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 /
Material type: TextSeries: Lecture Notes in Artificial Intelligence ; 11979Publisher: Cham : Springer International Publishing : Imprint: Springer, 2019Edition: 1st ed. 2019Description: XII, 175 p. 56 illus., 42 illus. in color. online resourceContent type:- text
- computer
- online resource
- 9783030374464
- Artificial intelligence
- Computer vision
- Computer engineering
- Computer networks
- Education -- Data processing
- Application software
- Artificial Intelligence
- Computer Vision
- Computer Engineering and Networks
- Computer Communication Networks
- Computers and Education
- Computer and Information Systems Applications
- 006.3 23
- Q334-342
- TA347.A78
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. .
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.
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