation for Bias Analysis -- Sources of Data -- Simple Bias-Adjustment for Differential Initial Participation -- Example -- Introduction to Bias Analysis -- Bias Analysis by Projecting the Exposed Proportion Among Nonparticipants -- Bias Analysis Using Selection Proportions -- Bias Analysis Using Inverse Probability of Participation Weighting -- Simple Bias-Adjustment for Differential Loss-to-Follow-up -- Example -- Bias Analysis by Modeling Outcomes -- Bias Analysis by Inverse Probability of Attrition Weighting -- Multidimensional Bias Analysis for Selection Bias -- Example -- References -- Chapter 5: Uncontrolled Confounders -- Introduction -- Key Concepts -- Definitions -- Motivation for Bias Analysis -- Data Sources -- Introduction to Simple Bias Analysis -- Approach -- Introduction to the Example -- Bias Parameters -- Implementation of Simple Bias Analysis -- Ratio Measures -- Example -- Difference Measures -- Person-time Designs -- Unmeasured Confounder in the Presence of Effect Measure Modification -- Polytomous Confounders -- Multidimensional Bias Analysis for Unmeasured Confounding -- Example -- Bounding the Bias Limits of an Unmeasured Confounding -- Analytic Approach -- The E-Value and G-Value -- Signed Directed Acyclic Graphs to Estimate the Direction of Bias -- References -- Chapter 6: Misclassification -- Introduction -- Definitions and Terms -- Differential vs. Nondifferential Misclassification -- Dependent vs. Independent Misclassification -- Directed Acyclic Graphs and Misclassification -- Calculating Classification Bias Parameters from Validation Data -- Sources of Data -- Bias Analysis of Exposure Misclassification -- Bias-Adjusting for Exposure Misclassification Using Sensitivity and Specificity: Nondifferential and Independent Errors -- Bias-Adjusting for Exposure Misclassification Using Predictive Values.
Bias-Adjustment for Nondifferential Outcome Misclassification Using Positive Predictive Values for the Risk Ratio Measure of A... -- Bias-Adjustments Using Sensitivity and Specificity: Differential Independent Errors -- Bias-Adjustments Using Sensitivity and Specificity: Internal Validation Data -- Overreliance on Nondifferential Misclassification Biasing Toward the Null -- Disease Misclassification -- Bias-Adjustments with Sensitivity and Specificity: Nondifferential and Independent Errors -- Disease Misclassification in Case-Control Studies -- Overreliance on Nondifferential Misclassification Biasing Toward the Null -- Covariate Misclassification -- Bias-Adjustments with Sensitivity and Specificity: Nondifferential and Differential Misclassification with Independent Errors -- Overreliance on Nondifferential Misclassification of Covariates Biasing Toward the Null -- Dependent Misclassification -- Matrix Method for Misclassification Adjustment -- Multidimensional Bias Analysis for Misclassification -- Limitations -- Negative Expected Cell Frequencies -- Other Considerations -- References -- Chapter 7: Preparing for Probabilistic Bias Analysis -- Introduction -- Preparing for Probabilistic Bias Analysis -- Statistical Software for Probabilistic Bias Analysis -- Summary Level Versus Record Level Probabilistic Bias Analysis -- Describing Uncertainty in the Bias Parameters -- Probability Distributions -- Uniform Distribution -- Generalized Method for Sampling from Distributions -- Trapezoidal Distribution -- Triangular Distribution -- Normal Distribution -- Beta Distribution -- Bernoulli and Binomial Distributions -- Other Probability Distributions -- Sensitivity to Chosen Distributions -- Correlated Distributions -- Conclusions -- References -- Chapter 8: Probabilistic Bias Analysis for Simulation of Summary Level Data -- Introduction.
