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

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.