TY - JOUR
T1 - Self-service Data Science for Healthcare Professionals: A Data Preparation Approach
AU - Vijlbrief, D
AU - Spruit, Marco R
AU - Deding, Thomas
N1 - Publisher Copyright:
© 2020 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Applied Data Science
KW - CRISP-DM
KW - Data Analytics
KW - Domain Expertise
KW - Healthcare
KW - Knowledge Discovery
KW - Meta-algorithmic Modelling
U2 - 10.5220/0009169507240734
DO - 10.5220/0009169507240734
M3 - Article
VL - 5
SP - 724
EP - 734
JO - HealthINF 2020
JF - HealthINF 2020
ER -