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

52,99 €

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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.