Artificial Intelligence in Medicine

21st International Conference on Artificial Intelligence in Medicine, AIME 2023, Portorož, Slovenia, June 12–15, 2023, Proceedings

AvJose M. Juarez,Mar Marcos

Häftad, Engelska, 2023

Del i serien Lecture Notes in Computer Science

896 kr

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Beskrivning

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.

Produktinformation

Utgivningsdatum:2023-06-06
Mått:155 x 235 x 23 mm
Vikt:616 g
Format:Häftad
Språk:Engelska
Serie:Lecture Notes in Computer Science
Antal sidor:388
Upplaga:23001
Förlag:Springer International Publishing AG
ISBN:9783031343438

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Innehållsförteckning

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.

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Artificial Intelligence in Medicine

21st International Conference on Artificial Intelligence in Medicine, AIME 2023, Portorož, Slovenia, June 12–15, 2023, Proceedings

Beskrivning

Produktinformation

Utforska kategorier

Innehållsförteckning

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