Artificial Intelligence in Medicine: Knowledge Representation and Transparent and Explainable Systems
Editat de Mar Marcos, Jose M. Juarez, Richard Lenz, Grzegorz J. Nalepa, Slawomir Nowaczyk, Mor Peleg, Jerzy Stefanowski, Gregor Stiglicen Limba Engleză Paperback – 4 ian 2020
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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Specificații
ISBN-13: 9783030374457
ISBN-10: 3030374459
Pagini: 188
Ilustrații: XII, 175 p. 56 illus., 42 illus. in color.
Dimensiuni: 155 x 235 x 11 mm
Greutate: 0.3 kg
Ediția:1st ed. 2019
Editura: Springer
Locul publicării:Cham, Switzerland
ISBN-10: 3030374459
Pagini: 188
Ilustrații: XII, 175 p. 56 illus., 42 illus. in color.
Dimensiuni: 155 x 235 x 11 mm
Greutate: 0.3 kg
Ediția:1st ed. 2019
Editura: Springer
Locul publicării:Cham, Switzerland
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Cuprins
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.