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A Multimodal Mathematical and Computational Framework for Personalized Medication-Adherence and Cognitive-Risk Modeling in Alzheimer's Disease
Arafat F.. Research Square, 2026-09-20
Topic: Brain imaging, connectivity and assessment
Digital medication-management tools for Alzheimer's disease (AD) typically record only whether a dose was taken, leaving the temporal and behavioral structure of adherence largely unused as a predictive signal. This working paper proposes a seven-layer mathematical and computational research framework that (i) formalizes multimodal behavioral data — medication-taking events, response latency, activity, and cognitive-task performance — as a time-indexed state vector; (ii) applies probabilistic and statistical inference to identify features associated with adherence difficulty; (iii) compares logistic, ensemble, neural, and temporal machine-learning model families under a common, pre-registered evaluation protocol; and (iv) tests whether personalized, participant-conditioned models outperform population-level baselines. No real patient data have been collected or analyzed. Instead, we validate the complete pipeline end-to-end on a synthetic, computer-generated cohort (150 synthetic participants, 9,000 participant-days) with a known data-generating process, and report the genuine simulation outputs: population-level models achieved AUROC 0.62–0.66, a temporal/personalized model achieved AUROC 0.83 and correctly recovered a personalization effect deliberately embedded in the synthetic data (bootstrap 95% CI on the AUROC gain excluded zero: [0.002, 0.084]), and permutation feature importance recovered the two dominant synthetic drivers (cognitive-score drift, response latency). These synthetic results validate the analysis pipeline only and make no claims about real AD patients. We situate the framework within the wearable- and sensor-based dementia-monitoring literature, specify testable hypotheses and evaluation metrics aligned with TRIPOD/PROBAST reporting standards, and describe how findings would extend an existing IoT-based medication-reminder system toward personalized, explainable adherence-risk prediction.
Key findings
- This working paper proposes a seven-layer mathematical and computational research framework that (i) formalizes multimodal behavioral data — medication-taking events, response latency, activity, and cognitive-task performance — as a time-indexed state vector; (ii) applies probabilistic and statistical inference to identify features associated with adherence difficulty; (iii) compares logistic, ensemble, neural, and temporal machine-learning model families under a common, pre-registered evaluation protocol; and (iv) tests whether personalized, participant-conditioned models outperform population-level baselines.
- These synthetic results validate the analysis pipeline only and make no claims about real AD patients.
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Study details
- Evidence strengthModerate
How this study scored 16/100
Preprint — not yet peer-reviewed. It scored 24 on the same rubric, but non-peer-reviewed sources are capped at 16, so it shows as 16. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it. We have no data on times cited, citations per year, impact for its field and year, cited by studies here, still being cited, senior author's track record, institution's track record and multi-centre collaboration, so those were left out: it earned 15 of the 62 points we could judge it on, which is 24 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 2 / 10 | Tier 4 — not a peer-reviewed journal (Research Square) |
| Journal's citation record | 0 / 4 | Not a journal article, so the journal's record does not count |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 3 / 5 | Rated “moderate” in our summary |
| Peer-review validation | 4 / 10 | Preprint — not yet peer-reviewed. Hosted on a preprint server. |
| How recent | 5 / 5 | 2026 — within 1 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | — / 7 | No citation count (not found in OpenAlex) — left out of this score |
| Citations per year | — / 5 | No citation count or no year — left out of this score |
| Impact for its field and year | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| Cited by studies here | — / 6 | Not yet checked against this library's citation graph — left out of this score |
| Still being cited | — / 4 | No yearly citation counts — left out of this score |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | — / 3 | No institution listed in OpenAlex — left out of this score |
| Multi-centre collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 1 / 2 | Not in PubMed, but has a DOI |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Points | 15 / 62 | Scored on 12 of 20 parts. We have no data for the rest, so they are left out: 15 of the 62 points we could judge it on is 24 out of 100. |
| Total | 24 / 100 | Capped to 16 because this is not a peer-reviewed article. |
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Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.
APA 7th
Arafat, F. (2026). A Multimodal Mathematical and Computational Framework for Personalized Medication-Adherence and Cognitive-Risk Modeling in Alzheimer's Disease. Research Square. https://doi.org/10.21203/rs.3.rs-11042021/v1
Vancouver
Arafat F. A Multimodal Mathematical and Computational Framework for Personalized Medication-Adherence and Cognitive-Risk Modeling in Alzheimer's Disease. Research Square. 2026. doi:10.21203/rs.3.rs-11042021/v1.
BibTeX
@article{arafat2026-cd543ea2,
author = {Arafat, F.},
title = {{A Multimodal Mathematical and Computational Framework for Personalized Medication-Adherence and Cognitive-Risk Modeling in Alzheimer's Disease}},
journal = {Research Square},
year = {2026},
doi = {10.21203/rs.3.rs-11042021/v1},
url = {https://doi.org/10.21203/rs.3.rs-11042021/v1}
}