Investigating the feasibility of machine learning to guide personalized red blood cell (RBC) transfusion: analyzing the heterogeneity of RBC transfusion in septic patients with hemoglobin levels of 7–9 g/dL based on the causal forest model

RBC transfusions among sepsis patients with Hb levels of 7-9 g/dL exhibit heterogenous treatment effects, which reduces the mortality of patients with high ITE. Although the causal forest model can guide personalized transfusion in these cases, randomized controlled trials are needed to validate these findings.

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Publication Details

Journal
Frontiers in Pharmacology
Published
2025-08-28
DOI
https://doi.org/10.3389/fphar.2025.1615618
Citations
1
Primary Topic
Sepsis Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
1.35
Controls
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article

Investigating the feasibility of machine learning to guide personalized red blood cell (RBC) transfusion: analyzing the heterogeneity of RBC transfusion in septic patients with hemoglobin levels of 7–9 g/dL based on the causal forest model

Lina Yu, Jie He, Qihong Chen, Penglei Yang et al.
1 citations
Frontiers in Pharmacology
Sepsis Diagnosis and Treatment
1.35
article

Investigating the feasibility of machine learning to guide personalized red blood cell (RBC) transfusion: analyzing the heterogeneity of RBC transfusion in septic patients with hemoglobin levels of 7–9 g/dL based on the causal forest model

Lina Yu, Jie He, Qihong Chen, Penglei Yang, Jun Yuan, Xue Gu, Xizhen Ding
article en
1 citations

Abstract

RBC transfusions among sepsis patients with Hb levels of 7-9 g/dL exhibit heterogenous treatment effects, which reduces the mortality of patients with high ITE. Although the causal forest model can guide personalized transfusion in these cases, randomized controlled trials are needed to validate these findings.

Frontiers in PharmacologyVol. 16
Northern Jiangsu People's Hospital (CN), Hangzhou Hospital of Traditional Chinese Medicine (CN), Anyang Hospital of Traditional Chinese Medicine (CN), Yangzhou University (CN)
Openalex Percentile: Top 16%
Sepsis Diagnosis and Treatment
1.35
DOIOpen Access PDFOpenAlex
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Investigating the feasibility of machine learning to guide personalized red blood cell (RBC) transfusion: analyzing the heterogeneity of RBC transfusion in septic patients with hemoglobin levels of 7–9 g/dL based on the causal forest model — Lina Yu, Jie He, et al. · Frontiers in Pharmacology (2025) | TGRS Research Map | TGRS