DiagnosisAssistant for Diagnosing Rare Diseases.
Top 10 Finalist for
2023 Inter-University Big Data and AI Challenge
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Diagnosing rare diseases often presents significant challenges due to their complex, unique characteristics, leading to delays in appropriate interventions and impacting patient outcomes. Addressing this issue, we introduce the ‘DiagnosisAssistant,’ an innovative web-based application designed to assist medical professionals in efficiently and accurately diagnosing rare diseases. Integrating artificial intelligence and machine learning, DiagnosisAssistant facilitates the recognition of Human Phenotype Ontology (HPO) concepts in unstructured medical text. This integration made possible through the PhenoTagger tool, enables healthcare professionals to input patient symptoms and signs in a natural, descriptive manner, streamlining the diagnostic process. Furthermore, the application leverages the comprehensive HPO and Orphanet databases, crucial resources in rare diseases. Our performance testing on ten varied case reports of rare diseases has shown promising results, reinforcing the app’s potential in complex diagnostic processes. Despite its dependency on the completeness and accuracy of the HPO and Orphanet databases and its current inability to account for patient-specific factors such as age, medical history, and genetic data, the tool represents a significant step forward in rare disease diagnostics. With its source code openly accessible for continuous improvement, the DiagnosisAssistant holds immense potential for transforming the diagnosis of rare diseases, thus significantly enhancing patient care and treatment outcomes.
Diagnosis Assistant, Rare Diseases, Artificial Intelligence, Named Entity Recognition, Big Data, Healthcare Informatics
The full text of the paper is available on Link
The web app is available for test upon request. Please send me email.
User can list every symptom and sign which involve skin yellowing.
Result of searching with the symptoms and signs from a case report.