• Journal of Korean Medical classics
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• v.35 no.3
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• pp.41-58
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• 2022

Objectives : In this paper, the outline and overall content of the Majinhwiseong, a specialized medical text on measles written by Lee Wonpung was introduced, along with its academic historical meaning. Methods : The entire Majinhwiseong was analyzed according to content and form. In terms of form, organization, construction, cited literature, etc., were studied, while in terms of content, diagnosis of disease pattern and treatment formulas were studied. Later, based on cited medical texts and the author's social position, the academic historical meaning of this book was discussed. Results : Through the Majinhwiseong, Lee Wonpung strengthened the credibility of the text by not only providing medical knowledge on measles but listing their sources and comparing and analyzing related contents. In the diagnosis part, Lee focused on the changes in symptom, shape, color, and pulse of measles, discussing in detail its differential diagnostic methods. In the treatment part, while listing numerous formulas suggested by Ming (明) masters, Lee did not leave out treatment experiences of Joseon physicians. Meanwhile, the Majinhwiseong is indicative of measles medicine in 18th century Joseon having been progressed in the private sector rather than the official, and how the results of private sector medicine were being absorbed into the official realm through the Uiyakdongcham (議藥同參) system. Conclusions : The Majinhwiseong is a practical treatment manual written by clinician Lee Wonpung to deal measles which was widely spread at the time. The author organized existing medical knowledge on measles for clinicians while reflecting outcomes and medical situation of Joseon physicians in this book. Based on these findings, we could verify that medicine in 18th century Joseon had been progressing actively around the private medical sector.

• Progress in Medical Physics
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• v.1 no.1
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• pp.3-10
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• 1990

No Abstract, See Full Text

• Progress in Medical Physics
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• v.1 no.1
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• pp.11-21
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• 1990

No Abstract, See Full Text

• Proceedings of the Korean Society of Medical Physics Conference
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• 2002.09a
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• pp.48-49
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• 2002

No Abstract, See Full Text

• Journal of the Korean Medical Association
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• v.61 no.11
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• pp.699-699
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• 2018

• Journal of The Korea Institute of Healthcare Architecture
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• v.24 no.2
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• pp.27-36
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• 2018

Purpose: In the case of mass disaster, the establishment of temporary medical facilities for the first aid and treatment is required for the stable accommodation of patients caused by the disaster. However, the criteria for decision making related to the deployment of field emergency medical facilities are not specified. So, The purpose of this study is to draw considerable factors needed for the deployment of field emergency medical facilities and to make proposal for site selection process of field emergency medical facilities on the basis of the factor. Methods: This study performs text mining of disaster-related laws, guidelines and documents to derive key factors affecting site selection, also proposes a decision making process and conducts virtual deployment to validate the process. Results: The key factors for the site selection derived as the size of the damage, the size of the DMAT inputs, the location of available place, and distance to the disaster base hospital. As a result of virtual deployment following proposed decision making process, It is confirmed that the site of field emergency medical facilities is changed depending on the type of disaster, even if the scope of the disaster damage was the same. Implications: The deployment of field emergency medical facilities requires a separate criteria for each type of disaster, not uniform, as a future research a quantitative approach of the criteria needs to be performed.

• The Journal of the Convergence on Culture Technology
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• v.10 no.2
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• pp.453-458
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• 2024

Optical Character Recognition is the technology that recognizes text in images and converts them into digital format. Deep learning-based OCR is being used in many industries with large quantities of recorded data due to its high recognition performance. To improve medical services, deep learning-based OCR was actively introduced by the medical industry. In this paper, we discussed trends in OCR engines and medical OCR and provided a roadmap for development of medical OCR. By using natural language processing on detected text data, current medical OCR has improved its recognition performance. However, there are limits to the recognition performance, especially for non-standard handwriting and modified text. To develop advanced medical OCR, databaseization of medical data, image pre-processing, and natural language processing are necessary.