Analytic Approach for Summary Level Probabilistic Bias Analysis -- Exposure Misclassification Implementation -- Step 1: Identify the Source of Bias -- Step 2: Select the Bias Parameters -- Step 3: Assign Probability Distributions to Each Bias Parameter -- Step 4: Use Simple Bias Analysis Methods to Incorporate Uncertainty in the Bias Parameters and Random Error -- Step 4a: Sample from the Bias Parameter Distributions -- Step 4b: Generate Bias-Adjusted Data Using Simple Bias Analysis Methods and the Sampled Bias Parameters -- Step 4c: Incorporate Conventional Random Error by Sampling Summary Statistics -- Step 4c (Alternate): Resample the Prevalence of Misclassification Adjusted Exposure -- Step 5: Save the Bias-Adjusted Estimate and Repeat Steps 4a-c -- Step 6: Summarize the Bias-Adjusted Estimates with a Frequency Distribution that Yields a Central Tendency and Simulation Inte... -- Misclassification Implementation: Predictive Values -- Misclassification Implementation: Predictive Values - Alternative -- Misclassification of Outcomes and Confounders -- Uncontrolled Confounding Implementation -- Step 1: Identify the Source of Bias -- Step 2: Identify the Bias Parameters -- Step 3: Assign Probability Distributions to Each Bias Parameter -- Step 4: Use Simple Bias Analysis Methods to Incorporate Uncertainty in the Bias Parameters and Random Error -- Step 4a: Sample from the Bias Parameter Distributions -- Step 4b: Generate Bias-Adjusted Data Using Simple Bias Analysis Methods and the Sampled Bias Parameters -- Step 4c: Sample the Bias-Adjusted Effect Estimate -- Steps 5 and 6: Resample, Save and Summarize -- Confounding Implementation Alternative: Relative Risk Due to Confounding -- Step 4: Use Simple Bias Analysis Methods to Incorporate Uncertainty in the Bias Parameters and Random Error -- Step 4a: Sample from the Bias Parameter Distributions.
Step 4b: Generate Bias-Adjusted Results Using Simple Bias Analysis Methods and the Sampled Bias Parameters -- Step 4c: Sample the Bias-Adjusted Effect Estimate -- Steps 5 and 6: Resample, Save and Summarize -- Selection Bias Implementation -- An Example of Probabilistic Bias Analysis in the Presence of Substantial Source Population Data -- Step 1: Identify the Source of Bias -- Step 2: Identify the Bias Parameters -- Step 3: Assign Probability Distributions to Each Bias Parameter -- Step 4: Use Simple Bias Analysis Methods to Incorporate Uncertainty in the Bias Parameters and Random Error -- Step 4a: Sample from the Bias Parameter Distributions -- Step 4b: Generate Bias-Adjusted Data Using Simple Bias Analysis Methods and the Sampled Bias Parameters -- Steps 4c: Sample the Bias-Adjusted Effect Estimate -- Steps 5 and 6: Resample, Save and Summarize -- Selection Bias Adjustment Using Selection Probabilities -- Step 1: Identify the Source of Bias -- Step 2: Identify the Bias Parameters -- Step 3: Assign Probability Distributions to Each Bias Parameter -- Step 4: Use Simple Bias Analysis Methods to Incorporate Uncertainty and Random Error -- Step 4a: Sample from the Bias Parameter Distributions -- Step 4b: Generate Bias-Adjusted Data Using Simple Bias Analysis Methods and the Sampled Bias Parameters -- Steps 4c: Sample the Bias-Adjusted Effect Estimate -- Steps 5 and 6: Resample, Save and Summarize -- Computing Issues with Summary Level Probabilistic Bias Analysis -- Bootstrapping -- Impossible Values for Bias Parameters and Model Diagnostic Plots -- Conclusions -- Appendix: Sampling Models for Exposure Misclassification -- References -- Chapter 9: Probabilistic Bias Analysis for Simulation of Record-Level Data -- Introduction -- Exposure Misclassification Implementation -- Step 1: Identify the Source of Bias -- Step 2: Select the Bias Parameters.
Step 3: Assign Probability Distributions to Each Bias Parameter.
Record Nr. UNINA-9910556891103321
Lash Timothy L.  
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Applying quantitative bias analysis to epidemiologic data / / Matthew P. Fox, Richard F. MacLehose, and Timothy L. Lash
Applying quantitative bias analysis to epidemiologic data / / Matthew P. Fox, Richard F. MacLehose, and Timothy L. Lash
Autore Lash Timothy L.
