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• The Impact of Engineering Enabling Technologies on the Further Development of Personalized Orthopedics
• The Development of Objective and Quantitative Eye-Tracking-Based Method for the Diagnostics of Oculomotor Dysfunctions
• A Novel Algorithm for the Compensation of Hemoglobin Interference on Bilirubin Measurement Applied to a Two-Wavelengths Reflectance Photometer
• Method for Muscle Fatigue Detection Using Inertial Sensors
• Accurate Registration of 3D Models with Organs in Augmented Reality Through Inertial Sensors
• Dynamic Testing of Piezoresistive Fabrics for Use in Smart Wearable Personal Protective Equipment
• Studies of UV-A inactivation of Escherichia coli by 365 nm Light
• A Prototype Device for Continuous Monitoring of Breathing Mode: Nasal Versus Mouth
• Investigation of Electrospun Keratin Mats Containing Biosynthesized Silver Nanoparticles
• Finite Element Method Modelling of Iron - Oxide Nanoparticle Heat Generation Under Low Radio Frequency Field Conditions
• Detection of Physical Stressors in Patients with Frailty Syndrome Using Wearable Devices
• Expediency of Using a Physical and Mathematical Model in Cell Engineering
• Smart textile in Post Stroke Patient Rehabilitation Exercises Evaluation for Lower Extremities
• Application of DAid Smart Shirt in Rehabilitation for Subacromial Pain Syndrome Patients Participating in High-risk for Overuse Injury Sports
• Effects of Tactical Boots on Foot and Ankle Biomechanics
• The Reverse Engineering of Human Organs Based on the Application of Method of Anatomical Features

This book reports on new trends, challenges and solutions, in the multidisciplinary fields of biomedical engineering and medical physics. Contributions spans from biomechanics, to robotic rehabilitation, radiation oncology, and image and signal processing, among many other topics. They cover advanced devices for diagnosis or patient monitoring, as well as for therapy (non-invasive surgery, rehabilitation and more). Gathering the proceedings of the 19th Nordic-Baltic Conference on Biomedical Engineering and Medical Physics, NBC 2023, held on June 12-14, 2023, in Liepaja, Latvia, this book is expected to inform a wide audience of researchers, engineers and other professionals working in the broad field of biomedical engineering, and to offer a timely snapshot of research and projects that have been carried out within Nordic and Baltic countries, in particular, but not limited to them

• 1. Introduction to three-dimensional printing in medicine
• 2. 3D printing families: laser, powder, and nozzle-based techniques
• 3. Materials for 3D printing in medicine: metals, polymers, ceramics, and hydrogels
• 4. 3D-printed and computational models: a combined approach for patient-specific studies
• 5. 3D printers for surgeons practice
• 6. Patient-specific 3D bioprinting for in situ tissue engineering and regenerative medicine
• 7. 3D-bioprinted in vitro disease models
• 8. 3D printed pharmaceutical products
• 9. High-resolution 3D printing for healthcare
• 10. (Bio)fabrication of microfluidic devices and organs-on-a-chip
• 11. Four-dimension printing in healthcare
• 12. Present and future of standardization of additive manufacturing in the medical field
• 13. Social and ethical considerations of bioprinted organs
• 14. Medical 3D printing, intellectual property, and regulation.
• (source: Nielsen Book Data)

3D Printing in Medicine, Second Edition examines the rapidly growing market of 3D-printed biomaterials and their clinical applications. With a particular focus on both commercial and premarket tools, the book looks at their applications within medicine and the future outlook for the field. The chapters are written by field experts actively engaged in educational and research activities at the top universities in the world. The earlier chapters cover the fundamentals of 3D printing, including topics such as materials and hardware. The later chapters go on to cover innovative applications within medicine such as computational analysis of 3D printed constructs, personalized 3D printing - including 3D cell and organ printing and the role of AI - with a subsequent look at the applications of high-resolution printing, 3D printing in diagnostics, drug development, 4D printing, and much more. This updated new edition features completely revised content, with additional new chapters covering organs-on-chips, bioprinting regulations and standards, intellectual properties, and socio-ethical implications of organs-on-demand.
(source: Nielsen Book Data)

