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Record Nr. |
UNINA9910624305103321 |
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Titolo |
Intelligent systems in medicine and health : the role of AI / / Trevor Cohen, Vimla L. Patel, Edward Hance Shortliffe (editors) |
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Pubbl/distr/stampa |
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Cham, Switzerland : , : Springer, , [2022] |
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©2022 |
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ISBN |
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Descrizione fisica |
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1 online resource (607 pages) |
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Collana |
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Cognitive informatics in biomedicine and healthcare |
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Disciplina |
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Soggetti |
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Artificial intelligence - Medical applications |
Machine learning |
Medical informatics |
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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 bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Intro -- Foreword -- Preface -- The State of AI in Medicine -- Introducing Intelligent Systems in Medicine and Health: The Role of AI -- Structure and Content -- Guide to Use of This Book -- Acknowledgments -- Contents -- Contributors -- Part I: Introduction -- Chapter 1: Introducing AI in Medicine -- The Rise of AIM -- Knowledge-Based Systems -- Neural Networks and Deep Learning -- Machine Learning and Medical Practice -- The Scope of AIM -- From Accurate Predictions to Clinically Useful AIM -- The Cognitive Informatics Perspective -- Why CI? -- The Complementarity of Human and Machine Intelligence -- Mediating Safe and Effective Human Use of AI-Based Tools -- Concluding Remarks -- References -- Chapter 2: AI in Medicine: Some Pertinent History -- Introduction -- Artificial Intelligence: The Early Years -- Modern History of AI -- AI Meets Medicine and Biology: The 1960s and 1970s -- Emergence of AIM Research at Stanford University -- Three Influential AIM Research Projects from the 1970s -- INTERNIST-1/QMR -- CASNET -- MYCIN -- Cognitive Science and AIM -- Reflecting on the 1970s -- Evolution of AIM During the 1980s and 1990s -- AI Spring and Summer Give Way to AI Winter -- AIM Deals with the Tumult of the 80s and 90s -- The Last 20 Years: Both AI and AIM Come of Age -- References -- Chapter 3: Data and Computation: A Contemporary Landscape -- |
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Understanding the World Through Data and Computation -- Types of Data Relevant to Biomedicine -- Knowing Through Computation -- Motivational Example -- Computational Landscape -- Knowledge Representation -- Machine Learning -- Data Integration to Better Understand Medicine: Multimodal, Multi-Scale Models -- Distributed/Networked Computing -- Data Federation Models -- Interoperability -- Computational Aspects of Privacy -- Trends and Future Challenges -- Ground Truth. |
Open Science and Mechanisms for Open data -- Data as a Public Good -- References -- Part II: Approaches -- Chapter 4: Knowledge-Based Systems in Medicine -- What Is a Knowledge-Based System? -- How Is Knowledge Represented in a Computer? -- Rules: Inference Steps -- Patterns: Matching -- Probabilistic Models -- Naive Bayes -- Bayesian Networks -- Decision Analysis and Influence Diagrams -- Causal Mechanisms: How Things Work -- How Is Knowledge Acquired? -- Ontologies and Their Tools -- Knowledge in the Era of Machine Learning -- Incorporating Knowledge into Machine Learning Models -- Graph-Based Models -- Graph Representation Learning -- Biomedical Applications of Graph Machine Learning -- Text-Based Models -- Leveraging Expert Systems to Train Models -- Looking Forward -- References -- Chapter 5: Clinical Cognition and AI: From Emulation to Symbiosis -- Augmenting Human Expertise: Motivating Examples -- Cognitive Science and Clinical Cognition -- Symbolic Representations of Clinical Information -- Clinical Text Understanding -- Clinical Cognition, Reasoning and the Evolution of AI -- Bridging Cognition to Medical Reasoning -- Models of Medical Reasoning -- Knowledge Organization, Expert Perception and Memory -- Understanding Clinical Practice for AI Systems -- The Role of Distributed Cognition -- AI, Machine Learning, and Human Cognition -- Reinforcing the Human Component -- Augmenting Clinical Comprehension -- Supporting Specific Cognitive Tasks -- Mental Models of AI Systems -- Conclusion -- References -- Chapter 6: Machine Learning Systems -- Identifying Problems Suited to Machine Learning -- The Machine Learning Workflow: Components of a Machine Learning Solution -- Evaluating Machine Learning Models: Validation Metrics -- Supervised Machine Learning -- The Structure of a Supervised Machine Learning Algorithm. |
