People, sensors, decisions
Bibliographic record
Abstract
The ratio of healthcare professionals to care recipients is dropping at an alarming rate, particularly for the older population. It is estimated that the number of persons with Alzheimer's disease, for example, will top 100 million worldwide by the year 2050 [Alzheimer's Disease International 2009]. It will become harder and harder to provide needed health services to this population of older adults. Further, patients are becoming more aware and involved in their own healthcare decisions. This is creating a void in which technology has an increasingly important role to play as a tool to connect providers with recipients. Examples of interactive technologies range from telecare for remote regions to computer games promoting fitness in the home. Currently, such technologies are developed for specific applications and are difficult to modify to suit individual user needs. The future potential economic and social impact of technology in the healthcare field therefore lies in our ability to make intelligent devices that are customizable by healthcare professionals and their clients, that are adaptive to users over time, and that generalize across tasks and environments. A wide application area for technology in healthcare is for assistance and monitoring in the home. As the population ages, it becomes increasingly dependent on chronic healthcare, such as assistance for tasks of everyday life (washing, cooking, dressing), medication taking, nutrition, and fitness. This article will present a summary of work over the past decade on the development of intelligent systems that provide assistance to persons with cognitive disabilities. These systems are unique in that they are all built using a common framework, a decision-theoretic model for general-purpose assistance in the home. In this article, we will show how this type of general model can be applied to a range of assistance tasks, including prompting for activities of daily living, assistance for art therapists, and stroke rehabilitation. This model is a Partially Observable Markov Decision Process (POMDP) that can be customized by end-users, that can integrate complex sensor information, and that can adapt over time. These three characteristics of the POMDP model will allow for increasing uptake and long-term efficiency and robustness of technology for assistance.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.007 |
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".