• 1. Introduction to non-invasive biomedical signals for healthcare
• 2. Signal Acquisition Preprocessing and Feature Extraction Techniques for Biomedical Signals
• 3. The Role of EEG as Neuro-Markers for Patients with Depression: A systematic Review
• 4. Brain-Computer Interface (BCI) Based on the EEG Signal Decomposition Butterfly Optimization and Machine Learning
• 5. Advances in the analysis of electrocardiogram in context of mass screening: technological trends and application of artificial intelligence anomaly detection
• 6. Application of Wavelet Decomposition and Machine Learning for the sEMG Signal based Gesture Recognition
• 7. Review of EEG Signals Classification using Machine Learning and Deep-learning Techniques
• 8. "Biomedical signal processing and artificial intelligence in EOG signals"
• 9. Peak Spectrogram and Convolutional Neural Network-based Segmentation and Classification for Phonocardiogram Signals
• 10. Eczema skin lesions segmentation using deep neural network (U-net)
• 11. Biomedical signal processing for automated detection of sleep arousals Based on Multi-Physiological Signals with Ensemble learning methods
• 12. Deep Learning Assisted Biofeedback
• 13. Estimations of Emotional Synchronization Indices for Brain regions using Electroencephalogram Signal Analysis
• 14. Recognition Enhancement of Dementia Patients' Working Memory using Entropy-based Features and Local Tangent Space Alignment Algorithm.

This book presents the modern technological advancements and revolutions in the biomedical sector. Progress in the contemporary sensing, Internet of Things (IoT) and machine learning algorithms and architectures have introduced new approaches in the mobile healthcare. A continuous observation of patients with critical health situation is required. It allows monitoring of their health status during daily life activities such as during sports, walking and sleeping. It is realizable by intelligently hybridizing the modern IoT framework, wireless biomedical implants and cloud computing. Such solutions are currently under development and in testing phases by healthcare and governmental institutions, research laboratories and biomedical companies. The biomedical signals such as electrocardiogram (ECG), electroencephalogram (EEG), Electromyography (EMG), phonocardiogram (PCG), Chronic Obstructive Pulmonary (COP), Electrooculography (EoG), photoplethysmography (PPG), and image modalities such as positron emission tomography (PET), magnetic resonance imaging (MRI) and computerized tomography (CT) are non-invasively acquired, measured, and processed via the biomedical sensors and gadgets. These signals and images represent the activities and conditions of human cardiovascular, neural, vision and cerebral systems. Multi-channel sensing of these signals and images with an appropriate granularity is required for an effective monitoring and diagnosis. It renders a big volume of data and its analysis is not feasible manually. Therefore, automated healthcare systems are in the process of evolution. These systems are mainly based on biomedical signal and image acquisition and sensing, preconditioning, features extraction and classification stages. The contemporary biomedical signal sensing, preconditioning, features extraction and intelligent machine and deep learning-based classification algorithms are described. Each chapter starts with the importance, problem statement and motivation. A self-sufficient description is provided. Therefore, each chapter can be read independently. To the best of the editors⁰́₉ knowledge, this book is a comprehensive compilation on advances in non-invasive biomedical signal sensing and processing with machine and deep learning. We believe that theories, algorithms, realizations, applications, approaches, and challenges, which are presented in this book will have their impact and contribution in the design and development of modern and effective healthcare systems.

• 1. Anxiety recognition using a new EEG signal analysis approach based on sample density in a Chebyshev chaotic map / Faezeh Daneshmand-Bahman and Ateke Goshvarpour
• 2. Evaluating cognitive load during lexical decision tasks for monolinguals and bilinguals using EEG / Mahesh Veezhinathan, B. Geethanjali, Sukeerthi Sakthivel and RaamaNarayanan AnanthaNarayanan
• 3. Detection of psychological stress using principal component analysis of phonocardiography signals / Amandeep Cheema and Mandeep Singh
• 4. Affective computational advertising based on perceptual metrics / Soujanya Narayana, Shweta Jain, Harish Katti, Roland Goecke and Ramanathan Subramanian
• 5. Machine-learning-based emotion recognition in arousal-valence space using photoplethysmogram signals / Md. Nazmul Islam Shuzan, Moajjem Hossain Chowdhury, Md. Shafayet Hossain, Md. Sakib Abrar Hossain and Muhammad E.H. Chowdhury
• 6. EEG-based human emotion classification from channel-wise feature extraction and feature selection / Lucky Odirile Mohutsiwa and Rodrigo S. Jamisola Jr.
• 7. Detection of physiological body movements in affective disorder patients using EEG signals and deep neural networks / Jason Elroy Martis, M.S. Sannidhan, Sunil Kumar Aithal, R. Balasubramani and K.B. Sudeepa
• 8. Voice-enabled real-time affective framework for negative emotion monitoring / Parker Wilmoth and Prabha Sundaravadivel
• 9. Differential diagnosis tool in healthcare application using respiratory sounds and convolutional neural network / Rajkumar Palaniappan, Kenneth Sundaraj, Fizza Ghulam Nabi and Vikneswaran Vijean
• 10. Virtual reality and augmented reality based affective computing applications in healthcare, challenges, and its future direction / Suma Dawn.

