Advancing Multimorbidity Analysis: A Computational Approach to Frequency-Based Odds Ratios and Temporal Disease Progression Modeling with Potential for Use in Clinical Assessment

Multimorbidity — the presence of multiple medical conditions occurring simultaneously or over time within an individual — presents significant challenges in clinical practice and epidemiological research. Traditional Odds Ratios (ORs) provide static associations between conditions but fail to capture diagnostic frequency as an index of disease severity and the temporal evolution of multimorbidity. To address these limitations, this study introduces refined Frequency-Based Odds Ratios (FORs) and Temporal Ratios of Ratios, implemented in Python-based computational tools designed for large-scale clinical datasets. These analytical scripts, developed with assistance from ChatGPT-4.o and presented at the 2024 World Psychiatry Association Congress in Mexico, integrate Fast Fourier Transform (FFT) and sequence-based analysis to quantify disease progression dynamically. The computational models were embedded into graphical user interfaces (GUIs) that facilitate interactive visualization of multimorbidity progression. These tools enable clinicians to assess disease trajectories in real time, optimize personalized treatment planning, and identify high-risk patients based on diagnostic patterns. The implementation of FORs and Temporal Ratios of Ratios in clinical decision-making supports proactive, data-informed interventions, making these computational tools valuable for precision medicine, epidemiology, and public health planning. This study underscores the transformative role of AI-assisted analytics in advancing multimorbidity research and clinical management.