Machine Learning and Artificial Intelligence in Healthcare Systems: Tools and Techniques

Description

This book provides applications of machine learning in healthcare systems and seeks to close the gap between engineering and medicine by combining design and problem-solving skills of engineering with health sciences to advance healthcare treatment. Machine Learning and Artificial Intelligence in Healthcare Systems: Tools and Techniques discusses AI-based smart paradigms for reliable prediction of infectious disease dynamics which can help or prevent disease transmission. It highlights the different aspects of using extended reality for diverse healthcare applications and aggregates the current state of research. The book offers intelligent models of the smart recommender system for personal well-being services and computer-aided drug discovery and design methods. Case studies illustrating the business processes that underline the use of big data and health analytics to improve healthcare delivery are center stage. Innovative techniques used for extracting user social behavior known as sentiment analysis for healthcare-related purposes round out the diverse array of topics this reference book covers. Contributions from experts in the field, this book is useful to healthcare professionals, researchers, and students of industrial engineering, systems engineering, biomedical, computer science, electronics, and communications engineering. Cover Half Title Series Page Title Page Copyright Page Contents 1. Artificial Intelligence Challenges, Principles, and Applications in Smart Healthcare Systems Introduction The Smart Healthcare Concept Primary Objectives for AI Applications Artificial Intelligence Benefits in Health Care Healthcare Benefits of Artificial Intelligence Accessibility Improved Early Diagnosis Speed Upgrade and Cost Savings Surgery Assistance that is Effective and Unique Support for Mental Health and Enhanced Human Ability Artificial Intelligence Challenges in Healthcare Digitization and Consolidation of Data Updating Regulations Human Interventions Prospective V / s Retrospective Research As an Evidence Gold Standard, Randomized Controlled Trials with Peer Review Clinical Value is not Always Reflected in Metrics Comparing Different Algorithms is Difficult Difficulties in the Science of Machine Learning Shift in Dataset Fitting Confounders by Accident vs. True Signal Difficulties in Generalisation to New Populations and Settings Bias in the Algorithmic Process Bias Susceptibility to Adversarial Attack or Manipulation Difficulties in Logistically Implementing AI Systems Achieving Robust Regulation and Rigorous Quality Control Human Resistance to Artificial Intelligence (AI) in Healthcare Improving our Understanding of the Humans' and Algorithms' Interaction Risks of Artificial Intelligence in Healthcare Injuries and Errors Privacy Issues Discrimination and Inequality Professional Reshuffling In the Healthcare System, Applications of Artificial Intelligence (AI) Application of Artificial Intelligence in Modern Medicine Alginate and Artificial Intelligence in Biomedical Fields Conclusion References 2. Systematic View and Impact of Artificial Intelligence in Smart Healthcare Systems, Principles, Challenges and Applications Introduction Artificial Intelligence: A Reference Point for Innovation Practice and Design Principles Artificial Intelligence's growth in Healthcare Domain Professional Support to Health Care in AI Performance Indicators for the progress of Health Care in AI Applications in Diagnosis and Treatment Inference about Healthcare Fear and Expectations about AI Expectations from AI in Healthcare More Efficient Healthcare Logistics Taking Drug Development to New Levels Improve the Working Conditions of Medical Workers While Saving Lives Identifying Novel Links Between Risks and Illnesses Bringing the Art of Medicine Into a New Age Assist in the Prediction of Future Outbreaks and Pandemics Artificial Intelligence Will Not Assist You in Healthcare Medical Practitioners will be Replaced by Artificial Intelligence Artificial Intelligence will Have the same Level of Understanding as a Real-Life Doctor Patient Interaction In Healthcare, Artificial Intelligence will Help with Privacy Difficulties Artificial Intelligence will Develop Completely Autonomous Surgical Robots Medical Choices will be Made Solely by Artificial Intelligence AI Will not be Prejudiced AI Will Think in the Same way that Humans Do Overview of current AI in Healthcare Radiology for Diagnosis Pathology Ophthalmology Cardiology Challenges and Solutions of AI in healthcare Artificial Intelligence Model Development and Validation Model Development Important Considerations in Model Development Learning in Model Development Model Validation Framework AI/ML Solution Hazard Tiering Fairness & Bias Validating the