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Advanced machine learning technologies and applications : proceedings of AMLTA 2021 / / edited by Aboul-Ella Hassanien, Kuo-Chi Chang, Tang Mincong
| Advanced machine learning technologies and applications : proceedings of AMLTA 2021 / / edited by Aboul-Ella Hassanien, Kuo-Chi Chang, Tang Mincong |
| Pubbl/distr/stampa | Gateway East, Singapore : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (1,144 pages) : illustrations |
| Disciplina | 006.31 |
| Collana | Advances in Intelligent Systems and Computing |
| Soggetto topico |
Machine learning
Aprenentatge automàtic COVID-19 Intel·ligència artificial en medicina |
| Soggetto genere / forma |
Congressos
Llibres electrònics |
| ISBN | 3-030-69717-7 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910484064003321 |
| Gateway East, Singapore : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Advances in Artificial Intelligence, Computation, and Data Science : For Medicine and Life Science / / edited by Tuan D. Pham, Hong Yan, Muhammad W. Ashraf, Folke Sjöberg
| Advances in Artificial Intelligence, Computation, and Data Science : For Medicine and Life Science / / edited by Tuan D. Pham, Hong Yan, Muhammad W. Ashraf, Folke Sjöberg |
| Edizione | [1st ed. 2021.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 |
| Descrizione fisica | 1 online resource (373 pages) |
| Disciplina | 610.285 |
| Collana | Computational Biology |
| Soggetto topico |
Bioinformatics
Artificial intelligence Artificial intelligence - Data processing Computer science Biomathematics Image processing - Digital techniques Computer vision Computational and Systems Biology Artificial Intelligence Data Science Theory of Computation Mathematical and Computational Biology Computer Imaging, Vision, Pattern Recognition and Graphics Intel·ligència artificial en medicina Investigació mèdica Ciències de la vida Processament de dades |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-030-69951-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Part I: Review of Recent Developments in AI, Computational Models for Complex Data Analysis, and Data Science -- 1. Recent Developments in AI -- 2. Recent Developments in Computational Models for Data Analysis -- 3. Recent Developments in Data Science -- Part II: Applications in Medicine and Physiology -- 4. Cancer -- 5. Neuroscience -- 6. Cardiology -- 7. Critical Care -- 8. Health Care -- 9. Digital Pathology -- Part III: Applications in Life Science -- 10. Systems Biology -- 11. Cell Biology -- 12. Biochemistry -- 13. Chemo-metrics -- 14. Food Technology. |
| Record Nr. | UNISA-996464404503316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Applications of Artificial Intelligence in COVID-19 / / edited by Sachi Nandan Mohanty, Shailendra K. Saxena, Suneeta Satpathy, Jyotir Moy Chatterjee
| Applications of Artificial Intelligence in COVID-19 / / edited by Sachi Nandan Mohanty, Shailendra K. Saxena, Suneeta Satpathy, Jyotir Moy Chatterjee |
| Edizione | [1st ed. 2021.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2021 |
| Descrizione fisica | 1 online resource (593 pages) |
| Disciplina | 610.285 |
| Collana | Medical Virology: From Pathogenesis to Disease Control |
| Soggetto topico |
Virology
Epidemiology Artificial intelligence Artificial Intelligence 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 | Chapter 1.Comprehensive Claims of AI for Healthcare Applications-Coherence towards COVID-19 -- Chapter 2. Artificial Intelligence based systems for combating COVID-19 -- Chapter 3. Artificial intelligence mediated medical diagnosis of COVID-19 -- Chapter 4. AI combined with medical imaging enables rapid diagnosis for COVID-19 -- Chapter 5. Role of Artificial Intelligence in COVID-19 prediction based on Statistical Methods -- Chapter 6. Data Driven symptom Analysis and Location Prediction Model for Clinical Health Data Processing and Knowledgebase Development for COVID 19 -- Chapter 7. A decision support System using Rule based Expert System For COVID -19 Prediction and Diagnosis -- Chapter 8. A Predictive Mechanism to Intimate the Danger of Infection via nCOVID-19 through Unsupervised Learning -- Chapter 9. AI-enabled prognosis technologies for SARS Co-2. Chapter 10. Intelligent agent Based Case Base Reasoning Systems Build Knowledge Representation in COVID-19 analysis of Recovery, Infectious Patients -- Chapter 11. Epidemic Analysis of COVID 19 Using Machine Learning -- Chapter 12. Machine learning application in COVID-19 drug development -- Chapter 13. COVID 19 Epidemic Analysis Using Linear and Polynomial Regression Approach -- Chapter 14. Prediction & Analysis of outbreak of COVID-19 Pandemic Using Machine Learning -- Chapter 15. Predictive Risk Analysis by using Machine Learning during Covid-19 -- Chapter 16. Analysis and Validation of Risk Prediction by Stochastic Gradient Boosting Along With Recursive Feature Elimination for COVID-19 -- Chapter 17. Artificial intelligence in mental healthcare during COVID-19 pandemic -- Chapter 18. Effect of Covid-19 on Autism Spectrum Disorder: Prognosis, diagnosis and