11405nam 2200577 450 991062430510332120231110224819.03-031-09108-6(MiAaPQ)EBC7134136(Au-PeEL)EBL7134136(CKB)25299477700041(OCoLC)1350690099(PPN)266353150(EXLCZ)992529947770004120230321d2022 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierIntelligent systems in medicine and health the role of AI /Trevor Cohen, Vimla L. Patel, Edward Hance Shortliffe (editors)Cham, Switzerland :Springer,[2022]©20221 online resource (607 pages)Cognitive informatics in biomedicine and healthcarePrint version: Cohen, Trevor A. Intelligent Systems in Medicine and Health Cham : Springer International Publishing AG,c2022 9783031091070 Includes bibliographical references and index.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 -- 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 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: 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.Cognitive Informatics in Biomedicine and Healthcare Artificial intelligenceMedical applicationsMachine learningMedical informaticsArtificial intelligenceMedical applications.Machine learning.Medical informatics.610.285Cohen Trevor A.Patel Vimla L.Shortliffe Edward H.MiAaPQMiAaPQMiAaPQBOOK9910624305103321Intelligent systems in medicine and health3072848UNINA