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[null,null,"Titolo",null,"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"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024"]
[null,null,"ISBN",null,"3-031-67278-X"]
[null,null,"Edizione",null,"[1st ed. 2024.]"]
[null,null,"Descrizione fisica",null,"1 online resource (320 pages)"]
[null,null,"Collana",null,"Lecture Notes in Computer Science, , 1611-3349 ; ; 14975"]
[null,null,"Disciplina",null,"610.28563"]
[null,null,"Soggetti",null,"Artificial intelligence"]
[{"$nulls":4},"Machine learning"]
[{"$nulls":4},"Image processing - Digital techniques"]
[{"$nulls":4},"Computer vision"]
[{"$nulls":4},"Information technology - Management"]
[{"$nulls":4},"Artificial Intelligence"]
[{"$nulls":4},"Machine Learning"]
[{"$nulls":4},"Computer Imaging, Vision, Pattern Recognition and Graphics"]
[{"$nulls":4},"Computer Application in Administrative Data Processing"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"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 "]
[{"$nulls":4},"Algorithm -- 3.5 Fine-Tuning for the Classification Tasks -- 3.6 Evaluating Embedding Spaces -- 3.7 Interpretability -- 4 Results and Evaluation -- 4.1 Model Evaluation -- 5 Discussion -- 5.1 Limitations -- 6 Conclusion -- References -- Identifying Clusters on Multiple Long-Term Conditions for Adults with Learning Disabilities -- 1 Introduction -- 2 Dataset -- 3 Methods -- 3.1 Selecting the Optimal Number of Clusters Across Algorithms -- 4 Results -- 4.1 Selecting the Number of Clusters (k) for Each Clustering Algorithm -- 4.2 Comparison of the Clustering Algorithms -- 4.3 Assessing the Allocation of Patients to Clusters -- 5 Conclusion -- References -- Interpreting Pretrained Speech Models for Automatic Speech Assessment of Voice Disorders -- 1 Background."]
[{"$nulls":4},"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."]
[{"$nulls":4},"2.1 Methods -- 2.2 Existing Datasets -- 3 Dataset -- 3.1 Data Processing -- 4 Methods -- 4.1 Cross-sectional Models -- 4.2 Longitudinal Models -- 5 Experiments and Results -- 5.1 Experiment Setup -- 5.2 Evaluation Metrics -- 5.3 Results -- 6 Conslusion -- References -- Contrastive Multitask Transformer for Hospital Mortality and Length-of-Stay Prediction -- 1 Introduction -- 2 Related Work -- 2.1 Clinical Domain and Imputation -- 2.2 Multitask Learning -- 2.3 Temporal Deep Learning -- 3 Methodology -- 3.1 Problem Definition -- 3.2 STraTs Model -- 3.3 Multitask -- 3.4 Forecast Pretraining -- 3.5 Contrastive Pretraining -- 3.6 Datasets -- 4 Experiments -- 4.1 Evaluation Metrics -- 4.2 Finetuning -- 4.3 Ablation Study -- 4.4 Discussion -- 5 Conclusion -- References -- AI Driven Robotics for Healthcare -- Development of Life Support Devices by Using Inclusive Design -- 1 Introduction -- 2 Development of the Life Support Devices by Using Inclusive Design -- 3 Walking Promoting System Considering the Emotion and Muscle Fatigue -- 3.1 Walking Assist Device -- 3.2 Emotion Recognition in Real Time -- 3.3 Muscle "]
[{"$nulls":4},"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."]
[{"$nulls":4},"References -- Exploration of AI-Enhanced Wearable Devices for Advanced Cardiovascular Monitoring in the Elderly -- 1 Introduction -- 2 Methods -- 3 Results and Discussion -- 4 Conclusion -- References -- Development of a Control Algorithm for an Ankle Joint Rehabilitation Device -- 1 Introduction -- 2 Rehabilitation Robotics for Human Ankle Joint Rehabilitation -- 3 Development of a Control Algorithm for an Ankle Joint Rehabilitation Device -- 4 Discussion and Results -- 5 Conclusion -- References -- Towards Quantification of Eye Contacts Between Trainee Doctors and Simulated Patients in Consultation Videos -- 1 Introduction and Background Works -- 1.1 Importance of Non-verbal Communication Skills Among Trainee Doctors -- 1.2 Why We Need Eye Contact in Non-verbal Communications? -- 1.3 Background Works for Analysing Non-verbal Communications in Videos/images -- 2 Proposed Methodology in Quantifying Eye Contacts -- 2.1 Stage 1: Data Collection -- 2.2 Stage 2: Dataset Formation and Curation -- 2.3 Stage 3: Selection of Models and Developing Architectures -- 2.4 Stage 4: Model Evaluation Metrics -- 3 Experimental Results and Discussions -- 3.1 Challenges in the Dataset: Collection and Annotations -- 3.2 Results -- 4 Conclusions and Future Works -- References -- Laboratory Experiences with an Intelligent Robotic Crank for Arm Exercises -- 1 Introduction -- 2 Biomechanics of Arm Exercise -- 3 Design of an Intelligent Crank -- 4 Laboratory Test -- 4.1 Procedure for Lab Test -- 5 Test Results -- 6 Conclusions -- References -- ADALINE Neurons Used for Targeting Performance on the Deep Brain Stimulation Platform -- 1 Introduction -- 2 Data Collection for ADALINE Neuron Application -- 3 ADALINE Neuron Training and Validation Results -- 4 Conclusion -- References -- AI in Mental Health."]
[{"$nulls":4},"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 "]
[{"$nulls":4},"System for Digital Mental Health Interventions -- 3.1 KOKOROBO -- 3.2 Main Challenges -- 4 Methodology -- 4.1 Topic Recognition of User Concerns Based on Customized Categories -- 4.2 Concern Severity Prediction -- 4.3 User Satisfaction Prediction -- 5 Experiments -- 5.1 Experimental Details -- 5.2 Results and Discussion -- 6 Conclusions -- References -- AI in Proactive Care and Intervention -- Unveiling Disparities in Maternity Care: A Topic Modelling Approach to Analysing Maternity Incident Investigation Reports -- 1 Introduction -- 2 Methodology -- 2.1 Dataset of Maternity Incident Investigation Reports."]
[{"$nulls":4},"2.2 Topic Modelling and Semantic Network Visualisation Methods."]
[null,null,null,"Sommario/riassunto","The two-volume set LNCS 14975 + 14976 constitutes the proceedings of the First International Conference on Artificial Intelligence in Healthcare, AIiH 2024, which took place in Swansea, UK, in September 2024. The 47 full papers included in the proceedings were carefully reviewed and selected from 70 submissions. They were organized in the following topical sections: Part I: Personalised Healthcare and Medicine; AI driven early diagnosis and prevention; AI driven robotics for healthcare; AI in mental health; Part II: AI in proactive care and intervention; AI-aided medical imaging and analysis; Medical signal and image processing; Assisted living technology; Digital twinning, virtual pathology and oncology; Patient data, privacy and ethics."]