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AI in healthcare : how artificial intelligence is changing IT operations and infrastructure services / / Robert Shimonski
AI in healthcare : how artificial intelligence is changing IT operations and infrastructure services / / Robert Shimonski
Autore Shimonski Robert
Pubbl/distr/stampa Indianapolis, Indiana : , : Wiley, , [2021]
Descrizione fisica 1 online resource (291 pages)
Disciplina 610.28563
Soggetto topico Artificial intelligence - Medical applications
Medical informatics
Artificial intelligence
ISBN 1-119-68004-2
1-119-68007-7
1-119-68005-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910677765003321
Shimonski Robert  
Indianapolis, Indiana : , : Wiley, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
AI in healthcare : how artificial intelligence is changing IT operations and infrastructure services / / Robert Shimonski
AI in healthcare : how artificial intelligence is changing IT operations and infrastructure services / / Robert Shimonski
Autore Shimonski Robert
Pubbl/distr/stampa Indianapolis, Indiana : , : Wiley, , [2021]
Descrizione fisica 1 online resource (291 pages)
Disciplina 610.28563
Soggetto topico Artificial intelligence - Medical applications
Medical informatics
Artificial intelligence
ISBN 1-119-68004-2
1-119-68007-7
1-119-68005-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910820298103321
Shimonski Robert  
Indianapolis, Indiana : , : Wiley, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
AI to improve e-governance and eminence of life : Kalyanathon 2020 / / Somnath Mukhopadhyay [and three others], editors
AI to improve e-governance and eminence of life : Kalyanathon 2020 / / Somnath Mukhopadhyay [and three others], editors
Edizione [First edition.]
Pubbl/distr/stampa Singapore : , : Springer Nature Singapore Pte Ltd., , [2023]
Descrizione fisica 1 online resource (189 pages)
Disciplina 006.3
Collana Studies in Big Data
Soggetto topico Artificial intelligence - Agricultural applications
Artificial intelligence - Industrial applications
Artificial intelligence - Medical applications
ISBN 981-9946-77-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Artificial Intelligence for Rural Healthcare Management: Prognosis, Diagnosis and Treatment -- A Study using Support Vector Machine as a Tool for Patient’s Satisfaction for SARS-CoV-2 Cases Using Telemedicine -- Applications of AI and IoT technology in Protected Cultivation for Enhancing Agricultural Productivity: A Concise Review -- IoT Based Smart Farming Using AI -- Random Forest Algorithm for Plant Disease Prediction.
Record Nr. UNINA-9910746087803321
Singapore : , : Springer Nature Singapore Pte Ltd., , [2023]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Application of artificial intelligence in Covid-19 / / Sachi Nandan Mohanty [and three others], editors
Application of artificial intelligence in Covid-19 / / Sachi Nandan Mohanty [and three others], editors
Pubbl/distr/stampa Gateway East, Singapore : , : Springer, , [2021]
Descrizione fisica 1 online resource (593 pages)
Disciplina 610.285
Collana Medical virology: from pathogenesis to disease control series
Soggetto topico Artificial intelligence - Medical applications
COVID-19
Intel·ligència artificial en medicina
Soggetto genere / forma Llibres electrònics
ISBN 981-15-7317-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Foreword 1 -- Foreword 2 -- Preface -- Acknowledgements -- Contents -- About the Editors -- Part I: AI as a Source of Prides for Healthcare -- 1: Comprehensive Claims of AI for Healthcare Applications-Coherence Towards COVID-19 -- 1.1 Orientation of Artificial Intelligence in Healthcare Research -- 1.2 Correlated Investigational Analysis of AI Appliances in Healthcare System and Various Clinical Diseases -- 1.2.1 Disease Detection and Diagnosis -- 1.2.2 Automated Robert Treatment and Drug Design, Discovery -- 1.2.3 Healthcare Data Management Supported by Digital Managerial Application -- 1.2.4 AI in Public and Clinical Health -- 1.3 Motivational AI Devices for Healthcare -- 1.3.1 AI-Administered Devices with Machine Learning and Deep Learning -- 1.3.2 AI Attributed Devices with IOT -- 1.3.3 AI Supervised Devices with Big Data and Data Science -- 1.3.4 AI-Based Mining and NLP -- 1.3.5 AI-Enabled Expert System -- 1.4 Demand of AI for COVID-19 -- 1.4.1 Prior Alert Generation -- 1.4.2 Continuous Tracing and Following COVID-19 Symptoms -- 1.4.3 Diagnosis and Prognosis -- 1.4.4 Treatment and Possible Drug Design and Discovery -- 1.4.5 Control over Society and People with Guidelines -- 1.5 Conclusions and Future Work -- 1.6 Executive Summary -- References -- 2: Artificial Intelligence-Based Systems for Combating COVID-19 -- 2.1 Introduction -- 2.2 How Technology Can Help in Containing the Pandemic? -- 2.3 Technological Approach Vs Non-technological Approach of Treatment of COVID-19 -- 2.4 Existing Technologies to Detect/Diagnose the Virus -- 2.4.1 Non-contact Infrared Thermometers -- 2.5 Thermal Screening via Thermal Cameras -- 2.5.1 Symptom-Based Diagnosis -- 2.5.2 Ventilators -- 2.6 Means of Prevention from COVID-19 -- 2.6.1 Masks -- 2.6.2 Sanitizers/Hand Rub -- 2.6.3 Sanitizing Tunnels for Public Areas.
