Skip to Main content Skip to Navigation
New interface
Journal articles

Automatic and Explainable Labeling of Medical Event Logs with Autoencoding

Abstract : Process mining is a suitable method for knowledge extraction from patient pathways. Structured in event logs, medical events are complex, often described using various medical codes. An efficient labeling of these events before applying process mining analysis is challenging. This paper presents an innovative methodology to handle the complexity of events in medical event logs. Based on autoencoding, accurate labels are created by clustering similar events in latent space. Moreover, the explanation of created labels is provided by the decoding of its corresponding events. Tested on synthetic events, the method is able to find hidden clusters on sparse binary data, as well as accurately explain created labels. A case study on real healthcare data is performed. Results confirm the suitability of the method to extract knowledge from complex event logs representing patient pathways.
Complete list of metadata
Contributor : Vincent Augusto Connect in order to contact the contributor
Submitted on : Tuesday, February 2, 2021 - 11:14:52 AM
Last modification on : Sunday, June 26, 2022 - 9:36:34 AM
Long-term archiving on: : Monday, May 3, 2021 - 6:28:23 PM


Files produced by the author(s)


  • HAL Id : emse-03128429, version 1


Hugo de Oliveira, Vincent Augusto, Baptiste Jouaneton, Ludovic Lamarsalle, Martin Prodel, et al.. Automatic and Explainable Labeling of Medical Event Logs with Autoencoding. IEEE Journal of Biomedical and Health Informatics, 2020. ⟨emse-03128429⟩



Record views


Files downloads