Mobile health screening platform for degenerative neurological disorders
Bibliographic record
Abstract
The new and dynamic field of mHealth - the use of mobile technology applications for healthcare - could improve the well-being of people around the world. Mobile applications can lower costs and improve the quality of healthcare as well as shift behavior to strengthen prevention, all of which can improve health outcomes over the long term. Technologies of mHealth field are a valuable partner in health care's shift towards a delivery model that is patient-centered and value-based. A broad category of brain diseases is Dementia, which causes long term loss of the ability to think and reason clearly that is severe enough to affect a person's daily functioning. The most common form of Dementia is Alzheimer's disease (75%). Although Alzheimer's disease develops differently for every individual, there are many common symptoms. Early symptoms are often mistakenly thought to be 'age-related' concerns, or manifestations of stress. Neurology cognitive tests used for symptoms recognition to such degenerative disorders are generally applied by paper-and-pencil until now. These kinds of tests implemented until now on PCs have typically unfriendly interface and do not take people's capabilities into consideration. Our aim was to develop a mHealth screening platform that combines various neurological tests that will help the diagnosis of early dementia and Alzheimer disease by easy performed self-testing applications, provided by a friendly GUI. The application is composed of the best 3 tests chosen from the paper-and-pencil standard tests, Clock drawing test (CDT), Mini Cog test (MCT) and Montreal Cognitive Assessment test (MoCA). This thesis is important for being one of the first mobile applications for degenerative neurological disorders self-testing that is compatible for tablets and smart-phones regardless witch operating system is running
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.015 |
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".