therapeutics based On AI -- Chapter 19. Use of mobile phone apps for contact tracing to control the COVID-19 pandemic: A Literature Review -- Chapter 20. Role of IoT and Social Networking in Mental Healthcare of Transgender Community in Covid-19 Pandemic -- Chapter 21. TECHNOLOGY ACCEPTANCE AND USE OF IOT DURING COVID 19 PANDEMIC-CASE STUDY OF HEALTH SECTOR IN INDIA. Chapter 22. Artificial Intelligence – The Strategies used in COVID-19 for Diagnosis -- Chapter 23. Impact of Isolation and Quarantine on Covid-19 Patients and Potential Role of Technology in Mitigation -- Chapter 24. Impact of loneliness and Quarantine on COVID-19 patients with artificial intelligence applications -- Chapter 25. Can Technology fight the loneliness Lockdown: A study of factors Affecting Loneliness in NCR during COVID 19 -- Chapter 26. Psycho-economic Impact of Obligatory Job Switching during Covid-19 Pandemic: A Study of Hawkers in Bhubaneswar (India) -- Chapter 27. AI’s Role in Essential Commodities during a Pandemic Situation -- Chapter 28.Impact of COVID-19 on Manufacturing and Operational Ecosystem in India -- Chapter 29. Impact of Repatriated Migrants on the Production Possibility of Agricultural Sector owing to Covid: A Study on the basis of Inferential Statistics -- Chapter 30. Nicotine in Covid-19: Friend or Foe”;? -- Chapter 31. Artificial Intelligence in Covid’19: Application and Legal Conundrums. |
| Record Nr. | UNINA-9910502987803321 |
| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2021 | ||
| 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
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| Gateway East, Singapore : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Artificial intelligence for information management : a healthcare perspective / / K.G. Srinivasa, Siddesh G.M., S.R. Mani Sekhar, editors
| Artificial intelligence for information management : a healthcare perspective / / K.G. Srinivasa, Siddesh G.M., S.R. Mani Sekhar, editors |
| Pubbl/distr/stampa | Singapore : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (332 pages) |
| Disciplina | 610.285 |
| Collana | Studies in big data |
| Soggetto topico |
Artificial intelligence - Medical applications
Intel·ligència artificial en medicina Processament de dades |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 981-16-0415-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910483957203321 |
| Singapore : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Artificial Intelligence in Healthcare : First International Conference, AIiH 2024, Swansea, UK, September 4–6, 2024, Proceedings, Part II / / edited by Xianghua Xie, Iain Styles, Gibin Powathil, Marco Ceccarelli
| Artificial Intelligence in Healthcare : First International Conference, AIiH 2024, Swansea, UK, September 4–6, 2024, Proceedings, Part II / / edited by Xianghua Xie, Iain Styles, Gibin Powathil, Marco Ceccarelli |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (353 pages) |
| Disciplina | 610.28563 |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Artificial intelligence
Artificial Intelligence Intel·ligència artificial en medicina Aprenentatge automàtic Processament digital d'imatges |
| Soggetto genere / forma |
Congressos
Llibres electrònics |
| ISBN | 3-031-67285-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents - Part II -- Contents - Part I -- AI in Proactive Care and Intervention -- Cluster and Trajectory Analysis of Multiple Long-Term Conditions in Adults with Learning Disabilities -- 1 Introduction -- 2 Methods -- 2.1 Dataset -- 2.2 Dataset Preparation -- 2.3 Cluster Analysis -- 3 Results -- 3.1 Identifying Clusters of MLTC for Adults with Learning Difficulties -- 3.2 What Were the Baseline Patient Characteristics Across the Identified Clusters? -- 3.3 What Were the Most Common Combinations of Multiple LTCs and the Most Frequent Trajectories for the Most Dominant Clusters? -- 4 Conclusion -- References -- A Deep Learning Framework for Assessing the Risk of Transvenous Lead Extraction Procedures -- 1 Introduction -- 2 Automatic Object Detection -- 2.1 Selecting Geometric Features -- 2.2 The Detection of the Approximate Location of SVC -- 2.3 The Detection of Pacing Leads and Coils -- 2.4 The Detection of Coils -- 3 The Extraction of Geometric Features -- 3.1 The Determination of the Coil Position Related to the SVC -- 3.2 The Lead Angulations -- 3.3 Detecting the Number of Leads in the SVC -- 4 Feature Selection and Machine Learning Model -- 4.1 Feature Selection -- 4.2 Machine Learning Model for Risk Assessment -- 5 Conclusions -- References -- AI-Aided Medical Imaging and Analysis -- Assessing the Impact of Deep Learning Backbones for Mass Detection in Breast Imaging -- 1 Introduction -- 2 Related Work -- 3 Materials and Methods -- 3.1 Dataset -- 3.2 Deep Learning Models -- 3.3 Training -- 3.4 Metrics -- 4 Results -- 4.1 Effect of Backbone Pre-training -- 4.2 Effect of Backbone Fine-Tuning Inside the Detection Network -- 4.3 Backbone Comparison -- 4.4 Performance Relative to the Size of the Network -- 4.5 Comparison with Other Works on CBIS-DDSM -- 5 Conclusion -- References.