Edizione [Second edition.]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (475 pages)
Disciplina 614.4072
Collana Statistics for Biology and Health
Soggetto topico Epidemiology - Research
Social sciences - Methodology
Epidemiologia
Estadística matemàtica
Soggetto genere / forma Llibres electrònics
ISBN 3-030-82673-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Acknowledgments -- Contents -- Chapter 1: Introduction, Objectives, and an Alternative -- Introduction: Biases in Health Research -- Statistical Inference in Public Health Research -- The Treatment of Uncertainty in Nonrandomized Research -- When Bias Analysis Will Be Most Useful -- Judgments Under Uncertainty -- The Dual-Process Model of Cognition -- Anchoring and Adjustment -- Overconfidence -- Failure to Account for the Base-Rate -- Conclusion -- References -- Chapter 2: A Guide to Implementing Quantitative Bias Analysis -- Introduction -- Reducing Error -- Reducing Error by Design -- Reducing Error in the Analysis -- Quantifying Error -- Evaluating the Potential Value of Quantitative Bias Analysis? -- Planning for Bias Analysis -- Creating a Data Collection Plan for Bias Analysis -- Creating an Analytic Plan for a Bias Analysis -- Type of Data: Record-Level Versus Summary -- Type of Bias Analysis -- Order of Bias Analysis Adjustments -- Bias Analysis Techniques -- Simple Bias Analysis -- Multidimensional Bias Analysis -- Probabilistic Bias Analysis -- Multiple Bias Modeling -- Direct Bias Modeling and Missing Data Methods -- Bayesian Bias Analysis -- Assigning Values and Distributions to Bias Parameters -- Directed Acyclic Graphs -- Conclusion -- References -- Chapter 3: Data Sources for Bias Analysis -- Bias Parameters -- Internal Data Sources -- Selection Bias -- Uncontrolled Confounding -- Information Bias -- Design of Internal Validation Studies -- Limitations of Internal Validation Studies -- External Data Sources -- Selection Bias -- Unmeasured Confounder -- Information Bias -- Expert Opinion -- Summary -- References -- Chapter 4: Selection Bias -- Introduction -- Definitions and Terms -- Conceptual -- Depicting Selection Bias Using Causal Graphs -- Design Considerations -- Bias Analysis.
Motivation for Bias Analysis -- Sources of Data -- Simple Bias-Adjustment for Differential Initial Participation -- Example -- Introduction to Bias Analysis -- Bias Analysis by Projecting the Exposed Proportion Among Nonparticipants -- Bias Analysis Using Selection Proportions -- Bias Analysis Using Inverse Probability of Participation Weighting -- Simple Bias-Adjustment for Differential Loss-to-Follow-up -- Example -- Bias Analysis by Modeling Outcomes -- Bias Analysis by Inverse Probability of Attrition Weighting -- Multidimensional Bias Analysis for Selection Bias -- Example -- References -- Chapter 5: Uncontrolled Confounders -- Introduction -- Key Concepts -- Definitions -- Motivation for Bias Analysis -- Data Sources -- Introduction to Simple Bias Analysis -- Approach -- Introduction to the Example -- Bias Parameters -- Implementation of Simple Bias Analysis -- Ratio Measures -- Example -- Difference Measures -- Person-time Designs -- Unmeasured Confounder in the Presence of Effect Measure Modification -- Polytomous Confounders -- Multidimensional Bias Analysis for Unmeasured Confounding -- Example -- Bounding the Bias Limits of an Unmeasured Confounding -- Analytic Approach -- The E-Value and G-Value -- Signed Directed Acyclic Graphs to Estimate the Direction of Bias -- References -- Chapter 6: Misclassification -- Introduction -- Definitions and Terms -- Differential vs. Nondifferential Misclassification -- Dependent vs. Independent Misclassification -- Directed Acyclic Graphs and Misclassification -- Calculating Classification Bias Parameters from Validation Data -- Sources of Data -- Bias Analysis of Exposure Misclassification -- Bias-Adjusting for Exposure Misclassification Using Sensitivity and Specificity: Nondifferential and Independent Errors -- Bias-Adjusting for Exposure Misclassification Using Predictive Values.
