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Academic Units

Faculty of Business and Law

United Nations SDGs

03 - Good Health and Well-being

Publication year

Between 2000 and 2024

Results 31 resources

Abstracts

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
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
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
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
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
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
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
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
Paulo do Vale Madeiro, J., César Cortez, Arnaldo Aires Peixoto Júnior, P., Alexandre Lôbo Marques, J., Alisson Pessoa Guimarães, A., & Hebert da Silva Felix, J. (2015). A Heart Rate Variability-based Smart Approach to Analyze Frailty in Older Adults. The Smart Computing Review. https://doi.org/10.6029/smartcr.2015.04.002
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
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.
Marques, J. A. L., Gois, F. N. B., Xavier-Neto, J., & Fong, S. J. (2021). Predictive Models for Decision Support in the COVID-19 Crisis. Springer International Publishing. https://doi.org/10.1007/978-3-030-61913-8
Marques, J. A. L., Cortez, P. C., Madeiro, J. P. D. V., Fong, S. J., Schlindwein, F. S., & Albuquerque, V. H. C. D. (2019). Automatic Cardiotocography Diagnostic System Based on Hilbert Transform and Adaptive Threshold Technique. IEEE Access, 7, 73085–73094. https://doi.org/10.1109/ACCESS.2018.2877933
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
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., & 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
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.
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

Last update from database: 4/26/24, 2:21 AM (UTC)

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