Observational studyAMIA ... Annual Symposium proceedings. AMIA Symposium2017
Identification of Clinically Meaningful Plasma Transfusion Subgroups Using Unsupervised Random Forest Clustering.
Observational study in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Artificial Intelligence in Patient Blood Management: A Systematic Review of Predictive, Diagnostic, and Decision Support Applications.Journal of clinical medicine · 2025Review
- Defining Clinically Meaningful Subgroups in Patients Undergoing Arthroscopic Rotator Cuff Repair Using Unsupervised Machine Learning.Orthopaedic journal of sports medicine · 2025Article
- Identifying clinically meaningful subgroups following open reduction and internal fixation for proximal humerus fractures: a risk stratification analysis for mortality and 30-day complications using machine learning.JSES international · 2024Article
- Machine learning in transfusion medicine: A scoping review.Transfusion · 2024Article
- Identifying treatment heterogeneity in atrial fibrillation using a novel causal machine learning method.American heart journal · 2023Article
- The role of machine learning in clinical research: transforming the future of evidence generation.Trials · 2021Review
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Authors and funding
7 authors.
Funding
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Abstract
Statistical techniques such as propensity score matching and instrumental variable are commonly employed to "simulate" randomization and adjust for measured confounders in comparative effectiveness research. Despite such adjustments, the results of these methods apply essentially to an "average" patient. However, as patients show significant heterogeneity in their responses to treatments, this average effect is of limited value. It does not account for individual level variabilities, which can deviate substantially from the population average. To address this critical problem, we present a framework that allows the discovery of clinically meaningful homogeneous subgroups with differential effects of plasma transfusion using unsupervised random forest clustering. Subgroup analysis using two blood transfusion datasets show that considerable variablilities exist between the subgroups and population in both the treatment effect of plasma transfusion on bleeding and mortality and risk factors for these outcomes. These results support the customization of blood transfusion therapy for the individual patient.
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Identifiers
29854202PMC5977681What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.