Transferable Variational Feedback Network for Vendor Generalization in Accelerated MRI -- 1 Introduction -- 2 Materials and Methods -- 2.1 Background and Problem Formulation -- 2.2 Base Architecture: Variational Feedback Network -- 2.3 Proposed Feature Transfer Learning Architecture -- 3 Experiments and Results -- 3.1 Datasets -- 3.2 Training Protocol -- 3.3 Vendor Transfer -- 3.4 Learning Without Forgetting in Accelerated MRI -- 3.5 Further Discussion -- 4 Conclusion -- References -- CVD_Net: Head and Neck Tumor Segmentation and Generalization in PET/CT Scans Across Data from Multiple Medical Centers -- 1 Introduction -- 2 Background -- 3 Method -- 3.1 CNN Encoder -- 3.2 Domain-Specific Batch Normalization (DSBN) -- 3.3 Transfomer Encoder -- 3.4 CNN Decoder -- 4 Experiments -- 4.1 Dataset -- 4.2 Training and Testing -- 4.3 Results -- 5 Conclusions -- References -- Applying Deep Learning Based Super-Resolution to Knee Imaging -- 1 Introduction -- 2 SR Models -- 2.1 SRCNN -- 2.2 ExSRCNN -- 2.3 RBSRCNN -- 3 Dataset and Experimental Setup -- 4 Experimental Results -- 5 Conclusions -- References -- FM-LiteLearn: A Lightweight Brain Tumor Classification Framework Integrating Image Fusion and Multi-teacher Distillation Strategies -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Image Fusion Techniques(F-DCGAN) -- 3.2 Improvement Based on ResNet18(T-ResNet18) -- 3.3 Multi-teacher Knowledge Distillation(MT-KD) -- 4 Experimental Setup -- 4.1 Datasets -- 4.2 Implementation Details -- 4.3 Evaluation Metrics -- 4.4 Experimental Results -- 5 Conclusion -- References -- Towards Improving Single-Cell Segmentation in Heterogeneous Configurations of Cardiomyocyte Networks -- 1 Introduction -- 2 Background -- 3 Methodology -- 3.1 The HL-1 Cardiac Cell Network Imaging Dataset -- 3.2 Data Preparation -- 3.3 Cell Annotation. 3.4 Segmentation Algorithms and Training -- 3.5 Evaluation Metrics -- 4 Validation Results -- 5 Cellular Network Analysis -- 6 Conclusion -- References -- Texture Feature Analysis for Classification of Early-Stage Prostate Cancer in MpMRI -- 1 Introduction -- 2 Datasets -- 3 Methods -- 4 Results -- 4.1 Best-Performing Machine Learning Classifiers -- 4.2 Feature Value Ranges and Correlations -- 4.3 Feature Impact on Model Output: Shapley Values -- 4.4 RF Classifiers Trained Without Feature Selection -- 5 Conclusions -- References -- Medical Signal and Image Processing -- DELRecon: Depth Electrode Reconstruction Toolbox for Stereo-EEG -- 1 Introduction -- 2 Method -- 2.1 Toolbox Installation and Requirements -- 2.2 Module 1: Image Processing -- 2.3 Module 2: Electrode Localization -- 2.4 Module 3: Individual Contact Labelling -- 2.5 Toolbox Outputs -- 2.6 Dataset Used for Validation -- 3 Results -- 3.1 MRI and CT Image Processing -- 3.2 Initial SEEG Electrode Clustering -- 3.3 Iterative Tracking of SEEG Electrodes -- 3.4 Contact Labelling -- 3.5 Evaluation -- 4 Discussion -- 5 Conclusion -- References -- Segmenting Breast Ultrasound Scans Using a Generative Adversarial Network Embedding U-Net -- 1 Introduction -- 2 Background and Design Motivations -- 3 Resources and Methods -- 3.1 Dataset -- 3.2 Training the Model -- 3.3 Experimental Setup -- 3.4 Results -- 4 Conclusion -- References -- Enhancing Predictive Accuracy in Embryo Implantation: The Bonna Algorithm and its Clinical Implications -- 1 Introduction -- 2 Methodology -- 2.1 Dataset Collection and Preparation -- 2.2 Data Processing and Flow -- 2.3 Model Architecture and Implementation. -- 3 Results -- 3.1 Model Performance Evaluation -- 3.2 Confidence and Predictive Accuracy -- 4 Discussion and Conclusion -- References. Bacterial Behaviour Analysis Through Image Segmentation Using Deep Learning Approaches -- 1 Introduction -- 2 Literature Review -- 3 Data Description and Terminologies -- 3.1 Data Description -- 3.2 Explored Attributes of Bacteria -- 4 Methodology -- 5 Result and Analysis -- 6 Conclusion -- References -- Assisted Living Technology -- Innovations in Mosquito Identification: Integrating Deep Learning with Citizen Science -- 1 Introduction -- 2 Similar Works -- 3 Method -- 3.1 Dataset Properties -- 3.2 Data Preprocessing -- 3.3 Model Architecture -- 3.4 Training and Evaluation -- 4 Results -- 4.1 Accuracy and Generalization -- 4.2 Loss Analysis -- 4.3 Evaluation Metrics -- 4.4 Robustness Across Scenarios -- 5 Comparison with Existing Literature -- 6 Discussion -- 6.1 Addressing Unbalanced Class Distribution -- 6.2 Generalization to Citizen Science Data -- 6.3 Future Directions -- 6.4 Implications for Public Health -- 7 Conclusion -- References -- Action Recognition for Privacy-Preserving Ambient Assisted Living -- 1 Introduction -- 2 Related Works -- 2.1 Real-Time Performance of Skeleton-Based Action Recognition -- 2.2 Data Augmentation Strategies -- 3 Method -- 3.1 TD-GDSCN -- 3.2 Data Augmentation Techniques -- 4 Experiments -- 4.1 DatasetsThe University Research Ethics Committee Approved the Dataset. Approval ID: EPS21036. -- 4.2 Implementation Details -- 4.3 Data Augmentation Techniques -- 4.4 Evaluation of Computational Efficiency -- 4.5 Comparison to State-of-the-Art Methods -- 5 Conclusion -- References -- Digital Twinning, Virtual Pathology and Oncology -- Weight Perturbations for Simulating Virtual Lesions in a Convolutional Neural Network -- 1 Introduction -- 1.1 Human Visual Recognition -- 1.2 Computational Models -- 1.3 CNNs as Computational Models -- 1.4 Aim and Hypothesis of the Study -- 1.5 Five Incremental Goals. 