Bias-Adjustment for Nondifferential Outcome Misclassification Using Positive Predictive Values for the Risk Ratio Measure of A... -- Bias-Adjustments Using Sensitivity and Specificity: Differential Independent Errors -- Bias-Adjustments Using Sensitivity and Specificity: Internal Validation Data -- Overreliance on Nondifferential Misclassification Biasing Toward the Null -- Disease Misclassification -- Bias-Adjustments with Sensitivity and Specificity: Nondifferential and Independent Errors -- Disease Misclassification in Case-Control Studies -- Overreliance on Nondifferential Misclassification Biasing Toward the Null -- Covariate Misclassification -- Bias-Adjustments with Sensitivity and Specificity: Nondifferential and Differential Misclassification with Independent Errors -- Overreliance on Nondifferential Misclassification of Covariates Biasing Toward the Null -- Dependent Misclassification -- Matrix Method for Misclassification Adjustment -- Multidimensional Bias Analysis for Misclassification -- Limitations -- Negative Expected Cell Frequencies -- Other Considerations -- References -- Chapter 7: Preparing for Probabilistic Bias Analysis -- Introduction -- Preparing for Probabilistic Bias Analysis -- Statistical Software for Probabilistic Bias Analysis -- Summary Level Versus Record Level Probabilistic Bias Analysis -- Describing Uncertainty in the Bias Parameters -- Probability Distributions -- Uniform Distribution -- Generalized Method for Sampling from Distributions -- Trapezoidal Distribution -- Triangular Distribution -- Normal Distribution -- Beta Distribution -- Bernoulli and Binomial Distributions -- Other Probability Distributions -- Sensitivity to Chosen Distributions -- Correlated Distributions -- Conclusions -- References -- Chapter 8: Probabilistic Bias Analysis for Simulation of Summary Level Data -- Introduction.
Analytic Approach for Summary Level Probabilistic Bias Analysis -- Exposure Misclassification Implementation -- Step 1: Identify the Source of Bias -- Step 2: Select the Bias Parameters -- Step 3: Assign Probability Distributions to Each Bias Parameter -- Step 4: Use Simple Bias Analysis Methods to Incorporate Uncertainty in the Bias Parameters and Random Error -- Step 4a: Sample from the Bias Parameter Distributions -- Step 4b: Generate Bias-Adjusted Data Using Simple Bias Analysis Methods and the Sampled Bias Parameters -- Step 4c: Incorporate Conventional Random Error by Sampling Summary Statistics -- Step 4c (Alternate): Resample the Prevalence of Misclassification Adjusted Exposure -- Step 5: Save the Bias-Adjusted Estimate and Repeat Steps 4a-c -- Step 6: Summarize the Bias-Adjusted Estimates with a Frequency Distribution that Yields a Central Tendency and Simulation Inte... -- Misclassification Implementation: Predictive Values -- Misclassification Implementation: Predictive Values - Alternative -- Misclassification of Outcomes and Confounders -- Uncontrolled Confounding Implementation -- Step 1: Identify the Source of Bias -- Step 2: Identify the Bias Parameters -- Step 3: Assign Probability Distributions to Each Bias Parameter -- Step 4: Use Simple Bias Analysis Methods to Incorporate Uncertainty in the Bias Parameters and Random Error -- Step 4a: Sample from the Bias Parameter Distributions -- Step 4b: Generate Bias-Adjusted Data Using Simple Bias Analysis Methods and the Sampled Bias Parameters -- Step 4c: Sample the Bias-Adjusted Effect Estimate -- Steps 5 and 6: Resample, Save and Summarize -- Confounding Implementation Alternative: Relative Risk Due to Confounding -- Step 4: Use Simple Bias Analysis Methods to Incorporate Uncertainty in the Bias Parameters and Random Error -- Step 4a: Sample from the Bias Parameter Distributions.
