Abstract: We define and illustrate a "deep visualization" paradigm for the analysis of a relatively large and complex clinical database for psoriasis (PSO) and psoriatic arthritis (PsA). This paradigm supports a growing number of machine learning and exploratory analyses, and it provides a framework for Shiny applications and dashboards used to communicate results with internal and external clinicians. Our R platform implements a "whole-patient" data view including omics, imaging, and hundreds of anatomical assessments (scores) on multiple tissues, such as skin, joints, bones, entheses, etc. The package makes extensive use of anatomical metadata objects implemented as reference classes (Chambers, 2016), both for computing over anatomical structures and for visualizing disease state both at specific anatomical locations and at the patient-level. We present examples including visualization of bone and joint structural damage assessment scores, clustering of patients according to their disease trajectories, and association of pain to clinical endpoints over time.
Speaker: David James
Title: An R package for Data Science and Deep Visualization of a complex clinical database
Abstract_Sanitzed: We define and illustrate a "deep visualization" paradigm for the analysis of a relatively large and complex clinical database for psoriasis (PSO) and psoriatic arthritis (PsA). This paradigm supports a growing number of machine learning and exploratory analyses, and it provides a framework for Shiny applications and dashboards used to communicate results with internal and external clinicians. Our R platform implements a "whole-patient" data view including omics, imaging, and hundreds of anatomical assessments (scores) on multiple tissues, such as skin, joints, bones, entheses, etc. The package makes extensive use of anatomical metadata objects implemented as reference classes (Chambers, 2016), both for computing over anatomical structures and for visualizing disease state both at specific anatomical locations and at the patient-level. We present examples including visualization of bone and joint structural damage assessment scores, clustering of patients according to their disease trajectories, and association of pain to clinical endpoints over time.