• Journal of the Korean Society of Physical Medicine
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• v.14 no.3
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• pp.55-62
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• 2019

PURPOSE: Text-mining has been shown to be useful for understanding the clinical characteristics and patients' concerns regarding a specific disease. Low back pain (LBP) is the most common disease in modern society and has a wide variety of causes and symptoms. On the other hand, it is difficult to understand the clinical characteristics and the needs as well as demands of patients with LBP because of the various clinical characteristics. This study examined online texts on LBP to determine of text-mining can help better understand general characteristics of LBP and its specific elements. METHODS: Online data from www.spine-health.com were used for text-mining. Keyword frequency analysis was performed first on the complete text of postings (full-text analysis). Only the sentences containing the highest frequency word, pain, were selected. Next, texts including the sentences were used to re-analyze the keyword frequency (pain-text analysis). RESULTS: Keyword frequency analysis showed that pain is of utmost concern. Full-text analysis was dominated by structural, pathological, and therapeutic words, whereas pain-text analysis was related mainly to the location and quality of the pain. CONCLUSION: The present study indicated that text-mining for a specific element (keyword) of a particular disease could enhance the understanding of the specific aspect of the disease. This suggests that a consideration of the text source is required when interpreting the results. Clinically, the present results suggest that clinicians pay more attention to the pain a patient is experiencing, and provide information based on medical knowledge.

• Korean Journal of Schizophrenia Research
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• v.23 no.2
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• pp.58-64
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• 2020

Objectives: In this study, we conducted an exploratory analysis of the current media trends on schizophrenia using text-mining methods. Methods: First, web-crawling techniques extracted text data from 575 news articles in 10 major newspapers between 2018 and 2019, which were selected by searching "schizophrenia" in the Naver News. We had developed document-term matrix (DTM) and/or term-document matrix (TDM) through pre-processing techniques. Through the use of DTM and TDM, frequency analysis, co-occurrence network analysis, and topic model analysis were conducted. Results: Frequency analysis showed that keywords such as "police," "mental illness," "admission," "patient," "crime," "apartment," "lethal weapon," "treatment," "Jinju," and "residents" were frequently mentioned in news articles on schizophrenia. Within the article text, many of these keywords were highly correlated with the term "schizophrenia" and were also interconnected with each other in the co-occurrence network. The latent Dirichlet allocation model presented 10 topics comprising a combination of keywords: "police-Jinju," "hospital-admission," "research-finding," "care-center," "schizophrenia-symptom," "society-issue," "family-mind," "woman-school," and "disabled-facilities." Conclusion: The results of the present study highlight that in recent years, the media has been reporting violence in patients with schizophrenia, thereby raising an important issue of hospitalization and community management of patients with schizophrenia.

• Proceedings of the Korean Society for Information Management Conference
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• 2010.08a
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• pp.21-28
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• 2010

본 연구에서는 PubMed에 등재되었으나 Medical Subject Headings(MeSH)가 부여되지 않은 국내 의학학술지의 문헌을 대상으로 미국국립의학도서관 (NLM: National Library of Medicine)의 Medical Text Indexer(MTI)를 활용하여 MeSH 용어를 추천받은 후, PubMed 레코드의 유사주제문헌 (Relation Citations, PRC)에 부여된 MeSH와의 일치여부를 분석하였다. 또한 논문의 저자가 부여한 키워드(저자키워드)와 PRC MeSH의 일치여부도 비교하였다. PRC MeSH와 MTI MeSH 추천어의 일치율은 주표목이 21.1%였고, 체크태그는 18.1%, 부표목은 16.5%로 나타났다. 우리나라 의학논문에 나타난 저자키워드의 중요한 특징은 MeSH 주표목 위주이고, 체크태그와 부표목은 거의 사용하지 않는 것이다. 따라서 저자키워드와 PRC MeSH 주표목과의 일치율은 23.4%에 이르지만, 체크태그와 부표목의 일치율은 각각 1%, 2.1%였다. 색인전문가가 통제어휘를 사용하여 색인하는 과정에서 PRC와 MTI의 MeSH 주표목과 저자키워드가 일치하는 용어를 주표목으로 부여하고, PRC와 MTI가 추천하는 체크태그와 부표목을 활용하는 등 국내 의학문헌의 MeSH 용어 부여 작업을 반자동화(semi-indexing)하면, 정확하고 신속한 MeSH 부여 작업이 가능할 것이다.