Abstract
Precision Medicine has emerged as a computational approach to provide a personalized diagnosis, based on the individual variability in genes, environment, and lifestyle. Success in such aim requires extensible, adaptive, and ontologically well-grounded Information Systems to store, manage, and analyze the large amounts of data generated by the scientific community. Using an existing adaptive information system (Delfos platform) supported by a conceptual schema and an AI algorithm, the contribution of this work is to describe how the system has been improved to address specific challenges regarding the clinical significance of DNA variants. To do so, the following topics are addressed: i) provide an ontologically-consistent representation of the problem domain; ii) improve the management of clinical significance conflicts; iii) ease the addition of new data sources; and iv) provide a scalable environment more aligned with the data analysis requirements in a clinical context. The aim of the work has been achieved by using a Model-Driven Engineering approach.
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Acknowledgements
This work was supported by the Spanish State Research Agency [grant number TIN2016-80811-P]; and the Generalitat Valenciana [grant number PROMETEO/2018/176] co-financed with European Regional Development Fund (ERDF).
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León, A., García S., A., Costa, M., Vañó Ribelles, A., Pastor, O. (2021). Evolution of an Adaptive Information System for Precision Medicine. In: Nurcan, S., Korthaus, A. (eds) Intelligent Information Systems. CAiSE 2021. Lecture Notes in Business Information Processing, vol 424. Springer, Cham. https://doi.org/10.1007/978-3-030-79108-7_1
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DOI: https://doi.org/10.1007/978-3-030-79108-7_1
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