Healthcare is one of the most promising applications of affective computing, where advances using biomedical signals have grown rapidly. This comprehensive book begins with an introduction to affective computing and affective computing models, artificial intelligence, probability theory, and statistical learning. It discusses topics such as noise elimination and baseline wandering effects of biomedical signals, and then proceeds to review biomedical signal acquisition and pre-processing. In this book, biomedical signals in affective states are discussed as well as artificial intelligence techniques used to classify biomedical signals, such as artificial neural networks and support vector machines. A discussion of affective computing applications in neurodegenerative diseases and neurological disorders concludes the book. Recent research is discussed alongside future challenges in these areas. Part of IPEM-IOP Series in Physics and Engineering in Medicine and Biology.

• Machine Learning for Drug Discovery and Manufacturing
• Knowledge Strategies Influencing on The Epidemiologists Performance of The Qeshm Island's Health Centers
• Healthcare: In the Era of Blockchain
• Securing Healthcare records using Blockchain: Applications and Challenges
• Authentication Schemes For Healthcare Data Using Emerging Computing Technologies
• Biomedical data classification using fuzzy clustering
• Applications of Machine Learning in healthcare With a Case Study of Lung Cancer Detection Through Deep Learning Approach
• Fetal Health Status Prediction During Labor and Delivery Based on Cardiotocogram Data using Machine and Deep Learning
• Blockchain and AI: Disruptive Digital Technologies in Designing the Potential Growth of Healthcare Industries.

This book presents state-of-the-art blockchain and AI advances in health care. Healthcare service is increasingly creating the scope for blockchain and AI applications to enter the biomedical and healthcare world. Today, blockchain, AI, ML, and deep learning are affecting every domain. Through its cutting-edge applications, AI and ML are helping transform the healthcare industry for the better. Blockchain is a decentralization communication platform that has the potential to decentralize the way we store data and manage information. Blockchain technology has potential to reduce the role of middleman, one of the most important regulatory actors in our society. Transactions are simultaneously secure and trustworthy due to the use of cryptographic principles. In recent years, blockchain technology has become very trendy and has penetrated different domains, mostly due to the popularity of cryptocurrencies. One field where blockchain technology has tremendous potential is health care, due to the need for a more patient-centric approach in healthcare systems to connect disparate systems and to increase the accuracy of electronic healthcare records (EHRs).

Just months ago, millions of people were stunned by ChatGPT's amazing abilities -- and its bizarre hallucinations. But that was 2022. GPT-4 is now here: smarter, more accurate, with deeper technical knowledge. GPT-4 and its competitors and followers are on the verge of transforming medicine. But with lives on the line, you need to understand these technologies -- stat. What can they do? What can't they do -- yet? What shouldn't they ever do? To decide, experience the cutting edge for yourself. Join three insiders who've had months of early access to GPT-4 as they reveal its momentous potential -- to improve diagnoses, summarize patient visits, streamline processes, accelerate research, and much more. You'll see real GPT-4 dialogues -- unrehearsed and unfiltered, brilliant and blundering alike -- all annotated with invaluable context, candid commentary, real risk insights, and up-to-the-minute takeaways.

• Foreword / by Sam Altman
• First contact / by Peter Lee
• Medicina ex machina / by Peter Lee
• The big question : Does it “understand?” / by Peter Lee
• Trust but verify / by Isaac "Zak" Kohane
• The AI-augmented patient / by Carey Goldberg
• So much more : Math, coding, and logic / by Peter Lee
• The ultimate paperwork shredder / by Peter Lee
• Smarter science / by Isaac "Zak" Kohane
• Safety first / by Isaac "Zak" Kohane, Carey Goldberg, and Peter Lee
• The big black bag / by Carey Goldberg and Isaac "Zak" Kohane
• Epilogue / by Peter Lee.

