Selected Research
Ribeiro, P., Marques, J. A. L., Pordeus, D., Zacarias, L., Leite, C. F., Sobreira-Neto, M. A., Peixoto, A. A., de Oliveira, A., Madeiro, J. P. do V., & Rodrigues, P. M. (2024). Machine learning-based cardiac activity non-linear analysis for discriminating COVID-19 patients with different degrees of severity. Biomedical Signal Processing and Control, 87, 105558. https://doi.org/10.1016/j.bspc.2023.105558
Guilherme Cunha Santos, A., Alexandre Lobo Marques, J., Rigo Jr., L., & Paulo Madeiro, J. (2023, November 26). Exploring EEG Signal Features for Predicting Post Cardiac Arrest Prognosis. 2023 Computing in Cardiology Conference. https://doi.org/10.22489/CinC.2023.312
Rodrigues, P. M., Madeiro, J. P., & Marques, J. A. L. (2023). Enhancing Health and Public Health through Machine Learning: Decision Support for Smarter Choices. Bioengineering, 10(7), 792. https://doi.org/10.3390/bioengineering10070792
Lobo Marques, J. A. (2023, May 26). 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 [Conference]. 2023 7th International Conference on Data Mining, Communications and Information Technology (DMCIT & CSMO 2023), Chongqin, China.
Ribeiro, P., Marques, J. A. L., & Rodrigues, P. M. (2023). COVID-19 Detection by Means of ECG, Voice, and X-ray Computerized Systems: A Review. Bioengineering, 10(2), 198. https://doi.org/10.3390/bioengineering10020198
Bernardo Gois, F. N., & Lobo Marques, J. A. (2023). Segmentation of CT-Scan Images Using UNet Network for Patients Diagnosed with COVID-19. In J. A. Lobo Marques & S. J. Fong (Eds.), Computerized Systems for Diagnosis and Treatment of COVID-19 (pp. 29–44). Springer International Publishing. https://doi.org/10.1007/978-3-031-30788-1_3
Bernardo Gois, F. N., Lobo Marques, J. A., & Fong, S. J. (2023). Classification of COVID-19 CT Scans Using Convolutional Neural Networks and Transformers. In J. A. Lobo Marques & S. J. Fong (Eds.), Computerized Systems for Diagnosis and Treatment of COVID-19 (pp. 79–97). Springer International Publishing. https://doi.org/10.1007/978-3-031-30788-1_6
Bernardo Gois, F. N., Lobo Marques, J. A., & Fong, S. J. (2023). TPOT Automated Machine Learning Approach for Multiple Diagnostic Classification of Lung Radiography and Feature Extraction. In J. A. Lobo Marques & S. J. Fong (Eds.), Computerized Systems for Diagnosis and Treatment of COVID-19 (pp. 117–135). Springer International Publishing. https://doi.org/10.1007/978-3-031-30788-1_8
Caldas, W. L., do Vale Madeiro, J. P., Pedrosa, R. C., Gomes, J. P. P., Du, W., & Marques, J. A. L. (2023). Noise Detection and Classification in Chagasic ECG Signals Based on One-Dimensional Convolutional Neural Networks. In R. Lee (Ed.), Computer and Information Science (pp. 117–129). Springer International Publishing. https://doi.org/10.1007/978-3-031-12127-2_8
Cavalcante, C. H. L., Primo, P. E. O., Sales, C. A. F., Caldas, W. L., Silva, J. H. M., Souza, A. H., Marinho, E. S., Pedrosa, R. C., Marques, J. A. L., Santos, H. S., Madeiro, J. P. V., Cavalcante, C. H. L., Primo, P. E. O., Sales, C. A. F., Caldas, W. L., Silva, J. H. M., Souza, A. H., Marinho, E. S., Pedrosa, R. C., … Madeiro, J. P. V. (2023). Sudden cardiac death multiparametric classification system for Chagas heart disease’s patients based on clinical data and 24-hours ECG monitoring. Mathematical Biosciences and Engineering, 20(5), 9159–9178. https://doi.org/10.3934/mbe.2023402
dos Santos Silva, B. R., Cesar Cortez, P., Crosara Motta, P., & Lobo Marques, J. A. (2023). Covid-19 Detection Based on Chest X-Ray Images Using Multiple Transfer Learning CNN Models. In J. A. Lobo Marques & S. J. Fong (Eds.), Computerized Systems for Diagnosis and Treatment of COVID-19 (pp. 45–63). Springer International Publishing. https://doi.org/10.1007/978-3-031-30788-1_4