2.6.4 Washing Hands with Soap for 20s -- 2.6.5 Avoiding Handshakes -- 2.7 Use of Modern Technologies for Making Diagnosis Faster, Easier, and Effective -- 2.8 Proposed Techniques to Effectively Control the Rise in Cases of COVID-19 -- 2.8.1 Crowdsource-Based Applications -- 2.9 Conclusion -- References -- Part II: AI Warfare in COVID-19 Diagnosis, Detection, Prediction, Prognosis and Knowledge Representation -- 3: Artificial Intelligence-Mediated Medical Diagnosis of COVID-19 -- 3.1 Introduction -- 3.2 Pathogenesis and Diagnostic Windows -- 3.3 AI Assisted COVID-19 Diagnosis -- 3.3.1 Potential Application for Infection Detection -- 3.3.2 Application of AI on `Omics´ Big-Data -- 3.3.3 Use of AI on Radiology Data -- 3.4 Future Directions -- References -- 4: Artificial Intelligence (AI) Combined with Medical Imaging Enables Rapid Diagnosis for Covid-19 -- 4.1 Introduction -- 4.1.1 Reverse Transcription-Polymerase Chain Reaction -- 4.1.2 Isothermal Amplification Assays -- 4.1.3 Antigen Tests -- 4.1.4 Serological Tests -- 4.1.5 Rapid Diagnostic Tests (RDT) -- 4.1.6 Enzyme-Linked ImmunoSorbent Assay (ELISA) -- 4.1.7 Neutralization Assay -- 4.1.8 Chemiluminescent Immunoassay -- 4.2 AI-Based Diagnosis -- 4.2.1 Chest CT or X-ray CT Scans -- 4.2.2 Chest Radiography -- 4.2.2.1 Limitation -- 4.3 Other Predictive Measures for Covid-19 Diagnosis -- 4.3.1 Pulse Oximetry -- 4.3.2 Thermal Screening -- 4.4 Conclusions -- References -- 5: Role of Artificial Intelligence in COVID-19 Prediction Based on Statistical Methods -- 5.1 Introduction -- 5.2 Related Work -- 5.3 Dataset Description -- 5.4 Experimental Results -- 5.4.1 Combinatorial (Quick) Approach -- 5.4.2 Stepwise Forward Selection Approach -- 5.4.3 Stepwise Mixed Selection Approach -- 5.4.4 GMDH Neural Network Approach -- 5.5 Comparison Between the Algorithms Based on MAE, RMSE, SD, Correlation.
5.6 Conclusion -- References -- 6: Data-Driven Symptom Analysis and Location Prediction Model for Clinical Health Data Processing and Knowledgebase Developmen... -- 6.1 Introduction -- 6.2 Related Work -- 6.3 Rudiments of Random Forest Machine Learning Algorithm -- 6.4 Case Study for Symptom Analysis and Its Prediction with Random Forest Using COVID-19 WHO Data Set -- 6.4.1 Step Wise Experimental Result Analysis and Discussions -- 6.4.2 Calculation of Average Baseline Error -- 6.4.3 Classifying Into Zones -- 6.4.3.1 Setting Threshold Value -- 6.4.4 Color Attribute of Map with Zones (Green, Orange, and Red) -- 6.5 Augmented Enhancements to the Detection and Prediction Analysis for COVID 19 -- 6.5.1 Appending a New Drop-Down Menu in the Detection Page -- 6.6 Aligning Output of This Research as a Supplement to Heighten Up Healthcare and Public Health -- 6.7 Conclusions -- 6.8 Future Work -- References -- 7: A Decision Support System Using Rule-Based Expert System for COVID-19 Prediction and Diagnosis -- 7.1 Introduction -- 7.2 Background -- 7.2.1 Machine Learning-Based Data-Oriented Approach -- 7.2.2 Expert System-Based Knowledge-Oriented Approach -- 7.3 Overview of Expert System -- 7.3.1 Fundamentals -- 7.3.2 Expert System Architecture -- 7.3.3 Expert System Design Issues -- 7.4 Case Study: COVID-19 -- 7.4.1 Feasibility of Expert System on COVID-19 -- 7.4.2 Problem Description -- 7.4.3 Proposed Expert System: ESCOVID -- 7.4.3.1 Rule Set and Knowledgebase -- 7.4.3.2 Inference Mechanism -- 7.5 Implementation and Testing -- 7.6 Conclusion -- References -- 8: A Predictive Mechanism to Intimate the Danger of Infection via nCOVID-19 Through Unsupervised Learning -- 8.1 Introduction -- 8.2 Literature Survey -- 8.3 Methodology -- 8.3.1 Data Collection -- 8.3.2 Relevant Dataset -- 8.3.3 Data Processing -- 8.3.3.1 Algorithm of Clustering -- 8.4 Result Analysis.
