Diverse Unity: Exploring Shared Phenotypic Traits Across a Heterogeneous Patient Cohort via Visual Analytics

Presentation Time: 03:30 PM - 03:45 PM

Abstract Keywords: Information Extraction, Population Health, Data Transformation/ETL
Primary Track: Applications
Programmatic Theme: Clinical Research Informatics

Clinical data, such as electronic health records (EHRs), hold valuable insights for personalized patient care. However, the complexity of these data often hinders the extraction of actionable clinical evidence. We present an interactive system that leverages patient similarity-based visual analytics to explore clinical evidence embedded in EHRs to promote informed decision-making in clinical practice. Our system constructs patient vectors for EHR data structured in the OMOP Common Data Model (CDM) to represent demographics, conditions, drugs, and laboratory test measurements. We employ dimensionality reduction techniques to project these high-dimensional vectors onto a 2D plane, facilitating intuitive exploration through interactive visualizations. Clinicians can flexibly select and compare patient clusters, gaining insights into similar and dissimilar characteristics. A case study demonstrates the system's capability to identify distinct patient clusters with varying conditions, such as those related to post-acute sequelae of SARS-CoV-2 infection (PASC). The interactive visualizations link algorithmic results back to the original EHR data, enhancing the understanding of patient cohorts and extraction of actionable insights into clinical care.

Speaker(s):
Xingbo Wang, PhD
Weill Cornell Medicine

Author(s):
Xingbo Wang, PhD - Weill Cornell Medicine; Mark Weiner, MD - Weill Cornell Medicine / Population Health Sciences; Qiannan Zhang, PhD - Weill Cornell Medicine; Jyotishman Pathak, PhD - Weill Cornell Medical College, Cornell University; Fei Wang, PhD - Weill Cornell Medicine;