Assessment of cognitive impairment using artificial intelligence from user-web-mobile interaction
Bibliographic record
Abstract
The World Health Organization forecasts a population of 2,000 million people over 60 years by the year 2050, with 7% of this population suffering from dementia, a disease of public priority. Making a constant evaluation of older adults allows early detection of the disease and provides a better quality of life in the patient. In this sense, the research and development of innovative technological systems for the management of the growing number of patients with cognitive diseases has increased in recent years, integrating data collection and its automatic processing based on geriatric metrics into these systems using artificial intelligence (AI) methods, such that they can establish disease detection at an early stage and follow-up of it, in order to support the increase in patients expected in the coming years in the clinical area. This research presents an interactive web platform that allows users with internet connection from any mobile device, computer, laptop, or other devices, to remotely perform an automated assessment of the Montreal Cognitive Assessment (MoCA) test. This test detects and assesses cognitive deterioration. We use AI and neural network methods for binary and multiclass classification to obtain assessment scores according to geriatric metrics. Subsequently, this test is validated remotely by a mental health specialist. The tests carried out show a correct correspondence in the handling of the information and the results regarding the reference data for comparison. Our system provides an automated and easyto-use digital evaluation metric.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".