Human-Centred Time Series Modeling of Daily Surgery Volumes in a Community Hospital: a Comparative Study of SARIMA, XGBoost, LSTM and Hybrid Methods, with XAI Integration
Bibliographic record
Abstract
This thesis predicts daily surgery numbers for a community hospital network using time series methods, including SARIMA, XGBoost, and LSTM, hybrid SARIMA-LSTM and hybrid SARIMA-XGBoost methods. Each technique offers unique benefits: traditional time series analysis (SARIMA), ensemble learning (XGBoost), and sequential modelling (LSTM). By comparing these methods, the research aims to assess their effectiveness in predicting surgery volumes. The research adopts a human-centred approach, emphasizing stakeholder engagement and model transparency in health care data science. Incorporating human-centred principles ensures the predictive models are not only technically proficient but also interpretable and actionable for hospital decision-makers. A key component is the use of eXplainable Artificial Intelligence (XAI) methods, which provide insights into complex predictive models, enhancing their transparency and usability for stakeholders.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".