Abstract
Currently, there is a large amount of digitized data. In the field of health, an important part of the data is obtained from electronic health records, related to the health of people.
This stream of data has led to a multitude of research efforts in the healthcare domain, focusing on various aspects such as disease prediction, clinical risk assessment, mortality analysis, and more. These predictions assist healthcare providers in early identification of potential risks, leading to better patient care.
The objective of this work is to propose a meta-learning model applied to clinical data in which the different algorithms included can be compared and the benefits of each of them appreciated. Meta-learning involves algorithms learning about other algorithms through experience. However, the application of meta-learning to diagnosis poses considerable practical challenges.
A case study is used to test the model and assess its effectiveness. In this study, the application of meta-learning showed very promising initial results, adapting the algorithm to the database used depending on the disease to be predicted.
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Acknowledgements
This result has been supported through the Spanish Government by the project PID2021-127275OB-I00, FEDER “Una manera de hacer Europa”.
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Amador, S., Turbay, K., Montoro, A., Espinilla, M., Mora, H., Gil, D. (2023). Meta-learning. An Approach Applied to Clinical Data. In: Bravo, J., Urzáiz, G. (eds) Proceedings of the 15th International Conference on Ubiquitous Computing & Ambient Intelligence (UCAmI 2023). UCAmI 2023. Lecture Notes in Networks and Systems, vol 842. Springer, Cham. https://doi.org/10.1007/978-3-031-48642-5_23
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