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Artificial Intelligence in Medicine [[electronic resource] ] : 21st International Conference on Artificial Intelligence in Medicine, AIME 2023, Portorož, Slovenia, June 12–15, 2023, Proceedings / / edited by Jose M. Juarez, Mar Marcos, Gregor Stiglic, Allan Tucker
| Artificial Intelligence in Medicine [[electronic resource] ] : 21st International Conference on Artificial Intelligence in Medicine, AIME 2023, Portorož, Slovenia, June 12–15, 2023, Proceedings / / edited by Jose M. Juarez, Mar Marcos, Gregor Stiglic, Allan Tucker |
| Autore | Juarez Jose M |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (398 pages) |
| Disciplina | 006.3 |
| Altri autori (Persone) |
MarcosMar
StiglicGregor TuckerAllan |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Artificial intelligence
Social sciences—Data processing Education—Data processing Computer networks Database management Data mining Artificial Intelligence Computer Application in Social and Behavioral Sciences Computers and Education Computer Communication Networks Database Management Data Mining and Knowledge Discovery |
| Soggetto non controllato |
Medicine
Medical |
| ISBN | 3-031-34344-1 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 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. . |
| Record Nr. | UNISA-996538667403316 |
Juarez Jose M
|
||
| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Artificial Intelligence in Medicine [[electronic resource] ] : 21st International Conference on Artificial Intelligence in Medicine, AIME 2023, Portorož, Slovenia, June 12–15, 2023, Proceedings / / edited by Jose M. Juarez, Mar Marcos, Gregor Stiglic, Allan Tucker
| Artificial Intelligence in Medicine [[electronic resource] ] : 21st International Conference on Artificial Intelligence in Medicine, AIME 2023, Portorož, Slovenia, June 12–15, 2023, Proceedings / / edited by Jose M. Juarez, Mar Marcos, Gregor Stiglic, Allan Tucker |
| Autore | Juarez Jose M |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (398 pages) |
| Disciplina | 006.3 |
| Altri autori (Persone) |
MarcosMar
StiglicGregor TuckerAllan |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Artificial intelligence
Social sciences—Data processing Education—Data processing Computer networks Database management Data mining Artificial Intelligence Computer Application in Social and Behavioral Sciences Computers and Education Computer Communication Networks Database Management Data Mining and Knowledge Discovery |
| Soggetto non controllato |
Medicine
Medical |
| ISBN | 3-031-34344-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 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. . |
| Record Nr. | UNINA-9910728928403321 |
|
Juarez Jose M
|
||
| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Artificial Intelligence in Medicine: Knowledge Representation and Transparent and Explainable Systems [[electronic resource] ] : AIME 2019 International Workshops, KR4HC/ProHealth and TEAAM, Poznan, Poland, June 26–29, 2019, Revised Selected Papers / / edited by Mar Marcos, Jose M. Juarez, Richard Lenz, Grzegorz J. Nalepa, Slawomir Nowaczyk, Mor Peleg, Jerzy Stefanowski, Gregor Stiglic
| Artificial Intelligence in Medicine: Knowledge Representation and Transparent and Explainable Systems [[electronic resource] ] : AIME 2019 International Workshops, KR4HC/ProHealth and TEAAM, Poznan, Poland, June 26–29, 2019, Revised Selected Papers / / edited by Mar Marcos, Jose M. Juarez, Richard Lenz, Grzegorz J. Nalepa, Slawomir Nowaczyk, Mor Peleg, Jerzy Stefanowski, Gregor Stiglic |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (XII, 175 p. 56 illus., 42 illus. in color.) |
| Disciplina | 610.285 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Artificial intelligence
Optical data processing Computer organization Computers Education—Data processing Application software Artificial Intelligence Image Processing and Computer Vision Computer Systems Organization and Communication Networks Information Systems and Communication Service Computers and Education Computer Applications |
