ArticleFrontiers in medical technology2026
Enhancing context-aware SARS disorder management: a proposed multi-agent simulation framework with machine learning and bio-sensor data integration.
Article in Frontiers in medical technology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Digital and Remote Interventions for Musculoskeletal Aging: Real-Time Muscle Strain Severity Detection Using Artificial Intelligence.Biosensors · 2026Article
- Article
- HybridCATNet: an explainable CNN-Transformer framework with early multi-channel fusion and uncertainty estimation for pneumonia detection from chest X-rays.Frontiers in digital health · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this work, SARS disorder denotes a generic acute severe respiratory distress condition characterized by abnormal respiratory rate, oxygen saturation, fever, and cardiovascular stress indicators, and does not represent a COVID-19 diagnostic system. Our research aims at analyzing a context-aware SARS disorder management system through the implementation of a multi-agent simulation framework using the NetLogo setting. The system relies on the use of interacting agents as well as non-monotonic, context-sensitive reasoning to reduce uncertainty and deal with the possible inconsistencies that happen due to biosensor recordings. A knowledge-based inference component is the combination of physiological sensor outputs and domain specific contextual data to assist in making informed decisions. The research involved the use of several machine-learning classifiers, that is, Naïve Bayes, Multinomial Naïve Bayes, Decision Table, Logistic Regression, and Sequential Minimal Optimization (SMO) so as to evaluate their appropriateness in being incorporated into the developed structure. To measure the system performance, standard evaluation measures were used such as True Positive (TP), False Positive (FP), Precision, Recall, F-Measure, Matthews Correlation Coefficient (MCC), Receiver Operating Characteristic (ROC) curve, and the Precision-Recall curve (PRC). The framework includes a list of physiological, environmental, and contextual variables, such as electrocardiographic parameters, heart-rate parameters, blood-pressure parameters, arterial oxygen saturation parameters, core body temperature, room temperature, the past history of the patient, and parameters that relate to alerts. The classification task is to produce probabilistic forecasts that help to define whether a patient should be alerted or clinical staff members informed in order to facilitate context-specific healthcare response.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.