dos Santos Silva, B. R., Cesar Cortez, P., Gomes Aguiar, R., Rodrigues Ribeiro, T., Pereira Teixeira, A., Bernardo Gois, F. N., & Lobo Marques, J. A. (2023). Lung Segmentation of Chest X-Rays Using Unet Convolutional Networks. In J. A. Lobo Marques & S. J. Fong (Eds.), Computerized Systems for Diagnosis and Treatment of COVID-19 (pp. 15–28). Springer International Publishing. https://doi.org/10.1007/978-3-031-30788-1_2
dos Santos Silva, B. R., Cortez, P. C., da Silva Neto, M. G., & Lobo Marques, J. A. (2023). X-Ray Machine Learning Classification with VGG-16 for Feature Extraction. In J. A. Lobo Marques & S. J. Fong (Eds.), Computerized Systems for Diagnosis and Treatment of COVID-19 (pp. 65–78). Springer International Publishing. https://doi.org/10.1007/978-3-031-30788-1_5
Gois, F. N. B., Marques, J. A. L., de Oliveira Dantas, A. B., Santos, M. C., Neto, J. V. S., de Macêdo, J. A. F., Du, W., & Li, Y. (2023). Malaria Blood Smears Object Detection Based on Convolutional DCGAN and CNN Deep Learning Architectures. In R. Lee (Ed.), Computer and Information Science (pp. 197–212). Springer International Publishing. https://doi.org/10.1007/978-3-031-12127-2_14
Lobo Marques, J. A., & Fong, S. J. (Eds.). (2023). Computerized Systems for Diagnosis and Treatment of COVID-19. Springer International Publishing. https://doi.org/10.1007/978-3-031-30788-1
Lobo Marques, J. A., & Fong, S. J. (2023). Technology Developments to Face the COVID-19 Pandemic: Advances, Challenges, and Trends. In J. A. Lobo Marques & S. J. Fong (Eds.), Computerized Systems for Diagnosis and Treatment of COVID-19 (pp. 1–13). Springer International Publishing. https://doi.org/10.1007/978-3-031-30788-1_1
Lobo Marques, J. A., Macedo, D. S., Motta, P., dos Santos Silva, B. R., Carvalho, F. H. C., Kehdi, R. C., Cavalcante, L. R. L., da Silva Viana, M., Lós, D., & Fiorenza, N. G. (2023). Exploratory Data Analysis on Clinical and Emotional Parameters of Pregnant Women with COVID-19 Symptoms. In J. A. Lobo Marques & S. J. Fong (Eds.), Computerized Systems for Diagnosis and Treatment of COVID-19 (pp. 179–209). Springer International Publishing. https://doi.org/10.1007/978-3-031-30788-1_11
Motta, P. C., Cesar Cortez, P., & Lobo Marques, J. A. (2023). COVID-19 Classification Using CT Scans with Convolutional Neural Networks. In J. A. Lobo Marques & S. J. Fong (Eds.), Computerized Systems for Diagnosis and Treatment of COVID-19 (pp. 99–116). Springer International Publishing. https://doi.org/10.1007/978-3-031-30788-1_7
Pordeus, D., Ribeiro, P., Zacarias, L., de Oliveira, A., Marques, J. A. L., Rodrigues, P. M., Leite, C., Neto, M. A., Peixoto, A. A., & do Vale Madeiro, J. P. (2023). Training Strategies for Covid-19 Severity Classification. In I. Rojas, O. Valenzuela, F. Rojas Ruiz, L. J. Herrera, & F. Ortuño (Eds.), Bioinformatics and Biomedical Engineering (pp. 514–527). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-34953-9_40
Pordeus, D., Ribeiro, P., Zacarias, L., Paulo Madeiro, J., Lobo Marques, J. A., Miguel Rodrigues, P., Leite, C., Alves Neto, M., Aires Peixoto Jr, A., & de Oliveira, A. (2023). Classification of Severity of COVID-19 Patients Based on the Heart Rate Variability. In J. A. Lobo Marques & S. J. Fong (Eds.), Computerized Systems for Diagnosis and Treatment of COVID-19 (pp. 155–177). Springer International Publishing. https://doi.org/10.1007/978-3-031-30788-1_10
