Achieving Inclusive Healthcare through Integrating Education and Research with AI and Personalized Curricula.

Precision medicine promises significant health benefits but faces challenges such as the need for complex data management and analytics, interdisciplinary collaboration, and education of researchers, healthcare professionals, and participants. Addressing these needs requires the integration of computational experts, engineers, designers, and healthcare professionals to develop user-friendly systems and shared terminologies. The widespread adoption of large language models (LLMs) like GPT-4 and Claude 3 highlights the importance of making complex data accessible to non-specialists.

VISAGE: A Query Interface for Clinical Research.

We present the design and implementation of VISAGE (VISual AGgregator and Explorer), a query interface for clinical research. We follow a user-centered development approach and incorporate visual, ontological, searchable and explorative features in three interrelated components: Query Builder, Query Manager and Query Explorer. The Query Explorer provides novel on-line data mining capabilities for purposes such as hypothesis generation or cohort identification.

User requirements for exploring a resource inventory for clinical research.

The CTSA Inventory of Resources Explorer facilitates searching and finding relevant biomedical resources in this rich, federated inventory. We used efficient and non-traditional formal usability methods to define requirements and to design the Explorer, which may be extended to similar web-based tools.

The nested structure of cancer symptoms. Implications for analyzing co-occurrence and managing symptoms.

Objective: Although many cancer patients experience multiple concurrent symptoms, most studies have either focused on the analysis of single symptoms, or have used methods such as factor analysis that make a priori assumptions about how the data is structured. This article addresses both limitations by first visually exploring the data to identify patterns in the co-occurrence of multiple symptoms, and then using those insights to select and develop quantitative measures to analyze and validate the results.

Methods: We used networks to visualize how 665 cancer patients reported 18 symptoms, and then quantitatively analyzed the observed patterns using degree of symptom overlap between patients, degree of symptom clustering using network modularity, clustering of symptoms based on agglomerative hierarchical clustering, and degree of nestedness of the symptoms based on the most frequently co-occurring symptoms for different sizes of symptom sets.

Discovering hidden relationships between renal diseases and regulated genes through 3D network visualizations.

Background: In a recent study, two-dimensional (2D) network layouts were used to visualize and quantitatively analyze the relationship between chronic renal diseases and regulated genes. The results revealed complex relationships between disease type, gene specificity, and gene regulation type, which led to important insights about the underlying biological pathways. Here we describe an attempt to extend our understanding of these complex relationships by reanalyzing the data using three-dimensional (3D) network layouts, displayed through 2D and 3D viewing methods.