Advancing Multimorbidity Analysis: A Computational Approach to Frequency-Based Odds Ratios and Temporal Disease Progression Modeling with Potential for Use in Clinical Assessment
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".