Ribeiro, P., Pordeus, D., Zacarias, L., Leite, C., Alves Neto, M., Aires Peixoto Jr, A., de Oliveira, A., Paulo Madeiro, J., Lobo Marques, J. A., & Miguel Rodrigues, P. (2023). Evaluation of ECG Non-linear Features in Time-Frequency Domain for the Discrimination of COVID-19 Severity Stages. In J. A. Lobo Marques & S. J. Fong (Eds.), Computerized Systems for Diagnosis and Treatment of COVID-19 (pp. 137–154). Springer International Publishing. https://doi.org/10.1007/978-3-031-30788-1_9
Singh, P., Chaudhary, G., & Lobo Marques, J. A. (2023). Medical Information Extraction of Clinical Notes and Pictorial Visualisation of Electronic Medical Records Summary Interface. In Smart Distributed Embedded Systems for Healthcare Applications (pp. 29–40). CRC Press. https://www.routledge.com/Smart-Distributed-Embedded-Systems-for-Healthcare-Applications/Nagrath-Alzubi-Singla-Rodrigues-Verma/p/book/9781032183473
Yan, K., Li, T., Marques, J. A. L., Gao, J., Fong, S. J., Yan, K., Li, T., Marques, J. A. L., Gao, J., & Fong, S. J. (2023). A review on multimodal machine learning in medical diagnostics. Mathematical Biosciences and Engineering, 20(5), 8708–8726. https://doi.org/10.3934/mbe.2023382
Lobo Marques, J. A. (2022, August 19). IoT-based smart health system for ambulatory maternal and fetal monitoring [Symposium]. Symposium on Intelligent Manufacturing and Artificial Intelligence Technologies (ISIMAIT 2022), Zhejiang, China.
Khatoon, F., Kumar, M., Khalid, A. A., Alshammari, A. D., Khan, F., Alshammari, R. D., Balouch, Z., Verma, D., Mishra, P., Abotaleb, M., Makarovskikh, T., El-kenawy, E. M., Dutta, P. K., & Marques, J. A. (2022). Quality of life during the pandemic: a cross sectional study about attitude, individual perspective and behavior change affecting general population in daily life. 379–383. https://doi.org/10.1049/icp.2023.0596
Lobo Marques, J. A., & Fong, S. (Eds.). (2022). Epidemic analytics for decision supports in COVID19 crisis. Springer. https://link.springer.com/book/10.1007/978-3-030-95281-5#about-this-book
Motta, P., Silva, B., Furtado, F., & Lobo Marques, J. A. (2022). Automatic Classification System for Subjects Exposed to Short-Term Stress Based on Facial Expression Analysis and ElectroDermal Activity [Conference]. XXVIII Brazilian Congress of Biomedical Engineering CBEB 2022, Florianópolis.
Ensure healthy lives and promote well-being for all at all ages
Mental-health education
Health and well-being are essential for a sustainable society. In the 2022-2023 academic year, 12% of our students graduated with degrees in the field of Psychology, equipped to promote the mental health and well-being of communities.
Policies and support services
USJ provides free mental health support to our students and staff through our counselling services, which also refer sexual health issues to the appropriate health services. USJ is a smoke-free campus. The use of tobacco products is prohibited in enclosed buildings and facilities, as well as during events, whether indoor or outdoor. To accommodate the needs of some students and staff, we do provide a designated ‘smoking allowed’ restricted area away from the main part of campus.
Collaborations
Partnerships are vital to the promotion of the SDGs. USJ partners with the Macao government and other organisations to promote the health of Macao’s population.
In 2022-2023 academic year, USJ collaborated with local organisation to conduct a study, which aimed to help the local government entities to improve the mental well-being of aging population.
Health service
We provide mental health support to the community at a reduced rate, through our counselling services, and physical health is promoted through our sports facilities, which are shared with a local secondary school.