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Browse normalized, publication-ready research with direct links to its evidence and source.
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Readable research linked to original sources
Browse normalized, publication-ready research with direct links to its evidence and source.
Loading articles data…
Readable research linked to original sources
Browse normalized, publication-ready research with direct links to its evidence and source.
3 articles
Newest firstBackgroundElectronic Health Records face a fundamental challenge: the semantic gap between relational data storage and clinical reasoning patterns. Traditional databases struggle with complex healthcare queries requiring multiple joins and temporal analysis, creating performance bottlenecks that limit real-time clinical applications. MethodsWe developed a Neo4j-based framework integrating MIMIC-IV clinical data (1…
Open article record in new tab ↗BackgroundIdentifying rare disease (RD) patients in electronic health records (EHR) is challenging, as more than 10,000 rare diseases are not typically captured by clinical coding systems. This limits the assessment of clinical outcomes for RD patients. This study introduces a semiautomated approach to map RDs to appropriate codes, that is applicable across various EHR systems. By improving RD patient identificati…
BackgroundThe digitisation of healthcare records has generated vast amounts of unstructured data, presenting opportunities for improvements in disease diagnosis when clinical coding falls short, such as in the recording of patient symptoms. This study presents an approach using natural language processing to extract clinical concepts from free-text which are used to automatically form diagnostic criteria for lung…