Machine Learning–Driven Functional Chain Scheduling for Medical Emergency Assistance in Smart Healthcare
Bibliographic record
Abstract
To effectively manage classification and prioritization in medical emergency assistance, this study proposes a machine learning (ML)–driven functional chain scheduling framework for health medical information networks. Medical emergency services are classified into four categories based on urgency level and service area, with corresponding priority weights assigned. A system architecture integrating Software-Defined Networking (SDN) and Network Function Virtualization (NFV) is designed to support dynamic and scalable service deployment. To minimize the total weighted completion time, a scheduling optimization model is formulated. For different problem scales, two complementary ML-based algorithms are employed: a Matching Game algorithm for small-scale instances and a Q-learning reinforcement learning algorithm for large-scale, dynamic scenarios. The Q-learning model adaptively optimizes scheduling policies through continuous interaction with network states and service demands, enhancing real-time decision-making efficiency. Simulation results show a reduction of up to 26.6% in total weighted completion time for small-scale problems using the Matching Game approach and up to 40 % improvement for large-scale problems with Q-learning compared to traditional heuristic algorithms. These results demonstrate the effectiveness of the proposed ML-driven framework in achieving accurate service prioritization, optimal resource utilization, and rapid emergency response, providing a foundation for intelligent and adaptive smart healthcare infrastructures.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".