• The Korean Society of Law and Medicine
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• v.24 no.3
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• pp.59-104
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• 2023

This essay attempts to analyze the public health welfare conception within the text of the Beveridge Report and its realization via the NHS in Great Britain. Once referring to the influence of the Report to create the foundations of the 20th-century welfare system, the quest to scrutinize the original intentions of the Report and its succession to the NHS is certainly intriguing. Furthermore, when regarding the significance of public health policy for a modern state, the effort to engage in Beveridge's conception and its realization is more than timely. In light of such a premise, this paper indulges in its study by the following methods. First, the historical background of the Report - namely, the role of the spirit of the age and the experience of the Emergency Medical Service are to be analyzed to identify the origins of the welfare policies proposed by Beveridge. Furthermore, the public health welfare conception of the Report conceived from its time is reflected upon by engaging on the goal towards social welfare and public health scheme. Lastly, the aims of the NHS and its management, treatment classification, and rehabilitation program are reviewed for comparative analysis with the Report to survey the realization of Beveridge's design. In this process, this paper not only takes into account the original text of the Report - but also other essential works of law and public policy, including the NHS Constitution for England and the National Health Service Act of 1946. The intentions of this study are not bound by merely coinciding with the Report, but resonate significance via reflecting upon the Beveridgian legacy on the modern welfare state from the current perspective. The structured analysis to research the aims and policies of the Report and to compare them to the reality of the NHS may provide an opportunity to confirm the realization of Beveridge's scheme in British society. In addition, this essay is part of an academic endeavor to critically assess the past and the present of the welfare institution in the public health sector. As such, it is hopeful that the essay sheds light on further studies concerning the constructive remedies of the Korean welfare system as well.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.44 no.3
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• pp.407-416
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• 2024

