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Mortality prediction in intensive care units (ICUs) using a deep rule-based fuzzy classifier.

Raheleh Davoodi1, Mohammad Hassan Moradi1

  • 1Department of Biomedical Engineering, Amirkabir University of Technology, Iran.

Journal of Biomedical Informatics
|February 23, 2018
PubMed
Summary

A new Deep Rule-Based Fuzzy System (DRBFS) accurately predicts intensive care unit (ICU) patient mortality using electronic health records. This AI model handles complex data and outperforms existing methods for early mortality assessment.

Keywords:
Deep learningFuzzy classifierIntensive care unitsMixed dataMortality prediction

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence
  • Data Science

Background:

  • Electronic health records (EHRs) are valuable for clinical research.
  • Accurate prediction of in-hospital mortality in intensive care units (ICUs) is crucial for patient management.
  • Existing methods may struggle with the complexity and heterogeneity of EHR data.

Purpose of the Study:

  • To propose a Deep Rule-Based Fuzzy System (DRBFS) for accurate in-hospital mortality prediction in ICU patients.
  • To develop a system capable of handling big data with heterogeneous mixed categorical and numeric attributes.
  • To create an interpretable model for mortality risk assessment.

Main Methods:

  • A Deep Rule-Based Fuzzy System (DRBFS) was developed, utilizing interpretable fuzzy rules in its hidden layers.
  • A modified supervised fuzzy k-prototype clustering was employed for fuzzy rule generation, leveraging soft partitioning.
  • A stacked approach was used, where each base building unit processes the input space and the prediction results from the previous unit.

Main Results:

  • The DRBFS model achieved an Area Under the Receiver Operating Characteristic Curve (AUROC) of 73.90% on a dataset of 10,972 ICU admissions from the MIMIC-III database.
  • The proposed DRBFS outperformed several common classifiers, including Naïve Bayes (73.51%), Gradient Boosting (72.98%), and Deep Belief Networks (70.07%).
  • The system demonstrated effectiveness in handling large, heterogeneous datasets while maintaining interpretable rule bases.

Conclusions:

  • The Deep Rule-Based Fuzzy System (DRBFS) offers a superior and interpretable approach for in-hospital mortality prediction in ICU patients.
  • DRBFS effectively manages complex, mixed-type data, making it suitable for large-scale electronic health record analysis.
  • The proposed method provides a scalable and accurate tool for early mortality risk assessment in critical care settings.