Tracking clusters of patients over time enables extracting information from medico-administrative databases.
J Biomed Inform
; 139: 104309, 2023 03.
Article
in En
| MEDLINE
| ID: mdl-36796599
ABSTRACT
CONTEXT Identifying clusters (i.e., subgroups) of patients from the analysis of medico-administrative databases is particularly important to better understand disease heterogeneity. However, these databases contain different types of longitudinal variables which are measured over different follow-up periods, generating truncated data. It is therefore fundamental to develop clustering approaches that can handle this type of data. OBJECTIVE:
We propose here cluster-tracking approaches to identify clusters of patients from truncated longitudinal data contained in medico-administrative databases. MATERIAL ANDMETHODS:
We first cluster patients at each age. We then track the identified clusters over ages to construct cluster-trajectories. We compared our novel approaches with three classical longitudinal clustering approaches by calculating the silhouette score. As a use-case, we analyzed antithrombotic drugs used from 2008 to 2018 contained in the Échantillon Généraliste des Bénéficiaires (EGB), a French national cohort.RESULTS:
Our cluster-tracking approaches allow us to identify several cluster-trajectories with clinical significance without any imputation of data. The comparison of the silhouette scores obtained with the different approaches highlights the better performances of the cluster-tracking approaches.CONCLUSION:
The cluster-tracking approaches are a novel and efficient alternative to identify patient clusters from medico-administrative databases by taking into account their specificities.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Data Management
/
Clinical Relevance
Type of study:
Prognostic_studies
Limits:
Humans
Language:
En
Journal:
J Biomed Inform
Journal subject:
INFORMATICA MEDICA
Year:
2023
Document type:
Article