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.
| Original language | English |
|---|---|
| Title of host publication | HEALTHINF 2020 - 13th International Conference on Health Informatics, Proceedings; Part of 13th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2020 |
| Editors | Federico Cabitza, Ana Fred, Hugo Gamboa |
| Publisher | SciTePress |
| Pages | 724-734 |
| Number of pages | 11 |
| ISBN (Electronic) | 9789897583988 |
| DOIs | |
| Publication status | Published - 2020 |
| Event | 13th International Conference on Health Informatics, HEALTHINF 2020 - Part of 13th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2020 - Valletta, Malta Duration: 24 Feb 2020 → 26 Feb 2020 |
Conference
| Conference | 13th International Conference on Health Informatics, HEALTHINF 2020 - Part of 13th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2020 |
|---|---|
| Country/Territory | Malta |
| City | Valletta |
| Period | 24/02/20 → 26/02/20 |
Keywords
- Applied data science
- CRISP-DM
- Data analytics
- Domain expertise
- Healthcare
- Knowledge discovery
- Meta-algorithmic modelling
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