No abstract available.
Information Storage and Retrieval*

OBJECTIVES: The purpose of this study was to investigate the effects of query expansion algorithms for MEDLINE retrieval within a pseudo-relevance feedback framework. METHODS: A number of query expansion algorithms were tested using various term ranking formulas, focusing on query expansion based on pseudo-relevance feedback. The OHSUMED test collection, which is a subset of the MEDLINE database, was used as a test corpus. Various ranking algorithms were tested in combination with different term re-weighting algorithms. RESULTS: Our comprehensive evaluation showed that the local context analysis ranking algorithm, when used in combination with one of the reweighting algorithms - Rocchio, the probabilistic model, and our variants - significantly outperformed other algorithm combinations by up to 12% (paired t-test; p < 0.05). In a pseudo-relevance feedback framework, effective query expansion would be achieved by the careful consideration of term ranking and re-weighting algorithm pairs, at least in the context of the OHSUMED corpus. CONCLUSIONS: Comparative experiments on term ranking algorithms were performed in the context of a subset of MEDLINE documents. With medical documents, local context analysis, which uses co-occurrence with all query terms, significantly outperformed various term ranking methods based on both frequency and distribution analyses. Furthermore, the results of the experiments demonstrated that the term rank-based re-weighting method contributed to a remarkable improvement in mean average precision.
Information Storage and Retrieval ; Models, Statistical

It is a common view that the integration of Chinese medicine (CM) and modern Western medicine is an efficient way to facilitate the development of CM. Integrative medicine is a kind of complex interventions. Scientific therapeutic evaluation plays a crucial role in making integrative medicine universally acknowledged. However, the modern method of clinical study, which is based on the concept of evidence-based medicine, mostly focuses on the population characteristics and single interventional factor. As a result, it is difficult for this method to totally adapt to the clinical features of CM and integrative medicine as complex interventions. One possible way to solve this issue is to improve and integrate with the existing method and to utilize the evaluation model on complex interventions from abroad. As an interdisciplinary technique, data mining involves database technology, artificial intelligence, machine learning, statistics, neural network and some other latest technologies, and has been widely used in the field of CM. Therefore, the application of data mining in the therapeutic evaluation of integrative medicine has broad prospects.
Information Storage and Retrieval ; Integrative Medicine

For regulatory approval of a new drug, the most preferred and reliable source of evidence would be randomized controlled trials (RCT). However, a great number of drugs, being developed as well as already marketed and being used, usually lack proper indications for children. It is imperative to develop properly evaluated drugs for children. And expanding the use of already approved drugs for other indications will benefit patients and the society. Nevertheless, to get an approval for expansion of indications, most often with off-label experiences, for drugs that have been approved or for the development of pediatric indications, either during or after completing the main drug development, conducting RCTs may not be the only, if not right, way to take. Extrapolation strategies and modelling & simulation for pediatric drug development are paving the road to the better approval scheme. Making the use of data sources other than RCT such as EHR and claims data in ways that improve the efficiency and validity of the results (e.g., randomized pragmatic trial and randomized registry trial) has been the topic of great interest all around the world. Regulatory authorities should adopt new methodologies for regulatory approval processes to adapt to the changes brought by increasing availability of big and real world data utilizing new tools of technological advancement.
Child ; Humans ; Information Storage and Retrieval

OBJECTIVES: Nowadays, as the amounts of biological data are rapidly increasing, bioinformatics has become one of the important research issues. The bioinformatics data sources are, however, distributed and heterogeneous, and therefore, often poorly integrated and difficult to use together. As many bioinformatics analyses need to make use of multiple information sources, the problem of integration of bioinformatics data sources has become an important one. The purpose of this paper is to present an integration system for bioinformatics data sources. METHODS: To solve this problem, we present an integration system for bioinformatics data sources using a global MDR, which provides users with efficiency and convenience as if they use one system. We deal with the extraction of data elements for bioinformatics MDRs by using ISO 11179 mandatory attributes. RESULTS: A global bioinformatics MDR schema for given MDRs and the results of query processing are presented. CONCLUSIONS: The proposed system and concepts in this paper may be a good solution for the integration of diverse bioinformatics data sources.
Computational Biology ; Information Storage and Retrieval

OBJECTIVE: Gene expression microarrays become a widely used tool in biomedicine. With growing needs of microarray data sharing, there are efforts for the development of microarray standards. MAGE-OM(Microarray Gene Expression Object Model) is a data exchange model and MAGE-ML is an XML-based data exchange format. Most database, however, do not have a suitable structure for MAGE-ML storage and maximum use of the data. Therefore, we have created relational database implementing MAGE-OM for the storage of MAGE-ML with importing and exporting capabilities. METHODS: A relational schema is derived from MAGE-OM with simple object-relational mapping strategy to reduce complexity of MAGE-OM. Data transfer between database and MAGE-ML document is performed via MAGE-OM using the MAGE Software Toolkit(MAGEstk). RESULTS: Our database accepts microarray data as MAGE-ML files through web-based interface, classifying into two types of submission, array or experiment. MAGE-ML import-export function is flexible to accommodate changing data model by separating model definition and implementation layers. CONCLUSION: Standard-based implementation of gene expression database enhances the collection and the structured storage of large-scale gene expression data from heterogeneous data sources.
Information Storage and Retrieval ; Gene Expression* ; Information Dissemination

In the paper, we discuss a series of problems in the hospital PACS storage system and introduce the implementation of hierarchical storages in this system. The detailed technique requirements of this system are also discussed.
Hospital Information Systems ; Information Storage and Retrieval

No abstract available.
Information Storage and Retrieval ; Korea ; Nutrition Surveys

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

PURPOSE: To establish the methods for sinogram formation and correction in order to appropriately apply the filtered backprojection (FBP) reconstruction algorithm to the data acquired using PET scanner with multiple scintillation crystal layers. MATERIAL AND METHODS: Formation for raw PET data storage and conversion methods from listmode data to histogram and sinogram were optimized. To solve the various problems occurred while the raw histogram was converted into sinogram, optimal sampling strategy and sampling efficiency correction method were investigated. Gap compensation methods that is unique in this system were also investigated. All the sinogram data were reconstructed using 2D filtered backprojection algorithm and compared to estimate the improvements by the correction algorithms. RESULTS: Optimal radial sampling interval and number of angular samples in terms of the sampling theorem and sampling efficiency correction algorithm were pitch/2 and 120, respectively. By applying the sampling efficiency correction and gap compensation, artifacts and background noise on the reconstructed image could be reduced. CONCLUSION: Conversion method from the histogram to sinogram was investigated for the FBP reconstruction of data acquired using multiple scintillation crystal layers. This method will be useful for the fast 2D reconstruction of multiple crystal layer PET data.
Artifacts ; Compensation and Redress ; Information Storage and Retrieval ; Noise