Step 4b: Generate Bias-Adjusted Results Using Simple Bias Analysis Methods and the Sampled Bias Parameters -- Step 4c: Sample the Bias-Adjusted Effect Estimate -- Steps 5 and 6: Resample, Save and Summarize -- Selection Bias Implementation -- An Example of Probabilistic Bias Analysis in the Presence of Substantial Source Population Data -- Step 1: Identify the Source of Bias -- Step 2: Identify the Bias Parameters -- Step 3: Assign Probability Distributions to Each Bias Parameter -- Step 4: Use Simple Bias Analysis Methods to Incorporate Uncertainty in the Bias Parameters and Random Error -- Step 4a: Sample from the Bias Parameter Distributions -- Step 4b: Generate Bias-Adjusted Data Using Simple Bias Analysis Methods and the Sampled Bias Parameters -- Steps 4c: Sample the Bias-Adjusted Effect Estimate -- Steps 5 and 6: Resample, Save and Summarize -- Selection Bias Adjustment Using Selection Probabilities -- Step 1: Identify the Source of Bias -- Step 2: Identify the Bias Parameters -- Step 3: Assign Probability Distributions to Each Bias Parameter -- Step 4: Use Simple Bias Analysis Methods to Incorporate Uncertainty and Random Error -- Step 4a: Sample from the Bias Parameter Distributions -- Step 4b: Generate Bias-Adjusted Data Using Simple Bias Analysis Methods and the Sampled Bias Parameters -- Steps 4c: Sample the Bias-Adjusted Effect Estimate -- Steps 5 and 6: Resample, Save and Summarize -- Computing Issues with Summary Level Probabilistic Bias Analysis -- Bootstrapping -- Impossible Values for Bias Parameters and Model Diagnostic Plots -- Conclusions -- Appendix: Sampling Models for Exposure Misclassification -- References -- Chapter 9: Probabilistic Bias Analysis for Simulation of Record-Level Data -- Introduction -- Exposure Misclassification Implementation -- Step 1: Identify the Source of Bias -- Step 2: Select the Bias Parameters.
Step 3: Assign Probability Distributions to Each Bias Parameter.
Record Nr. UNISA-996466557403316
Lash Timothy L.  
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Artificial Intelligence and Complex Dynamical Systems / / by Giorgos Tsironis
Artificial Intelligence and Complex Dynamical Systems / / by Giorgos Tsironis
Autore Tsironis Giorgos
Edizione [1st ed. 2025.]
Pubbl/distr/stampa Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025
Descrizione fisica 1 online resource (391 pages)
Disciplina 530.1
Collana Understanding Complex Systems
Soggetto topico System theory
Artificial intelligence
Quantum theory
Quantum electrodynamics
Biophysics
Epidemiology
Complex Systems
Artificial Intelligence
Quantum Physics
Quantum Electrodynamics, Relativistic and Many-body Calculations
Teoria de sistemes
Intel·ligència artificial
Teoria quàntica
Electrodinàmica quàntica
Biofísica
Epidemiologia
Sistemes complexos
Soggetto genere / forma Llibres electrònics
ISBN 9783031819469
3031819462
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Chapter 1. Complex systems and machine learning -- Chapter 2. Regression and Classification -- Chapter 3. Data manipulation techniques -- Chapter 4. Artificial neurons and deep learning -- Chapter 5. Powerful neural network architectures -- Chapter 6. Autoencoders and more -- Chapter 7. The Discrete Nonlinear Schr¨odinger Equation -- Chapter 8. Learning Analytical Solutions -- Chapter 9. The targeted energy transfer model -- Chapter 10. Dynamical embedding with autoencoders -- Chapter 11. Chimeras -- Chapter 12. Branching -- Chapter 13. Discrete breathers -- Chapter 14. Quantum targeted transfer with machine learning -- Chapter 15. Learning quantum systems -- Chapter 16. Action potential propagation in the heart -- Chapter 17. Machine learning cardiology -- Chapter 18. Epidemiology with physics informed machine learning -- Chapter 19. Foundations -- Chapter 20. Computational complexity and the butterfly effect.
Record Nr. UNINA-9910987692503321
Tsironis Giorgos  
Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Biochemical, Immunological and Epidemiological Analysis of Parasitic Diseases / / by P.K. Bandyopadhyay, N.R. Das, Amit Chattopadhyay
Biochemical, Immunological and Epidemiological Analysis of Parasitic Diseases / / by P.K. Bandyopadhyay, N.R. Das, Amit Chattopadhyay
Autore Bandyopadhyay Premansukumar
Edizione [1st ed. 2022.]
