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AI Trauma Search Strategy.pdf

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Preview of AI Trauma Search Strategy
🔗 Source: static-content.springer.com
📊 Size: 513 KB
👤 Author: Peng, Henry
⬇️ Downloads: 148

Summary

Artificial intelligence and machine learning are used to predict various problems in hemorrhagic trauma care. A literature search used 94 combinations of keywords, including "artificial intelligence," "machine learning," "trauma," "bleed," "hemorrhage," and others. Studies used different machine learning models, such as LDA, GNB, KNN, DT, and DNN, to predict mortality and outcomes in traumatic injuries. The models were trained and tested on data from patients in ICUs and emergency departments, with features such as sodium, chloride, bicarbonate, and lactate showing statistical significance to model accuracy. The best-performing models included Deep-FLAIM, Levenberg-Marquardt, and Bayesian regularization backpropagated ANN algorithms, with high accuracy and AUROC values.

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Description

Artificial intelligence and machine learning are used to predict various problems in hemorrhagic trauma care.

Technical Information

  • File Format: PDF
  • File Size: 513 KB
  • Pages: 39
  • Language: EN
  • Author: Peng, Henry
  • Total Downloads: 148
  • Last Updated: 4 hours ago

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This PDF document about AI Trauma Search Strategy provides comprehensive information and guidance. Whether you're a beginner or advanced user, this resource offers valuable insights into AI Trauma Search Strategy.

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