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February 11, 2026Scientific ReportsOpen Access

Identification of perception gaps between physicians and patients with neurological diseases and the prediction of these gaps using machine learning

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Authors

GOGenko OyamaYTYuji TomizawaTTTaiji Tsunemi

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Overview

Observational study identifies and predicts perception gaps in patient-physician interactions, suggesting improved understanding for better care.

Key Points

  • The study aims to identify perception gaps between patients with neurological diseases and their physicians and develop predictive models for these gaps using machine learning.
  • Conducted a single-center observational study with 197 patients and 12 physicians.
  • Utilized questionnaires to assess patient satisfaction, shared decision-making, and quality of life.
  • Analyzed data to determine factors influencing perception gaps and used machine learning to develop predictive models.
  • Minimal perception gaps were identified in patient satisfaction and quality of life.
  • Physician attributes like age and experience significantly influenced these gaps.
  • The k-nearest neighbors algorithm outperformed other models in predicting perception gaps.

Cite This Study

Oyama et al. (2026) studied this question.

synapsesocial.com/papers/698c1cb3267fb587c655f48ehttps://doi.org/10.1038/s41598-025-33500-x
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