Mining Disease Courses across Organizations: A Methodology Based on Process Mining of Diagnosis Events Datasets

Publikation: Beiträge in SammelwerkenAufsätze in KonferenzbändenForschungbegutachtet


  • Paula de Toledo
  • Carolin Joppien
  • Maria Paz Sesmero
  • Paul Drews
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).
Titel2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
Anzahl der Seiten4
VerlagIEEE - Institute of Electrical and Electronics Engineers Inc.
ISBN (Print)978-1-5386-1312-2
ISBN (elektronisch)978-1-5386-1311-5
PublikationsstatusErschienen - 01.07.2019
Veranstaltung41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society - EMBC 2019: BIOMEDICAL ENGINEERING RANGING FROM WELLNESS TO INTENSIVE CARE - Berlin, Deutschland
Dauer: 23.06.201927.06.2019
Konferenznummer: 41 (Conference Proceeding)

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