Bioprofile Analysis: A New Approach for the Analysis of Biomedical Data in Alzheimer's Disease
Bibliographic record
Abstract
This article presents a new approach for the analysis of biomedical data to support the management and care of patients with Alzheimer's disease (AD). The increase in prevalence of neurodegenerative disorders such as AD has led to a need for objective means to assist clinicians with the analysis and interpretation of complex biomedical data. To this end, we propose a "Bioprofile" analysis to reveal the pattern of disease in the subject's biodata. From the Bioprofile, personal "Bioindices" that indicate how closely a subject's data resemble the pattern of AD can be derived. We used an unsupervised technique (k-means) to cluster variables of the ADNI database so that subjects are divisible into those with the Bioprofile of AD and those without it. Results revealed that there is an "AD pattern" in the biodata of most AD and mild cognitive impairment (MCI) patients and some controls. This pattern agrees with a recent hypothetical model of AD evolution. We also assessed how the Bioindices changed with time and we found that the Bioprofile was associated with the risk of progressing from MCI to AD. Hence, the Bioprofile analysis is a promising methodology that may potentially provide a complementary new way of interpreting biomedical data. Furthermore, the concept of the Bioprofile could be extended to other neurodegenerative diseases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".