Summary of Robust Meta-model For Predicting the Need For Blood Transfusion in Non-traumatic Icu Patients, by Alireza Rafiei et al.
Robust Meta-Model for Predicting the Need for Blood Transfusion in Non-traumatic ICU Patients
by Alireza Rafiei, Ronald Moore, Tilendra Choudhary, Curtis Marshall, Geoffrey Smith, John D. Roback, Ravi M. Patel, Cassandra D. Josephson, Rishikesan Kamaleswaran
First submitted to arxiv on: 1 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY); Applications (stat.AP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper presents a machine learning-based approach to accurately predict the likelihood of blood transfusions being necessary within the next 24 hours in a diverse group of non-traumatic Intensive Care Unit (ICU) patients. The proposed model aims to overcome limitations of existing clinical decision support systems by considering a broader range of patient demographics and medical conditions, as well as different types of blood transfusions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The goal is to develop a more effective way to allocate resources and assess patient risk for blood transfusions in ICU settings. This study uses machine learning techniques to predict the probability of transfusion necessity over the next 24 hours for a diverse range of non-traumatic ICU patients, building on existing models that focused primarily on specific patient demographics and medical conditions. |
Keywords
* Artificial intelligence * Likelihood * Machine learning * Probability
