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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 2629, 2019, Revised Selected Papers 2019 ed. [Mīkstie vāki]

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  • Formāts: Paperback / softback, 175 pages, height x width: 235x155 mm, weight: 454 g, 42 Illustrations, color; 14 Illustrations, black and white; XII, 175 p. 56 illus., 42 illus. in color., 1 Paperback / softback
  • Sērija : Lecture Notes in Artificial Intelligence 11979
  • Izdošanas datums: 04-Jan-2020
  • Izdevniecība: Springer Nature Switzerland AG
  • ISBN-10: 3030374459
  • ISBN-13: 9783030374457
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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 2629, 2019, Revised Selected Papers 2019 ed.Palielināt
  • Formāts: Paperback / softback, 175 pages, height x width: 235x155 mm, weight: 454 g, 42 Illustrations, color; 14 Illustrations, black and white; XII, 175 p. 56 illus., 42 illus. in color., 1 Paperback / softback
  • Sērija : Lecture Notes in Artificial Intelligence 11979
  • Izdošanas datums: 04-Jan-2020
  • Izdevniecība: Springer Nature Switzerland AG
  • ISBN-10: 3030374459
  • ISBN-13: 9783030374457
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9783030374457.html
Keywords:
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.
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.