Over the past 20 years, there has been a rapid increase in the number and size of subway stations and underground structures worldwide, and the importance of safety for subway users has also continuously grown. Subway stations, due to their structural characteristics, have limited visibility and escape routes in disaster situations, posing a high risk of human casualties and economic losses. Therefore, an analysis of disaster vulnerabilities is essential not only for existing subway systems but also for deep underground facilities like GTX. This paper presents a case study applying a betweenness centrality-based disaster vulnerability analysis framework to the case of Gwanggyo Central Station. The analysis of Gwanggyo Central Station's base model and various disaster scenarios revealed that the betweenness centrality distribution is symmetrical, following the symmetrical spatial structure of the station, with high centrality concentrated in the central areas of basement levels one and two. These areas exhibited values more than 220% above the average, indicating a high likelihood of bottleneck phenomena during evacuation in disaster situations. To mitigate this vulnerability, scenarios were proposed to distribute evacuation flows concentrated in the central areas, enhancing the usability of peripheral areas as evacuation routes by connecting staircases continuously. This modification, when considered, showed a decrease in centrality concentration, confirming that the proposed addition of evacuation paths could effectively contribute to dispersing the flow of evacuation in Gwanggyo Central Station. This case study demonstrates the effectiveness of the proposed framework for assessing evacuation vulnerability in enhancing subway station user safety and can be effectively applied in disaster response and management plans for major underground facilities.

• The Journal of the Korean life insurance medical association
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• v.24
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• pp.49-78
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• 2005

Ⅰ. 연구(硏究) 배경(背景) 및 목적(目的) o 우리나라 보험시장의 세대가입율은 86%로 보험시장 성숙기에 진입하였으며 기존의 전통적인 전업채널에서 방카슈랑스의 도입, 온라인전문보험사의 출현, TM 영업의 성장세 等멀티채널로 진행되고 있음 o LTC(장기간병), CI(치명적질환), 실손의료보험 등(等)선 진형 건강상품의 잇따른 출시로 보험리스크 관리측면에서 언더라이팅의 대비가 절실한 시점임 o 상품과 마케팅 等언더라이팅 측면에서 매우 밀접한 영역의 변화에 발맞추어 언더라이팅의 인수기법의 선진화가 시급히 요구되는 상황하에서 위험을 적절히 분류하고 평가하는 선진적 언더라이팅 기법 구축이 필수 적임 o 궁극적으로 고객의 다양한 보장니드 충족과 상품, 마케팅, 언더라이팅의 경쟁력 강화를 통한 보험사의 종합이익 극대화에 기여할 수 있는 방안을 모색하고자 함 Ⅱ. 선진보험시장(先進保險市場)Risk 세분화사례(細分化事例) 1. 