Enhancing Health and Public Health through Machine Learning: Decision Support for Smarter Choices

Pedro Miguel Rodrigues, João Paulo Madeiro, João Alexandre Lobo Marques, Pedro Miguel Rodrigues, João Paulo Madeiro, João Alexandre Lobo Marques

Abstract

In recent years, the integration of Machine Learning (ML) techniques in the field of healthcare and public health has emerged as a powerful tool for improving decision-making processes [...].

Conflict of interest statement

The authors declare no conflict of interest.

References

    1. Kourou K., Exarchos T.P., Exarchos K.P., Karamouzis M.V., Fotiadis D.I. Machine learning applications in cancer prognosis and prediction. Comput. Struct. Biotechnol. J. 2015;13:8–17. doi: 10.1016/j.csbj.2014.11.005.
    1. Bhatt C.M., Patel P., Ghetia T., Mazzeo P.L. Effective Heart Disease Prediction Using Machine Learning Techniques. Algorithms. 2023;16:88. doi: 10.3390/a16020088.
    1. Rodrigues P.M., Bispo B.C., Garrett C., Alves D., Teixeira J.P., Freitas D. Lacsogram: A New EEG Tool to Diagnose Alzheimer’s Disease. IEEE J. Biomed. Health Inform. 2021;25:3384–3395. doi: 10.1109/JBHI.2021.3069789.
    1. Santangelo O.E., Gentile V., Pizzo S., Giordano D., Cedrone F. Machine Learning and Prediction of Infectious Diseases: A Systematic Review. Mach. Learn. Knowl. Extr. 2023;5:175–198. doi: 10.3390/make5010013.
    1. Mirniaharikandehei S., Abdihamzehkolaei A., Choquehuanca A., Aedo M., Pacheco W., Estacio L., Cahui V., Huallpa L., Quiñonez K., Calderón V., et al. Automated Quantification of Pneumonia Infected Volume in Lung CT Images: A Comparison with Subjective Assessment of Radiologists. Bioengineering. 2023;10:321. doi: 10.3390/bioengineering10030321.
    1. Chen C., Liu G., Chu C., Zheng W., Ma Q., Liao Y., Yan Y., Sun Y., Wang D., Mu J. A Novel and Noninvasive Risk Assessment Score and Its Child-to-Adult Trajectories to Screen Subclinical Renal Damage in Middle Age. Bioengineering. 2023;10:257. doi: 10.3390/bioengineering10020257.
    1. Zhang X., Wu D., Li H., Fang Y., Xiong H., Li Y. Early Diagnosis of Intracranial Internal Carotid Artery Stenosis Using Extracranial Hemodynamic Indices from Carotid Doppler Ultrasound. Bioengineering. 2022;9:422. doi: 10.3390/bioengineering9090422.
    1. Barnawi A., Boulares M., Somai R. Simple and Powerful PCG Classification Method Based on Selection and Transfer Learning for Precision Medicine Application. Bioengineering. 2023;10:294. doi: 10.3390/bioengineering10030294.
    1. Ribeiro P., Marques J.A.L., Rodrigues P.M. COVID-19 Detection by Means of ECG, Voice, and X-ray Computerized Systems: A Review. Bioengineering. 2023;10:198. doi: 10.3390/bioengineering10020198.
    1. Battineni G., Chintalapudi N., Hossain M.A., Losco G., Ruocco C., Sagaro G.G., Traini E., Nittari G., Amenta F. Artificial Intelligence Models in the Diagnosis of Adult-Onset Dementia Disorders: A Review. Bioengineering. 2022;9:370. doi: 10.3390/bioengineering9080370.
    1. Sebastiani M., Vacchi C., Manfredi A., Cassone G. Personalized Medicine and Machine Learning: A Roadmap for the Future. J. Clin. Med. 2022;11:4110. doi: 10.3390/jcm11144110.
    1. Kim S., Yoon J., Kwon O. Biomedical Relation Extraction Using Dependency Graph and Decoder-Enhanced Transformer Model. Bioengineering. 2023;10:586. doi: 10.3390/bioengineering10050586.
    1. Khadem H., Nemat H., Elliott J., Benaissa M. Blood Glucose Level Time Series Forecasting: Nested Deep Ensemble Learning Lag Fusion. Bioengineering. 2023;10:487. doi: 10.3390/bioengineering10040487.
    1. Zeng D., Cao Z., Neill D.B. Chapter 22—Artificial intelligence–enabled public health surveillance—From local detection to global epidemic monitoring and control. In: Xing L., Giger M.L., Min J.K., editors. Artificial Intelligence in Medicine. Academic Press; Cambridge, MA, USA: 2021. pp. 437–453.
    1. Rodrigues P.M., Ribeiro P., Tavaria F.K. Distinction of Different Colony Types by a Smart-Data-Driven Tool. Bioengineering. 2022;10:26. doi: 10.3390/bioengineering10010026.
    1. Goh Y.S., Ow Yong J.Q.Y., Chee B.Q.H., Kuek J.H.L., Ho C.S.H. Machine Learning in Health Promotion and Behavioral Change: Scoping Review. J. Med. Internet Res. 2022;24:e35831. doi: 10.2196/35831.
    1. Promsri A., Cholamjiak P., Federolf P. Walking Stability and Risk of Falls. Bioengineering. 2023;10:471. doi: 10.3390/bioengineering10040471.
    1. Gupta M.V., Vaikole S., Oza A.D., Patel A., Burduhos-Nergis D.P., Burduhos-Nergis D.D. Audio-Visual Stress Classification Using Cascaded RNN-LSTM Networks. Bioengineering. 2022;9:510. doi: 10.3390/bioengineering9100510.
    1. Tawhid A., Teotia T., Elmiligi H. Chapter 13—Machine learning for optimizing healthcare resources. In: Kumar P., Kumar Y., Tawhid M.A., editors. Machine Learning, Big Data, and IoT for Medical Informatics. Academic Press; Cambridge, MA, USA: 2021. pp. 215–239. Intelligent Data-Centric Systems.
    1. da Silva J.H.B., Cortez P.C., Jagatheesaperumal S.K., de Albuquerque V.H.C. ECG Measurement Uncertainty Based on Monte Carlo Approach: An Effective Analysis for a Successful Cardiac Health Monitoring System. Bioengineering. 2023;10:115. doi: 10.3390/bioengineering10010115.

Source: PubMed

Sponsorzy i współpracownicy

Warunki medyczne

Interwencje lekowe

3
Subskrybuj