Hospital Management Based On Semantic Process Mining: A Systematic Review
Bibliographic record
Abstract
Introduction: Semantic Process Mining is the extension field of process mining that is based on getting knowledge of conceptual event logs (based on ontologies) for analyzing frequent and rare processes. In the healthcare studies, semantic process mining has been used in different hospitals in order to improve processes.Material and Methods: A review of the usages of semantic process mining in hospitals is done. This review contains 65 articles from PubMed, dblp and Google scholar. It is searched from 2000 to 2017. One of them was duplicated and finally, we received 64 articles. Data were extracted according to PRISMA guidelines.Results: Out of 64 articles, 6 of them were related with inclusion and exclusion criteria. Most of them detect business process mining. In 80% of studies, the semantic process mining was useful and effective to improve hospital processes and improve its management.Conclusion: This review can show an overview the application of process mining in hospitals. It can help researchers to compare semantic process mining with other methods for improving processes in hospitals and finally, it shows the use of semantic process mining to enhance hospitals processes.
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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 | 0.010 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.020 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".