"Just months ago, millions of people were stunned by ChatGPT's amazing abilities, and its bizarre hallucinations. That was 2022. AI's next generation is coming fast : smarter, more accurate, with deeper technical knowledge. GPT-4 and its competitors are on the verge of transforming medicine. Whether you're a healthcare leader, provider, or a patient, you need to understand these technologies, stat. What can they do? What can't they do, yet? What shouldn't they ever do? To decide, experience the cutting edge for yourself. Join three insiders who've had months of early access to GPT-4 as they reveal its momentous potential ; to improve diagnoses, summarize patient visits, streamline processes, accelerate research, as well as its risks. You'll see real GPT-4 dialogues ; unrehearsed and unfiltered, brilliant and blundering alike, all annotated with invaluable context, candid commentary, real risk insights about potential downsides, and up-to-the-minute takeaways."-- taken from back cover.

• Chapter 1. ALGINATE BASED MATRIX TABLET FOR DRUG DELIVERY.-
• Chapter 2. Alginate based micro particulate systems for drug delivery.-
• Chapter 3. Alginate based nanocarriers for controlled drug delivery applications.-
• Chapter 4. Alginate based carriers for topical drug delivery.-
• Chapter 5. Alginate based Hydrogel in drug delivery and biomedical applications.-
• Chapter 6. Alginate based interpenetrating polymer network (IPN) in drug delivery and biomedical applications.-
• Chapter 7. Alginate based micelle in biomedical applications.-
• Chapter 8. Alginate based polyelectrolyte complexes for drug delivery and biomedical applications.-
• Chapter 9. Alginate-Based Inhalable Particles for Controlled Pulmonary Drug Delivery.-
• Chapter 10. Biomedical Applications of Alginate in the Delivery System for Natural Products.-
• Chapter 11. Alginate in cancer therapy.-
• Chapter 12. Alginate carriers in wound healing applications.-
• Chapter 13. Alginate as support material in enzyme Immobilization.-
• Chapter 14. Alginate in Gene and Vaccine Delivery.-
• Chapter 15. Alginate based scaffolds in Tissue engineering and Regenerative Medicine.
• (source: Nielsen Book Data)

This book focuses on recent trends of research on alginate-based biomaterials in Drug Delivery strategies and biomedical engineering. It contains the widely used alginate-based biomaterials as micro to nano-controlled drug delivery (oral, ocular delivery, topical delivery, etc.) and its fabrication technology, characterization, and biomedical aspects (such as cancer therapy, tissue engineering, gene delivery, vaccine delivery, enzyme immobilization, wound healing, dental applications, etc.) in a single book. The chapters cover updated information, current research trends, informatics, and all aspects of applications. Alginate is a U.S. Food and Drug Administration (FDA)-approved natural biomaterial and has diverse biomedical applications. In recent years, researchers and scientists are working on the alginate-based drug delivery systems that have been designed and characterized as a matrix, micro to nanocarriers, fibers, composite/scaffolds, etc. Alginate has versatile properties such as biodegradable, biocompatible, nontoxic, and easily available. This book especially highlights both the drug delivery strategies and biomedical engineering aspects such as controlled drug delivery, drug targeting to the site of action, cancer therapy, gene and vaccine delivery, enzyme immobilization, tissue engineering, and regenerative medicine. .
(source: Nielsen Book Data)

• An Adaptive Fuzzy based Clustering Model for Healthcare-Wireless Sensor Networks.- Artificial Pancreas (AP) based on the JAYA Optimized PI Controller (JAYA-PIC).- Security Issues and Solutions for Reliable WBAN Based e-Healthcare Systems: A Systematic Review.- Brain Tumor Detection by Multilevel Thresholding using Opposition Equilibrium Optimizer.- RescuePlus.- Skeletal Bone Age Determination using Deep Learning.- Chimp Optimization Algorithm Based Feature Selection for Cardiac Image-based Heart Disease Diagnosis.- The Hospitality Industry's Impact on the Covid-19 Epidemic: A Case Study of Ukraine.- Society in front of a COVID-19 Pandemic: India vs Ukraine.- Breast Cancer Detection using Concatenated Deep Learning Model.- Recommendation of Pesticides based on Automation Detection of Citrus Fruits and Leaves Diseases using Deep Learning.- Comprehensive Information Retrieval using Fine-tuned BERT model and Topic-assisted Query Expansion.
• (source: Nielsen Book Data)

