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ECAI2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI, including:




**An extensive programme of workshops, tutorials, and invited speakers**

**The Prestigious Applications of Intelligent Systems conference (PAIS)**

**The Frontiers in AI track sessions**

**The Lab To Market event**

**The Women in AI time**

**The EU Challenges forum**

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ECAI 2020

Maintaining blood glucose in a target range is essential for people living with Type 1 diabetes in order to avoid excessive periods in hypoglycemia and hyperglycemia which can result in severe complications. Accurate blood glucose prediction can reduce this risk and enhance early interventions to improve diabetes management. However, due to the complex nature of glucose metabolism and the various lifestyle related factors which can disrupt this, diabetes management still remains challenging. In this work we propose a novel deep learning model to predict future BG levels based on the historical continuous glucose monitoring measurements, meal ingestion, and insulin delivery. We adopt a modified architecture of the generative adversarial network that comprises of a generator and a discriminator. The generator computes the BG predictions by a recurrent neural network with gated recurrent units, and the auxiliary discriminator employs a one-dimensional convolutional neural network to distinguish between the predictive and real BG values. Two modules are trained in an adversarial process with a combination of loss. The experiments were conducted using the OhioT1DM dataset that contains the data of six T1D contributors over 40 days. The proposed algorithm achieves an average root mean square error (RMSE) of 18.34 ± 0.17 mg/dL with a mean absolute error (MAE) of 13.37 ± 0.18 mg/dL for the 30-minute prediction horizon (PH) and an average RMSE of 32.31 ± 0.46 mg/dL with a MAE of 24.20 ± 0.42 for the 60-minute PH. The results are compared for clinical relevance using the Clarke error grid which confirms the promising performance of the proposed model.

Blood Glucose Prediction for Type 1 Diabetes Using Generative Adversarial Networks

technical paper

Prognosis Prediction in Covid-19 Patients from Lab Tests and X-ray Data through Randomized Decision Trees

In Silico Comparison of Continuous Glucose Monitor Failure Mode Strategies for an Artificial Pancreas

Investigating potentials and pitfalls of knowledge distillation across datasets for blood glucose forecasting

Towards Causal Knowledge Graphs - Position Paper

Uncertainty Quantification in Chest X-Ray Image Classification using Bayesian Deep Neural Networks

In Silico Comparison of Continuous Glucose Monitor Failure Mode Strategies for and Artificial Pancreas

Knowledge Discovery and Visualization in Healthcare Datasets using Formal Concept Analysis and Graph Databases

Interactive Explanations of Internal Representations of Neural Network Layers: An Exploratory Study on Outcome Prediction of Comatose Patients

Assessing the clinicians' pathway to embed artificial intelligence for assisted diagnostics of fracture detection

A Personalized and Interpretable Deep Learning Based Approach to Predict Blood Glucose Concentration inType 1 Diabetes

Blood Glucose Prediction for Type 1 Diabetes Using Generative Adversarial Networks

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Prognosis Prediction in Covid-19 Patients from Lab Tests and X-ray Data through Randomized Decision Trees

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A Deep Learning Approach for Blood Glucose Prediction of Type 1 Diabetes