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Incorporating clinical expertise into machine learning models can simplify them. This study found that using filtered features reduced model complexity with minimal impact on mortality prediction performance in severe asthma cases.

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Area of Science:

  • Artificial Intelligence
  • Clinical Informatics
  • Biomedical Data Science

Background:

  • Machine learning (ML) models offer potential for improved interpretability when expert knowledge is integrated during training.
  • Clinical expert knowledge integration aims to enhance ML model transparency and clinical utility.
  • Severe asthma mortality prediction presents a complex challenge for standard ML approaches.

Purpose of the Study:

  • To investigate a feature engineering approach for incorporating clinical expert knowledge into ML models.
  • To evaluate the impact of this approach on model complexity and predictive performance.
  • To assess the efficacy of clinical input versus discriminative scores for feature selection.

Main Methods:

  • Four ML models were developed to predict mortality in severe asthma patients.
  • Feature engineering incorporated clinical expert knowledge and utilized discriminative scores for feature selection.
  • Experiments compared baseline ML models with models using filtered features informed by clinical input and discriminative scores.

Main Results:

  • Discriminative score-based feature selection showed limited precision in identifying clinically meaningful features.
  • Models utilizing fewer features, informed by clinical input and discriminative scores, exhibited reduced complexity compared to baseline models.
  • A small performance difference was observed between baseline and filtered-feature ML models for mortality prediction.
  • Encoding demographic and triplet information within filtered-feature models suggested potential performance improvements.

Conclusions:

  • Feature filtering, guided by clinical expertise, can effectively reduce ML model complexity.
  • The integration of clinical knowledge alongside feature selection methods offers a promising strategy for developing interpretable and efficient ML models.
  • While discriminative scores alone are insufficient, their combination with clinical input aids in refining ML models for clinical applications.