Article ; Online: Machine-learning prediction models for any blood component transfusion in hospitalized dengue patients.
Hematology, transfusion and cell therapy
2023
Abstract: Background: Blood component transfusions are a common and often necessary medical practice during the epidemics of dengue. Transfusions are required for patients when they developed severe dengue fever or thrombocytopenia of 10×10: Methods: Eight ... ...
Abstract | Background: Blood component transfusions are a common and often necessary medical practice during the epidemics of dengue. Transfusions are required for patients when they developed severe dengue fever or thrombocytopenia of 10×10 Methods: Eight predictive models were developed based on retrospective data from a private group of hospitals in India. A python package SHAP (SHapley Additive exPlanations) was used to explain the output of the "XGBoost" model. Results: Sixteen vital variables were finally selected as having the most significant effects on blood component transfusion prediction. The XGBoost model presented significantly better predictive performance (area under the curve: 0.793; 95 % confidence interval: 0.699-0.795) than the other models. Conclusion: Predictive modelling techniques can be utilized to streamline blood component preparation procedures and can help in the triage of high-risk patients and readiness of caregivers to provide blood component transfusions when required. This study demonstrates the potential of multilayer algorithms to reasonably predict any blood component transfusion needs which may help healthcare providers make more informed decisions regarding patient care. |
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Language | English |
Publishing date | 2023-11-17 |
Publishing country | Brazil |
Document type | Journal Article |
ISSN | 2531-1387 |
ISSN (online) | 2531-1387 |
DOI | 10.1016/j.htct.2023.09.2365 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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