8.4.1 Overall Behavior of All Unsupervised Learning Model (Figs. 8.9 and 8.10) -- 8.5 Conclusion -- References -- 9: Artificial Intelligence-Enabled Prognosis Technologies for SARS-CoV-2/COVID-19 -- 9.1 Introduction -- 9.1.1 Epidemiology and Phylogeography of Pathogen -- 9.1.2 Human-to-Human Transmission -- 9.1.3 Clinical Phenotype Variations and Pathogenesis -- 9.2 Current Prognosis Practices -- 9.2.1 Diagnosis Services -- 9.2.2 Control Practices -- 9.2.2.1 Sanitization -- 9.2.2.2 Treatment -- 9.3 Challenges of SARS-CoV-2 -- 9.3.1 Phylogeography and Clinical Features -- 9.3.2 Mass Community and Healthcare Management -- 9.3.3 Transmission and Distancing -- 9.3.4 Diagnosis and Treatment -- 9.3.5 Disease Modeling Approaches -- 9.3.6 Data Security Concerns -- 9.4 Advanced Technologies -- 9.4.1 Internet of Things (IoT) -- 9.4.2 Artificial Intelligence (AI) -- 9.4.3 Databases and Analytics -- 9.4.4 Advanced Genomics and proteomics -- 9.4.5 Cloud Computing and Optimization -- 9.4.6 Digital Medicine and Healthcare -- 9.4.7 Biosensor and Bioelectronics -- 9.5 Integrated Technology and Logical Products -- 9.5.1 AI, Cloud, Sensor and IoT -- 9.6 AI-Enabled Prognosis Technology, Product, and Model Description -- 9.6.1 Technology and Product: AI Analysis and Program in Healthcare -- 9.6.2 Product and Technology: AI-Based sanitization Machine Using Cloud computing and Optimization -- 9.6.3 Product and Technology: IOT-Based AI-Enabled Touchless Hand Sanitizer Machine -- 9.6.4 Technology and Model: Prognosis Healthcare Model for Mass Community -- 9.6.4.1 Standard Prognosis Practices -- 9.7 Adaptation of AI-Enabled Technology and Disease Research -- 9.7.1 Hygiene, Distancing, and Virus Control -- 9.7.2 Understanding of Pathogenic Consequences -- 9.8 Conclusion -- 9.9 Future Prospects -- References.
10: Intelligent Agent Based Case Base Reasoning Systems Build Knowledge Representation in COVID-19 Analysis of Recovery of Inf... -- 10.1 Introduction -- 10.2 Related Work -- 10.3 COVID-19 -- 10.4 Symptom of COVID-19 -- 10.5 Artificial Intelligence -- 10.6 Machine Learning -- 10.7 Natural Language Processing -- 10.8 Robotics -- 10.9 Autonomous Vehicles -- 10.10 Vision -- 10.11 Clinical Artificial Intelligence -- 10.12 Expert System -- 10.13 Machine Learning -- 10.14 Intelligent Agent -- 10.15 Characteristic Agents -- 10.16 Clinical Intelligent Agent -- 10.17 Multi-Agent System -- 10.18 Java Agent Framework (JADE) -- 10.19 Clinical Multi-Agents -- 10.20 Case Base Reasoning -- 10.21 The CBR Cycle -- 10.22 JCOLIBRI -- 10.23 Clinical Case Base Reasoning Systems -- 10.24 Knowledge Base System -- 10.25 Clinical Knowledge Base System -- 10.26 Amalgamation OF CAI, CIA, CMAS, CCBR Using in KBSCOVID-19 Model -- 10.27 Implementation of MASCBR-Based Knowledge Base Patients Recovery from COVID-19 Pandemic -- 10.28 Conclusion -- 10.29 Future Work -- References -- Part III: Machine Learning Solicitation for COVID 19 -- 11: Epidemic Analysis of COVID-19 Using Machine Learning Techniques -- 11.1 Introduction -- 11.2 Related Work -- 11.3 Pattern Identification for COVID-19 -- 11.4 Experiment Analysis -- 11.4.1 Dataset 1: Based on Geographic Distribution (https://www.ecdc.europa.eu/en/publications-data/download-todays-data-geogr... -- 11.4.1.1 Description of the Dataset -- 11.4.1.2 Correlation Between the Variables -- 11.4.1.3 Generating Heat Map of the Correlation -- 11.4.2 Dataset 2 (https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-counties.csv) -- 11.4.2.1 Snapshot of the dataset -- 11.4.2.2 Generating Pair Plot -- 11.5 Pattern Prediction of Covid-19 Using Machine Learning Approaches -- 11.6 Conclusions -- References.
12: Machine Learning Application in COVID-19 Drug Development.