Model Healthcare tools using AI Amazon's Alexa Voice Assistant has a New Trick Health Chatbots OneRemission Youper Babylon Health Florence Healthily Ada Health Sensely Buoy Health Infermedica GYANT Woebot Cancer Chatbot Case Study Using IBM Watson to aid Oncologists in India is a Case Study The Influence of AI on Data Privacy and Ethical Issues Examining the Impact of Artificial Intelligence on Physicians Conclusion References 3. Application of Machine Learning Techniques in COVID-19 Epidemiology: A Glimpse Introduction Attributes of COVID-19 Epidemiology Distribution Geographic Distribution Disease Trajectory Determinants Transmission Dynamics Clinical Features Immune Response Risk of Reinfection Machine Learning in COVID-19 Epidemiology at a Glance Distribution Geographic Distribution Disease Trajectory Determinants Transmission Dynamics of COVID-19 Clinical Features Immune Response Risk of Reinfection Conclusion References 4. Automated Seven-Level Skin Cancer Staging Diagnosis in Dermoscopic images using Deep Learning Introduction Skin Cancer Stages of Skin Cancer Limitation of Machine Learning Background and Related Work Theoretical Concepts Preprocessing in Deep Learning Normalization Standardization Classification Convolutional Neural Network (CNN) Layers in Convolutional Neural Network Filter (or) Kernel Padding Strided Convolution Convolution over Volume Pooling Layer Activation Function Soft Max Convolutional Layer Fully-connected Layers Methods and Results Proposed Methodology Dataset Preprocessing Splitting the Dataset Classification Performance Analysis Expected Result and Predicted Result Confusion Matrix Conclusion and Future Enhancement References 5. Ensemble Classifier Based Predictive Model for Type-2 Diabetes Mellitus Prediction Introduction Literature Survey Theoretical Concepts Used Boosting Gradient Boosting Classifier Light Gradient Boosting Machine (LGBM) Bagging Random Forest Proposed System Methodology Dataset Collection and Description Preprocessing of Data Exploratory Data Analysis (EDA) Feature Correlation Feature Engineering Model Development Process Model Ensemble Hyper Parameter Tuning Model Evaluation Receiver Operating Characteristics (ROC) Results and Discussion Conclusion and Future Work References 6. Machine Learning Approaches for Analysis in Smart Healthcare Informatics Introduction Smart Health Smart Healthcare Applications Diagnosis Healthcare Administration Diseases and Risk Analysis Smart Hospitals that are Forward-Thinking Assisting with Drug Development IoT and Healthcare Machine Learning Algorithms Learning under Supervision Learning Without Supervision Hyperparameters Reinforcement Learning in Health Care Applications of Reinforcement Learning in Healthcare Dynamic Treatment Regimens (DTRs) Medical Diagnostic Scheduling and Allocating Healthcare Resources Drug Research, Development, and Design Health Administration The Difficulties of Reinforcement Learning in Healthcare Scarcity of Data Partially Observable Formulation and Design of Rewards Case Study An Extensive Medicare Data Exploration CMS (Content Management System) Conclusion and Future Scope References 7. Smart Approaches for Diagnosis of Brain Disorders Using Artificial Intelligence Introduction Brain MRI Physics of MRI MRI Imaging Sequences Application Brain MRI Artifacts Use of Deep Learning in Health Diagnosis Deep Learning in Medical Imaging Deep Learning Methods Deep Learning in the Brain Electroencephalogram (EEG) Characteristic Nature of Electroencephalogram (EEG) Signals EEG Signal Analysis and Classification EEG Data Processing Preprocessing Feature Extraction Classification Deep Learning for Detection of Brain Disorders using EEG Conclusion References 8. Bridging the Gap Between Technology and Medicine: Approaches of Artificial Intelligence in Healthcare Introduction Application Areas of Artificial Intelligence in Healthcare Diagnosis and Treatment of Diseases Electronic Health Records (EHRs) Drug Discovery Radiology Machine Learning Algorithms in Healthcare Industry Naive Bayes Logistic Regression k-Nearest Neighbor Support Vector Machines Deep Learning in Healthcare Performance Metrics for Model Evaluation Confusion Matrix True Positives (TP) True Negatives (TN) False Positives (FP) False Negatives (FN) Precision Recall F1-score Accuracy AUC-ROC Curve Logarithmic Loss (Log Loss) Challenges to Artificial Intelligence in Healthcare Informed Consent Safety, Transparency, and Effectiveness Fairness and bias of algorithms Data privacy Conclusion References 9. Brain Tumor Classification Using Transfer Learning Introduction Related Works Convolutional Neural Network (CNN) based Approach Support Vector Machine (SVM) based Approach Challenges Proposed Model Data PreProcessing Canny Edge Detection Data Augmentation Transfer Learning EfficientNet B0 ResNet50 DenseNet121 MobileNetV3-Small