The Application of Machine Learning in Clustering Borderline Mild Cognitive Impairment among Aging Thai People Living with HIV
Bibliographic record
Abstract
Many people living with HIV (PLWH) have cognitive impairment. Details of cognitive impairment subtypes are lacking.�Unsupervised machine learning�(ML)�can reveal hidden subgroups within heterogeneous data. The study aimed to determine clusters of aging Thai PLWH with borderline cognitive impairment using unsupervised�ML. HIV-NAT 207 study enrolled Thai PLWH aged ?50 years. Cognitive performance was evaluated by the Thai-validated Montreal Cognitive Assessment (MoCA).�This study included participants who scored between 23 and�27. The score of each cognitive domain served as cluster variables for the K-means algorithm. Among 340 PLWH, 177 (52.1%) scored between 23 and 27.�Median�age was 54 (IQR = 51-58) years, 118 (66.7%) were male, median CD4 was 620 (IQR = 489-795) cells/?L, and 170 (96.1%) were virally suppressed. K-means cluster demonstrated five clusters�of all participants: 22.0% cluster 1 (marked memory with mild language impairment), 25.4% cluster 2 (mild visuospatial/executive function-language-memory impairment), 19.2% cluster 3 (moderate abstraction with mild visuospatial/executive function-language-memory impairment), 18.6% cluster 4 (marked language with mild memory impairment), 14.7% cluster 5 (marked language-abstraction impairment). A longitudinal study is warranted to identify differences in clinical significance and prognosis between each cluster.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".