Workshop Program

This year's workshop will be held virtually, via Digital ECAI 2020's partner platform, Underline.io. However, the workshop's home location is Santiago de Compostela, Spain, which is in the Central European Summer Time (CEST) zone. Therefore, all program times listed below are for CEST. The Whova app for Digital ECAI 2020 may be of assistance in displaying your local time.

Saturday, August 29, 2020

KDH 2020 Main Track

13:00-13:15 KDH 2020 Welcome and Introduction, by Kerstin Bach and Cindy Marling

13:15-13:55 Keynote Address: The Potential for AI in Public Health: Lessons Learned from Developing and Testing a Patient-Centered Mobile App, by Kerstin Bach

13:55-14:10 Short Break

14:10-14:25 Interactive Explanations of Internal Representations of Neural Network Layers: An Exploratory Study on Outcome Prediction of Comatose Patients, by Meike Nauta, Michel van Putten, Marleen C. Tjepkema-Cloostermans, Jeroen Bos, Maurice van Keulen and Christin Seifert

14:25-14:40 Assessing the Clinicians' Pathway to Embed Artificial Intelligence for Assisted Diagnostics of Fracture Detection, by Carlos Francisco Moreno-García, Truong Dang, Kyle Martin, Manish Patel, Andrew Thompson, Lesley Leishman and Nirmalie Wiratunga

14:40-14:55 Knowledge Discovery and Visualization in Healthcare Datasets using Formal Concept Analysis and Graph Databases, by Diana Cristea, Christian Sacarea and Diana Șotropa

14:55-15:10 Towards Causal Knowledge Graphs - Position Paper, by Eva Blomqvist, Marjan Alirezaie and Marina Santini

15:10-16:10 Long Break

16:10-16:25 Prognosis Prediction in Covid-19 Patients from Lab Tests and X-ray Data through Randomized Decision Trees, by Alfonso Emilio Gerevini, Roberto Maroldi, Luca Putelli, Matteo Olivato and Ivan Serina

16:25-16:40 Uncertainty Quantification in Chest X-Ray Image Classification using Bayesian Deep Neural Networks, by Yumin Liu, Claire Zhao and Jonathan Rubin

16:40-16:55 Region Proposal Network for Lung Nodule Detection and Segmentation, by Mohammad Hesam Hesamian, Wenjing Jia, Sean He and Paul Kennedy

16:55-17:10 Short Break

17:10-17:25 Comparison of Forecasting Algorithms for Type 1 Diabetic Glucose Prediction on 30 and 60-Minute Prediction Horizons, by Richard McShinsky and Brandon Marshall

17:25-17:40 A General Neural Architecture for Carbohydrate and Bolus Recommendations in Type 1 Diabetes Management, by Jeremy Beauchamp, Razvan Bunescu and Cindy Marling

17:40-17:55 In Silico Comparison of Continuous Glucose Monitor Failure Mode Strategies for an Artificial Pancreas, by Yunjie Lisa Lu, Abigail Koay and Michael Mayo

17:55-18:30 Discussion and KDH Main Track Wrap-Up, by Kerstin Bach and Cindy Marling

Sunday, August 30, 2020

Blood Glucose Level Prediction (BGLP) Challenge

12:00-12:15 BGLP Challenge Welcome and Introduction, by Razvan Bunescu and Cindy Marling

12:15-12:30 Neural Multi-Class Classification Approach to Blood Glucose Level Forecasting with Prediction Uncertainty Visualisation, by Michael Mayo and Tomas Koutny

12:30-12:45 Multi-Scale Long Short-Term Memory Network with Multi-Lag Structure for Blood Glucose Prediction, by Tao Yang, Ruikun Wu, Rui Tao, Shuang Wen, Ning Ma, Yuhang Zhao, Xia Yu and Hongru Li

12:45-13:00 Online Blood Glucose Prediction Using Autoregressive Moving Average Model with Residual Compensation Network, by Ning Ma, Yuhang Zhao, Shuang Wen, Tao Yang, Ruikun Wu, Rui Tao, Xia Yu and Hongru Li

13:00-13:15 A Personalized and Interpretable Deep Learning Based Approach to Predict Blood Glucose Concentration in Type 1 Diabetes, by Giacomo Cappon, Lorenzo Meneghetti, Francesco Prendin, Jacopo Pavan, Giovanni Sparacino, Simone Del Favero and Andrea Facchinetti

13:15-13:30 Short Break

13:30-13:45 Analysis of the Performance of Genetic Programming on the Blood Glucose Level Prediction Challenge 2020, by David Joedicke, Oscar Garnica, Gabriel Kronberger, José Manuel Colmenar, Stephan Winkler, Jose Manuel Velasco, Sergio Contador and Ignacio Hidalgo

13:45-14:00 Blood Glucose Prediction for Type 1 Diabetes Using Generative Adversarial Networks, by Taiyu Zhu, Xi Yao, Kezhi Li, Pau Herrero and Pantelis Georgiou

14:00-14:15 Personalized Machine Learning Algorithm based on Shallow Network and Error Imputation Module for an Improved Blood Glucose Prediction, by Jacopo Pavan, Francesco Prendin, Lorenzo Meneghetti, Giacomo Cappon, Giovanni Sparacino, Andrea Facchinetti and Simone Del Favero

14:15-14:30 Personalised Glucose Prediction via Deep Multitask Networks, by John Daniels, Pau Herrero and Pantelis Georgiou

14:30-15:30 Long Break

15:30-15:45 Deep Residual Time-Series Forecasting: Application to Blood Glucose Prediction, by Harry Rubin-Falcone, Ian Fox and Jenna Wiens

15:45-16:00 Prediction of Blood Glucose Levels for People with Type 1 Diabetes using Latent-Variable-based Model, by Xiaoyu Sun, Mudassir Rashid, Mert Sevil, Nicole Hobbs, Rachel Brandt, Mohammad Reza Askari, Andrew Shahidehpour and Ali Cinar

16:00-16:15 A Deep Learning Approach for Blood Glucose Prediction and Monitoring of Type 1 Diabetes Patients, by Jonas Freiburghaus, Aïcha Rizzotti-Kaddouri and Fabrizio Albertetti

16:15-16:30 Blood Glucose Level Prediction as Time-Series Modeling using Sequence-to-Sequence Neural Networks, by Ananth Reddy Bhimireddy, Priyanshu Sinha, Bolu Oluwalade, Judy Wawira Gichoya and Saptarshi Purkayastha

16:30-16:45 Short Break

16:45-17:00 Investigating Potentials and Pitfalls of Knowledge Distillation across Datasets for Blood Glucose Forecasting, by Hadia Hameed and Samantha Kleinberg

17:00-17:15 Experiments in Non-Personalized Future Blood Glucose Level Prediction, by Robert Bevan and Frans Coenen

17:15-17:30 Multi-lag Stacking Approach for Blood Glucose Level Prediction, by Heydar Khadem, Hoda Nemat, Jackie Elliott and Mohammed Benaissa

17:30-17:45 Data Fusion of Activity and CGM for Predicting Blood Glucose Level, by Hoda Nemat, Heydar Khadem, Jackie Elliott and Mohammed Benaissa

17:45-18:30 Discussion and BGLP Challenge Wrap-Up, by Razvan Bunescu and Cindy Marling