The book is a collection of research papers presented at the First International Conference on International Conference on Ambient Intelligence in Health Care (ICAIHC 2021) organized by Institute of Technical Education and Research, Siksha 'O' Anusandhan (Deemed to be University) University, Bhubaneswar, India, during April 15-16, 2022. It includes papers in the research area of e-health care, telemedicine, other medical technologies, life support systems, fast detection and diagnoses, developed technologies and innovative solutions, bioinformatics, and solutions for monitoring smart intelligent systems in health care.
(source: Nielsen Book Data)

• 1. Emergence of analytical techniques . Kamil Reza Khondakar
• 2. Electrochemical techniques for biomedical nanotechnology / Jyotsana Mehta, Neeraj Dilbaghi and Sandeep Kumar
• 3. UV-visible spectroscopy in biomedical nanotechnology / Syed S. Razi, Vivek Kumar Nautiyal and Gaurav Hitkari
• 4. FTIR spectroscopy and microscopy in biomedical nanotechnology / Yash Thakare, Swapnil Dharaskar and Ritesh Palkar
• 5. Raman spectroscopy and microscopy in biomedical nanotechnology / Zohreh Shahnavaz, Muhammad J.A. Shiddiky, Kamelia Saremi, Guanjun Qiao and Nam-Young Kim
• 6. Quartz crystal microbalance for biomedical nanotechnology / Kshitij R.B. Singh, Vanya Nayak and Ravindra Pratap Singh
• 7. Application of nuclear magnetic resonance spectroscopy in biomedical nanotechnology / Avtar Singh, Jaspreet Dhau, Rajeev Kumar, Kirandeep Singh and Amritpal Singh
• 8. Mass spectroscopy in biomedical nanotechnology / Priyanka Mankotia, Kartikey Verma, Kashma Sharma, Vishal Sharma, Vijay Kumar and Rakesh Sehgal
• 9. Magnetic measurement systems for biomedical nanotechnology / Md. Mottahir Alam, Mohd Imran, Akhilesh Kumar Gupta, Afzal Khan, Tansir Ahamad and Ajeet Kaushik
• 10. Application of x-ray diffraction for biomedical nanotechnologies : current insights and perspectives / Rahul Bhattacharjee, Priya Mitra, Srija Chakrabarty, Radheka Bhaduri, Sulagna Kar and Abhijit Dey
• 11. Scanning electron microscopy for biomedical nanotechnology / Ravikumar B. Shinde, Shashank S. Pawitwar and Ajeet Kaushik
• 12. Transmission electron microscopy for biomedical nanotechnology / Sayan Mukherjee, Sandeep Kaushik, Poonam Singh, S. Shweta and Ishwar Prasad Sahu
• 13. Energy dispersive spectroscopy for biomedical nanotechnology / Pooja Rawat, Shraddha Dorlikar, Vidhu Malik, Parshant Kumar Sharma and Jong Soo Rhyee
• 14. Atomic force microscopy for biomedical nanotechnology / Chinmaya Mahapatra
• 15. Microfluidics system for biomedical nanotechnology / Nitish Sagar, Prosenjit Sen and Saurabh Kumar
• 16. Dynamic light scattering (DLS) particle size analysis for biomedical nanotechnology / Vikas Shukla, Karishma Niveria, Poonam Shashidhar and Anita Kamra Verma
• 17. Zeta potential measurements and analysis for biomedical nanotechnology / Sanjoy Kumar Das, Soumalya Chakraborty, Rajan Rajabalaya and Bhaskar Mazumder
• 18. Electron paramagnetic (spin) resonance spectroscopy for biomedical nanotechnology / S.V. Satya Prasad, Kartikey Verma and Subhash Singh
• 19. X-ray photoelectron spectroscopy for biomedical applications / Anindita Tarafdar, Hima Sree Buddhiraju and Aravind Kumar Rengan
• 20. AI and IOT for biomedical smart applications / Shilpa Bhatkande, Hirak Mazumdar and Hardik Gohel
• 21. Summary and aspects of techniques for biotechnology / Ajeet Kaushik, Sesha S. Srinivasan and Yogendra K. Mishra.

