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Quantitative Analysis of Diagnostic Reasoning Using Initial Electronic Medical Records
Shinya Takeuchi1, Yoshiyasu Okuhara2, Yutaka Hatakeyama2
1Department of Disaster and Emergency Medicine, Kochi Medical School, Kochi University, Nankoku 783-8505, Kochi, Japan.
Diagnostics (Basel, Switzerland)
|June 26, 2025
Summary
Initial electronic medical records (EMRs) contain valuable linguistic cues for diagnostic reasoning. Text analysis of EMRs can reveal clinical reasoning patterns, aiding medical education and diagnostic support systems.
Area of Science:
- Clinical Informatics
- Medical Education
- Natural Language Processing
Background:
- Diagnostic reasoning is crucial but often automated and opaque.
- Limited quantitative studies assess clinical reasoning in electronic medical records (EMRs).
- Need to explore EMRs for insights into diagnostic processes.
Purpose of the Study:
- Investigate if initial EMRs hold diagnostic reasoning information.
- Assess text analysis and logistic regression for visualizing reasoning.
- Enhance understanding of clinical decision-making.
Main Methods:
- Retrospective analysis of EMRs (2008-2022) at Kochi University Hospital.
- Text analysis using Japanese NLP library (GiNZA).
- Logistic regression to link terms with final diagnoses in dizziness and headache cohorts.
Main Results:
- Identified 48 significant diagnostic terms in 248 dizziness cases.
- Identified 46 significant diagnostic terms in 616 headache cases.
- Term presence and expression (affirmative/negative) significantly correlated with diagnoses.
Conclusions:
- Initial EMRs contain quantifiable linguistic data reflecting diagnostic reasoning.
- Simple analytical methods can reveal reasoning patterns.
- Findings support medical education and explainable AI in diagnostics.
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