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Produktbild: Artificial Intelligence in Medicine: Knowledge Representation and Transparent and Explainable Systems
Band 11979

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

Aus der Reihe Lecture Notes in Artificial Intelligence

Fr. 72.90

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

04.01.2020

Abbildungen

XII, 175 p. 56 illus., 42 illus. in color.

Herausgeber

Mar Marcos + weitere

Verlag

Springer

Seitenzahl

175

Maße (L/B/H)

23.5/15.5/1.1 cm

Gewicht

295 g

Auflage

1st ed. 2019

Sprache

Englisch

ISBN

978-3-030-37445-7

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

04.01.2020

Abbildungen

XII, 175 p. 56 illus., 42 illus. in color.

Herausgeber

Verlag

Springer

Seitenzahl

175

Maße (L/B/H)

23.5/15.5/1.1 cm

Gewicht

295 g

Auflage

1st ed. 2019

Sprache

Englisch

ISBN

978-3-030-37445-7

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: ProductSafety@springernature.com

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  • Produktbild: Artificial Intelligence in Medicine: Knowledge Representation and Transparent and Explainable Systems

  • 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.