Self-service data science for healthcare professionals: A data preparation approach
Publication date
2020
Editors
Cabitza, Federico
Fred, Ana
Gamboa, Hugo
Advisors
Supervisors
Document Type
Part of book
Metadata
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License
cc_by_nc_nd
Abstract
Knowledge Discovery and Data Mining are two well-known and still growing fields that, with the advancements of data collection and storage technologies, emerged and expanded with great strength by the many possibilities and benefits that exploring and analyzing data can bring. However, it is a task that requires great domain expertise to really achieve its full potential. Furthermore, it is an activity which is done mainly by data experts who know little about specific domains, like the healthcare sector, for example. Thus, in this research, we propose means for allowing domain experts from the medical domain (e.g. doctors and nurses) to also be actively part of the Knowledge Discovery process, focusing in the Data Preparation phase, and use the specific domain knowledge that they have in order to start unveiling useful information from the data. Hence, a guideline based on the CRISP-DM framework, in the format of methods fragments is proposed to guide these professionals through the KD process.
Keywords
Applied data science, CRISP-DM, Data analytics, Domain expertise, Healthcare, Knowledge discovery, Meta-algorithmic modelling, Biomedical Engineering, Electrical and Electronic Engineering
Citation
Spruit, M, Dedding, T & Vijlbrief, D 2020, Self-service data science for healthcare professionals : A data preparation approach. in F Cabitza, A Fred & H Gamboa (eds), HEALTHINF 2020 - 13th International Conference on Health Informatics, Proceedings; Part of 13th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2020. SciTePress, pp. 724-734, 13th International Conference on Health Informatics, HEALTHINF 2020 - Part of 13th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2020, Valletta, Malta, 24/02/20. https://doi.org/10.5220/0009169507240734, conference