2 Experiment Materials and Methods -- 2.1 Matching Task -- 3 Experiment Results -- 3.1 Accuracy -- 3.2 Reaction Times -- 3.3 TMS Experiment Discussion -- 4 Modeling -- 4.1 Modeling Approach -- 4.2 Modeling Results -- 5 Discussion -- 5.1 Hypothesis Questions -- 5.2 Summary -- References -- Using GANs to Visualise Class-Specific Features in Digital Histopathology Images -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 Dataset -- 3.2 Training -- 4 Experimentation -- 4.1 Domain Representation - Comparison Between Models -- 4.2 Domain Representation - Class-Specific Representation of HPV Status -- 4.3 Linear Interpolation -- 5 Results -- 5.1 Domain Representation - Comparison Between Models -- 5.2 Domain Representation - Class Specific Representation of HPV Status -- 5.3 Linear Interpolation - Transition from HPV- to HPV+ -- 6 Discussion -- 7 Conclusion -- References -- Artificial Intelligence for Predicting Responses to Thyroid Cancer Treatment -- 1 Introduction -- 2 Methods -- 2.1 Dataset Information -- 2.2 Data Preprocessing -- 2.3 Experiments -- 2.4 Model Development and Evaluation -- 3 Results -- 3.1 Overall Performance -- 3.2 Performance Based on the Number of Classes -- 3.3 Performance Based on Features Used -- 4 Discussion -- 4.1 Principal Findings -- 4.2 Practical and Research Implications -- 4.3 Limitations -- 5 Conclusion -- References -- Patient Data, Privacy and Ethics -- ZMAM: A ZKP-Based Mutual Authentication Scheme for the IoMT -- 1 Introduction -- 2 Related Work -- 3 Background, Threat Model and Assumptions -- 3.1 Background: Zero-Knowledge Proof, Trusted Boot -- 3.2 Threat Model and Assumption -- 4 ZMAM Scheme Design -- 4.1 System Components -- 4.2 Security Goals -- 4.3 Registration Protocol (RP) -- 4.4 Normal Communication Protocol -- 4.5 Communication in Emergency Protocol -- 5 Security Analysis -- 5.1 Formal Analysis. 5.2 Informal Analysis. |
| Record Nr. | UNINA-9910881100203321 |
| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Artificial Intelligence in Healthcare : First International Conference, AIiH 2024, Swansea, UK, September 4–6, 2024, Proceedings, Part I / / edited by Xianghua Xie, Iain Styles, Gibin Powathil, Marco Ceccarelli
| Artificial Intelligence in Healthcare : First International Conference, AIiH 2024, Swansea, UK, September 4–6, 2024, Proceedings, Part I / / edited by Xianghua Xie, Iain Styles, Gibin Powathil, Marco Ceccarelli |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (320 pages) |
| Disciplina | 610.28563 |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Artificial intelligence
Machine learning Image processing - Digital techniques Computer vision Information technology - Management Artificial Intelligence Machine Learning Computer Imaging, Vision, Pattern Recognition and Graphics Computer Application in Administrative Data Processing Intel·ligència artificial en medicina Aprenentatge automàtic Processament digital d'imatges |
| Soggetto genere / forma |
Congressos
Llibres electrònics |
| ISBN | 3-031-67278-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents - Part I -- Contents - Part II -- Personalised Healthcare and Medicine -- Assessing the Significance of Longitudinal Data in Alzheimer's Disease Forecasting -- 1 Introduction and Related Literature -- 2 Materials and Methods -- 2.1 Dataset -- 2.2 Input Features -- 2.3 Model -- 2.4 Training -- 2.5 Evaluation -- 3 Experiments -- 3.1 Experimental Details -- 3.2 Results -- 3.3 Impact of Data Collection Frequency -- 4 Conclusion -- References -- GraphDDI: Graph Neural Network for Prediction of Drug-Drug Interaction -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Problem Formulation -- 3.2 Inputs -- 3.3 Model Architecture -- 4 Experiments and Results -- 4.1 Datasets -- 4.2 Comparative Analysis -- 4.3 Ablation Study -- 4.4 Case Study -- 5 Conclusion -- References -- Augmenting Infrequent Relationships in Clinical Language Models with Graph-Encoded Hierarchical Ontologies -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Cohort Creation -- 3.2 Modelling -- 3.3 Hierarchical Structures -- 3.4 Journey of Patient Data Through the Algorithm -- 3.5 Fine-Tuning for the Classification Tasks -- 3.6 Evaluating Embedding Spaces -- 3.7 Interpretability -- 4 Results and Evaluation -- 4.1 Model Evaluation -- 5 Discussion -- 5.1 Limitations -- 6 Conclusion -- References -- Identifying Clusters on Multiple Long-Term Conditions for Adults with Learning Disabilities -- 1 Introduction -- 2 Dataset -- 3 Methods -- 3.1 Selecting the Optimal Number of Clusters Across Algorithms -- 4 Results -- 4.1 Selecting the Number of Clusters (k) for Each Clustering Algorithm -- 4.2 Comparison of the Clustering Algorithms -- 4.3 Assessing the Allocation of Patients to Clusters -- 5 Conclusion -- References -- Interpreting Pretrained Speech Models for Automatic Speech Assessment of Voice Disorders -- 1 Background.