환경적위험(環境的危險)에 따른 보험료(保險料) 차등(差等) (1) 위험직업 보험료 할증 o 미국, 유럽등(等) 대부분의 선진시장에서는 가입당시 피보험자의 직업위험도에 따라 보험료를 차등 적용중(中)임 o 가입하는 보장급부에 따라 직업 분류방법 및 할증방식도 상이하며 일반사망과 재해사망,납입면제, DI에 대해서 별도의 방법을 사용함 o 할증적용은 표준위험율의 일정배수를 적용하여 할증 보험료를 산출하거나, 가입금액당 일정한 추가보험료를 적용하고 있음 - 광부의 경우 재해사망 가입시 표준위험율의 300% 적용하며, 일반사망 가입시 $1,000당 $2.95 할증보험료 부가 (2) 위험취미 보험료 할증 o 취미와 관련 사고의 지속적 다발로 취미활동도 위험요소로 인식되어 보험료를 차등 적용중(中)임 o 할증보험료는 보험가입금액당 일정비율로 부가(가입 금액과 무관)하며, 신종레포츠 등(等)일부 위험취미는 통계의 부족으로 언더라이터가 할증율 결정하여 적용함 - 패러글라이딩 년(年)$26{\sim}50$회(回) 취미생활의 경우 가입금액 $1,000당 재해사망 $2, DI보험 8$ 할증보험료 부가 o 보험료 할증과는 별도로 위험취미에 대한 부담보를 적용함. 위험취미 활동으로 인한 보험사고 발생시 사망을 포함한 모든 급부에 대한 보장을 부(不)담보로 인수함. (3) 위험지역 거주/ 여행 보험료 할증 o 피보험자가 거주하고 있는 특정국가의 임시 혹은 영구적 거주시 기후위험, 거주지역의 위생과 의료수준, 여행위험, 전쟁과 폭동위험 등(等)을 고려하여 평가 o 일반사망, 재해사망 등(等)보장급부별로 할증보험료 부가 또는 거절 o 할증보험료는 보험全기간에 대해 동일하게 적용 - 러시아의 경우 가입금액 $1,000당 일반사망은 2$의 할증보험료 부가, 재해사망은 거절 (4) 기타 위험도에 대한 보험료 차등 o 비행관련 위험은 세가지로 분류(항공운송기, 개인비행, 군사비행), 청약서, 추가질문서, 진단서, 비행이력 정보를 바탕으로 할증보험료를 부가함 - 농약살포비행기조종사의 경우 가입금액 $1,000당 일반사망 6$의 할증보험료 부가, 재해사망은 거절 o 미국, 일본등(等)서는 교통사고나 교통위반 관련 기록을 활용하여 무(無)사고운전자에 대해 보험료 할인(우량체 위험요소로 활용) 2. 신체적위험도(身體的危險度)에 따른 보험료차등(保險料差等) (1) 표준미달체 보험료 할증 1) 총위험지수 500(초과위험지수 400)까지 인수 o 300이하는 25점단위, 300점 초과는 50점 단위로 13단계로 구분하여 할증보험료를 적용중(中)임 2) 삭감법과 할증법을 동시 적용 o 보험금 삭감부분만큼 할증보험료가 감소하는 효과가 있어 청약자에게 선택의 기회를 제공할수 있으며 고(高)위험 피보험자에게 유용함 3) 특정암에 대한 기왕력자에 대해 단기(Temporary)할증 적용 o 질병성향에 따라 가입후 $1{\sim}5$년간 할증보험료를 부가하고 보험료 할증 기간이 경과한 후에는 표준체보험료를 부가함 4) 할증보험료 반환옵션(Return of the extra premium)의 적용 o 보험계약이 유지중(中)이며, 일정기간 생존시 할증보험료가 반환됨 (2) 표준미달체 급부증액(Enhanced annuity) o 영국에서는 표준미달체를 대상으로 연금급부를 증가시킨 증액형 연금(Enhanced annuity) 상품을 개발 판매중(中)임 o 흡연, 직업, 병력 등(等)다양한 신체적, 환경적 위험도에 따라 표준체에 비해 증액연금을 차등 지급함 (3) 우량 피보험체 가격 세분화 o 미국시장에서는 $8{\sim}14$개 의적, 비(非)의적 위험요소에 대한 평가기준에 따라 표준체를 최대 8개 Class로 분류하여 할인보험료를 차등 적용 - 기왕력, 혈압, 가족력, 흡연, BMI, 콜레스테롤, 운전, 위험취미, 거주지, 비행력, 음주/마약 등(等) o 할인율은 회사, Class, 가입기준에 따라 상이(최대75%)하며, 가입연령은 최저 $16{\sim}20$세, 최대 $65{\sim}75$세, 최저보험금액은 10만달러(HIV검사가 필요한 최저 금액) o 일본시장에서는 $3{\sim}4$개 위험요소에 따라 $3{\sim}4$개 Class로 분류 우량체 할인중(中)임 o 유럽시장에서는 영국 등(等)일부시장에서만 비(非)흡연할인 또는 우량체할인 적용 Ⅲ. 국내보험시장(國內保險市場) 현황(現況)및 문제점(問題點) 1. 환경적위험도(環境的危險度)에 따른 가입한도제한(加入限度制限) (1) 위험직업 보험가입 제한 o 업계공동의 직업별 표준위험등급에 따라 각 보험사 자체적으로 위험등급별 가입한도를 설정 운영중(中)임. 비(非)위험직과의 형평성, 고(高)위험직업 보장 한계, 수익구조 불안정화 등(等)문제점을 내포하고 있음 - 광부의 경우 위험1급 적용으로 사망 최대 1억(億), 입원 1일(日) 2만원까지 제한 o 금융감독원이 2002년(年)7월(月)위험등급별 위험지수를 참조 위험율로 인가하였으나, 비위험직은 70%, 위험직은 200% 수준으로 산정되어 현실적 적용이 어려움 (2) 위험취미 보험가입 제한 o 해당취미의 직업종사자에 준(準)하여 직업위험등급을 적용하여 가입 한도를 제한하고 있음. 