Preventing Data Ambiguity in Infectious Diseases with Four-Dimensional and Personalized Evaluations

Area of Science:

• Immunology
• Infectious Diseases
• Data Science

Background:

• Diagnostic errors in infectious diseases arise from analyzing immune responses across multiple temporal scales.
• Pre-selected temporal scales may miss crucial faster or slower biological responses.
• A pilot study evaluated a novel four-dimensional (4D) method to capture disease complexity and dynamics.

Purpose of the Study:

• To evaluate a four-dimensional (4D) method for diagnosing infectious diseases.
• To prevent diagnostic errors by capturing multi-scale temporal dynamics.
• To explore leukocyte-microbial-temporal data in canine and human infections.

Main Methods:

• Compared non-structured (isolated variable) and structured (interacting variables) data analysis approaches.
• Tested four alternatives of the structured method, including noise-reduction, 3D interactions, temporal directionality, and a full 4D version.
• Explored leukocyte-microbial-temporal data in bacterial and/or viral infections in canines and humans.

Main Results:

• Non-structured and single-line structured data approaches yielded ambiguous results, failing to distinguish biological conditions.
• Structuring data as complex 3D interactions with temporal directionality avoided ambiguity.
• The 4D method detected rapid changes in immune profiles within a day of antibiotic administration.

Conclusions:

• Infectious disease data can be inherently ambiguous.
• Four-dimensional methods offer a way to prevent diagnostic ambiguity.
• 4D methods provide earlier, in vivo, dynamic, complex, and personalized information for improved diagnostics and antimicrobial therapy selection.