1.1 Voice for Health -- 1.2 Deep Learning Speech Model for Automatic Speech Assessment -- 1.3 Interpreting Speech Models -- 2 Methodology -- 2.1 Data Selection -- 2.2 Model Training -- 2.3 Model Decision Interpretation -- 3 Result -- 3.1 Model Performances -- 3.2 Analysis -- 4 Conclusion -- Appendix -- Model Configuration -- Visualisations -- References -- AI Driven Early Diagnosis and Prevention -- Word or Phoneme? To Optimise Prosodic Features to Predict Lung Function with Helicopter Task -- 1 Introduction -- 1.1 Helicopter Task -- 1.2 Three-Tier Features at Word and Phoneme Levels -- 2 Method -- 2.1 Participants -- 2.2 Procedure -- 3 Results -- 4 Conclusion -- References -- Electrical Impedance Spectroscopy Based Preterm Birth Prediction with Machine Learning -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 3.1 Data Description -- 3.2 Data Balancing -- 3.3 Machine Learning Algorithms -- 3.4 Hyper-parameters Setting -- 3.5 Models Evaluation -- 4 Experimental Design and Results -- 4.1 Experimental Setup -- 4.2 Model Performance on the Original Dataset -- 4.3 Model Performance on Rebalanced Data -- 5 Conclusion -- References -- Transfer Learning in Hypoglycemia Classification -- 1 Introduction -- 2 Related Work -- 3 Data and Methods -- 4 Results -- 4.1 T1D to Pre-diabetes -- 4.2 Pre-diabetes to T1D -- 4.3 Threshold Exploration -- 5 Discussion and Conclusion -- References -- A Comparative Analysis of Eleven Augmentation Techniques for Enhanced Retinal Pathology Recognition -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Geometric and Colour Space Transformations -- 3.2 Random Deletion -- 3.3 Mixing Images -- 3.4 Style Transfer -- 4 Performance Evaluation -- 4.1 Experimental Set-Up -- 4.2 Results -- 5 Conclusion -- References -- Multi-stage Chronic Kidney Disease Classification on Longitudinal Data -- 1 Introduction -- 2 Related Works. 2.1 Methods -- 2.2 Existing Datasets -- 3 Dataset -- 3.1 Data Processing -- 4 Methods -- 4.1 Cross-sectional Models -- 4.2 Longitudinal Models -- 5 Experiments and Results -- 5.1 Experiment Setup -- 5.2 Evaluation Metrics -- 5.3 Results -- 6 Conslusion -- References -- Contrastive Multitask Transformer for Hospital Mortality and Length-of-Stay Prediction -- 1 Introduction -- 2 Related Work -- 2.1 Clinical Domain and Imputation -- 2.2 Multitask Learning -- 2.3 Temporal Deep Learning -- 3 Methodology -- 3.1 Problem Definition -- 3.2 STraTs Model -- 3.3 Multitask -- 3.4 Forecast Pretraining -- 3.5 Contrastive Pretraining -- 3.6 Datasets -- 4 Experiments -- 4.1 Evaluation Metrics -- 4.2 Finetuning -- 4.3 Ablation Study -- 4.4 Discussion -- 5 Conclusion -- References -- AI Driven Robotics for Healthcare -- Development of Life Support Devices by Using Inclusive Design -- 1 Introduction -- 2 Development of the Life Support Devices by Using Inclusive Design -- 3 Walking Promoting System Considering the Emotion and Muscle Fatigue -- 3.1 Walking Assist Device -- 3.2 Emotion Recognition in Real Time -- 3.3 Muscle Fatigue Recognition in Real Time -- 3.4 System Configuration -- 3.5 Example of Walking Experiment -- 4 Conclusion -- References -- Design and Operation Requirements for an Ankle Assisting Device -- 1 Introduction -- 2 Ankle Anatomy and Problems -- 3 Requirements for Motion Assistance -- 4 An Example of Design Analysis -- 5 Conclusions -- References -- Promoting Healthy Eating Habits via Intelligent Virtual Assistants, Improving Monitoring by Nutritional Specialists: State of the Art -- 1 Introduction -- 2 Methodology -- 2.1 Conducting the Review -- 2.2 Reviewing Document -- 3 Virtual Nutrition Assistants to Promote Healthy Eating Habits and Optimize Patient Monitoring -- 4 Synthesis of Findings: Results -- 5 Discussion and Conclusions -- 6 Future Work. References -- Exploration of AI-Enhanced Wearable Devices for Advanced Cardiovascular Monitoring in the Elderly -- 1 Introduction -- 2 Methods -- 3 Results and Discussion -- 4 Conclusion -- References -- Development of a Control Algorithm for an Ankle Joint Rehabilitation Device -- 1 Introduction -- 2 Rehabilitation Robotics for Human Ankle Joint Rehabilitation -- 3 Development of a Control Algorithm for an Ankle Joint Rehabilitation Device -- 4 Discussion and Results -- 5 Conclusion -- References -- Towards Quantification of Eye Contacts Between Trainee Doctors and Simulated Patients in Consultation Videos -- 1 Introduction and Background Works -- 1.1 Importance of Non-verbal Communication Skills Among Trainee Doctors -- 1.2 Why We Need Eye Contact in Non-verbal Communications? -- 1.3 Background Works for Analysing Non-verbal Communications in Videos/images -- 2 Proposed Methodology in Quantifying Eye Contacts -- 2.1 Stage 1: Data Collection -- 2.2 Stage 2: Dataset Formation and Curation -- 2.3 Stage 3: Selection of Models and Developing Architectures -- 2.4 Stage 4: Model Evaluation Metrics -- 3 Experimental Results and Discussions -- 3.1 Challenges in the Dataset: Collection and Annotations -- 3.2 Results -- 4 Conclusions and Future Works -- References -- Laboratory Experiences with an Intelligent Robotic Crank for Arm Exercises -- 1 Introduction -- 2 Biomechanics of Arm Exercise -- 3 Design of an Intelligent Crank -- 4 Laboratory Test -- 4.1 Procedure for Lab Test -- 5 Test Results -- 6 Conclusions -- References -- ADALINE Neurons Used for Targeting Performance on the Deep Brain Stimulation Platform -- 1 Introduction -- 2 Data Collection for ADALINE Neuron Application -- 3 ADALINE Neuron Training and Validation Results -- 4 Conclusion -- References -- AI in Mental Health. Evaluating the Feasibility and Acceptability of a GPT-Based Chatbot for Depression Screening: A Mixed-Methods Study -- 1 Background and Introduction -- 2 Methods -- 2.1 Ethical Approval -- 2.2 Study Design and Participants -- 2.3 HopeBot -- 2.4 Statistical Analysis -- 3 Results -- 3.1 Self-evaluation Result -- 3.2 Feedback Questionnaire Result -- 3.3 Mann-Whitney U Test Result -- 4 Discussion -- 4.1 Principal Finding -- 4.2 Limitations -- 4.3 Future's Work -- 4.4 Conclusion -- References -- Structural Brain Network Generation via Brain Denoising Diffusion Probabilistic Model -- 1 Introduction -- 2 Related Works -- 2.1 Brain Network Generation -- 2.2 Diffusion Model -- 2.3 Graph Neural Networks -- 3 Methodology -- 3.1 Problem Defination -- 3.2 Overall Structure -- 3.3 Channel Adaption Module -- 3.4 Brain Network Generation Module -- 3.5 Symmetry Correction Module -- 4 Experiment Result and Analysis -- 4.1 Datasets and Pre-processing -- 4.2 Experiment Settings -- 4.3 Results -- 4.4 Ablation Studies -- 5 Conclusion -- References -- Conversation Analysis of Remote Dialogue System for Mental Health Interventions -- 1 Introduction -- 2 Related Work -- 2.1 Dialogue Systems -- 2.2 Language Models -- 3 Dialogue System for Digital Mental Health Interventions -- 3.1 KOKOROBO -- 3.2 Main Challenges -- 4 Methodology -- 4.1 Topic Recognition of User Concerns Based on Customized Categories -- 4.2 Concern Severity Prediction -- 4.3 User Satisfaction Prediction -- 5 Experiments -- 5.1 Experimental Details -- 5.2 Results and Discussion -- 6 Conclusions -- References -- AI in Proactive Care and Intervention -- Unveiling Disparities in Maternity Care: A Topic Modelling Approach to Analysing Maternity Incident Investigation Reports -- 1 Introduction -- 2 Methodology -- 2.1 Dataset of Maternity Incident Investigation Reports. 2.2 Topic Modelling and Semantic Network Visualisation Methods. |
| Record Nr. | UNINA-9910879593003321 |
| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Artificial Intelligence in Medicine : 22nd International Conference, AIME 2024, Salt Lake City, UT, USA, July 9–12, 2024, Proceedings, Part II / / edited by Joseph Finkelstein, Robert Moskovitch, Enea Parimbelli
| Artificial Intelligence in Medicine : 22nd International Conference, AIME 2024, Salt Lake City, UT, USA, July 9–12, 2024, Proceedings, Part II / / edited by Joseph Finkelstein, Robert Moskovitch, Enea Parimbelli |
| Autore | Finkelstein Joseph |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (387 pages) |
| Disciplina | 006.3 |
| Altri autori (Persone) |
MoskovitchRobert
ParimbelliEnea |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Artificial intelligence
Education - Data processing Computer networks Database management Data mining Application software Artificial Intelligence Computers and Education Computer Communication Networks Database Management Data Mining and Knowledge Discovery Computer and Information Systems Applications Intel·ligència artificial en medicina |
| Soggetto genere / forma |
Congressos
Llibres electrònics |
| ISBN |
9783031665356
9783031665349 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | -- Medical imaging analysis. -- 3T to 7T Whole Brain + Skull MRI Translation with Densely Engineered U-Net Network. -- A Sparse Convolutional Autoencoder for Joint Feature Extraction and Clustering of Metastatic Prostate Cancer Images. -- AI in Neuro-Oncology: Predicting EGFR Amplification in Glioblastoma from Whole Slide Images using Weakly Supervised Deep Learning. -- An Exploration of Diabetic Foot Osteomyelitis X-ray Data for Deep Learning Applications. -- Automated Detection and Characterization of Small Cell Lung Cancer Liver Metastases on CT. -- Content-Based Medical Image Retrieval for Medical Radiology Images. -- Cross-Modality