추가질문서를 활용하여 자격증 유무, 동호회 가입등(等)에 대한 세부정보를 입수하지 않음 - 패러글라이딩의 경우 위험2급을 적용, 사망보장 최대 2 억(億)까지 제한 (3) 거주지역/ 해외여행 보험가입 제한 o 각(各)보험사별로 지역적 특성상 사고재해 다발 지역에 대해 보험가입을 제한하고 있음 - 강원, 충청 일부지역 상해보험 가입불가 - 전북, 태백 일부지역 입원급여금 1일(日)2만원이내 o 해외여행을 포함한 해외체류에 대해서는 일정한 가입 요건을 정하여 운영중(中)이며, 가입한도 설정 보험가입을 제한하거나 재해집중보장 상품에 대해 거절함 - 러시아의 경우 단기체류는 위험1급 및 상해보험 가입 불가, 장기 체류는 거절처리함 2. 신체적위험도(身體的危險度)에 따른 인수차별화(引受差別化) (1) 표준미달체 인수방법 o 체증성, 항상성 위험에 대한 초과위험지수를 보험금삭감법으로 전환 사망보험에 적용(최대 5년(年))하여 5년(年)이후 보험 Risk노출 심각 o 보험료 할증은 일부 회사에서 주(主)보험 중심으로 사용중(中)이며, 총위험지수 300(8단계)까지 인수 - 주(主)보험 할증시 특약은 가입 불가하며, 암 기왕력자는 대부분 거절 o 신체부위 39가지, 질병 5가지에 대해 부담보 적용(입원, 수술 등(等)생존급부에 부담보) (2) 비(非)흡연/ 우량체 보험료 할인 o 1999년(年)최초 도입 이래 $3{\sim}4$개의 위험요소로 1개 Class 운영중(中)임 S생보사의 경우 비(非)흡연우량체, 비(非)흡연표준체의 2개 Class 운영 o 보험료 할인율은 회사, 상품에 따라 상이하며 최대 22%(영업보험료기준)임. 흡연여부는 뇨스틱을 활용 코티닌테스트를 실시함 o 우량체 판매는 신계약의 $2{\sim}15%$수준(회사의 정책에 따라 상이) Ⅳ. 언더라이팅 기법(技法) 선진화(先進化) 방안(方案) 1. 직업위험도별 보험료 차등 적용 o 생 손보 직업위험등급 일원화와 연계하여 3개등급으로 위험지수개편, 비위험직 기준으로 보험요율 차별적용 2. 위험취미에 대한 부담보 적용 o 해당취미를 원인으로 보험사고(사망포함) 발생시 부담보 제도 도입 3. 표준미달체 인수기법 선진화를 통한 인수범위 대폭 확대 o 보험료 할증법 적용 확대를 통한 Risk 헷지로 총위험지수 $300{\rightarrow}500$으로 확대(거절건 최소화) 4. 보험료 할증법 보험금 삭감 병행 적용 o 삭감기간을 적용한 보험료 할증방식 개발, 고객에게 선택권 제공 5. 기한부 보험료할증 부가 o 위암, 갑상선암 등(等)특정암의 성향에 따라 위험도가 높은 가입초기에 평준할증보험료를 적용하여 인수 6. 보험료 할증법 부가특약 확대 적용, 부담보 병행 사용 o 정기특약 등(等)사망관련 특약에 할증법 확대, 생존급부 특약은 부담보 7. 표준체 고객 세분화 확대 o 콜레스테롤, HDL 등(等)위험평가요소 확대를 통한 Class 세분화 Ⅴ. 기대효과(期待效果) 1. 고(高)위험직종사자, 위험취미자, 표준미달체에 대한 보험가입 문호개방 2. 보험계약자간 형평성 제고 및 다양한 고객의 보장니드에 부응 3. 상품판매 확대 및 Risk헷지를 통한 수입보험료 증대 및 사차익 개선 4. 본격적인 가격경쟁에 대비한 보험사 체질 개선 5. 회사 이미지 제고 및 진단 거부감 해소, 포트폴리오 약화 방지 Ⅵ. 결론(結論) o 종래의 소극적이고 일률적인 인수기법에서 탈피하여 피보험자를 다양한 측면에서 위험평가하여 적정 보험료 부가와 합리적 가입조건을 제시하는 적절한 위험평가 수단을 도입하고, o 언더라이팅 인수기법의 선진화와 함께 언더라이팅 인력의 전문화, 정보입수 및 시스템 인프라의 구축 등이 병행함으로써, o 보험사의 사차손익 관리측면에서 뿐만 아니라 보험시장 개방 및 급변하는 보험환경에 대비한 한국 생보언더라이팅 경쟁력 강화 및 언더라이터의 글로벌화에도 크게 기여할 것임.

• Food preservation and processing industry
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• v.8 no.2
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• pp.6-12
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• 2009

면역반응은 외부 감염원으로부터 신체를 보호하고 외부감염원을 제거하고자 하는 주요항상성 유지기전의 하나이다. 이들 반응은 골수에서 생성되고 비장, 흉선 및 임파절 등에서 성숙되는 면역세포들에 의해 매개된다. 보통 태어나면서부터 얻어진 선천성 면역반응을 매개하는 대식세포, 수지상 세포 등과, 오랜기간 동안 감염된 다양한 면역원에 대한 경험을 토대로 얻어진 획득성 면역을 담당하는 T 임파구 등이 대표적인 면역세포로 알려져 있다. 다양한 면역질환이 최근 주요 사망률의 원인이 되고 있다. 최근, 암, 당뇨 및 뇌혈관질환 등이 생체에서 발생되는 급 만성염증에 의해 발생된다고 보고됨에 따라 면역세포 매개성 염증질환에 대한 치료제 개발을 서두르고 있다. 또한 암환자의 급격한 증가는 암발생의 주요 방어기전인 면역력 증강에 대한 요구들을 가중시키고 있다. 예로부터 사용되어 오던 고려인삼과 홍삼은 기를 보호하고 원기를 회복하는 명약으로 알려진 대표적인 우리나라 천연생약이다. 특별히, 홍삼은 단백질과 핵산의 합성을 촉진시키고, 조혈작용, 간기능 회복, 혈당강하, 운동수행 능력증대, 기억력 개선, 항피로작용 및 면역력 증대에 매우 효과가 좋은 것으로 보고되고 있다. 