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Handbook of Artificial Intelligence in Biomedical Engineering focuses on recent AI technologies and applications that provide some very promising solutions and enhanced technology in the biomedical field. Recent advancements in computational techniques, such as machine learning, Internet of Things (IoT), and big data, accelerate the deployment of biomedical devices in various healthcare applications.This volume explores how artificial intelligence (AI) can be applied to these expert systems by mimicking the human expert’s knowledge in order to predict and monitor the health status in real time. The accuracy of the AI systems is drastically increasing by using machine learning, digitized medical data acquisition, wireless medical data communication, and computing infrastructure AI approaches, helping to solve complex issues in the biomedical industry and playing a vital role in future healthcare applications. The volume takes a multidisciplinary perspective of employing these new applications in biomedical engineering, exploring the combination of engineering principles with biological knowledge that contributes to the development of revolutionary and life-saving concepts.
Design of Medical Expert Systems Using Machine Learning Techniques
From Design Issues to Validation: Machine Learning in Biomedical Engineering
Biomedical Engineering and Informatics Using Artificial Intelligence
Hybrid Genetic Algorithms for Biomedical Applications
Healthcare Applications of the Biomedical AI System
Applications of Artificial Intelligence in Biomedical Engineering
Biomedical Imaging Techniques Using AI Systems
Analysis of Heart Disease Prediction Using Machine Learning Techniques
A Review on Patient Monitoring and Diagnosis Assistance by Artificial Intelligence Tools
Semantic Annotation of Healthcare Data
Drug Side Effect Frequency Mining over a Large Twitter Dataset using Apache Spark
Deep Learning in Brain Segmentation
Security and Privacy Issues in Biomedical AI Systems and Potential Solutions
LiMoS—Live Patient Monitoring System
Real-Time Detection of Facial Expressions Using k-NN, SVM, Ensemble classifier and Convolution Neural Networks
Analysis and Interpretation of Uterine Contraction Signals Using Artificial Intelligence
Enhanced Classification Performance of Cardiotocogram Data for Fetal State Anticipation Using Evolutionary Feature Reduction Techniques
Deployment of Supervised Machine Learning and Deep Learning Algorithms in Biomedical Text Classification
Energy Efficient Optimum Cluster Head Estimation for Body Area Networks
Segmentation and Classification of Tumour Regions from Brain Magnetic Resonance Images by Neural Network-Based Technique
A Hypothetical Study in Biomedical Based Artificial Intelligence Systems using Machine Language (ML) Rudiments
Neural Source Connectivity Estimation using particle filter and Granger causality methods
Exploration of Lymph Node-Negative Breast Cancers by Support Vector Machines, Naïve Bayes, and Decision Trees: A Comparative Study