Automatic and Explainable Labeling of Medical Event Logs With Autoencoding

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...

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Bibliographic Details
Published in:IEEE journal of biomedical and health informatics 2020-11, Vol.24 (11), p.3076-3084
Main Authors: De Oliveira, Hugo, Augusto, Vincent, Jouaneton, Baptiste, Lamarsalle, Ludovic, Prodel, Martin, Xie, Xiaolan
Format: Article
Language:English
Subjects:
Artificial Intelligence
autoencoding
Binary data
Clustering
Complexity
Complexity theory
Computer Science
Data mining
Decoding
Ecology, environment
event log
Health
healthcare data
Hidden Markov models
Labelling
Labels
Life Sciences
Machine learning
Medical diagnosis
Medical diagnostic imaging
Medical services
patient pathways
Process mining
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Summary: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.
ISSN:2168-2194
2168-2208
DOI:10.1109/JBHI.2020.3021790