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Structure and Function of Platelets01:18

Structure and Function of Platelets

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The cell fragments known as platelets are disc-shaped, with an average diameter of about 3 μm and a thickness of roughly 1 μm. They play a crucial role in the body's vascular clotting system, which also involves plasma proteins, blood cells, and blood vessel tissues.
Platelets are continually replenished, circulating in the bloodstream for 9-12 days before being removed by phagocytes, primarily in the spleen. A microliter of circulating blood contains between 150,000 and 450,000...
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The platelet phase, the second stage of hemostasis, commences around 15-20 seconds after an injury. It follows and overlaps with the vascular phase, during which blood vessels constrict to minimize blood loss.
As the injured blood vessel contracts, endothelial cells undergo contraction, revealing collagen fibers in the basement membrane and underlying connective tissue. Furthermore, the plasma membrane of endothelial cells becomes adhesive, preparing the site for platelet adhesion. Platelets...
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Related Experiment Video

Updated: Oct 5, 2025

Microfluidics in Assessing Platelet Function
06:47

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Reduction of Platelet Outdating and Shortage by Forecasting Demand With Statistical Learning and Deep Neural

Maximilian Schilling1, Lennart Rickmann1, Gabriele Hutschenreuter2

  • 1Institute for Medical Informatics, University Hospital Aachen, RWTH Aachen University, Aachen, Germany.

JMIR Medical Informatics
|February 1, 2022
PubMed
Summary

Accurate platelet demand forecasting using statistical and deep learning models can significantly reduce blood product waste and shortages. This inventory management strategy could save hospitals approximately $250,000 annually.

Keywords:
LASSOLSTMblood inventory managementdeep learningdemand forecastingplateletsstatistical learningtime series forecasting

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Area of Science:

  • Hematology
  • Data Science
  • Health Informatics

Background:

  • Platelets are vital but perishable blood products with complex inventory management challenges.
  • High daily demand variability and short shelf-lives lead to significant waste and shortages.
  • Accurate platelet demand prediction is crucial for optimizing inventory and patient care.

Purpose of the Study:

  • To forecast hospital platelet demand using a statistical (LASSO) model and a deep neural network (RNN-LSTM).
  • To simulate platelet inventory management to quantify potential reductions in waste and shortage.
  • To assess the economic impact of improved platelet inventory management.

Main Methods:

  • Utilized historical clinical data (81 features) to train LASSO and RNN-LSTM models for daily platelet demand prediction.
  • Performed retrospective simulations of platelet inventory using model predictions versus historical data.
  • Calculated waste and shortage rates and prediction accuracy (mean absolute percent error).

Main Results:

  • Historical platelet waste and shortage rates were 10.1% and 6.5%, respectively.
  • Simulations showed potential waste reduction to ~5% and shortage reduction to ~2% with both models.
  • Both models achieved similar prediction accuracy, with 4-day predictions showing lower error rates.

Conclusions:

  • Both LASSO and RNN-LSTM models provide accurate platelet demand forecasts suitable for improving inventory management.
  • Implementing these predictive models can significantly decrease platelet waste and shortages.
  • Optimized platelet inventory management offers substantial cost savings, estimated at $250,000 per year.