Authors:
Hugo Boisaubert
1
;
Chloé Grivaud
1
;
Antoine Bouchet
1
;
Corinne Lejus-Bourdeau
2
;
3
and
Christine Sinoquet
1
Affiliations:
1
Nantes Université, CNRS, LS2N UMR 6004, 2 Chemin de la Houssinière, 44300 Nantes, France
;
2
Nantes Université, LESiMU, 9 rue Bias, 44000 Nantes, France
;
3
Department of Anaesthesia and Intensive Care, CHU Nantes, 1 place Alexis Ricordeau, 44000 Nantes, France
Keyword(s):
Process-Mining, Anesthesia, Healthcare, Model Comparison, Machine Learning, Expertise, Personal Data.
Abstract:
Digital tools accessible for healthcare are often based on models representing a medical process and learned from medical data.
Unfortunately, those data are protected by privacy regulation and therefore are quite rare. This rarity leads to process models mainly based on the expertise of caregivers. Those expertise-based model and data-based models are rarely compared to show their common characteristics and differences. When both model can be produced for the same situation multiple questions arise. Should the expertise-based model be invalidated if it is not in full conformity to the data-based model ? Are those models' characteristics the same? In this article, we present a comparison of expertise-based models and data-based models produced for a surgery under general anesthesia with 204 real cases.
We conducted a process mining algorithm performance comparison on our specific real data to identify the most promising learning method. Then we compared the produced data-based mode
ls to the expertise-based models with some metrics.
The comparison results show strong differences between the two types of models, the expertise-based model is very much smaller than the data-based model, but we have noticed that the expert-based model is included in a data-based model. Therefore, the main difference between the two models appears to be on a level of abstraction.
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