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Lopes, A. & Amaral, B. (2023). A machine learning approach for mapping and accelerating multiple sclerosis research. In Martinho, R., Rijo, R., Cruz-Cunha, M. M., Domingos, D., and Peres, E. (Ed.), Procedia Computer Science. (pp. 1193-1199). Lisboa: Elsevier.

A. L. Lopes and B. A. Tiago,  "A machine learning approach for mapping and accelerating multiple sclerosis research", in Procedia Computer Science, Martinho, R., Rijo, R., Cruz-Cunha, M. M., Domingos, D., and Peres, E., Ed., Lisboa, Elsevier, 2023, vol. 219, pp. 1193-1199

@inproceedings{lopes2023_1714185488860,
	author = "Lopes, A. and Amaral, B.",
	title = "A machine learning approach for mapping and accelerating multiple sclerosis research",
	booktitle = "Procedia Computer Science",
	year = "2023",
	editor = "Martinho, R., Rijo, R., Cruz-Cunha, M. M., Domingos, D., and Peres, E.",
	volume = "219",
	number = "",
	series = "",
	doi = "10.1016/j.procs.2023.01.401",
	pages = "1193-1199",
	publisher = "Elsevier",
	address = "Lisboa",
	organization = "",
	url = "https://www.sciencedirect.com/journal/procedia-computer-science"
}

TY  - CPAPER
TI  - A machine learning approach for mapping and accelerating multiple sclerosis research
T2  - Procedia Computer Science
VL  - 219
AU  - Lopes, A.
AU  - Amaral, B.
PY  - 2023
SP  - 1193-1199
SN  - 1877-0509
DO  - 10.1016/j.procs.2023.01.401
CY  - Lisboa
UR  - https://www.sciencedirect.com/journal/procedia-computer-science
AB  - The medical field, as many others, is overwhelmed with the amount of research-related information available, such as journal papers, conference proceedings and clinical trials. The task of parsing through all this information to keep up to date with the most recent research findings on their area of expertise is especially difficult for practitioners who must also focus on their clinical duties. Recommender systems can help make decisions and provide relevant information on specific matters, such as for these clinical practitioners looking into which research to prioritize. In this paper, we describe the early work on a machine learning approach, which through an intelligent reinforcement learning approach, maps and recommends research information (papers and clinical trials) specifically for multiple sclerosis research. We tested and evaluated several different machine learning algorithms and present which one is the most promising in developing a complete and efficient model for recommending relevant multiple sclerosis research.
ER  -