Building and Prospectively Evaluating a Prediction Model to Forecast Urgent Dialysis Needs across Four Tertiary Hospitals
Bibliographic record
Abstract
INTRODUCTION: Urgent dialysis is labor-intensive and expensive because it requires specialized nursing staff. Most hospitals schedule a fixed number of nurses daily for urgent dialysis needs, but daily dialysis demand fluctuates, leading to inefficiencies. METHODS: We developed statistical, machine learning, and deep learning models to predict the next 7 days' dialysis needs. Our study included a retrospective (April 1, 2018, to March 31, 2023) and prospective component (November 1 to 30, 2023, and May 31 to June 27, 2024) across four hospitals (hospital A for one hospital and hospital B for three hospitals combined). To avoid model over-fitting, we divided our data into three sets: training, testing, and validation. The latter was performed prospectively during two silent deployment periods. The primary outcome measure was the mean absolute error (MAE). RESULTS: The mean daily dialysis volume in the retrospective data was 16.0 (standard deviation [SD], 5.7) for hospital A and 4.5 (SD, 2.3) for hospital B. The best performing models were autoregressive integrated moving average (ARIMA) and temporal convolutional network; both resulted in an MAE of 3.0 procedures for hospital A and 1.5 procedures for hospital B, compared to 4.4 and 1.9, respectively, for the benchmark. During our two prospective evaluations, the mean daily dialysis volume was 16.8 (SD, 4.5) for hospital A and 4.2 (SD, 2.5) for hospital B. The ARIMA model resulted in the lowest MAE at 2.2 and 1.5 procedures, respectively. CONCLUSIONS: Our multicenter, 6-year study demonstrated that urgent in-hospital dialysis needs can be accurately forecasted.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".