Mining Disease Courses across Organizations: A Methodology Based on Process Mining of Diagnosis Events Datasets
Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › begutachtet
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Mining Disease Courses across Organizations : A Methodology Based on Process Mining of Diagnosis Events Datasets. / de Toledo, Paula; Joppien, Carolin; Paz Sesmero, Maria et al.
2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019. IEEE - Institute of Electrical and Electronics Engineers Inc., 2019. S. 354-357 8857149 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › begutachtet
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RIS
TY - CHAP
T1 - Mining Disease Courses across Organizations
T2 - 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society - EMBC 2019
AU - de Toledo, Paula
AU - Joppien, Carolin
AU - Paz Sesmero, Maria
AU - Drews, Paul
N1 - Conference code: 41
PY - 2019/7/1
Y1 - 2019/7/1
N2 - This work proposes the use of Process Mining methodologies on healthcare datasets containing diagnosis information as a means to identify the course of a disease across organizations. Datasets containing diagnosis information for administrative purposes are a good candidate due to its standardized format, widespread availability and coverage. We present a methodology to preprocess, cluster and mine diagnosis information and the results of a preliminary use case with diabetes type II. Some meaningful disease courses have been found but less useful patterns do also emerge. Future work involves lowering the level of granularity chosen (ICD three digit codes) and extending the time span of the data available (three years).
AB - This work proposes the use of Process Mining methodologies on healthcare datasets containing diagnosis information as a means to identify the course of a disease across organizations. Datasets containing diagnosis information for administrative purposes are a good candidate due to its standardized format, widespread availability and coverage. We present a methodology to preprocess, cluster and mine diagnosis information and the results of a preliminary use case with diabetes type II. Some meaningful disease courses have been found but less useful patterns do also emerge. Future work involves lowering the level of granularity chosen (ICD three digit codes) and extending the time span of the data available (three years).
KW - Business informatics
UR - https://ieeexplore.ieee.org/document/8857149
UR - http://www.scopus.com/inward/record.url?scp=85077849572&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/9964ab5d-63cb-3333-87d1-8f5763428f3e/
U2 - 10.1109/EMBC.2019.8857149
DO - 10.1109/EMBC.2019.8857149
M3 - Article in conference proceedings
C2 - 31945914
SN - 978-1-5386-1312-2
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 354
EP - 357
BT - 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
PB - IEEE - Institute of Electrical and Electronics Engineers Inc.
Y2 - 23 June 2019 through 27 June 2019
ER -