TY - GEN
T1 - Multi-view Clustering of Heterogeneous Health Data: Application to Systemic Sclerosis
AU - José-garcía, Adán
AU - Jacques, Julie
AU - Filiot, Alexandre
AU - Handl, Julia
AU - Launay, David
AU - Sobanski, Vincent
AU - Dhaenens, Clarisse
PY - 2022/8/15
Y1 - 2022/8/15
N2 - Electronic health records (EHRs) involve heterogeneous data types such as binary, numeric and categorical attributes. As traditional clustering approaches require the definition of a single proximity measure, different data types are typically transformed into a common format or amalgamated through a single distance function. Unfortunately, this early transformation step largely pre-determines the cluster analysis results and can cause information loss, as the relative importance of different attributes is not considered. This exploratory work aims to avoid this premature integration of attribute types prior to cluster analysis through a multi-objective evolutionary algorithm called MVMC. This approach allows multiple data types to be integrated into the clustering process, explore trade-offs between them, and determine consensus clusters that are supported across these data views. We evaluate our approach in a case study focusing on systemic sclerosis (SSc), a highly heterogeneous auto-immune disease that can be considered a representative example of an EHRs data problem. Our results highlight the potential benefits of multi-view learning in an EHR context. Furthermore, this comprehensive classification integrating multiple and various data sources will help to understand better disease complications and treatment goals.
AB - Electronic health records (EHRs) involve heterogeneous data types such as binary, numeric and categorical attributes. As traditional clustering approaches require the definition of a single proximity measure, different data types are typically transformed into a common format or amalgamated through a single distance function. Unfortunately, this early transformation step largely pre-determines the cluster analysis results and can cause information loss, as the relative importance of different attributes is not considered. This exploratory work aims to avoid this premature integration of attribute types prior to cluster analysis through a multi-objective evolutionary algorithm called MVMC. This approach allows multiple data types to be integrated into the clustering process, explore trade-offs between them, and determine consensus clusters that are supported across these data views. We evaluate our approach in a case study focusing on systemic sclerosis (SSc), a highly heterogeneous auto-immune disease that can be considered a representative example of an EHRs data problem. Our results highlight the potential benefits of multi-view learning in an EHR context. Furthermore, this comprehensive classification integrating multiple and various data sources will help to understand better disease complications and treatment goals.
U2 - 10.1007/978-3-031-14721-0_25
DO - 10.1007/978-3-031-14721-0_25
M3 - Conference contribution
T3 - Lecture Notes in Computer Science
SP - 352
EP - 367
BT - Parallel Problem Solving from Nature – PPSN XVII
T2 - 17th International Conference on Parallel Problem Solving from Nature
Y2 - 10 September 2022 through 14 September 2022
ER -