@book{lobo_marques_computerized_2023, address = {Cham}, title = {Computerized {Systems} for {Diagnosis} and {Treatment} of {COVID}-19}, isbn = {978-3-031-30787-4 978-3-031-30788-1}, url = {https://link.springer.com/10.1007/978-3-031-30788-1}, language = {en}, urldate = {2023-10-10}, publisher = {Springer International Publishing}, editor = {Lobo Marques, Joao Alexandre and Fong, Simon James}, year = {2023}, doi = {10.1007/978-3-031-30788-1}, keywords = {Artificial Intelligence, Biofeedback, Computerized Diagnostic Support, Covid-19, Signal and Image Processing}, } @book{lobo_marques_epidemic_2022, address = {Cham, Switzerland}, title = {Epidemic analytics for decision supports in {COVID19} crisis}, isbn = {978-3-030-95281-5 978-3-030-95280-8}, url = {https://link.springer.com/book/10.1007/978-3-030-95281-5#about-this-book}, abstract = {Covid-19 has hit the world unprepared, as the deadliest pandemic of the century. Governments and authorities, as leaders and decision makers fighting against the virus, enormously tap on the power of AI and its data analytics models for urgent decision supports at the greatest efforts, ever seen from human history. This book showcases a collection of important data analytics models that were used during the epidemic, and discusses and compares their efficacy and limitations. Readers who from both healthcare industries and academia can gain unique insights on how data analytics models were designed and applied on epidemic data. Taking Covid-19 as a case study, readers especially those who are working in similar fields, would be better prepared in case a new wave of virus epidemic may arise again in the near future.}, language = {eng}, publisher = {Springer}, editor = {Lobo Marques, Joao Alexandre and Fong, Simon}, year = {2022}, } @book{marques_predictive_2021, series = {{SpringerBriefs} in {Applied} {Sciences} and {Technology}}, title = {Predictive {Models} for {Decision} {Support} in the {COVID}-19 {Crisis}}, isbn = {978-3-030-61912-1}, url = {https://www.springer.com/gp/book/9783030619121}, abstract = {COVID-19 has hit the world unprepared, as the deadliest pandemic of the century. Governments and authorities, as leaders and decision makers fighting the virus, enormously tap into the power of artificial intelligence and its predictive models for urgent decision support. This book showcases a collection of important predictive models that used during the pandemic, and discusses and compares their efficacy and limitations. Readers from both healthcare industries and academia can gain unique insights on how predictive models were designed and applied on epidemic data. Taking COVID19 as a case study and showcasing the lessons learnt, this book will enable readers to be better prepared in the event of virus epidemics or pandemics in the future.}, language = {en}, urldate = {2021-01-29}, publisher = {Springer International Publishing}, author = {Marques, João Alexandre Lobo and Gois, Francisco Nauber Bernardo and Xavier-Neto, Jose and Fong, Simon James}, year = {2021}, doi = {10.1007/978-3-030-61913-8}, }