Design and Evaluation of an Abdominal Voice Wearable System for Mitigating Hemodialysis-Related Complications in End-Stage Renal Disease Patients
Bibliographic record
Abstract
End-stage renal disease (ESRD) patients often face complications during hemodialysis, such as hypotension and gastrointestinal disorders.Still, traditional monitoring methods cannot capture patients' dynamic physiological changes in real time.To address this problem, this study focuses on the design and evaluation of a wearable abdominal sound system with a human-computer interface: the device can detect and analyze physiological signals such as bowel peristalsis in real-time, and at the same time, through multimodal data processing and visualization interface, provide personalized health management advice to healthcare teams and patients.Based on this system, we further propose a cross-disciplinary model that integrates dietary management and health monitoring, not only to reduce the risk of complications during hemodialysis but also to improve patients' awareness of their condition and quality of life.Overall, this study focuses on the potential value of human-computer interaction technology in clinical monitoring and patient selfmanagement and explores how wearable devices can enhance patient engagement and compliance through real-time feedback and userfriendly interfaces.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".