Record Nr. UNINA-9910502987803321
Gateway East, Singapore : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Applications of medical artificial intelligence : first international workshop, AMAI 2022, held in conjunction with MICCAI 2022, Singapore, September 18, 2022, proceedings / / edited by Shandong Wu, Behrouz Shabestari, and Lei Xing
Applications of medical artificial intelligence : first international workshop, AMAI 2022, held in conjunction with MICCAI 2022, Singapore, September 18, 2022, proceedings / / edited by Shandong Wu, Behrouz Shabestari, and Lei Xing
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (171 pages)
Disciplina 006.3
Collana Lecture Notes in Computer Science Ser.
Soggetto topico Artificial intelligence - Medical applications
Diagnostic imaging - Data processing
ISBN 3-031-17721-5
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Organization -- Contents -- Increasing the Accessibility of Peripheral Artery Disease Screening with Deep Learning -- 1 Problem -- 2 Related Work -- 3 Data Collection Study -- 4 System Development -- 5 Validation Study -- 6 Conclusion -- References -- Deep Learning Meets Computational Fluid Dynamics to Assess CAD in CCTA -- 1 Introduction -- 2 Automated Assessment of CAD in CCTA -- 2.1 Straightened Representation of the Coronary Vessels -- 2.2 Representing Ground-Truth Segmentation as a 3D Mesh -- 2.3 Segmentation of Vessels Using U-Nets in Upsampled CTTA -- 2.4 Blood Flow Simulation -- 3 Experimental Validation -- 4 Conclusions and Future Work -- References -- Machine Learning for Dynamically Predicting the Onset of Renal Replacement Therapy in Chronic Kidney Disease Patients Using Claims Data -- 1 Introduction -- 2 Methods -- 2.1 Dataset Description -- 2.2 Task Definition -- 2.3 Data Representation and Processing -- 2.4 Model Description -- 2.5 Model Evaluation -- 3 Experiments and Results -- 3.1 Study Population and Dataset -- 3.2 Model Performance -- 4 Conclusions -- References -- Uncertainty-Aware Geographic Atrophy Progression Prediction from Fundus Autofluorescence -- 1 Introduction -- 2 Method -- 2.1 Data -- 2.2 Model Development -- 2.3 Uncertainty Estimation Using Deep Ensemble -- 3 Results -- 4 Conclusions -- References -- Automated Assessment of Renal Calculi in Serial Computed Tomography Scans -- 1 Introduction -- 1.1 Our Contributions -- 2 Materials and Methods -- 2.1 Data -- 2.2 Calculi Detection and Segmentation -- 2.3 Registration and Stone Matching -- 2.4 Manual Review and Tracking -- 2.5 Evaluation of Performance -- 2.6 Statistical Analysis -- 3 Results -- 3.1 Cohort Characteristics -- 3.2 Performance of the Stone Detection and Segmentation -- 3.3 Performance of Stone Tracking -- 4 Discussion -- References.
Prediction of Mandibular ORN Incidence from 3D Radiation Dose Distribution Maps Using Deep Learning -- 1 Introduction -- 2 Methods and Materials -- 2.1 Data -- 2.2 Prediction Models -- 2.3 Model Evaluation -- 2.4 Statistical Analysis -- 3 Results -- 4 Discussion -- 4.1 ORN Prediction -- 4.2 Study Limitations and Future Work -- 5 Conclusion -- References -- Analysis of Potential Biases on Mammography Datasets for Deep Learning Model Development -- 1 Introduction -- 2 Materials and Methods -- 2.1 Mammography Dataset -- 2.2 Bias Analysis -- 2.3 Bias Correction Techniques -- 2.4 Experimental Setup -- 3 Results and Discussion -- 4 Conclusions -- References -- ECG-ATK-GAN: Robustness Against Adversarial Attacks on ECGs Using Conditional Generative Adversarial Networks -- 1 Introduction -- 2 Methodology -- 2.1 Generator and Discriminator -- 2.2 Objective Function and Individual Losses -- 2.3 Adversarial Attacks -- 3 Experiments -- 3.1 Data Set Preparation -- 3.2 Hyper-parameters -- 3.3 Quantitative Evaluation -- 3.4 Qualitative Evaluation -- 4 Conclusions and Future Work -- References -- CADIA: A Success Story in Breast Cancer Diagnosis with Digital Pathology and AI Image Analysis -- 1 Introduction -- 2 Methods -- 2.1 Starting Point Analysis and Functional Requirement Collection -- 2.2 Sample Selection and Collection -- 2.3 Digital Image Annotation -- 2.4 Model Development -- 2.5 Model Deployment and Integration -- 3 Results -- 4 Conclusions and Future Perspectives -- References -- Was that so Hard? Estimating Human Classification Difficulty -- 1 Introduction -- 2 Estimating Image Difficulty -- 3 Datasets -- 4 Experiments -- 5 Results -- 6 Discussion and Conclusion -- References -- A Deep Learning-Based Interactive Medical Image Segmentation Framework -- 1 Introduction -- 2 Related Work -- 3 Applicative Scope -- 4 Methodology -- 4.1 System.