Synthesis of T1c MRI from Non-Contrast Images Using GANs: Implications for Brain Tumor Research. -- Harnessing the Power of Graph Propagation in Lung Nodule Detection. -- Histology Image Artifact Restoration with Lightweight Transformer and Diffusion Model. -- Improved Glioma Grade Prediction with Mean Image Transformation. -- Learning to Predict the Optimal Template in Stain Normalization For Histology Image Analysis. -- MRI Brain Cancer Image Detection Application of an Integrated U-Net and ResNet50 Architecture. -- MRI Scan Synthesis Methods based on Clustering and Pix2Pix. -- Supervised Pectoral Muscle Removal in Mammography Images. -- TinySAM-Med3D: A Lightweight Segment Anything Model for Volumetric Medical Imaging with Mixture of Experts. -- Towards a Formal Description of Artificial Intelligence Models and Datasets in Radiology. -- Towards Aleatoric and Epistemic Uncertainty in Medical Image Classification. -- Ultrasound Image Segmentation via a Multi-Scale Salient Network. -- Data integration and multimodal analysis. -- A 360-Degree View for Large Language Models: Early Detection of Amblyopia in Children using Multi-View Eye Movement Recordings. -- Enhancing Anti-VEGF Response Prediction in Diabetic Macular Edema through OCT Features and Clinical Data Integration based on Deep Learning. -- Expert Insight-Enhanced Follow-up Chest X-Ray Summary Generation. -- Integrating multimodal patient data into attention-based graph networks for disease risk prediction. -- Integrative analysis of amyloid imaging and genetics reveals subtypes of Alzheimer progression in early stage. -- Modular Quantitative Temporal Transformer for Biobank-scale Unified Representations. -- Multimodal Fusion of Echocardiography and Electronic Health Records for the Detection of Cardiac Amyloidosis. -- Multi-View $k$-Nearest Neighbor Graph Contrastive Learning on Multi-Modal Biomedical Data. -- Quasi-Orthogonal ECG-Frank XYZ Transformation with Energy-based models and clinical text. -- Explainable AI. -- Do you trust your model explanations? An analysis of XAI performance under dataset shift. -- Explainable AI for Fair Sepsis Mortality Predictive Model. -- Explanations of Augmentation Methods For Deep Learning ECG Classification. -- Exploring the possibility of arrhythmia interpretation of time domain ECG using XAI: a preliminary study. -- Improving XAI Explanations for Clinical Decision-Making – Physicians’ Perspective on Local Explanations in Healthcare. -- Manually-Curated Versus LLM-Generated Explanations for Complex Patient Cases: An Exploratory Study with Physicians. -- On Identifying Effective Investigations with Feature Finding using Explainable AI: an Ophthalmology Case Study. -- Towards Interactive and Interpretable Image Retrieval-Based Diagnosis: Enhancing Brain Tumor Classification with LLM Explanations and Latent Structure Preservation. -- Towards Trustworthy AI in Cardiology: A Comparative Analysis of Explainable AI Methods for Electrocardiogram Interpretation. |
| Record Nr. | UNINA-9910878044503321 |
|
Finkelstein Joseph
|
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| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Artificial Intelligence in Medicine : 22nd International Conference, AIME 2024, Salt Lake City, UT, USA, July 9–12, 2024, Proceedings, Part I / / edited by Joseph Finkelstein, Robert Moskovitch, Enea Parimbelli
| Artificial Intelligence in Medicine : 22nd International Conference, AIME 2024, Salt Lake City, UT, USA, July 9–12, 2024, Proceedings, Part I / / edited by Joseph Finkelstein, Robert Moskovitch, Enea Parimbelli |
| Autore | Finkelstein Joseph |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (438 pages) |
| Disciplina | 006.3 |
| Altri autori (Persone) |
MoskovitchRobert
ParimbelliEnea |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Artificial intelligence
Education - Data processing Computer networks Database management Data mining Application software Artificial Intelligence Computers and Education Computer Communication Networks Database Management Data Mining and Knowledge Discovery Computer and Information Systems Applications Intel·ligència artificial en medicina |
| Soggetto genere / forma |
Congressos
Llibres electrònics |
