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Predicting of anaphylaxis in big data EMR by exploring machine learning approaches.

Isabel Segura-Bedmar1, Cristobal Colón-Ruíz1, Miguél Ángel Tejedor-Alonso2

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Summary

Machine learning effectively classifies anaphylaxis cases in electronic medical records (EMR). This automates epidemiological studies, improving efficiency and reducing manual review time for anaphylaxis research.

Keywords:
AnaphylaxisBag of centroidsBalancing strategiesEMR classificationMachine learning

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Area of Science:

  • Medical Informatics
  • Computational Biology
  • Allergy and Immunology

Background:

  • Anaphylaxis is a severe allergic reaction requiring epidemiological study for prevention and treatment strategies.
  • Electronic Medical Records (EMR) offer rich data for anaphylaxis epidemiology but manual review is labor-intensive.
  • Automating EMR analysis is crucial for efficient and cost-effective epidemiological research.

Purpose of the Study:

  • To explore machine learning techniques for automatic classification of anaphylaxis cases in EMR data.
  • To reduce the manual effort required for large-scale anaphylaxis epidemiological studies.
  • To compare the effectiveness of various text classification methods and document representations for EMR data.

Main Methods:

  • Utilized diverse machine learning classifiers including Logistic Regression, Linear SVM, Multilayer Perceptron, Random Forest, and Convolutional Neural Networks (CNN).
  • Compared document representations such as Bag of Words (BoW) and word embedding models (average, bag of centroids).
  • Implemented a novel clustering-based undersampling technique to address the class imbalance in EMR data (anaphylaxis cases <1%).

Main Results:

  • Most classifiers and representations achieved high performance (F1 score >90%).
  • Logistic Regression, Linear SVM, Multilayer Perceptron, and Random Forest demonstrated F1 scores around 95%, with linear methods showing faster training times.
  • Convolutional Neural Networks (CNN) achieved a slightly superior F1 score of 95.6%.

Conclusions:

  • Machine learning, particularly CNNs, offers an effective and efficient solution for automatically identifying anaphylaxis cases in EMR.
  • Automated classification significantly reduces the time and cost associated with anaphylaxis epidemiological studies.
  • The developed methods provide a valuable tool for advancing anaphylaxis research and public health strategies.