Healthcare Monitoring System for Remote Areas
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: Doctors now place a high importance on ongoing patient health monitoring since it gives them the chance to save a patient's life. So, the primary objective is to develop a patient monitoring system that can monitor a patient's various physiological data when they are in a remote location and provide the doctor with this information in real time. The information is made public online so that any doctor in the globe can access it. The necessity for a patient to visit the doctor is lessened via remote patient monitoring. The Raspberry Pi employed here is not only a sensor node but also a CPU, and IOT plays a significant part in this complete system by delivering several apps and services. This data can be sensed, gathered, and published online by an intelligent gadget. The paper suggests a general health monitoring system utilising a neural network-based HDPS (Heart Disease Prediction System). The HDPS system forecasts a patient's risk of developing heart disease. The technique uses medical parameters like sex, blood pressure, age, height, and weight for prediction. as an improvement over the work done in this area up until now.
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 | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 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