| Vol-2675 urn:nbn:de:0074-2675-4 Copyright © 2020 for the individual papers by the papers' authors. Copyright © 2020 for the volume as a collection by its editors. This volume and its papers are published under the Creative Commons License Attribution 4.0 International (CC BY 4.0).

# KDH 2020
Knowledge Discovery in Healthcare Data 2020

### Proceedings of the 5th International Workshop on Knowledge Discovery in Healthcare Data
co-located with 24th European Conference on Artificial Intelligence (ECAI 2020)

### Santiago de Compostela, Spain & Virtually, August 29-30, 2020.

Edited by

### Kerstin Bach *
Razvan Bunescu **
Cindy Marling **
Nirmalie Wiratunga ***

* Norwegian University of Science and Technology, Department of Computer Science, Sem Sælandsvei 9, NO-7491 Trondheim, Norway
** Ohio University, School of Electrical Engineering and Computer Science, Russ College of Engineering and Technology, Athens, OH 45701
*** Robert Gordon University, School of Computing Science and Digital Media, Aberdeen, United Kingdom

## Table of Contents

- Preface
  1-4

### KDH 2020 Technical Papers

- Interactive Explanations of Internal Representations of Neural Network Layers: An Exploratory Study on Outcome Prediction of Comatose Patients 5-11
  Meike Nauta, Michel van Putten, Marleen C. Tjepkema-Cloostermans, Jeroen Bos, Maurice van Keulen, Christin Seifert
- Comparison of Forecasting Algorithms for Type 1 Diabetic Glucose Prediction on 30 and 60-Minute Prediction Horizons 12-18
  Richard McShinsky, Brandon Marshall
- Uncertainty Quantification in Chest X-Ray Image Classification using Bayesian Deep Neural Networks 19-26
  Yumin Liu, Claire Zhao, Jonathan Rubin
- Prognosis Prediction in Covid-19 Patients from Lab Tests and X-ray Data through Randomized Decision Trees 27-34
  Alfonso Emilio Gerevini, Roberto Maroldi, Matteo Olivato, Luca Putelli, Ivan Serina
- Knowledge Discovery and Visualization in Healthcare Datasets using Formal Concept Analysis and Graph Databases 35-42
  Diana Cristea, Christian Sacarea, Diana Șotropa
- A General Neural Architecture for Carbohydrate and Bolus Recommendations in Type 1 Diabetes Management 43-47
  Jeremy Beauchamp, Razvan Bunescu, Cindy Marling
- Region Proposal Network for Lung Nodule Detection and Segmentation 48-52
  Mohammadhesam Hesamian, Wenjing Jia, Sean He, Paul Kennedy
- In Silico Comparison of Continuous Glucose Monitor Failure Mode Strategies for an Artificial Pancreas 53-57
  Yunjie Lisa Lu, Abigail Koay, Michael Mayo
- Towards Causal Knowledge Graphs - Position Paper 58-62
  Eva Blomqvist, Marjan Alirezaie, Marina Santini
- Assessing the Clinicians' Pathway to Embed Artificial Intelligence for Assisted Diagnostics of Fracture Detection 63-70
  Carlos Francisco Moreno-García, Truong Dang, Kyle Martin, Manish Patel, Andrew Thompson, Lesley Leishman, Nirmalie Wiratunga

### Blood Glucose Level Prediction Challenge Papers

- The OhioT1DM Dataset for Blood Glucose Level Prediction: Update 2020 71-74
  Cindy Marling, Razvan Bunescu
- A Personalized and Interpretable Deep Learning Based Approach to Predict Blood Glucose Concentration in Type 1 Diabetes 75-79
  Giacomo Cappon, Lorenzo Meneghetti, Francesco Prendin, Jacopo Pavan, Giovanni Sparacino, Simone Del Favero, Andrea Facchinetti
- Neural Multi-class Classification Approach to Blood Glucose Level Forecasting with Prediction Uncertainty Visualisation 80-84
  Michael Mayo, Tomas Koutny
- Investigating Potentials and Pitfalls of Knowledge Distillation Across Datasets for Blood Glucose Forecasting 85-89
  Hadia Hameed, Samantha Kleinberg
- Blood Glucose Prediction for Type 1 Diabetes Using Generative Adversarial Networks 90-94
  Taiyu Zhu, Xi Yao, Kezhi Li, Pau Herrero, Pantelis Georgiou
- Personalized Machine Learning Algorithm based on Shallow Network and Error Imputation Module for an Improved Blood Glucose Prediction 95-99
  Jacopo Pavan, Francesco Prendin, Lorenzo Meneghetti, Giacomo Cappon, Giovanni Sparacino, Andrea Facchinetti , Simone Del Favero
- Experiments in Non-Personalized Future Blood Glucose Level Prediction 100-104
  Robert Bevan, Frans Coenen
- Deep Residual Time-Series Forecasting: Application to Blood Glucose Prediction 105-109
  Harry Rubin-Falcone, Harry Rubin-Falcone, Ian Fox, Jenna Wiens
- Personalised Glucose Prediction via Deep Multitask Networks 110-114
  John Daniels, Pau Herrero, Pantelis Georgiou
- Prediction of Blood Glucose Levels for People with Type 1 Diabetes using Latent-Variable-based Model 115-119
  Xiaoyu Sun, Mudassir Rashid, Mert Sevil, Nicole Hobbs, Rachel Brandt, Mohammad Reza Askari, Andrew Shahidehpour, Ali Cinar
- Data Fusion of Activity and CGM for Predicting Blood Glucose Levels 120-124
  Hoda Nemat, Heydar Khadem, Jackie Elliott, Mohammed Benaissa
- Blood Glucose Level Prediction as Time-Series Modeling using Sequence-to-Sequence Neural Networks 125-130
  Ananth Reddy Bhimireddy, Priyanshu Sinha, Bolu Oluwalade, Judy Wawira Gichoya, Saptarshi Purkayastha
- A Deep Learning Approach for Blood Glucose Prediction and Monitoring of Type 1 Diabetes Patients 131-135
  Jonas Freiburghaus, Aïcha Rizzotti-Kaddouri, Fabrizio Albertetti
- Multi-Scale Long Short-Term Memory Network with Multi-Lag Structure for Blood Glucose Prediction 136-140
  Tao Yang, Ruikun Wu, Rui Tao, Shuang Wen, Ning Ma, Yuhang Zhao, Xia Yu, Hongru Li
- Analysis of the performance of Genetic Programming on the Blood Glucose Level Prediction Challenge 2020 141-145
  David Joedicke, Gabriel Kronberger, José Manuel Colmenar, Stephan Winkler, Jose Manuel Velasco, Sergio Contador, Ignacio Hidalgo
- Multi-lag Stacking for Blood Glucose Level Prediction 146-150
  Heydar Khadem, Hoda Nemat, Jackie Elliott, Mohammed Benaissa
- Online Blood Glucose Prediction Using Autoregressive Moving Average Model with Residual Compensation Network 151-155
  Ning Ma, Yuhang Zhao, Shuang Wen, Tao Yang, Ruikun Wu, Rui Tao, Xia Yu, Hongru Li

2020-08-28: submitted by Kerstin Bach, metadata incl. bibliographic data published under Creative Commons CC0
2020-09-17: published on CEUR Workshop Proceedings (CEUR-WS.org, ISSN 1613-0073) |valid HTML5|