| ISBN | 3-030-37446-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | KR4HC/ProHealth - Joint Workshop on Knowledge Representation for Health Care and Process-Oriented Information Systems in Health Care -- A practical exercise on re-engineering clinical guideline models using different representation languages -- A method for goal-oriented guideline modeling in PROforma and ist preliminary evaluation -- Differential diagnosis of bacterial and viral meningitis using Dominance-Based Rough Set Approach -- Modelling ICU Patients to Improve Care Requirements and Outcome Prediction of Acute Respiratory Distress Syndrome: A Supervised Learning Approach -- Deep learning for haemodialysis time series classification -- TEAAM - Workshop on Transparent, Explainable and Affective AI in Medical Systems -- Towards Understanding ICU Treatments using Patient Health Trajectories -- An Explainable Approach of Inferring Potential Medication Effects from Social Media Data -- Exploring antimicrobial resistance prediction using post-hoc interpretable methods -- Local vs. Global Interpretability of Machine Learning Models in Type 2 Diabetes Mellitus Screening -- A Computational Framework towards Medical Image Explanation -- A Computational Framework for Interpretable Anomaly Detection and Classification of Multivariate Time Series with Application to Human Gait Data Analysis -- Self-organizing maps using acoustic features for prediction of state change in bipolar disorder -- Explainable machine learning for modeling of early postoperative mortality in lung cancer. . |
| Record Nr. | UNISA-996466422303316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Artificial Intelligence in Medicine: Knowledge Representation and Transparent and Explainable Systems : AIME 2019 International Workshops, KR4HC/ProHealth and TEAAM, Poznan, Poland, June 26–29, 2019, Revised Selected Papers / / edited by Mar Marcos, Jose M. Juarez, Richard Lenz, Grzegorz J. Nalepa, Slawomir Nowaczyk, Mor Peleg, Jerzy Stefanowski, Gregor Stiglic
| Artificial Intelligence in Medicine: Knowledge Representation and Transparent and Explainable Systems : AIME 2019 International Workshops, KR4HC/ProHealth and TEAAM, Poznan, Poland, June 26–29, 2019, Revised Selected Papers / / edited by Mar Marcos, Jose M. Juarez, Richard Lenz, Grzegorz J. Nalepa, Slawomir Nowaczyk, Mor Peleg, Jerzy Stefanowski, Gregor Stiglic |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (XII, 175 p. 56 illus., 42 illus. in color.) |
| Disciplina |
610.285
006.3 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Artificial intelligence
Optical data processing Computer organization Computers Education—Data processing Application software Artificial Intelligence Image Processing and Computer Vision Computer Systems Organization and Communication Networks Information Systems and Communication Service Computers and Education Computer Applications |
| ISBN | 3-030-37446-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | KR4HC/ProHealth - Joint Workshop on Knowledge Representation for Health Care and Process-Oriented Information Systems in Health Care -- A practical exercise on re-engineering clinical guideline models using different representation languages -- A method for goal-oriented guideline modeling in PROforma and ist preliminary evaluation -- Differential diagnosis of bacterial and viral meningitis using Dominance-Based Rough Set Approach -- Modelling ICU Patients to Improve Care Requirements and Outcome Prediction of Acute Respiratory Distress Syndrome: A Supervised Learning Approach -- Deep learning for haemodialysis time series classification -- TEAAM - Workshop on Transparent, Explainable and Affective AI in Medical Systems -- Towards Understanding ICU Treatments using Patient Health Trajectories -- An Explainable Approach of Inferring Potential Medication Effects from Social Media Data -- Exploring antimicrobial resistance prediction using post-hoc interpretable methods -- Local vs. Global Interpretability of Machine Learning Models in Type 2 Diabetes Mellitus Screening -- A Computational Framework towards Medical Image Explanation -- A Computational Framework for Interpretable Anomaly Detection and Classification of Multivariate Time Series with Application to Human Gait Data Analysis -- Self-organizing maps using acoustic features for prediction of state change in bipolar disorder -- Explainable machine learning for modeling of early postoperative mortality in lung cancer. . |
| Record Nr. | UNINA-9910370257703321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||