Biomedical subjects

Yugyung Lee

Publications and source records attributed to Yugyung Lee.

4 recordsLinked to original sources

PubMed2005

MachineProse: an ontological framework for scientific assertions.

OBJECTIVE: The idea of testing a hypothesis is central to the practice of biomedical research. However, the results of testing a hypothesis are published mainly in the form of prose articles. Encoding the results as scientific assertions that are both human and machine readable would greatly enhance the synergistic growth and dissemination of knowledge. DESIGN: We have developed MachineProse (MP), an ontological framework for the concise specification of scientific assertions. MP is based on the idea of an assertion constituting a fundamental unit of knowledge. This is in contrast to current approaches that use discrete concept terms from domain ontologies for annotation and assertions are only inferred heuristically. MEASUREMENTS: We use illustrative examples to highlight the advantages of MP over the use of the Medical Subject Headings (MeSH) system and keywords in indexing scientific articles. RESULTS: We show how MP makes it possible to carry out semantic annotation of publications that is machine readable and allows for precise search capabilities. In addition, when used by itself, MP serves as a knowledge repository for emerging discoveries. A prototype for proof of concept has been developed that demonstrates the feasibility and novel benefits of MP. As part of the MP framework, we have created an ontology of relationship types with about 100 terms optimized for the representation of scientific assertions. CONCLUSION: MachineProse is a novel semantic framework that we believe may be used to summarize research findings, annotate biomedical publications, and support sophisticated searches.

Artificial Intelligence↗

PubMed2005

Ontology integration: experience with medical terminologies.

To build a common controlled vocabulary is a formidable challenge in medical informatics. Due to vast scale and multiplicity in interpretation of medical data, it is natural to face overlapping terminologies in the process of practicing medical informatics [A. Rector, Clinical terminology: why is it so hard? Methods Inf. Med. 38 (1999) 239-252]. A major concern lies in the integration of seemingly overlapping terminologies in the medical domain and this issue has not been well addressed. In this paper, we describe a novel approach for medical ontology integration that relies on the theory of Algorithmic Semantic Refinement we previously developed. Our approach simplifies the task of matching pairs of corresponding concepts derived from a pair of ontologies, which is vital to terminology mapping. A formal theory and algorithm for our approach have been devised and the application of this method to two medical terminologies has been developed. The result of our work is an integrated medical terminology and a methodology and implementation ready to use for other ontology integration tasks.

Algorithms↗

PubMed2005

Semantic enrichment for medical ontologies.

The Unified Medical Language System (UMLS) contains two separate but interconnected knowledge structures, the Semantic Network (upper level) and the Metathesaurus (lower level). In this paper, we have attempted to work out better how the use of such a two-level structure in the medical field has led to notable advances in terminologies and ontologies. However, most ontologies and terminologies do not have such a two-level structure. Therefore, we present a method, called semantic enrichment, which generates a two-level ontology from a given one-level terminology and an auxiliary two-level ontology. During semantic enrichment, concepts of the one-level terminology are assigned to semantic types, which are the building blocks of the upper level of the auxiliary two-level ontology. The result of this process is the desired new two-level ontology. We discuss semantic enrichment of two example terminologies and how we approach the implementation of semantic enrichment in the medical domain. This implementation performs a major part of the semantic enrichment process with the medical terminologies, with difficult cases left to a human expert.

Artificial Intelligence↗

PubMed2005

OntoDiagram: automatic diagram generation for congenital heart defects in pediatric cardiology.

In pediatric cardiology as well as many other medical specialties, the accurate portrayal of a large volume of patient information is crucial to providing good patient care. Our research aims at utilizing clinical and spatial ontologies representing the human heart, to automatically generate a Mullins-like diagram based on a patient's information in the cardiology databases. Our ontology allows an intuitive way of modeling congenital defects with the structure of the human heart. A prototype system has been implemented using Protege and the Java Advanced Imaging package , and is currently under testing at the Children's Mercy Hospitals and Clinics. A pilot study showed the diagrammatical representation and characterization of congenital heart defects can be used to facilitate a better understanding of a complex congenital heart defects.

Anatomy, Cross-Sectional↗