Demand Forecasting for Platelet Usage: from Univariate Time Series to\n Multivariate Models
Bibliographic record
Abstract
Platelet products are both expensive and have very short shelf lives. As\nusage rates for platelets are highly variable, the effective management of\nplatelet demand and supply is very important yet challenging. The primary goal\nof this paper is to present an efficient forecasting model for platelet demand\nat Canadian Blood Services (CBS). To accomplish this goal, four different\ndemand forecasting methods, ARIMA (Auto Regressive Moving Average), Prophet,\nlasso regression (least absolute shrinkage and selection operator) and LSTM\n(Long Short-Term Memory) networks are utilized and evaluated. We use a large\nclinical dataset for a centralized blood distribution centre for four hospitals\nin Hamilton, Ontario, spanning from 2010 to 2018 and consisting of daily\nplatelet transfusions along with information such as the product\nspecifications, the recipients' characteristics, and the recipients' laboratory\ntest results. This study is the first to utilize different methods from\nstatistical time series models to data-driven regression and a machine learning\ntechnique for platelet transfusion using clinical predictors and with different\namounts of data. We find that the multivariate approaches have the highest\naccuracy in general, however, if sufficient data are available, a simpler time\nseries approach such as ARIMA appears to be sufficient. We also comment on the\napproach to choose clinical indicators (inputs) for the multivariate models.\n
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".