Highlights latest achievements in the use of artificial intelligence for disease surveillance, pandemic intelligence

Interconnects three major fields: Artificial intelligence, Medicine and clinical and public health informatics

Makes the emerging topics of digital health and AI in medicine accessible to a broad readership

Digital Technologies for Clinical, Public and Global Health Surveillance.- Imputing Fine-grain Patterns of Mental Health with Statistical Modelling of Online Data.- Lexical and Acoustic Correlates of Clinical Speech Disturbance in Schizo-phrenia.-A Prognostic Tool to Identify Youth at Risk of at Least Weekly Cannabis Use.- Neuro-symbolic Neurodegenerative Disease Modeling as Probabilistic Pro-grammed Deep Kernels.-Self-Disclosure in Opioid Use Recovery Forums.- Identifying Prepubertal Children with Risk for Suicide Using Deep Neural Network Trained on Multimodal Brain Imaging.- Improving Adverse Drug Event Extraction with SpanBERT on Different Text Typologies.- Machine Learning Identification of Self-Reported COVID-19 Symptoms from Tweets in Canada.- RRISK: Analyzing COVID-19 Risk in Food Establishments.- AWS CORD-19 Search: A Neural Search Engine for COVID-19 Literature.- Inferring COVID-19 Biological Pathways from Clinical Phenotypes via Topological Analysis.- The EpiBench Platform to Propel AI/ML-based Epidemic Forecasting: A Prototype Demonstration Reaching Human Expert-level Performance.- Interpretable Classification of Human Exercise Videos through Pose Esti-mation and Multivariate Time Series Analysis.- Interpreting Deep Neural Networks for Medical Imaging using Concept Graphs.- Do Deep Neural Networks Forget Facial Action Units? - Exploring the Ef-fects of Transfer Learning in Health Related Facial Expression Recognition.- Utilizing Predictive Analysis to Aid Emergency Medical Services.- Measuring Physiological Markers of Stress During Conversational Agent Interactions.- EvSys: A Relational Dynamic System for Sparse Irregular Clinical Events.- Predicting Patient Outcomes with Graph Representation Learning.- Patient-Specific Seizure Prediction Using Single Seizure Electroenceph-alography Recording.- Evaluation Metrics for Deep Learning Imputation Models.- Logistic Regression Is Also A Black Box. Machine Learning Can Help.