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

• Pubblicazione: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019
• Edizione: 1st ed. 2019.
• Descrizione fisica: 1 online resource (XII, 175 p. 56 illus., 42 illus. in color.)
• Disciplina: 610.285
• 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
• Persona (resp. second.): MarcosMar; JuarezJose M; LenzRichard; NalepaGrzegorz J; NowaczykSlawomir; PelegMor; StefanowskiJerzy; StiglicGregor
• 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. .
• Sommario/riassunto: 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.
• Titolo autorizzato: Artificial Intelligence in Medicine: Knowledge Representation and Transparent and Explainable Systems
• ISBN: 3-030-37446-7
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 996466422303316
• Lo trovi qui: Univ. di Salerno
• Serie: Lecture Notes in Artificial Intelligence ; ; 11979