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Articles linked to this work by shared authors, journal, and citation graph.

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Author Correction: Identification of right ventricular dysfunction with LogNNet based diagnostic model: A comparative study with supervised ML algorithms.

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Identification of right ventricular dysfunction with LogNNet based diagnostic model: A comparative study with

Mehmet Tahir Huyut1, Andrei Velichko2, Maksim Belyaev2

  • 1Department of Biostatistics and Medical Informatics, Faculty of Medicine, Erzincan Binali Yıldırım University, Erzincan, 24000, Turkey. tahir.huyut@erzincan.edu.tr.

Scientific Reports
|July 12, 2025
PubMed
Summary

Early detection of right ventricular dysfunction (RVD) in acute pulmonary embolism (PE) is vital. LogNNet machine learning models effectively identify key RVD predictors, improving diagnosis and patient outcomes.

Keywords:
Diagnostic modelsEdge computingFeature selectionLogNNetMachine learningMedical IoTPredictive analyticsPulmonary embolismRight ventricular dysfunctionRisk assessmentThrombosis

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Area of Science:

  • Cardiology
  • Medical Informatics
  • Machine Learning

Background:

  • Right ventricular dysfunction (RVD) significantly increases mortality in acute pulmonary embolism (PE) patients.
  • Early and accurate RVD detection is crucial for timely intervention and improved survival rates.
  • Identifying cost-effective RVD risk factors in acute PE is essential for clinical practice.

Purpose of the Study:

  • To evaluate LogNNet and supervised machine learning (ML) models for diagnosing RVD in acute PE patients.
  • To identify significant predictors of RVD using ML approaches.
  • To propose an ensemble-based LogNNet model for practical RVD diagnosis.

Main Methods:

  • Repeated stratified hold-out validation was employed to assess model performance.
  • LogNNet and supervised ML models were utilized for RVD diagnosis.
  • Feature importance analysis was conducted to identify key RVD predictors.

Main Results:

  • LogNNet identified gender, coronary artery disease, comorbid disease (hypertension), age (>74 years), thrombus segment, and laterality as significant RVD predictors.
  • Combinations of these features showed high predictive power for RVD.
  • The LogNNet model demonstrated robust performance with a limited set of features.

Conclusions:

  • LogNNet offers a practical and accessible tool for early RVD detection in PE patients, even in resource-limited settings.
  • The model's efficiency with few features supports its use in edge devices and clinical decision support systems.
  • Findings can integrate with digital health innovations for enhanced patient monitoring and resilience.