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Park Y.R.,Office of Clinical Research Information | Chun J.N.,Seoul National University | So I.,Seoul National University | Kim H.J.,Asan Medical Center | And 5 more authors.
Cancer Genomics and Proteomics | Year: 2016

Background: Experimental evidence has suggested that transient receptor potential (TRP) channels play a crucial role in tumor biology. However, clinical relevance and significance of TRP channels in cancer remain largely unknown. Materials and Methods: We applied a datadriven approach to dissect the expression landscape of 27 TRP channel genes in 14 types of human cancer using International Cancer Genome Consortium data. Results: TRPM2 was found overexpressed in most tumors, whereas TRPM3 was broadly down-regulated. TRPV4 and TRPA1 were found up- and down-regulated respectively in a cancer type-specific manner. TRPC4 was found to be closely associated with incidence of head and neck cancer and poor survival of patients with kidney cancer. TRPM8 was identified as a new molecular marker for lung cancer diagnosis and TRPP1 for kidney cancer prognosis. Conclusion: Our data-driven approach demonstrates that the variation in the expression of TRP channel genes is manifested across various human cancer types and genes, for certain TRP channels have strong predictive diagnostic and prognostic potential.


Baek S.,Asan Medical Center | Park S.H.,University of Ulsan | Won E.,NYU Langone Medical Center | Park Y.R.,Office of Clinical Research Information | And 2 more authors.
Korean Journal of Radiology | Year: 2015

The propensity score is defined as the probability of each individual study subject being assigned to a group of interest for comparison purposes. Propensity score adjustment is a method of ensuring an even distribution of confounders between groups, thereby increasing between group comparability. Propensity score analysis is therefore an increasingly applied statistical method in observational studies. The purpose of this article was to provide a step-by-step nonmathematical conceptual guide to propensity score analysis with particular emphasis on propensity score matching. A software program code used for propensity score matching was also presented. © 2015 The Korean Society of Radiology.


Shin Y.,Office of Clinical Research Information | Choi C.,Office of Clinical Research Information | Choi C.,University of Ulsan | Lee J.,Office of Clinical Research Information | And 4 more authors.
Studies in Health Technology and Informatics | Year: 2015

Hospitals have accumulated large amounts of data driven by hospital information system such as EMR, PACS, CPOE, and LIMS. While most data are stored in hospital systems, researchers have still experienced trouble to use clinical data. To overcome this problem and promote 'big data' research, clinical research information system is necessary. Here an example of such a system, ABLE (Asan BiomedicaL research Environment), will be introduced. © 2015 IMIA and IOS Press.


Shin S.-Y.,Asan Medical Center | Shin S.-Y.,Office of Clinical Research Information | Lyu Y.,Office of Clinical Research Information | Shin Y.,Office of Clinical Research Information | And 7 more authors.
Studies in Health Technology and Informatics | Year: 2013

To protect patients' privacy and to improve the convenience of research, Asan Medical Center (AMC) has been developing a de-identification system for biomedical research, which mainly consists of three components: de-identification tool, search tool, and chart review tool. The de-identification tool can substitute a randomly assigned research ID for a hospital patient ID, remove the identifiers in the structured format, and mask them in the unstructured format, i.e., texts. The search tool can find the number of patients which satisfies given criteria. The chart review tool can provide de-identified patient's clinical data for review. We found that clinical data warehouse was essential for successful implementation of de-identification system, and this system should be tightly linked to an electronic institutional review board system for easy operation of honest brokers. © 2013 IMIA and IOS Press.


Shin S.-Y.,Asan Medical Center | Shin S.-Y.,Office of Clinical Research Information | Park.Y.R. Yu Rang,Office of Clinical Research Information | Shin Y.,Office of Clinical Research Information | And 12 more authors.
Journal of Korean Medical Science | Year: 2015

De-identification of personal health information is essential in order not to require writtenpatient informed consent. Previous de-identification methods were proposed using naturallanguage processing technology in order to remove the identifiers in clinical narrative text,although these methods only focused on narrative text written in English. In this study, wepropose a regular expression-based de-identification method used to address bilingualclinical records written in Korean and English. To develop and validate regular expressionrules, we obtained training and validation datasets composed of 6,039 clinical notes of 20types and 5,000 notes of 33 types, respectively. Fifteen regular expression rules wereconstructed using the development dataset and those rules achieved 99.87% precision and96.25% recall for the validation dataset. Our de-identification method successfullyremoved the identifiers in diverse types of bilingual clinical narrative texts. This method willthus assist physicians to more easily perform retrospective research. © 2015 The Korean Academy of Medical Sciences.

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