Title: Bayesian Multi-view Clustering for Grouping of Patients in Critical Care
Speakers: Luwei (Demi) Wang
Abstract:

The COVID-19 pandemic underscored the need to tailor medical interventions to patient characteristics by identifying at-risk subgroups. While extensive research has explored associations between pre-existing conditions and COVID-19 complications, less attention has been given to the relationship between pre-existing conditions and symptom presentation. Latent Class Analysis (LCA) is commonly employed to investigate this association. However, traditional clustering methods like LCA falter by relying solely on a single data source, amalgamating disparate data streams and potentially compromising outcome accuracy. This approach overlooks individual clinical profiles’ distinctiveness. Integrating multiple data sources, such as medical history and acute symptom presentation, offers a more tailored approach. Our study aims to utilize various clustering methods to explore connections between pre-existing condition clusters and symptom clusters while preserving their uniqueness. Multi-view clustering (MVC) techniques, unlike single-view methods, leverage consensus and complementary information to enhance clustering accuracy across multiple data representations. We evaluate different MVC methods—Binary Multi-view Clustering (BMVC), Consensus Graph Learning (CGL), and Bayesian Consensus Clustering (BCC)—against our proposed Bayesian approach. Simulation studies showcase our approach’s ability to uncover target clustering structures and its advantage in discovering complementary clusters. Utilizing the ISARIC dataset further elucidates distinct characteristics of these methodologies. In conclusion, the performance of MVC methods varies depending on assumptions and model implementations. Our approach offers fast processing and data-driven insights, uniquely uncovering complementary clusters compared to existing methods.