| ISBN | 3-031-66538-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | -- Predictive modelling and disease risk prediction. -- Applying Gaussian Mixture Model for clustering analysis of emergency room patients based on intubation status. -- Bayesian Neural Network to predict antibiotic resistance. -- Boosting multitask decomposition: directness, sequentiality, subsampling, cross-gradients. -- Diagnostic Modeling to Identify Unrecognized Inpatient Hypercapnia Using Health Record Data. -- Enhancing Hypotension Prediction in Real-time Patient Monitoring Through Deep Learning: A Novel Application of XResNet with Contrastive Learning and Value Attention Mechanisms. -- Evaluating the TMR model for multimorbidity decision support using a community-of-practice based methodology. -- Frequent patterns of childhood overweight from longitudinal data on parental and early-life of infants health. -- Fuzzy neural network model based on uni-nullneuron in extracting knowledge about risk factors of Maternal Health. -- Identifying Factors Associated with COVID-19 All-Cause 90-Day Readmission: Machine Learning Approaches. -- Mining Disease Progression Patterns for Advanced Disease Surveillance. -- Minimizing Survey Questions for PTSD Prediction Following Acute Trauma. -- Patient-Centric Approach for Utilising Machine Learning to Predict Health-Related Quality of Life Changes during Chemotherapy. -- Predicting Blood Glucose Levels with LMU Recurrent Neural Networks: A Novel Computational Model. -- Prediction Modelling and Data Quality Assessment for Nursing Scale in a big hospital: a proposal to save resources and improve data quality. -- Process Mining for capacity planning and reconfiguration of a logistics system to enhance the intra-hospital patient transport. Case Study.. -- Radiotherapy Dose Optimization via Clinical Knowledge Based Reinforcement Learning. -- Reinforcement Learning with Balanced Clinical Reward for Sepsis Treatment. -- Secure and Private Vertical Federated Learning for Predicting Personalized CVA Outcomes. -- Smoking Status Classification: A Comparative Analysis of Machine Learning Techniques with Clinical Real World Data. -- The Impact of Data Augmentation on Time Series Classification Models: An In-Depth Study with Biomedical Data. -- The Impact of Synthetic Data on Fall Detection Application. -- Natural Language Processing. -- A Retrieval-Augmented Generation Strategy To Enhance Medical Chatbot Reliability. -- Beyond Self-Consistency: Ensemble Reasoning Boosts Consistency and Accuracy of LLMs in Cancer Staging. -- Clinical Reasoning over Tabular Data and Text with Bayesian Networks. -- Empowering Language Model with Guided Knowledge Fusion for Biomedical Document Re-ranking. -- Enhancing Abstract Screening Classification in Evidence-Based Medicine: Incorporating domain knowledge into pre-trained models. -- Exploring Pre-trained Language Models for Vocabulary Alignment in the UMLS. -- ICU Bloodstream Infection Prediction: A Transformer-Based Approach for EHR Analysis. -- Modeling multiple adverse pregnancy outcomes: Learning from diverse data sources. -- OptimalMEE: Optimizing Large Language Models for Medical Event Extraction through Fine-tuning and Post-hoc Verification. -- Self-Supervised Segment Contrastive Learning for Medical Document Representation 295. -- Sentence-aligned Simplification of Biomedical Abstracts. -- Sequence-Model-Based Medication Extraction from Clinical Narratives in German. -- Social Media as a Sensor: Analyzing Twitter Data for Breast Cancer Medication Effects Using Natural Language Processing. -- Bioinformatics and omics. -- Breast cancer subtype prediction model integrating domain adaptation with semi-supervised learning on DNA methylation profiles. -- CI-VAE for Single-Cell: Leveraging Generative-AI to Enhance Disease Understanding. -- ProteinEngine: Empower LLM with Domain Knowledge for Protein Engineering. -- Wearable devices, sensors, and robotics. -- Advancements in Non-Invasive AI-Powered Glucose Monitoring: Leveraging Multispectral Imaging Across Diverse Wavelengths. -- Anticipating Stress: Harnessing Biomarker Signals from a Wrist-worn Device for Early Prediction. -- Improving Reminder Apps for Home Voice Assistants. |
| Record Nr. | UNINA-9910878044603321 |
|
Finkelstein Joseph
|
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| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Artificial intelligence in medicine : applications, limitations and future directions / / edited by Manda Raz, Tam C. Nguyen, Erwin Loh
| Artificial intelligence in medicine : applications, limitations and future directions / / edited by Manda Raz, Tam C. Nguyen, Erwin Loh |
| Pubbl/distr/stampa | Singapore : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (255 pages) |
| Disciplina | 060 |
| Soggetto topico |
Artificial intelligence - Medical applications
Intel·ligència artificial en medicina |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 981-19-1223-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910578696003321 |
| Singapore : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
