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In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila
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Enhancing diagnostic precision for rare diseases using case-based reasoning.

Richard Noll1, Alexandra Berger2, Carlo Facchinello3

  • 1Institute of Medical Informatics, Goethe University Frankfurt, University Medicine, 60590 Frankfurt am Main, Germany.

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|June 27, 2025
PubMed
Summary
This summary is machine-generated.

Case-based reasoning (CBR) shows promise for rare disease diagnosis by comparing new patient cases with historical data. Using broader ICD-10 codes can improve initial diagnostic guidance and patient outcomes.

Keywords:
case-based reasoningdiagnostic techniques and proceduresinterdisciplinary researchmedical informaticsrare diseases

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Rare Disease Diagnostics

Background:

  • Rare diseases pose significant diagnostic challenges due to their low prevalence and diverse presentations.
  • Effective diagnostic tools are crucial for timely intervention and improved patient outcomes in rare disease cases.
  • Case-based reasoning (CBR) offers a potential framework for leveraging historical patient data to aid in diagnosing complex conditions.

Purpose of the Study:

  • To evaluate the effectiveness of Case-Based Reasoning (CBR) in enhancing the diagnostic process for rare diseases.
  • To compare different data representation methods (TF, TF-IDF, TF-IDF with semantic embeddings) for patient records within a CBR system.
  • To assess the impact of ICD-10 code granularity on the precision of diagnostic predictions.

Main Methods:

  • A dataset of 4295 patient cases from University Hospital Frankfurt was utilized, with data standardized using the OMOP Common Data Model.
  • Three text representation methods (TF, TF-IDF, TF-IDF with semantic vector embeddings) were applied to patient records for similarity searches.
  • Diagnostic precision was evaluated using cross-validation, expert ratings of concept relevance, and analysis of ICD-10 code granularity effects.

Main Results:

  • The TF-IDF method achieved high diagnostic precision, with an average positive predictive value of 91% within the 10 most similar cases.
  • No statistically significant differences were observed between the evaluated data representation methods.
  • Expert evaluation indicated moderate medical relevance for high-weighted concepts, and increased ICD-10 code granularity led to decreased prediction precision.

Conclusions:

  • CBR methods demonstrate broad applicability across multiple medical specialties for rare disease diagnosis.
  • Utilizing broader ICD-10 codes in CBR systems can enhance initial diagnostic guidance and potentially improve patient outcomes.
  • Further integration of Explainable AI and comprehensive lab value data is recommended to improve diagnostic transparency and system utility.