Supervised Learning: A Mathematical Formulation -- Augmenting Feature Representations: Basis Function Expansion -- Bias and Variance -- Regularization: Ridge and Lasso Regression -- Linear Models for Classification -- Discriminative Models: Logistic Regression -- Regularized Logistic Regression: Ridge and Lasso Models -- A Simple Clinical Example of Logistic Regression -- A Multivariate Clinical Example of Logistic Regression -- Generative Models: Gaussian Discriminant Analysis -- Factored Generative Models: Naive Bayes -- Bias and Variance in Generative Models -- Recap of Parametric Linear Models for Classification -- Non-linear Models -- Kernel Methods -- Similarity Functions for Kernel Methods -- Recap: How to Use Kernels for Classification -- Sparse Kernel Machines and Maximum Margin Classifiers -- Neural Networks: Stacked Logistic Models -- Parameterizing Feedforward Networks and the Forward Propagation Algorithm -- Learning the Parameters of a Feedforward Network -- Convolutional Networks -- Other Network Architectures -- Putting It All Together: The Workflow for Training Deep Neural Networks -- Ensembling Models -- Conclusion -- References -- Chapter 7: Natural Language Processing -- Introduction to NLP and Basic Linguistics Information -- Common Biomedical NLP Tasks and Methods -- Overview of Biomedical NLP Tasks -- Biomedical IE Tasks and Methods -- NER Examples and Methods -- RE Examples and Methods -- CN |
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Examples and Methods -- Current Biomedical NLP Tools and Corpora -- Biomedical NLP Tools -- Biomedical Text Resources -- Types of Biomedical Text -- Annotated Corpora from Past Challenges -- Applications, Challenges and Future Directions -- Applications of NLP -- Challenges and Future Directions -- Conclusion -- References -- Chapter 8: Explainability in Medical AI -- Introduction -- Current Trends in AI Explainability Research. |
Applying Additional Context to Understand Explainability in Medical AI -- Three Purposes of AI Explainability -- Expanding the Conception of AI Explainability Based on Cognitive Informatics -- Human Information Processing -- Human-AI Agents -- Sociotechnical Systems -- Implications of Explainability on Bias and the Regulatory Environment -- Explainability and Inherent Biases -- Effect of Explainability on Accountability for Decision Making -- The Current Regulatory Framework and Explainability -- Application of Explainability to Real World Examples of Medical AI -- Example: Continuous Blood Glucose Monitoring for Patients with Type 1 Diabetes -- Example: Digital Image Analysis Tools Assisting in Histopathological Diagnoses -- Example: Wearable Devices Informing Clinical Management -- Conclusion -- References -- Chapter 9: Intelligent Agents and Dialog Systems -- Introduction to Dialog Systems -- Definitions and Scope -- What's Hard About Getting Machines to Engage in Spontaneous Human Conversation? -- Machine Learning and Dialog Systems -- History of Dialog Systems in Healthcare -- Dialog System Technology -- Classic Symbolic Pipeline Architectures -- Neural Network Methods and End-to-End Architectures -- Approaches to Dialog System Evaluation -- Evaluation of Pipeline Architectures -- Automated Metrics for End-to-End Architectures -- System-Level Evaluation -- Example Patient- and Consumer-Facing Dialog Systems -- Example Provider-Facing Dialog Systems -- Safety Issues in Dialog Systems for Healthcare -- State of the Art: What We Currently Can and Can't Do -- Future Directions -- Conclusion -- References -- Part III: Applications -- Chapter 10: Integration of AI for Clinical Decision Support -- Challenges Faced by Clinicians -- Artificial Intelligence-Based CDS -- Degree of Automation in AI-CDS -- Application of AI-CDS in Clinical Care. |
Pitfalls of AI-CDS -- Regulation of AI-CDS -- Conclusions -- References -- Chapter 11: Predicting Medical Outcomes -- Clinical Outcomes: An Enlarged Perspective -- AI Approaches for Clinical Outcomes Prediction -- Preprocessing: Missing Values, Features Transformation and Latent Variables Extraction -- Missing Values -- Dimensionality Reduction and Feature Transformation -- Deep Learning -- Classification -- Regression -- Survival Analysis -- Time Lines and Trajectory Modeling -- Markov Models -- Performance Assessment -- Experimental Design for Learning -- Common Mistakes in the Design of Experimental Validation -- Experimental Design for Testing: External Validation -- Checking Performance Stability, Model Drifts, Diagnostics, and Model Revision -- Case Studies and Examples -- Type 2 Diabetes -- Myelodysplastic Syndromes -- The COVID-19 Pandemic -- Conclusion -- References -- Chapter 12: Interpreting Medical Images -- Overview -- Introduction to Medical Images -- Characteristics of Medical Images -- Historical Perspectives -- Pioneer CAD Systems -- Recent Successes in Deep Learning -- Clinical Needs and Existing Challenges -- Clinical Needs -- Medical Applications -- Technical Barriers -- Opportunities and Emerging Techniques -- Acquiring Annotation from Human Experts -- Utilizing Annotation by Advanced Models -- Extracting Features from Unannotated Images -- Conclusion -- References -- Chapter 13: |
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Public Health Applications -- Public Health and AI -- Public Health, Essential Public Health Functions, and Public Health Informatics -- The Nature of Essential Public Health Functions and the Application of AI -- A Vision for AI in Public Health -- Applications of AI in Public Health -- Examples of AI Applications to Public Health Functions -- Assessment -- Policy Development -- Assurance -- Barriers and Risks to AI Applications in Public Health. |
Future Applications of AI in Public Health. |
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