ArticlePloS one2020
Prediction of perioperative transfusions using an artificial neural network.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05228548 (Development and Interpretation of a Machine Learning Model for Perioperative Transfusion Prediction), which is not on this map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Development and Interpretation of a Machine Learning Model for Perioperative Transfusion Prediction
Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prediction of Complications and Prognostication in Perioperative Medicine: A Systematic Review and PROBAST Assessment of Machine Learning Tools.Anesthesiology · 2024Pooled it
- Artificial Intelligence in Patient Blood Management: A Systematic Review of Predictive, Diagnostic, and Decision Support Applications.Journal of clinical medicine · 2025Review
- Predicting blood transfusion demand in intensive care patients after surgery by comparative analysis of temporally extended data selection.BMC medical informatics and decision making · 2024Article
- Machine-learning prediction models for any blood component transfusion in hospitalized dengue patients.Hematology, transfusion and cell therapy · 2024Article
- Nano fuzzy alarming system for blood transfusion requirement detection in cancer using deep learning.Scientific reports · 2024Article
- A Novel Scoring System Predicting Red Blood Cell Transfusion Requirements in Patients Undergoing Invasive Spine Surgery.Journal of clinical medicine · 2024Article
- Robust Meta-Model for Predicting the Likelihood of Receiving Blood Transfusion in Non-traumatic Intensive Care Unit Patients.Health data science · 2024Article
- Machine learning in transfusion medicine: A scoping review.Transfusion · 2024Article
- Development of a machine learning model to predict intraoperative transfusion and guide type and screen ordering.Journal of clinical anesthesia · 2023Article
- Construction and effect evaluation of prediction model for red blood cell transfusion requirement in cesarean section based on artificial intelligence.BMC medical informatics and decision making · 2023Article
- Multivariable statistical models to predict red cell transfusion in elective surgery.Blood transfusion = Trasfusione del sangue · 2023Article
- A neural network analysis of the effect of high and low frailty index indicators on predicting elective surgery discharge destinations.PloS one · 2023Article
- Predicting willingness to donate blood based on machine learning: two blood donor recruitments during COVID-19 outbreaks.Scientific reports · 2022Article
- Personalized Surgical Transfusion Risk Prediction Using Machine Learning to Guide Preoperative Type and Screen Orders.Anesthesiology · 2022Article
- New method for visualizing the dose rate distribution around the Fukushima Daiichi Nuclear Power Plant using artificial neural networks.Scientific reports · 2021Article
- Classification of abnormal location in medium voltage switchgears using hybrid gravitational search algorithm-artificial intelligence.PloS one · 2021Article
- Precision medicine and Patient Blood Management - A good pairing.Blood transfusion = Trasfusione del sangueArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAccurate prediction of operative transfusions is essential for resource allocation and identifying patients at risk of postoperative adverse events. This research examines the efficacy of using artificial neural networks (ANNs) to predict transfusions for all inpatient operations.
methodsOver 1.6 million surgical cases over a two year period from the NSQIP-PUF database are used. Data from 2014 (750937 records) are used for model development and data from 2015 (885502 records) are used for model validation. ANN and regression models are developed to predict perioperative transfusions for surgical patients.
resultsVarious ANN models and logistic regression, using four variable sets, are compared. The best performing ANN models with respect to both sensitivity and area under the receiver operator characteristic curve outperformed all of the regression models (p < .001) and achieved a performance of 70-80% specificity with a corresponding 75-62% sensitivity.
conclusionANNs can predict >75% of the patients who will require transfusion and 70% of those who will not. Increasing specificity to 80% still enables a sensitivity of almost 67%. The unique contribution of this research is the utilization of a single ANN model to predict transfusions across a broad range of surgical procedures.
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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.