Machine learning integrated patient flow simulation: why and how?
Bibliographic record
Abstract
Stochastic distribution methods were used to construct patient flow simulation sub-models such as patient inflow, length of stay (LoS), cost of treatment (CoT) and clinical pathways (CPs). However, the patient inflow rate demonstrates seasonality, trend, and variation due to natural and human-made factors. LoS, CoT and CPs are determined by social-demographics factors, clinical and laboratory test results, resource availability and healthcare structure. For this reason, patient flow simulation models developed using stochastic methods have limitations including uncertainty, not recognising patient heterogeneity, and not representing personalised and value-based healthcare. This, in turn, results in a low acceptance level and implementation of solutions suggested by patient flow simulation models. On the other hand, machine learning becomes effective in predicting patient inflow, LoS, CoT, and CPs. This paper, therefore, describes why coupling machine learning with patient flow simulation is important, proposes a conceptual architecture for machine learning integrated patient flow simulation and demonstrates its implementation with examples.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".