AI-enabled resilience modeling for brain health
Bibliographic record
Abstract
One development in the growing field of Alzheimer's Disease and related neurological disorders (ADRD) is the consideration of brain resilience, the ability to respond to and recover from adversity, which builds on a growing literature on the role of lifestyle behaviours in ADRD prevention and response. This paper reviews definitions of 'brain health' and integrates these with innovations in resilience system models applied to ADRD. Based on a socio-ecological framework that links physiological, behavioral, economic, and social determinants of mental health, we propose a unified model of resilience and aging in this field. We contend that applications of a resilience analytical approach to brain health require innovation in Artificial Intelligence (AI) to harness the full potential of immense interdisciplinary data mining opportunities. These include: development of digital twins, precision health analytics, AI sensors, and Multimodal Large Language Models (MLLM), knowledge graph technologies, and cognitive/decision science modeling. We apply this model to research and clinical examples to elucidate its potential value, requirements, risks, and challenges in developing new research agendas.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".