Pubbl/distr/stampa Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2022
Descrizione fisica 1 online resource (325 pages)
Disciplina 616.962
Collana Biomedical and Life Sciences Series
Soggetto topico Parasitology
Epidemiology
Immunology
Molecular biology
Molecular Biology
Malalties parasitàries
Epidemiologia
Immunologia
Medicina preventiva
Soggetto genere / forma Llibres electrònics
ISBN 981-16-4384-9
981-16-4383-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Chapter 1. Introduction -- Chapter 2. Protozoan Parasites -- Chapter 3. Introduction to Parasitic Helminthes and Cestoda -- Chapter 4. Organisms Those Carry Disease Causing Organisms (Vector) -- Chapter 5. Epidemiology -- Chapter 6. Biochemistry -- Chapter 7. Molecular Biology and Molecular Genetics -- Chapter 8. Physiology -- Chapter 9. Immunology -- Chapter 10. Ecology and Evolution of Parasites Ecology of Parasites -- Chapter 11. Control.
Record Nr. UNINA-9910743377403321
Bandyopadhyay Premansukumar  
Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2022
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Biostatistics and Epidemiology : A Primer for Health and Biomedical Professionals / / by Sylvia Wassertheil-Smoller, Jordan Smoller
Biostatistics and Epidemiology : A Primer for Health and Biomedical Professionals / / by Sylvia Wassertheil-Smoller, Jordan Smoller
Autore Wassertheil-Smoller Sylvia
Edizione [5th ed. 2024.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2024
Descrizione fisica 1 online resource (239 pages)
Disciplina 610.724
Soggetto topico Biometry
Medicine - Research
Biology - Research
Epidemiology
Public health
Biostatistics
Biomedical Research
Public Health
Biometria
Assaigs clínics
Epidemiologia
Estadística matemàtica
Soggetto genere / forma Llibres electrònics
ISBN 3-031-53043-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 1. The Scientific Method -- 2. A Little Bit of Probability -- 3. Mostly About Statistics -- 4. Mostly About Epidemiology -- 5. Mostly About Screening -- 6. Mostly About Clinical Trials -- 7. Mostly About Quality of Life -- 8. Mostly About Genetic Epidemiology -- 9. Risk Prediction and Risk Classification -- 10. Research Ethics and Statistics -- Postscript -- Appendix A -- Appendix B -- Appendix C -- Appendix D -- Appendix E -- Appendix F -- Appendix G. References -- Suggested Readings -- Index.
Record Nr. UNINA-9910879594003321
Wassertheil-Smoller Sylvia  
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2024
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
The Cambridge World History of Human Disease / editor Kenneth F. Kiple
The Cambridge World History of Human Disease / editor Kenneth F. Kiple
Autore Kiple, Kenneth F.
Pubbl/distr/stampa Cambridge : University Press, c1993
Descrizione fisica XXIV, 1176 p. ; 29 cm.
Disciplina 616
Soggetto topico Epidemiologia
Medicina - Storia
ISBN 0521332869
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNISALENTO-991002032059707536
Kiple, Kenneth F.  
Cambridge : University Press, c1993
Materiale a stampa
Lo trovi qui: Univ. del Salento
Opac: Controlla la disponibilità qui
Cancer causes & control
Cancer causes & control
Pubbl/distr/stampa Oxford, U.K., : Rapid Communications of Oxford, Ltd., 1990-
Disciplina 614.59994
Soggetto topico Cancer - Epidemiology
Cancer - Prevention
Neoplasms - epidemiology
Neoplasms - prevention & control
Cancer - Épidémiologie
Cancer - Prévention
Càncer
Epidemiologia
Programes de prevenció
Soggetto genere / forma Periodicals.
Revistes electròniques.
ISSN 1573-7225
Formato Materiale a stampa
Livello bibliografico Periodico
Lingua di pubblicazione eng
Altri titoli varianti Cancer causes and control
CCC
Record Nr. UNINA-9910142558203321
Oxford, U.K., : Rapid Communications of Oxford, Ltd., 1990-
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui