Enhancing diagnostic precision for rare diseases using case-based reasoning.

Abstract

This study aims to enhance the diagnostic process for rare diseases using case-based reasoning (CBR). CBR compares new cases with historical data, utilizing both structured and unstructured clinical data. The study uses a dataset of 4295 patient cases from the University Hospital Frankfurt. Data were standardized using the OMOP Common Data Model. Three methods-TF, TF-IDF, and TF-IDF with semantic vector embeddings-were employed to represent patient records. Similarity search effectiveness was evaluated using cross-validation to assess diagnostic precision. High-weighted concepts were rated by medical experts for relevance. Additionally, the impact of different levels of ICD-10 code granularity on prediction outcomes was analyzed. The TF-IDF method showed a high degree of precision, with an average positive predictive value of 91% in the 10 most similar cases. The differences between the methods were not statistically significant. The expert evaluation rated the medical relevance of high-weighted concepts as moderate. The granularity of ICD-10 coding significantly influences the precision of predictions, with more granular codes showing decreased precision. The methods effectively handle data from multiple medical specialties, suggesting broad applicability. The use of broader ICD-10 codes with high precision in prediction could improve initial diagnostic guidance. The use of Explainable AI could enhance diagnostic transparency, leading to better patient outcomes. Limitations include standardization issues and the need for more comprehensive lab value integration. While CBR shows promise for rare disease diagnostics, its utility depends on the specific needs of the decision support system and its intended clinical application.