Nanotechnology is essential in the development of advanced technologies for medicine and healthcare. Advances depend on the ability to study these materials and nano-biotechnologies using advanced analytical techniques. Providing a comprehensive overview of analytical techniques for applications in biomedical nanotechnology at both the fundamental and applied level, this book provides a broad perspective of the development of analytical methods involved in materials science and electronics. It highlights the fundamentals and systematic developments of techniques to achieve better characterization, rapid diagnostics, cost-effective and user-friendly approaches, and state-of-the-art methodologies for personalized health management, and explains how this fundamental knowledge can be translated into applications in nano-enabled biomedical research. The multidisciplinary readership includes chemists, biologists, physicists, materials scientists, engineers and information technologists.

• 1. Main responsible of human infections
• 2. Nanomaterials to aid wound healing and infection control
• 3. Nanocomposite scaffolds and coatings for wound healing and infection control
• 4. Antiviral potential of nanomaterials: The fight against viruses
• 5. Antiviral potential of nanomaterials: Novel solutions for emerging challenges
• 6. Antimicrobial nanoparticles
• 7. Functionalization of nanoparticles
• 8. Toxicity of nanoparticles.
• (source: Nielsen Book Data)

Antimicrobial Activity of Nanoparticles: Applications in Wound Healing and Infection Treatment presents the state of the art among nanotechnological approaches used in the treatment of infections. This field has gained a large amount of interest over the past few years, in response to the increasing resistance of pathogens to antibiotics. Leading researchers from around the world discuss the synthesis routes of nanobiomaterials, characterization, and their applications as antimicrobial agents. The book covers various aspects: from antiviral and antibacterial nanoparticles, to the functionalization of nanoparticles and their toxicity to human cells. This book offers an advanced reference text for biomedical engineers, materials scientists, clinicians, and biochemists, with an interest in nanomedicine and infection control.
(source: Nielsen Book Data)

• Clinical health psychology : foundations, history, and future of a promising discipline and profession / Rafael Ballester-Arnal, Maria Dolores Gil-Llario, & Cristina Giménez-García
• The unified protocol for transdiagnostic treatment of emotional disorders : an introduction to the treatment protocol / Todd J. Farchione, Daniella M. Spencer-Laitt, Shannon Sauer-Zavala, David H. Barlow
• Transdiagnostic emotional disorder assessment and case formulation in health conditions / Anthony J. Rosellini, Jorge Osma, Carlos Suso, & Timothy A. Brown
• Application of the unified protocol in type 2 diabetes / Stephanie L. Leung, Todd J. Farchione, Elizabeth H. Eustis, Lindsey Pappalardo, & Jeffrey S. Gonzalez
• Application of a brief unified protocol program in group format to people living with HIV in a public health unit / Alicia González Baeza, Guadalupe Rua-Cebrian, Joanna Cano-Smith, Ignacio Pérez-Valero, & Jorge Osma
• A unified, transdiagnostic approach to pain management / Jenna Sandler Eilenberg & John D. Otis
• Application of the unified protocol in patients who have undergone bariatric surgery / Vanesa Ferreres-Galán, Alba Quilez-Orden, Óscar Peris-Baquero & Jorge Osma
• Unified protocol application in fertility problems / Elena Crespo-Delgado, Verónica Martínez-Borba, Laura Andreu-Pejó & Jorge Osma
• Unified protocol for endometriosis / Laura A. Payne & Antje M. Barreveld
• Cognitive-behavioral treatment for anxiety and depression in Parkinson's disease : efficacy and clinical application of the unified protocol / Gretchen O. Reynolds, Ryan Piers, & Alice Cronin-Golomb
• Application of the unified protocol in irritable bowel syndrome / Zahra Zanjani & Sanaz Joekar
• Treating depression and anxiety disorders simultaneously in patients with cardiovascular diseases utilizing the unified protocol / Phillip J. Tully & Suzanne M. Cosh
• The unified protocol for smoking cessation : conceptual basis and integrated treatment approach / Amanda R. Mathew & Chelsea Cox.