4.2 Training with Dynamic Data Generation -- 5 Experimental Results -- 5.1 Setup -- 5.2 Automated Evaluation -- 5.3 User Evaluation -- 6 Conclusion -- References -- Deep Neural Network Pruning for Nuclei Instance Segmentation in Hematoxylin and Eosin-Stained Histological Images -- 1 Introduction -- 2 Method -- 2.1 Datasets -- 2.2 Segmentation and Regression Models -- 2.3 Pruning -- 2.4 Merging and Post-processing -- 2.5 Evaluation Metrics -- 3 Results and Discussion -- 4 Conclusion -- References -- Spatial Feature Conservation Networks (SFCNs) for Dilated Convolutions to Improve Breast Cancer Segmentation from DCE-MRI -- 1 Introduction -- 2 Methods -- 2.1 Compensation Module -- 2.2 Network Architecture -- 2.3 Performance Evaluation -- 2.4 Image Dataset and Data Preparation -- 3 Results -- 4 Discussion and Conclusion -- References -- The Impact of Using Voxel-Level Segmentation Metrics on Evaluating Multifocal Prostate Cancer Localisation -- 1 Introduction -- 2 Materials and Methods -- 2.1 Prostate Lesion Segmentation for Procedure Planning -- 2.2 Voxel-Level Segmentation Metrics -- 2.3 Lesion-Level Object Detection Metrics -- 2.4 Lesion Detection Metrics for Multifocal Segmentation Output -- 2.5 Correlation, Pairwise Agreement and Impact on Evaluation -- 3 Results -- 3.1 Comparison Between DSC and HD -- 3.2 Comparison Between Voxel- and Lesion-Level Metrics -- 4 Conclusion -- References -- OOOE: Only-One-Object-Exists Assumption to Find Very Small Objects in Chest Radiographs -- 1 Introduction -- 2 Methods -- 2.1 Feature Extractor -- 2.2 Point Detection Head -- 3 Experiments -- 3.1 Datasets -- 3.2 Evaluation Metrics -- 3.3 Implementation Details -- 3.4 Comparison to Other Methods -- 3.5 A Closer Look at ET-tube vs. T-tube Detection Performance -- 4 Conclusion -- References -- Wavelet Guided 3D Deep Model to Improve Dental Microfracture Detection.
1 Introduction -- 2 Materials -- 3 Methods -- 4 Results and Discussion -- References -- Author Index.
Record Nr. UNISA-996490357403316
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Artificial intelligence and machine learning for healthcare : image and data analytics / / edited by Chee Peng Lim, [and four others]
Artificial intelligence and machine learning for healthcare : image and data analytics / / edited by Chee Peng Lim, [and four others]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2023]
Descrizione fisica 1 online resource (239 pages)
Disciplina 060
Collana Intelligent Systems Reference Library
Soggetto topico Artificial intelligence - Medical applications
ISBN 3-031-11154-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Contents -- 1 An Introduction to Artificial Intelligence in Healthcare -- 1.1 Introduction to Artificial Intelligence -- 1.2 Artificial Intelligence in Healthcare -- 1.2.1 Natural Language Processing (NLP) Technology -- 1.2.2 Machine Learning (ML) Algorithms -- 1.2.3 Artificial Neural Networks -- 1.2.4 Bayesian Classifier -- 1.2.5 Classification/Decision Trees. Random Forest -- 1.2.6 Survival Regression Models -- 1.2.7 Cluster Analysis -- 1.3 Advantages of Artificial Intelligence in Healthcare -- 1.4 Limitations of Artificial Intelligence in Healthcare -- 1.5 Successful Applications of Artificial Intelligence in Healthcare -- 1.6 Conclusions -- Appendix -- Books -- 2 Radiomics: Approach to Precision Medicine -- 2.1 Introduction -- 2.2 Materials and Methods -- 2.2.1 Building of a Database -- 2.2.2 Segmentation of Target Volume -- 2.2.3 Extraction and Selection of Useful Radiomics Features -- 2.2.4 Model Building Based on Machine Learning Technologies -- 2.3 Results and Discussion -- 2.4 Conclusions -- References -- 3 Artificial Intelligence Based Strategies for Data-Driven Radial MRI -- 3.1 Introduction -- 3.2 Related Work -- 3.2.1 Sparse Sampling Strategies -- 3.2.2 Contribution of the Manuscript -- 3.3 Problem Statement and Framework Description -- 3.3.1 Relationship Between Radial Projections and Image -- 3.3.2 Image Reconstruction, Resolution and Noise -- 3.3.3 Super-Resolution -- 3.3.4 Framework Details -- 3.3.5 Noise Threshold upper TT -- 3.4 Results and Discussion -- 3.5 Conclusion -- References -- 4 Unsupervised Domain Adaptation Approach for Liver Tumor Detection in Multi-phase CT Images -- 4.1 Introduction -- 4.1.1 Domain-Shift Problem -- 4.1.2 Domain Adaptation -- 4.2 Domain Adaptation Using Adversarial Learning -- 4.2.1 Anchor-free Detector.
