Modeling Mobile Visualization for Medical Reports of Complex Chronic Diseases

TL;DR

This paper explores visualization models for complex chronic disease medical reports, proposing a model to transform unstructured data into visual formats to aid understanding and treatment planning.

Contribution

It introduces a novel model for converting unstructured medical data of complex chronic diseases into structured visual representations.

Findings

Proposed a model for visualizing multifaceted medical data

Analyzed various visualization paradigms for medical reports

Demonstrated transformation of unstructured data into visual formats

Abstract

Visualizing medical histories of patients with complex chronic diseases (e.g., discordant chronic comorbidities (DCCs)) is a challenge for patients, their healthcare providers, and their support network. DCCs are health conditions in which patients have multiple, often unrelated, chronic illnesses that may need to be addressed concurrently but may also be associated with conflicting treatment instructions. Future work targeting to reduce treatment conflicts and improve patient quality of life and care should carefully examine and visualize DCCs medical reports, symptoms, and treatment recommendations. In this study, we explore various visualization models and paradigms. We analyze how these models and paradigms are applied to visualize multifaceted medical data. We then propose a model for transforming the unstructured data into temporal slices and depict them in a single graphic model. We report how we carefully moved multifaceted DCC records into; structured data tables, visualization graphs, and various hardware devices.