Telecommunication integration in e‐healthcare: technologies, applications and challenges
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The technological advancements in sensing, signal processing, computing and communication have had great influence in different aspect of our lives. One of the fields that benefit from these technologies is health care. Trustworthiness of sensors, electronic devices, networks and wireless communications have matured enough to replace traditional legacy systems. However, there remains unexplored aspects of e‐healthcare especially considering the challenges in service provisioning, integration and resource optimization. Pervasive and continuous health monitoring is an important service, and it specifically provides the elderly and chronically ill patients with continuous and seamless health monitoring, benefiting both of them and the caretakers. In this survey article, we review and discuss different elements of e‐healthcare, introduce wireless transmission technologies that are used in e‐healthcare, present the quality of service (QoS) requirements and discuss the potential challenges as in security and privacy of data, power allocation, scheduling, meeting the QoS needs and confronting the electromagnetic interference. Some of the future research directions in healthcare areas are also discussed. Copyright © 2016 John Wiley & Sons, Ltd.
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.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it