Predicting Individual Patient Platelet Demand in a Large Tertiary Care Hospital Using Machine Learning.

ABSTRACT:

Introduction

An increasing shortage of donor blood is expected, considering the demographic change in Germany. Due to the short shelf life and varying daily fluctuations in consumption, the storage of platelet concentrates (PCs) becomes challenging. This emphasizes the need for reliable prediction of needed PCs for the blood bank inventories. Therefore, the objective of this study was to evaluate multimodal data from multiple source systems within a hospital to predict the number of platelet transfusions in 3 days on a per-patient level.

Methods

Data were collected from 25,190 (42% female and 58% male) patients between 2017 and 2021. For each patient, the number of received PCs, platelet count blood tests, drugs causing thrombocytopenia, acute platelet diseases, procedures,

SUBMITTER: Engelke M 

PROVIDER: S-EPMC10521242 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature