Computational Intelligence and Sensor Networks for Biomedical Systems
Bibliographic record
Abstract
Sensor networks (SN) is an emergent technology which combines small sensors outfitted with wireless transmitters to form a network with more powerful sensing capabilities (Akyildiz, Su, Sankarasubramaniam, & Cayirci, 2002; Chong & Kumar, 2003). The primary application for SN technology is monitoring environmental changes making it ideal for deployment in patient monitoring systems. In contrast to other monitoring technologies such as video, SN offers a potentially cheaper solution consisting of cost effective interconnected sensors which cooperatively sense the surroundings. Individual sensor information is then fused to derive an instantaneous description of the environment. In this article, we review briefly the recent applications of CI and SN technologies in health care, mentioning some of the challenges in deploying these technologies. This is followed by an example of a biomedical system incorporating both technologies in a single paradigm. The state of current systems and their advantages over existing methods are highlighted with examples focusing primarily on intelligent automated diagnostic systems to augment clinician diagnoses and health care monitoring systems for continuous patient observation.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.009 |
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".