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Record Nr. |
UNINA9910370257703321 |
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Titolo |
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 |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
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ISBN |
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Edizione |
[1st ed. 2019.] |
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Descrizione fisica |
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1 online resource (XII, 175 p. 56 illus., 42 illus. in color.) |
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Collana |
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Lecture Notes in Artificial Intelligence ; ; 11979 |
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Disciplina |
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Soggetti |
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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 |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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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 |
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-- 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. . |
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Sommario/riassunto |
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This book constitutes revised selected papers from the AIME 2019 workshops KR4HC/ProHealth 2019, the Workshop on Knowledge Representation for Health Care and Process-Oriented Information Systems in Health Care, and TEAAM 2019, the Workshop on Transparent, Explainable and Affective AI in Medical Systems. The volume contains 5 full papers from KR4HC/ProHealth, which were selected out of 13 submissions. For TEAAM 8 papers out of 10 submissions were accepted for publication. |
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