Drug Intervention Follow up with Internet of Things: A Case Study
Bibliographic record
Abstract
Abstract Advancements on the Internet of Things (IoT) have enabled the development of advanced monitoring systems that can track human behavior and vital signs in real-time, which can have a real impact in the way healthcare is provided. This paper presents a system implementation to monitor and analyze a subject's behavior changes over time using IoT, with the objective of detecting the impact of an inhibitor drug on the subject's activity levels. In this research we present a case study by which we showed it is possible to follow the effect of an anticholinergic drug by means of an unobtrusive IoT system. We have monitored the physical activity of a subject in his residence for seven consecutive months to study the effect of the inhibiting drug doses introduced at three known specific timestamps. Following, we compared our detection results for the subject’s physical activity change timestamps with the medical staff medication doses timestamps. Our results show that we can detect the physical activity change at close timestamps compared to those indicated by the medical staff.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".