Clustering-based demand forecasting with an application to immunoglobulin products
Bibliographic record
Abstract
Efficient healthcare supply chain management can benefit greatly from accurate demand forecasting, which can help reduce costs and prevent patient treatment delays. This is particularly challenging for demand forecasting for the human immunoglobulin blood product stored in hospital blood banks due to the diverse patient population and therapeutic applications. Our study proposes an iterative clustering-based demand forecasting framework to address this issue. We cluster patients based on domain knowledge and demand pattern characteristics using the robust and sparse K-means algorithm. We then employ time-series analysis techniques to forecast demand for each cluster, aggregate the forecasts, and evaluate the performance. The potential variables affecting the clustering and forecasting results are identified to make this process iterative and to find the best clustering scheme based on forecast performance. For example, the optimal number of clusters, K , in a K-means algorithm is unknown. Therefore, we choose K to optimize the forecast performance. Clustering algorithms can also be sensitive to feature selection, so using an extension of K-means with weighted features, the bound on feature weights is included as an unknown input variable in the iterative process. We further enhance the forecasting model by incorporating individual patient-level predictions from the cluster identified with extended treatment plans, which contains patients with more data points and better individual predictability. The proposed framework outperforms baseline ARIMA and LSTM network models trained on aggregate demand data. Moreover, the results show improved performance as data size increases.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".