Neural network predicts need for RBC transfusion for patient
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AI can provide highly accurate risk scores for patients with suspected upper GI bleeding, and make a recommendation for discharge or hospitalization, according to recent research by Scientific Reports.

Acute gastrointestinal bleeding is the most common gastrointestinal cause for hospitalization. For high-risk patients requiring intensive care unit stay, predicting transfusion needs during the first 24 h using dynamic risk assessment may improve resuscitation with red blood cell transfusion in admitted patients with severe acute gastrointestinal bleeding.

A patient cohort admitted for acute gastrointestinal bleeding (N?=?2,524) was identified MIMIC-III critical care database and separated into training (N?=?2,032) and internal validation (N?=?492) sets. The external validation patient cohort was identified from the eICU collaborative database of patients admitted for acute gastrointestinal bleeding presenting to large urban hospitals (N?=?1,526).

62 demographic, clinical, and laboratory test features were consolidated into 4-h time intervals over the first 24 h from admission. The outcome measure was the transfusion of red blood cells during each 4-h time interval. A long short-term memory (LSTM) model, a type of Recurrent Neural Network, was compared to regression-based models on time-updated data. The LSTM model performed better than discrete-time regression-based models for both internal validation and external validation.

In particular, an LSTM model can be used to predict the need for transfusion of packed red blood cells over the first 24 h from admission to help personalize the care of high-risk patients with acute gastrointestinal bleeding.

Source: https://doi.org/10.1038/s41598-021-88226-3
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