4.2.2 Proposed Multi-phase Domain Adaptation Framework Using Adversarial Domain Classification Loss -- 4.3 Proposed Multi-phase Domain Adaptation Framework Using Adversarial Learning with Maximum Square Loss -- 4.3.1 Maximum Square Loss -- 4.3.2 Overall Framework with Adversarial Domain Classification and Maximum Square Loss -- 4.4 Experiments -- 4.4.1 Implementation Details -- 4.4.2 Dataset -- 4.4.3 Evaluation -- 4.4.4 Results -- 4.5 Conclusions -- References -- 5 Multi-stage Synthetic Image Generation for the Semantic Segmentation of Medical Images -- 5.1 Introduction -- 5.2 Related Works -- 5.2.1 Synthetic Image Generation -- 5.2.2 Image-to-Image Translation -- 5.2.3 Retinal Image Synthesis and Segmentation -- 5.2.4 Chest X-ray Image Synthesis and Segmentation -- 5.3 Multi-stage Image Synthesis -- 5.3.1 Image Generation -- 5.4 Evaluation of Multi-stage Methods -- 5.4.1 Datasets -- 5.4.2 Segmentation Network -- 5.4.3 Experimental Setup -- 5.4.4 Two-Stage Method Evaluation -- 5.4.5 Three-Stage Method Evaluation -- 5.5 Conclusions -- References -- 6 Classification of Arrhythmia Signals Using Hybrid Convolutional Neural Network (CNN) Model -- 6.1 Introduction -- 6.2 Literature Review -- 6.3 Methodology -- 6.4 Results and Discussion -- 6.5 Conclusions -- Appendix 1 -- Appendix 2 -- Appendix 3 -- References -- 7 Polyp Segmentation with Deep Ensembles and Data Augmentation -- 7.1 Introduction -- 7.2 Related Methods -- 7.2.1 Overview of the Propose System -- 7.2.2 Loss Functions -- 7.3 Data Augmentation -- 7.3.1 Shadows -- 7.3.2 Contrast and Motion Blur -- 7.3.3 Color Mapping -- 7.4 Experimental Results -- 7.4.1 Data and Testing Protocol -- 7.4.2 Experiments -- 7.5 Conclusions -- References -- 8 Autistic Verbal Behavior Parameters -- 8.1 Introduction -- 8.2 Estate of the Art -- 8.3 Proposal, Materials and Methods -- 8.4 Testing Protocol.
8.5 Analysis of Tests -- 8.6 Conclusions and Future Work -- References -- 9 Advances in Modelling Hospital Medical Wards -- 9.1 Introduction and Problem Addressed -- 9.2 Case Study and Data Analysis -- 9.3 Methodology and Results -- 9.4 Conclusion -- References -- 10 Tracking Person-Centred Care Experiences Alongside Other Success Measures in Hearing Rehabilitation -- 10.1 Person-Centred Care in Research and Practice -- 10.1.1 Situated Action-Understanding the Context as a Basis for Meaningful Measures -- 10.1.2 Situated AI for Achieving High-Quality Person-Centred Care -- 10.2 Co-design for Person-Centred Care Measures -- 10.2.1 Co-design of Evaluation Instruments -- 10.2.2 Artificial Intelligence and PCC -- 10.3 Case Study: Co-creation of PCC Measures and Dashboard with Hearing Rehabilitation Provider -- 10.3.1 Method -- 10.4 Results -- 10.4.1 Stakeholder Workshops-Development of Tools -- 10.4.2 Stakeholder Feedback -- 10.4.3 Piloting the Dashboard -- 10.5 Discussion -- 10.5.1 Summary of Case Study -- 10.5.2 Discussion on Opportunities and Challenges for AI -- 10.5.3 Quality of Data -- 10.6 Conclusions -- References -- 11 BioGNN: How Graph Neural Networks Can Solve Biological Problems -- 11.1 Overview of the Research Area -- 11.1.1 Biological Problems on Graphs -- 11.1.2 Deep Learning Models for Biological Graphs -- 11.2 Graph Neural Networks -- 11.2.1 The Graph Neural Network Model -- 11.2.2 Composite Graph Neural Networks -- 11.2.3 Layered Graph Neural Networks -- 11.2.4 Approximation Power of Graph Neural Networks -- 11.2.5 Software Implementation -- 11.3 Biological Applications -- 11.3.1 Prediction of Protein-Protein Interfaces -- 11.3.2 Drug Side-Effect Prediction -- 11.3.3 Molecular Graph Generation -- 11.4 Conclusions and Future Perspectives -- References.
