With the widespread dissemination of picture archiving and communication systems (PACSs) in hospitals, the amount of imaging data is rapidly increasing. Effective image retrieval systems are required to manage these complex and large image databases. The authors reviewed the past development and the present state of medical image retrieval systems including text-based and content-based systems. In order to provide a more effective image retrieval service, the intelligent content-based retrieval systems combined with semantic systems are required.
Radiology Information Systems ; Semantics

To realize the benefits of electronic health records, electronic health record information needs to be shared seamlessly and meaningfully. Clinical terminology systems, one of the current semantic interoperability solutions, were reviewed in this article. Definition, types, brief history, and examples of clinical terminologieswere introduced along with phases of clinical terminology use and issues on clinical terminology use in electronic health records. Other attempts to standardize the capture, representation and communication of clinical data were also discussed briefly with future needs.
Electronic Health Records ; Semantics*

Molecular mechanisms of biological processes are typically represented as "pathways" that have a graph-analogical network structure. However, due to the diversity of topics that pathways cover, their constituent biological entities are highly diverse and the semantics is embedded implicitly. The kinds of interactions that connect biological entities are likewise diverse. Consequently, how to model or process pathway data is not a trivial issue. In this review article, we give an overview of the challenges in pathway database development by taking the INOH project as an example.
Biological Processes ; Semantics ; Signal Transduction

The main purpose of this paper is to overview the current issues in the area of medical ontology. Ontology technology in Medical Informatics is evolved from the three different research areas: namely, web application for the Semantic Web, Knowledge Representation in Artificial Intelligence, and medical terminology system. In this paper we provide possible research agenda concerning medical ontology development from the above three perspectives at the same time.
Artificial Intelligence ; Medical Informatics* ; Semantics

OBJECTIVES: Extension of the standard model while retaining compliance with it is a challenging issue because there is currently no method for semantically or syntactically verifying an extended data model. A metadata-based extended model, named CCR+, was designed and implemented to achieve interoperability between standard and extended models. METHODS: Furthermore, a multilayered validation method was devised to validate the standard and extended models. The American Society for Testing and Materials (ASTM) Community Care Record (CCR) standard was selected to evaluate the CCR+ model; two CCR and one CCR+ XML files were evaluated. RESULTS: In total, 188 metadata were extracted from the ASTM CCR standard; these metadata are semantically interconnected and registered in the metadata registry. An extended-data-model-specific validation file was generated from these metadata. This file can be used in a smartphone application (Health Avatar CCR+) as a part of a multilayered validation. The new CCR+ model was successfully evaluated via a patient-centric exchange scenario involving multiple hospitals, with the results supporting both syntactic and semantic interoperability between the standard CCR and extended, CCR+, model. CONCLUSIONS: A feasible method for delivering an extended model that complies with the standard model is presented herein. There is a great need to extend static standard models such as the ASTM CCR in various domains: the methods presented here represent an important reference for achieving interoperability between standard and extended models.
Compliance ; Health Records, Personal* ; Humans ; Methods ; Semantics

OBJECTIVES: X-rays are widely used in medical examinations. In particular, chest X-rays are the most frequent imaging test. However, observations are usually recorded in a free-text format. Therefore, it is difficult to standardize the information provided to construct a database for the sharing of clinical data. Here, we describe a simple X-ray observation entry system that can interlock with an electronic medical record system. METHODS: We investigated common diagnosis indices. Based on the indices, we have designed an entry system which consists of 5 parts: 1) patient lists, 2) image selection, 3) diagnosis result entry, 4) image view, and 5) main menu. The X-ray observation results can be extracted in an Excel format. RESULTS: The usefulness of the proposed system was assessed in a study using over 500 patients' chest X-ray images. The data was readily extracted in a format that allowed convenient assessment. CONCLUSIONS: We proposed the chest X-ray observation entry system. The proposed X-ray observation system, which can be linked with an electronic medical record system, allows easy extraction of standardized clinical information to construct a database. However, the proposed entry system is limited to chest X-rays and it is impossible to interpret the semantic information. Therefore, further research into domains using other interpretation methods is required.
Electronic Health Records ; Humans ; Semantics ; Thorax

No abstract available.
Electrons* ; Negotiating* ; Positron-Emission Tomography* ; Semantics*

OBJECTIVES: Anaphora recognition is a process to identify exactly which noun has been used previously and relates to a pronoun that is included in a specific sentence later. Therefore, anaphora recognition is an essential element of a dialogue agent system. In the current study, all the merits of rule-based, machine learning-based, semantic-based anaphora recognition systems were combined to design and realize a new hybrid-type anaphora recognition system with an optimum capacity. METHODS: Anaphora recognition rules were encoded on the basis of the internal traits of referred expressions and adjacent contexts to realize a rule-based system and to serve as a baseline. A semantic database, related to predicate instances of sentences including referred expressions, was constructed to identify semantic co-relationships between the referent candidates (to which semantic tags were attached) and the semantic information of predicates. This approach would upgrade the anaphora recognition system by reducing the number of referent candidates. Additionally, to realize a machine learning-based system, an anaphora recognition model was developed on the basis of training data, which indicated referred expressions and referents. The three methods were further combined to develop a new single hybrid-based anaphora recognition system. RESULTS: The precision rate of the rule-based systems was 54.9%. However, the precision rate of the hybrid-based system was 63.7%, proving it to be the most efficient method. CONCLUSIONS: The hybrid-based method, developed by the combination of rule-based and machine learning-based methods, represents a new system with enhanced functional capabilities as compared to other pre-existing individual methods.
Delivery of Health Care* ; Natural Language Processing ; Semantics

This article gives a brief introduction to the content of aphasic discourse analysis, the theoretical frameworks applied, and critical findings . It also points out the problem that faces the study, the variety of study methods that arises, and the future trend. The structuralists tend to focus on the micro aspects of the discourse while the functionalists on its general structure and the meaning. The study in the future shall address the connection of these two levels.
Aphasia ; physiopathology ; Communication ; Humans ; Language ; Semantics

OBJECTIVE: The standard vocabularies need to cover a diverse and enriched field of medical content, thereby facilitating semantic information retrieval, clinical decision support and efficient care delivery. SNOMED-CT(Systematized Nomenclature of Human and Veterinary Medicine-Clinical Term) is a comprehensive and precise clinical reference terminology that provides unsurpassed clinical content and expressivity for clinical documentation and reporting. To investigate whether the SNOMED-CT can serve this function in Seoul National University Hospital(SNUH) environment, we evaluated the coverage of SNOMED-CT as compared with clinical terms in the discharge summary at SNUH. METHODS: We tested for discordance of clinical terms between SNUH discharge summary and those from SNOMED-CT. We extracted 9,554 concepts from 1,000 discharge summaries. From these concepts, we obtained 3,545 unique concepts which are normalized to map with SNOMED-CT. These normalized terms are mapped to concepts of SNOMED-CT with semi-automatic method. RESULTS: We found a degree of concordance between SNOMED-CT and the clinical terms used in the discharge summary. Approximately, 89% of medical terms in the discharge summary are matched and 11% of the concepts are not mapped to those of SNOMED-CT. CONCLUSION: Through this study, we confirmed that SNOMED-CT is appropriate reference terminology in SNUH environment.
Humans ; Information Storage and Retrieval ; Semantics ; Seoul ; Vocabulary