Applications of Medical IoT and Smart Sensor Paradigm for Handling Patients
Bibliographic record
Abstract
The rapid advancement of technology has led to the emergence of new paradigms in the field of healthcare, with medical Internet of Things (IoT) and smart sensors playing a pivotal role. This paper explores the application of medical IoT and smart sensors in handling patients, presenting a comprehensive overview of their benefits, challenges, and potential impact on healthcare delivery. Medical IoT refers to the interconnectivity of medical devices and systems, enabling the seamless exchange of data and information. Smart sensors, on the other hand, are small, embedded devices that can monitor and collect various physiological parameters in real time. By integrating these technologies, healthcare providers can enhance patient monitoring, diagnostics, and treatment, ultimately leading to improved patient outcomes and reduced healthcare costs. One key application of medical IoT and smart sensors is remote patient monitoring. Through wearable devices and sensors, healthcare professionals can continuously track vital signs, such as heart rate, blood pressure, and oxygen saturation, in real time. This remote monitoring enables early detection of abnormalities, timely intervention, and personalized care for patients, particularly those with chronic illnesses or those in remote areas with limited access to healthcare facilities. Moreover, the implementation of medical IoT and smart sensors facilitates the creation of comprehensive electronic health records (EHRs). These records contain up-to-date patient data, including medical history, test results, and treatment plans, accessible to healthcare providers across different settings. This interoperability improves care coordination, reduces medical errors, and enhances patient safety.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".