Record Nr. UNINA-9910627260103321
Cham, Switzerland : , : Springer, , [2023]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Artificial intelligence and machine learning for healthcare . Volume 2 : emerging methodologies and trends / / edited by Chee Peng Lim [and four others]
Artificial intelligence and machine learning for healthcare . Volume 2 : emerging methodologies and trends / / edited by Chee Peng Lim [and four others]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2023]
Descrizione fisica 1 online resource (282 pages)
Disciplina 006.31
Collana Intelligent Systems Reference Library
Soggetto topico Machine learning
Artificial intelligence - Medical applications
ISBN 3-031-11170-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Contents -- 1 Artificial Intelligence for the Future of Medicine -- 1.1 Introduction -- 1.2 How Do Machines Learn? -- 1.2.1 Machine Learning Process -- 1.2.2 Machine Learning in Medicine -- 1.3 Artificial Intelligence in Medicine -- 1.4 AI Applications in Medicine -- 1.4.1 Predictive Medicine -- 1.4.2 Participatory Medicine -- 1.4.3 Personalized Medicine -- 1.4.4 Preventive Medicine -- 1.5 Summary -- References -- 2 A Survival Analysis Guide in Oncology -- 2.1 Introduction -- 2.2 Survival Analysis -- 2.3 Kaplan-Meier Survival Curve -- 2.4 The Logrank Test -- 2.5 The Hazard Ratio -- 2.5.1 Cox Regression Model -- 2.6 Conclusions -- References -- 3 Social Media Sentiment Analysis Related to COVID-19 Vaccinations -- 3.1 Introduction -- 3.2 Literature Review -- 3.2.1 Machine Learning-Based Sentiment Analysis Studies -- 3.2.2 Lexicon-Based Sentiment Analysis Studies -- 3.2.3 Hybrid Sentiment Analysis Studies -- 3.3 Methodology -- 3.3.1 Methodology Outline -- 3.4 Experiments -- 3.4.1 Dataset -- 3.4.2 Dataset Pre-Processing -- 3.4.3 Sentiment Analysis -- 3.5 Experimental Results -- 3.6 Conclusion -- 3.6.1 Discussion -- 3.6.2 Overview of Contribution -- 3.6.3 Future Directions -- References -- 4 Healthcare Support Using Data Mining: A Case Study on Stroke Prediction -- 4.1 Introduction -- 4.1.1 Data Mining -- 4.1.2 Data Mining in Healthcare -- 4.1.3 Applications of Data Mining in Healthcare -- 4.1.4 Chapter Overview -- 4.2 Literature Review -- 4.2.1 Data Mining Applications in Healthcare -- 4.2.2 Machine Learning Concepts Related with Healthcare Support -- 4.3 Methodology and Results -- 4.3.1 Methodology Outline -- 4.3.2 Experiments -- 4.4 Conclusion -- 4.4.1 Discussion -- 4.4.2 Issues and Challenges of Data Mining in Stroke Prediction and Healthcare -- 4.4.3 Future Directions and Insights -- References.
5 A Big Data Infrastructure in Support of Healthy and Independent Living: A Real Case Application -- 5.1 Introduction -- 5.2 Architecture -- 5.2.1 HomeHub -- 5.2.2 SB@App -- 5.2.3 Security Component -- 5.3 Clinical Interventions -- 5.3.1 Hearing Loss -- 5.3.2 Balance Disorders -- 5.4 Initial Implementation and Testing -- 5.4.1 Data Insights on the Platform -- 5.4.2 Data Insights on the SB@App -- 5.5 Data Analytics -- 5.6 Conclusion -- References -- 6 Virtual Reality-Based Rehabilitation Gaming System -- 6.1 Introduction -- 6.1.1 Rehabilitation -- 6.1.2 Stroke -- 6.1.3 Musculoskeletal Disorders (MSDs) -- 6.2 Classical Treatment Approaches -- 6.2.1 Methods and Procedures for Stroke Treatment -- 6.2.2 Treatment Approaches for Musculoskeletal Disorders -- 6.2.3 Limitations in Traditional Approaches -- 6.3 Modern Rehabilitation Technologies -- 6.3.1 Physical Prosthetics -- 6.3.2 Sensory Prosthetics -- 6.3.3 Robotics Rehabilitation -- 6.3.4 Brain-Computer Interface (BCI) -- 6.4 Virtual-Reality (VR) -- 6.4.1 Classification of Virtual Reality Based on Experience -- 6.4.2 Virtual Reality Devices -- 6.4.3 VR in Rehabilitation -- 6.4.4 Game-Based VR Rehabilitation -- 6.4.5 System Requirements -- 6.4.6 System Architecture -- 6.5 VR Applications for Rehabilitation -- 6.5.1 Virtual Reality in Mental Rehabilitation -- 6.5.2 Autism -- 6.5.3 Cerebral Palsy -- 6.5.4 Upper Limb Prosthetic Training -- 6.5.5 Sports Rehabilitation Exercises -- 6.6 Summary and Conclusion -- References -- 7 The Use of Serious Games for Developing Social and Communication Skills in Children with Autism Spectrum Disorders-Review -- 7.1 Introduction -- 7.2 Background -- 7.2.1 Autism Spectrum Disorder (ASD) -- 7.2.2 Application of Information and Communication Technologies in Therapy -- 7.2.3 Types of Technologies -- 7.2.4 Serious Games -- 7.3 Aim of the Study -- 7.4 Material and Methods.
7.4.1 Relevant Research -- 7.5 Discussion -- 7.6 Conclusion -- References -- 8 Deep Learning-based Coronary Stenosis Detection in X-ray Angiography Images: Overview and Future Trends -- 8.1 Introduction -- 8.2 Convolutional Neural Networks -- 8.3 Attention Mechanisms -- 8.4 Vision Transformers -- 8.5 Quantum Computing -- 8.6 Stenosis Detection Methods Based on Deep Learning -- 8.6.1 Object-based Classification -- 8.6.2 Image-Based Classification -- 8.7 Illustrative Study Cases -- 8.8 Challenges and Future Work -- 8.9 Conclusions -- References -- 9 Potential Benefits of Artificial Intelligence in Healthcare -- 9.1 Introduction -- 9.2 Artificial Intelligence in Healthcare -- 9.3 Research Design -- 9.3.1 Systematic Literature Review (SLR) -- 9.3.2 Generation of Hypotheses and Conceptual Model -- 9.3.3 Data Collection -- 9.4 Results -- 9.4.1 Data Analysis and Sample Characteristics -- 9.4.2 Examination of Quality Criteria -- 9.4.3 Evaluation of the SEM -- 9.5 Interpretation -- 9.6 Recommended Activities: Cooperation and Exchange Between Different Stakeholders -- 9.7 Conclusion and Outlook -- References -- 10 Barriers of Artificial Intelligence in the Health Sector -- 10.1 Introduction -- 10.2 Empirical Investigation -- 10.2.1 Research Design -- 10.2.2 Systematic Literature Review and Generation of Hypotheses -- 10.2.3 Data Collection -- 10.3 Results -- 10.3.1 Data Analysis -- 10.3.2 Empirical Findings and Model Conceptualization -- 10.4 Discussion -- 10.5 Limitations and Further Research -- References.
Record Nr. UNINA-9910627240503321
Cham, Switzerland : , : Springer, , [2023]
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Artificial intelligence and machine learning in healthcare / / Ankur Saxena, Shivani Chandra
Artificial intelligence and machine learning in healthcare / / Ankur Saxena, Shivani Chandra
Autore Saxena Ankur
Edizione [1st ed. 2021.]
Pubbl/distr/stampa Gateway East, Singapore : , : Springer, , [2021]
Descrizione fisica 1 online resource (XIX, 228 p. 119 illus., 88 illus. in color.)
Disciplina 610.285
Soggetto topico Artificial intelligence - Medical applications
Intel·ligència artificial en medicina
Aprenentatge automàtic
Soggetto genere / forma Llibres electrònics
ISBN 981-16-0811-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Chapter 1_Big Data Analytics and AI for Healthcare -- Chapter 2_Genetics with Big Data and AI -- Chapter 3_AI and Big Data for next-generation sequencing -- Chapter 4_Artificial Intelligence for Computational Biology -- Chapter 5_Artificial intelligence and machine learning in clinical development -- Chapter 6_Big data analytics for personalized medicine -- Chapter 7_Generating and Managing Healthcare data with AI -- Chapter 8_Big Data and Artificial Intelligence for diseases -- Chapter 9_Artificial Intelligence and Big Data for Public Health -- Chapter 10_Biasness in Healthcare Big Data and Computational Algorithms -- Chapter 11_AI and ML in Healthcare: An Ethical perspective.
Record Nr. UNINA-9910484050503321
Saxena Ankur  
Gateway East, Singapore : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Artificial intelligence and machine learning in public healthcare : opportunities and societal impact / / KC Santosh, Loveleen Gaur
Artificial intelligence and machine learning in public healthcare : opportunities and societal impact / / KC Santosh, Loveleen Gaur
Autore Santosh K. C.
Pubbl/distr/stampa Singapore : , : Springer, , [2021]
Descrizione fisica 1 online resource (xxiii, 74 pages) : illustrations
Disciplina 006.31
Collana SpringerBriefs in applied sciences and technology, Computational intelligence
Soggetto topico Artificial intelligence - Medical applications
Public health - Data processing
Machine learning
ISBN 981-16-6768-3
9789811667688
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910520081303321
Santosh K. C.  
Singapore : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Artificial intelligence and machine learning methods in COVID-19 and related health diseases / / edited by Victor Chang, Harleen Kaur, Simon James Fong
Artificial intelligence and machine learning methods in COVID-19 and related health diseases / / edited by Victor Chang, Harleen Kaur, Simon James Fong
Pubbl/distr/stampa Singapore : , : Springer, , [2022]
Descrizione fisica 1 online resource (254 pages)
Disciplina 060
Collana Studies in Computational Intelligence
Soggetto topico Artificial intelligence - Medical applications
ISBN 3-031-04597-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910735390603321
Singapore : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui