Noncontact Monitoring and AI‐Driven Stroke Prediction: National Center for Neurological Disorders‐Based Approach Using Smart Beds
Bibliographic record
Abstract
Background: Stroke is the second leading cause of death and third leading cause of disability worldwide and is the leading cause of death and disability among adults in China, with its incidence rate continuing to rise. In China, the average age of first-time stroke patients is 66.4 years, and the intravenous thrombolysis rate using recombinant tissue plasminogen activator within 3 h of onset is only 16%. Given this fact, there is a pressing need for real-time predictive tools, particularly for elderly individuals at home, that can provide early warnings for potential strokes. Methods: We collected continuous monitoring data from nonintrusive smart beds and multimodal temporal data from electronic medical records at the National Center for Neurological Disorders. The data included smart bed monitoring indicators, laboratory tests, nurse observations, and static data as potential predictors, with stroke as the outcome. We applied feature representation and feature selection techniques and then input the predictors into machine learning models. Additionally, deep learning models were used after preprocessing the irregular temporal data. Finally, we evaluated the performance of the stroke prediction models and assessed the importance of the features. We used continuously updated vital signs and clinical data during hospitalization to generate timely stroke risk alerts during the same period of admission. Results: A total of 37,041 samples were analyzed, of which 7020 patients were diagnosed with stroke. When only the smart bed features were used for prediction, the model achieved an area under the receiver operating characteristic curve (AUROC) of 0.59-0.63, with an accuracy ranging from 60%-65%. Among the four artificial intelligence algorithms, the random forest model demonstrated the best performance. After all the available features were incorporated, the AUROC increased to 0.94, and the accuracy improved to 92%. Conclusions: In this study, the occurrence of stroke was successfully identified by integrating multimodal temporal data from electronic medical records. Noncontact monitoring of respiration and heart rate offers a promising approach for daily stroke surveillance in home-based populations, particularly for elderly individuals living alone.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".