AI-Enabled Digital Twin Framework for Healthcare Task Offloading Strategies with MADDPG, AHP, Multimodal Digital Twins, DRM, and mHealth Applications
Bibliographic record
Abstract
Background: The AI-powered framework combines MADDPG, AHP, multimodal digital twins, DRM, and mHealth apps to improve real-time monitoring, optimize healthcare resource management, and promote individualized care using predictive analytics and the Internet of Things. Methods: It uses multimodal digital twins for real-time simulation, DRM for resource allocation, AHP for decision prioritization, MADDPG for reinforcement learning, and mHealth apps for ongoing monitoring. Objectives: The ultimate aim of enhancing these factors is to bring better decision-making, resource allocation, and work offloading to improve operational performance and results in the healthcare industry. Results: The findings are on improvement of scalability, adaptability, and patient care by achieving 94.5% accuracy, 92.8% precision, and 90% computing efficiency. Conclusion: By enhancing real-time reactions, resource management, and patient outcomes, the integrated AI-driven framework may provide a creative, effective solution for contemporary healthcare.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".