Aug 2026· Medical and Biological Engineering and Computing· 0 citations· 18 references
Medicine
TL;DR
A diagnosis classification model that automatically maps diagnosis spans in EMR data to the standardized clinical ontology Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT) is developed, demonstrating strong potential for scalable and privacy-preserving medical concept normalization in real-world clinical environments.
This work demonstrates how AI-driven informatics can enhance rare disease diagnosis and exemplifies the role of digital tools in transforming precision medicine and healthcare delivery.
J. Pérez-García, Federico García-Criado, F. Pazos et al.· iScience· 0 citations
This approach combines semantic understanding of clinical narratives with structural modeling of patient-disease-treatment relationships and successfully validates synthetic EHR data utility for privacy-preserving healthcare AI development while addressing critical requirements necessary for clinical decision support s...
U. Luke, P. Asuquo, Victor Anaga et al.· E3S Web of Conferences· 0 citations
This work constructs and shares joint embeddings for the International Classification of Diseases (ICD-10) and the Anatomical Therapeutic Chemical (ATC) classification system, providing reusable semantic representations of diagnoses and treatments from real-world claims data.
C. Faujour, S. Bouée, C. Emery et al.· JAMIA Journal of the America...· 0 citations
The Orphanet Nomenclature and Classification of RDs is the only RDs-specific interoperable medical terminology meeting the needs of health care, research, and public health systems by addressing the underrepresentation of RDs in medical terminologies.
C. Lucano, D. Lagorce, A. Olry et al.· JMIR Medical Informatics· 0 citations
xMICD provides a practical way to integrate embedding-based semantic relationships into interpretable clinical feature spaces for machine learning models, and achieves predictive performance comparable to embedding-based representations such as ICD2Vec across multiple clinical prediction tasks.
P. Vatiwutipong, Kumkup Keeratisiwakul, Albert Phuoc Kien Van Truong et al.· 0 citations
The NexusOpti model, incorporating the Enhanced Gated Recurrent Unit (E-GRU) layer, outperforms the existing GRU model in terms of NDCG and Hit Ratio metrics and highlights the effectiveness of the model in advancing personalised, safer, and data-driven medication prescriptions.
Harichandra Khalingarajah, A. Vasudevan, P. Abinaya et al.· Scientific Reports· 0 citations
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