Expanding SNOMED-CT through Spanish Drug Summaries of Product Characteristics (original) (raw)
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No standardized representation of drug indications is currently available that could be used in drug knowledge bases. We describe an object-oriented representation of indications that should make it possible to develop new tools for selecting drugs and checking prescriptions in computerized drug prescription systems. The model was developed using the results of a lexical and semantic analysis of drug indications, collected into a single file and processed using natural language processing software. It distinguishes both the diseases for which the drug may be given and the efficiency of the drug for a given indication. Two aspects of the model were evaluated: the differences if two independent evaluators filled the attributes independently and the loss of information induced by the use of the model. A system based on this model, making it possible for the physician to select all the drugs satisfying various criteria, is also presented.
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Journal of Biomedical Informatics, 2012
Standardized terminological systems for biomedical information have provided considerable benefits to biomedical applications and research. However, practical use of this information often requires mapping across terminological systems-a complex and time-consuming process. This paper demonstrates the complexity and challenges of mapping across terminological systems in the context of medication information. It provides a review of medication terminological systems and their linkages, then describes a case study in which we mapped proprietary medication codes from an electronic health record to SNOMED CT and the UMLS Metathesaurus. The goal was to create a polyhierarchical classification system for querying an i2b2 clinical data warehouse. We found that three methods were required to accurately map the majority of actively prescribed medications. Only 62.5% of source medication codes could be mapped automatically. The remaining codes were mapped using a combination of semi-automated string comparison with expert selection, and a completely manual approach. Compound drugs were especially difficult to map: only 7.5% could be mapped using the automatic method. General challenges to mapping across terminological systems include (1) the availability of up-to-date information to assess the suitability of a given terminological system for a particular use case, and to assess the quality and completeness of cross-terminology links; (2) the difficulty of correctly using complex, rapidly evolving, modern terminologies; (3) the time and effort required to complete and evaluate the mapping; (4) the need to address differences in granularity between the source and target terminologies; and (5) the need to continuously update the mapping as terminological systems evolve.
Categorical information in pharmaceutical terminologies
AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium, 2006
Drug information sources use category labels to assist in navigating and organizing information. Some category labels describe drugs from multiple perspectives (e.g., both structure and function). The National Drug File - Reference Terminology (NDF RT) is a drug information source that augments a "legacy" categorization system with a formal reference model specifying Chemical Structure, Cellular or Sub-Cellular Mechanism of Action, Organ- or System-Level Physiological Effect, and Therapeutic Intent categories. We examined drug category names from three sources to better understand their information content and evaluate NDF…
Proces. del Leng. Natural, 2016
The goal of the Deteami project is to develop tools that make clinicians aware of adverse drug reactions stated in electronic health records of the clinical digital history.The records produced in hospitals are a valuable though nearly unex-plored source of information among others due to the fact that are tough to get due to privacy and confidentiality restrictions. To leverage the clinicians work of reading and analyzing the health records looking for information about the health of the patients, in this project we explore the records automatically, identify among others disorder and drug entities, and infer medical information, in this case, adverse drug reactions. In this project a research-framework was settled with the Galdakao-Usansolo and Basurto Hospitals from Osakidetza (the Basque Health System). Osa-kidetza provided both the texts and the final user feedback, as well as, specialists that annotate the corpora, an in this way, we obtained a gold-standard.
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Proceedings - Natural Language Processing in a Deep Learning World, 2019
Information on drug administration is obtained traditionally from doctors and pharmacists, as well as leaflets which provide in most cases cumbersome and hard-tofollow details. Thus, the need for medical knowledge bases emerges to provide access to concrete and well-structured information which can play an important role in informing patients. This paper introduces a Romanian medical knowledge base focused on drug-drug interactions, on representing relevant drug information, and on symptom-disease relations. The knowledge base was created by extracting and transforming information using Natural Language Processing techniques from both structured and unstructured sources, together with manual annotations. The resulting Romanian ontologies are aligned with larger, well-established, English medical ontologies. Our knowledge base supports queries regarding drugs (e.g., active ingredients, concentration, expiration date), drug-drug interaction, symptom-disease relations, as well as drug-symptom relations (e.g., searching for the drug that might be most useful for treating a given set of symptoms).