• An Introduction to Artificial Intelligence in Healthcare.- Radiomics: Approach to Precision Medicine.- Artificial Intelligence Based Strategies for Data-Driven Radial MRI. .
• (source: Nielsen Book Data)

Artificial intelligence (AI) and machine learning (ML) have transformed many standard and conventional methods in undertaking health and well-being issues of humans. AL/ML-based systems and tools play a critical role in this digital and big data era to address a variety of medical and healthcare problems, improving treatments and quality of care for patients. This edition on AI and ML for healthcare consists of two volumes. The first presents selected AI and ML studies on medical imaging and healthcare data analytics, while the second unveils emerging methodologies and trends in AI and ML for delivering better medical treatments and healthcare services in the future. In this first volume, progresses in AI and ML technologies for medical image, video, and signal processing as well as health information and data analytics are presented. These selected studies offer readers theoretical and practical knowledge and ideas pertaining to recent advances in AI and ML for effective and efficient image and data analytics, leading to state-of-the-art AI and ML technologies for advancing the healthcare sector.
(source: Nielsen Book Data)

• Artificial Intelligence for the future of medicine.- A Survival Analysis Guide in Oncology.- Social Media Sentiment Analysis related to COVID-19 Vaccinations.- Healthcare support using data mining: A case study on stroke prediction. .
• (source: Nielsen Book Data)

In line with advances in digital and computing systems, artificial intelligence (AI) and machine learning (ML) technologies have transformed many aspects of medical and healthcare services, delivering tangible benefits to patents and the general public. This book is a sequel of the edition on "Artificial Intelligence and Machine Learning for Healthcare". The first volume is focused on utilization of AI and ML for image and data analytics in the medical and healthcare domains. In this second volume, emerging methodologies and future trends in AI and ML for advancing medical treatments and healthcare services are presented. The selected studies in this book provide readers a glimpse on current progresses in AI and ML for undertaking a variety of healthcare-related tasks. The advances in AI and ML technologies for future healthcare are also discussed, shedding light on the potential of AI and ML to realize the next-generation medical treatments and healthcare services for the betterment of our global society.
(source: Nielsen Book Data)

• Introduction to Human and Artificial Intelligence
• Knowledge Representation and Reasoning
• Methods of Machine Learning
• Supervised learning
• Unsupervised Learning
• Time-series analysis
• Artificial Intelligence in Healthcare
• Rule based expert systems
• Robotic Process Automation: A path to Intelligent Healthcare
• Tools and Technologies for implementing AI approaches in healthcare
• Learning Evaluation for Intelligence
• Ethics of Intelligence

This book presents a systematic evolution of artificial intelligence (AI), its applications, challenges and solutions in the field of healthcare. The book mainly covers the foundations and various methods of learning in artificial intelligence with its application in healthcare industry. This book provides a comprehensive introduction to data analysis using AI as a tool in the generation, normalization and analysis of healthcare data in association with several evaluation techniques and accuracy measurements. The book is divided into three major sections describing the basic foundations of AI and its associated algorithms, history of artificial intelligence in healthcare, recent developments and several modeling techniques for the same. The last section of the book provides insights into several implementations and methods of evaluation and accuracy prediction for healthcare analysis in AI. Extensive use of data for analysis and prediction using several technologies has transformed the lives of normal people indirectly effecting our process to communicate, learn, work and socialize within the society. Thus, the book also provides an insight into the ethics of AI that is very vital in the process of implementation and evaluation of healthcare data. The book provides an organized analysis to a considerable part of data in a digitized society. In view of this, it covers the theory, methodology, perfection and verification of empirical work for health-related data processing. Particular attention is devoted to in-depth experiments and applications

