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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Natural Language Processing for Clinical Text

Background:

  • Health care-associated infections (HAIs) pose a significant challenge in clinical settings.
  • Accurate and timely detection of HAIs is crucial for patient safety and effective treatment.
  • Automated methods for analyzing medical reports can improve HAI surveillance.

Purpose of the Study:

  • To compare the performance of deep learning (DL) and conventional machine learning (ML) models in detecting HAIs within French medical reports.
  • To evaluate different text representation techniques in conjunction with these models.
  • To assess the accuracy and reliability of automated HAI detection systems.

Main Methods:

  • A corpus of 1,531 deidentified French medical reports was analyzed.
  • Both deep learning (convolutional neural network - CNN) and various conventional ML models were employed.
  • Performance was evaluated using the F1 Score, with hyperparameter Bayesian optimization and four text representations (bag of words, TF-IDF, word2vec, GloVe).

Main Results:

  • The CNN model demonstrated superior performance compared to all conventional ML algorithms for HAI classification.
  • The best F1 Score achieved was 97.7% ± 3.6%, with an area under the curve of 99.8% ± 0.41%.
  • CNN achieved high sensitivity (0.962) and specificity (0.937), balancing detection capability with false notifications.

Conclusions:

  • Deep learning, particularly CNNs, is highly effective for automatically identifying HAIs in medical reports.
  • The opacity of CNNs can be addressed by analyzing inner layer activations to identify key phrases, aiding clinical practitioners.
  • This study confirms the superiority of DL approaches for automated HAI detection in clinical text.