Speech Title: Data Mining Theories and Techniques for COVID-19 Diagnosis

Abstract: COVID-19 is a pandemic disease that caused more than 6.65 million deaths until 12/Dec/2022. X-ray and CT scans are two popular medical imaging technique used in radiology to get detailed images of the body noninvasively for diagnostic purposes. Traditional manual labeling of X-ray or CT-based scans is tedious and error-prone. To solve the problem, our lab develops new data mining theories and techniques, such as advanced pooling-based networks, graph convolutional networks, attention neural networks, weakly supervised networks, etc. We also use cloud computing techniques to run our developed app on the remote server to help doctors in the suburban area. Two other chest-related diseases: secondary pulmonary tuberculosis and community-acquired pneumonia, will be covered in this talk.

Biography: Prof. Yudong Zhang is a Chair Professor at the School of Computing and Mathematical Sciences, University of Leicester, UK. His research interests include deep learning and medical image analysis. He is the Fellow of IET, Fellow of EAI, and Fellow of BCS. He is the Senior Member of IEEE and ACM. He is the Distinguished Speaker of ACM. He was the 2019, 2021 & 2022 recipient of Clarivate Highly Cited Researcher. He has (co)authored over 400 peer-reviewed articles. There are more than 60 ESI Highly Cited Papers and 5 ESI Hot Papers in his (co)authored publications. His citation reached 23250 in Google Scholar (h-index 84). He is the editor of Neural Networks, IEEE TITS, IEEE TCSVT, etc. He has conducted many successful industrial projects and academic grants from NIH, Royal Society, GCRF, EPSRC, MRC, Hope, British Council, and NSFC. He has served as (Co-)Chair for more than 60 international conferences (including more than 20 IEEE or ACM conferences). More than 50 news presses have reported his research outputs, such as Reuters, BBC, Telegraph, Physics World, UK Today News, etc.

Speech Title: Intelligent Data Fusion System for Assessing and Classifying the Long-Term Effects of Exposure to COVID-19 in Pregnancy (Long Covid): Associated Neurophysiological and Epigenetic Mechanisms and Consequences for Infant Development

Abstract: The Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2) is responsible for the most significant global public health crisis of the last 50 years, having brought severe damage to mental health, with about 36% of patients presenting neuropsychiatric symptoms during or in cases of “Post-Acute Sequelae of COVID-19” (PASC). Among these, we can mention cognitive impairment, fatigue, sleep disturbances, depression, post-traumatic stress, and substance use disorders observed for more than 6 months after infection in patients who required hospitalization and non-hospitalized in percentages ranging from 3-47%. The main objective of this work is to develop and validate an intelligent system based on data fusion and multiple Artificial Intelligence approaches to assess PASC neuropsychiatric symptoms in women exposed to the SARS-CoV-2 virus during pregnancy with PASC neuropsychiatric symptoms, comparing the exposed non-pregnant women and the involvement of neurophysiological and epigenetic mechanisms, as well as determining the consequences of PASC for the development of babies. The project provides the follow-up for 12 months of women with long-term COVID treated at the HUWC (University Hospital), performing Neuropsychological and Sleep Assessments carried out through directed anamnesis, and polysomnography, in addition to various tests. Additionally, non-invasive ambulatory monitoring of heart rate is carried out, and computerized analysis of its variability is carried out. Metrics will be extracted in the time domains (SDNN, SDANN, RMSSD, pNN50, among others), frequency (HF, LF, LF /HF, among others), geometric and non-linear (entropy, chaotic, among others). The data collected in the research are from multiple sources and of different types, including laboratory analysis, DNA methylation and RNA sequencing, questionnaires, and monitoring tests such as polysomnography, EEG, EOG, and ECG. The data is consolidated, integrated, and validated in a structured and unstructured data lake, which allows classifying the project as a Big Data solution. The system also provides a layer for viewing and interpreting the information obtained, either through diagnostic support dashboards or through the use of eXplainable Artificial Intelligence (XAI) techniques to indicate the most relevant attributes for certain types of analysis.

Biography: Associate Professor, Head of Department, Research Coordinator at the University of Saint Joseph, Macau, SAR China. Founder of the Laboratory of Applied Neurosciences (LAN/USJ). PostDoctorate and Honorary Research Fellow at the University of Leicester - UK. Visiting Associate Professor at the University of the Chinese Academy of Sciences (UCAS) - Shenzhen Institutes of Advanced Technologies (SIAT). PhD in Engineering Federal University of Ceará (2010). Associate Professor and Software Department Chief at University Gregorio Semedo (UGS), Angola (2016). Associate Professor at University Lusiada of Angola (2009-2015). Master in Artificial Intelligence UFC (2007). Has large experience in Artificial Intelligence, Bioengineering, and Applied Computer Science, focusing on signal and image processing. RESEARCH AREAS - Neuroscience applied to management (marketing, leadership, performance) - Business Analytics - Big Data Applications - Theory of Constraints - Project management - Digital Signal Processing - Bioengineering / Computer-Aided Diagnostic Systems - Artificial Intelligence - Deep Learning - Nonlinear analysis and dynamics of time series.