Enhanced Complex Event Processing Framework for Geriatric Remote Healthcare
Bibliographic record
Abstract
Advances in information and communication technology (ICT) have paved way for improved healthcare and facilitates remote health monitoring. Geriatric remote health monitoring system (GRHMS) uses WBAN (wireless body area network) which provides flexibility and mobility for the patients. GRHMS uses complex event processing (CEP) to detect the abnormality in patient's health condition, formulate contexts based on spatiotemporal relations between vital parameters, learn rules dynamically, and generate alerts in real time. Even though CEP is powerful in detecting abnormal events, its capability is limited due to uncertain incoming events, static rule base, and scalability problem. To address the above challenges, this chapter proposes an enhanced CEP (eCEP) which encompasses augmented CEP (a-CEP), a statistical event refinement model to minimize the error due to uncertainty, dynamic CEP (DCEP) to add and delete rules dynamically into the rule base and scalable CEP (SCEP) to address scalability problem. Experimental results show that the proposed framework has better accuracy in decision making.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".