• Machine Learning and Deep Learning
• Survival Hierarchical Agglomerative Clustering: A Semi-Supervised Clustering Method Incorporating Survival Data
• Boosted Random Forests for Predicting Treatment Failure of Chemotherapy Regimens
• A Binning Approach for Predicting Long-Term Prognosis in Multiple Sclerosis
• Decision Tree Approaches to Select High Risk Patients for Lung Cancer Screening based on the UK Primary Care Data
• Causal Discovery with Missing Data in a Multicentric Clinical Study
• Novel approach for phenotyping based on diverse top-k subgroup lists
• Patient Event Sequences for Predicting Hospitalization Length of Stay
• Autoencoder-based prediction of ICU clinical codes
• Explainability and Transfer Learning
• Hospital Length of Stay Prediction Based on Multi-modal Data towards Trustworthy Human-AI Collaboration in Radiomics
• Explainable Artificial Intelligence for Cytological Image Analysis
• Federated Learning to Improve Counterfactual Explanations for Sepsis Treatment Prediction
• Explainable AI for Medical Event Prediction for Heart Failure Patients
• Adversarial Robustness and Feature Impact Analysis for Driver Drowsiness Detection
• Computational Evaluation of Model-Agnostic Explainable AI using Local Feature Importance in Healthcare
• Batch Integrated Gradients: Explanations for Temporal Electronic Health Records
• Improving stroke trace classification explainability through counterexamples
• Spatial Knowledge Transfer with Deep Adaptation Network for Predicting Hospital Readmission
• Dealing with Data Scarcity in Rare Diseases: Dynamic Bayesian Networks and Transfer Learning to Develop Prognostic Models of Amyotrophic Lateral Sclerosis
• Natural Language Processing
• A Rule-free Approach for Cardiological Registry Filling from Italian Clinical Notes with Question Answering Transformers
• Classification of Fall Types in Parkinson Disease From Self-report Data Using Natural Language Processing
• BERT for complex systematic review screening to support the future of medical research
• GGTWEAK: Gene Tagging with Weak Supervision for German Clinical Text
• Soft-prompt tuning to predict lung cancer using primary care free-text Dutch medical notes
• Machine learning models for automatic Gene Ontology annotation of biological texts
• Image Analysis and Signal Analysis
• A Robust BKSVD Method for Blind Color Deconvolution and Blood Detection on H&E Histological Images
• Can knowledge transfer techniques compensate for the limited myocardial infarction data by leveraging hemodynamics? An in silico Study
• Covid-19 Diagnosis In 3D Chest CT Scans With Attention-Based Models
• Generalized Deep Learning-based Proximal Gradient Descent for MR Reconstruction
• Crowdsourcing segmentation of histopathological images using annotations provided by medical students
• Automatic sleep stage classification on EEG signals using time-frequency representation
• Learning EKG Diagnostic Models with Hierarchical Class Label Dependencies
• Discriminant audio properties in deep learning based respiratory insufficiency detection in Brazilian Portuguese
• ECGAN: Self-supervised generative adversarial network for electrocardiography
• Data Analysis and Statistical Models
• Nation-wide ePrescription Data Reveals Landscape of Physicians and their Drug Prescribing Patterns in Slovenia
• Machine Learning Based Prediction of Incident Cases of Crohn’s Disease Using Electronic Health Records from a Large Integrated Health System
• Prognostic prediction of paediatric DHF in two hospitals in Thailand
• The Impact of Bias on Drift Detection in AI Health Software
• A Topological Data Analysis Framework for Computational Phenotyping
• Ranking of Survival-Related Gene Sets through Integration of Single-Sample Gene Set Enrichment and Survival Analysis
• Knowledge Representation and Decision Support
• Supporting the prediction of AKI evolution through interval-based approximate temporal functional dependencies
• Integrating Ontological Knowledge with Probability Data to Aid Diagnosis in Radiology
• Ontology model for supporting process mining on healthcare-related data
• Real-World Evidence Inclusion in Guideline-Based Clinical Decision Support Systems: Breast Cancer Use Case
• Decentralized Web-based Clinical Decision Support using Semantic GLEAN Workflows
• An Interactive Dashboard for Patient Monitoring and Management: a Support Tool to the Continuity of Care Centre
• A general-purpose AI assistant embedded in an open-source radiology information system
• Management of patient and physician preferences and explanations for participatory evaluation of treatment with an ethical seal. .

This book constitutes the refereed proceedings of the 21st International Conference on Artificial Intelligence in Medicine, AIME 2023, held in Portoroz, Slovenia, in June12-15, 2023. The 23 full papers and 21 short papers presented together with 3 demonstration papers were selected from 108 submissions. The papers are grouped in topical sections on: machine learning and deep learning; explainability and transfer learning; natural language processing; image analysis and signal analysis; data analysis and statistical models; knowledge representation and decision support.