Feature Fusion Experimental Framework Implementation Details Loss Function Optimization Function Dataset Description Results and Discussion EfficientNet B0 ResNet50 DenseNet121 MobileNetV3-Small Conclusion and Future Works References 10. Advanced Bayesian Estimation of Weibull in Early Stage Eye Loss Prediction in Diabetic Retinopathy Introduction Literature Review Bayesian Model and Survival Model Bayesian Model Naïve Bayes Tree-Augmented Naive Bayes (TAN) Survival Model Censored Data Survival Function Proposed Approach: Bayesian Estimation of Weibull in Early Stage Formulation Approach Process Flow Diagram Prior Probability Extrapolation Implementation and Results Dataset Description Performance Evaluation Results and Discussion AUC Measure Accuracy Measure F-Measure Conclusion and Future Scope References 11. Automated Sleep Staging Using Single-Channel EEG Signal Based on Machine Learning Approaches Introduction Motivation Importance of Human Sleep Study Sleep Structure and Sleep Stages Sleep Stages Behavior Experimental Studies on Sleep Staging System Experimental Data Proposed Automatic Sleep Stage Detection Method Experimental Results and Discussion Classification Accuracy of Category-I Subject ISRUC-Sleep Database Classification Accuracy of Category-II Subject ISRUC-Sleep Database Classification Accuracy of Category-III Subject ISRUC-Sleep Database Summary of Comparative Analysis Results of ISRUC-Sleep (SG-I/SG-II/SG-III) data Conclusion References 12. Machine Learning-Based Intelligent Assistant for Smart Healthcare Introduction Literature Review Motivation for the Proposed Work Application of AI/Ml in Healthcare Industry Application of Chatbots in Healthcare Robots in Medical Labs Medical Image Diagnostics with AI and ML in Healthcare AI Empowered Health Companions Proposed ML Equipped Intelligent Solution For Smart Healthcare Application of ML Algorithms in Healthcare Prediction Logistic Regression K-Nearest Neighbor Algorithm (KNN Algorithm) Support Vector Machine (SVM) Gaussian Naive Bayes Decision Tree Random Forest Gradient Boost Logistic Regression ML Algorithm Case Study - Patient Data Analytics Using Logistic Regression ML Algorithm in Intelligent Virtual Assistant Application of Logistic Regression Model in Prediction Analysis Problem to be Addressed: Stroke Prediction Predicting Stroke Using Datasets Dataset Taken for Learning Purpose Attributes Information Unique Values for Attributes Observations Prediction Variable (Desired Target) Sample Python Code for Logistic Regression Model to Predict Stroke Disease Data Preprocessing Observations Statistical Analysis to assess the Prediction Efficiency Model Training and Prediction using Logarithmic Regression Model Summary of Findings from the Analysis Conclusion and Future Research Directions Conclusion Future Research Directions References Additional Readings 13. AI-enabled Sentiment Analysis on COVID-19 Vaccination: A Twitter Based Study Introduction Background and Literature Review Word2vec N-gram Tf-Idf VADER (Valence Aware Dictionary and sEntiment Reasoner) Pattern Sentiment Analysis of Twitter Data Related to Vaccines: A Case Study Dataset Description Data Preprocessing Exploratory Data Analysis Overall Sentiment Analysis Through VADER Tool Overall Sentiment Analysis Through Pattern Library Analysis of AstraZeneca Related Tweets Analysis of Pfizer Related Tweets Analysis of Covaxin Related Tweets Analysis of Moderna Related Tweets Analysis of Sputnik V Related Tweets Conclusion Notes References 14. An Early Diagnosis of Lung Nodule Using CT Images Based on Hybrid Machine Learning Techniques Introduction Related Works Problem Methodology Research Gap System Model Proposed EPC-HML Techniques Lung Nodule Segmentation Using ACS-ML Feature Extraction and Selection Using HBS Algorithm Lung Nodule Classification Using SM-CNN Simulation Results and Discussion Description of the Datasets Training and Implementation Advantages and Limitations of Lung Nodule Diagnosis Analysis of Segmentation and Feature Extraction Process Comparative Analysis of Classifiers Conclusion and Future Work References 15. Early Detection of Alzheimer's Disease Assisted by AI-Powered Human-Robot Communication Overview of Application of AI-powered Robots for Early Detection of Alzheimer's Disease (AD) Using Human-Robot Communication Introduction Alzheimer's Disease (AD) Detection of Alzheimer's Disease Human-robot Communication Human-robot Communication in Early Detection of AD Early Detection of AD Using Human-Robot Communication: A Scoping Review Results Description of Robots Performance Evaluation and Outcomes AI-powered Human-Robot Communication for Advancement of Early Detection of AD in Healthcare Services Study Participant Feedback The Future Use of Human-Robot Intervention for Early Detection of AD Conclusions Acknowledgment References Index