홍삼에 관한 많은 연구에 비해, 현재까지 홍삼이 면역력 증강에 미치는 효과에 대한 분자적 수준에서의 연구는 매우 미미한 것으로 확인되어져 있다. 홍삼의 투여는 NK 세포나 대식세포의 활성이 증가하고 항암제의 암세포 사멸을 증가시키는 것으로 확인되어졌다. 현재까지 알려진 주요 면역증강 성분은 산성다당류로 보고되었다. 또 한편으로 일부 진세노사이드류에서 항염증 효능이 확인되어졌으며, 이를 통해 피부염증 반응과 관절염에 대한 치료 효과가 있는 것으로 추측되고 있다 [본 연구는 KT&G 연구출연금 (2009-2010) 지원을 받아 이루어졌기에 이에 감사드린다]. 면역반응은 외부 감염물질의 침입으로 유도된 질병환경을 제거하고 수복하는 중요한 생체적 방어작용의 하나이다. 이들 과정은 체내로 유입된 미생물이나 미세화학물질들과 같은 독성물질을 소거하거나 파괴하는 것을 주요 역할로 한다. 외부로 부터 인체에 들어온 이물질에 대한 방어기전은 현재 두 가지 종류의 면역반응으로 구분해서 설명한다. 즉, 선천성 면역 반응 (innate immunity)과 후천성 면역 반응 (adaptive immunity)이 그것이다. 선천성 면역반응은 1) 피부나 점막의 표면과 같은 해부학적인 보호벽 구조와 2) 체온과 낮은 pH 및 chemical mediator (리소자임, collectin류) 등과 같은 생리적 방어구조, 3) phagocyte류 (대식세포, 수지상세포 및 호중구 등)에 의한 phagocytic/endocytic 방어, 그리고 4) 마지막으로 염증반응을 통한 감염에 저항하는 면역반응 등으로 구분된다. 후천성 면역반응은 획득성면역이라고도 불리고 특이성, 다양성, 기억 및 자기/비자기의 인식이라는 네 가지의 특징을 가지고 있으며, 외부 유입물질을 제거하는 반응에 따라 체액성 면역 반응 (humoral immune response)과 세포성 면역반응 (cell-mediated immune response)으로 구분된다. 체액성 면역은 침입한 항원의 구조 특이적으로 생성된 B cell 유래 항체와의 반응과 간이나 대식세포 등에서 합성되어 분비된 혈청내 보체 등에 의해 매개되는 반응으로 구성되어 있다. 세포성 면역반응은 T helper cell (CD4+), cytotoxic T cell (CD8+), B cell 및antigen presenting cell 중개를 통한 세포간 상호 작용에 의해 발생되는 면역반응이다. 선천성 면역반응의 하나인 염증은 우리 몸에서 가장 빈번히 발생되고 있는 방어작용의 하나이다. 예를 들면 감기에 걸렸을 경우, 환자의 편도선내 대식세포나 수지상세포류는 감염된 바이러스 단독 혹은 동시에 감염된 박테리아를 상대로 다양한 염증성 반응을 유도하게 된다. 또한, 상처가 생겼을 경우에도 감염원을 통해 유입된 병원성 세균과 주위조직내 선천성 면역담당 세포들 간의 면역학적 전투가 발생되게 된다. 이들 과정을 통해, 주위 세포나 조직이 손상되면, 즉각적으로 이들 면역세포들 (주로 phagocytes류)은 신속하게 손상을 극소화하고 더 나가서 손상된 부위를 원상으로 회복시키려는 일련의 염증반응을 유도하게 된다. 이들 반응은 우리가 흔히 알고 있는 발적 (redness), 부종 (swelling), 발열 (heat), 통증 (pain) 등의 증상으로 나타나게 된다. 즉, 손상된 부위 주변에 존재하는 모세혈관에 흐르는 혈류의 양이 증가하면서 혈관의 직경이 늘어나게 되고, 이로 인한 조직의 홍반과, 부어 오른 혈관에 의해 발열과 부종이 초래되는 것이다. 확장된 모세혈관의 투과성 증가는 체액과 세포들이 혈관에서 조직으로 이동하게 하는 원동력이 되고, 이를 통해 축적된 삼출물들은 단백질의 농도를 높여, 최종적으로 혈관에 존재하는 체액들이 조직으로 더 많이 이동되도록 유도하여 부종을 형성시킨다. 마지막으로 혈관 내 존재하는 면역세포들은 혈판 내벽에 점착되고 (margination), 혈관벽의 간극을 넓히는 역할을 하는 히스타민 (histamine)이나 일산화질소(nitric oxide : NO), 프로스타그린딘 (prostagladins : PGE2) 및 류코트리엔 (leukotriens) 등과 같은 chemical mediator의 도움으로 인해 혈관벽 사이로 삼출하게 되어 (extravasation), 손상된 부위로 이동하여 직접적인 외부 침입 물질의 파괴나 다른 면역세포들을 모으기 위한 cytokine (tumor necrosis factor [TNF]-$\alpha$, interleukin [IL]-1, IL-6 등) 혹은 chemokine (MIP-l, IL-8, MCP-l등)의 분비 등을 수행함으로써 염증반응을 매개하게 된다. 염증과정시 발생되는 여러 mediator 중 PGE2나 NO 및 TNF-$\alpha$ 등은 실험적 평가가 용이하여 이들 mediator 자체나 생성관련효소 (cyclooxygenase [COX] 및 nitric oxide synthase [NOS] 등)들은 현재항염증 치료제의 개발 연구시 주요 표적으로 연구되고 있다. 염증 반응은 지속기간에 따라 크게 급성염증과 만성염증으로 나뉘며, 삼출물의 종류에 따라서는 장액성, 섬유소성, 화농성 및 출혈성 염증 등으로 구분된다. 급성 염증 (acute inflammation)반응은 수일 내지 수주간 지속되는 일반적인 염증반응이라고 볼 수 있다. 국소반응은 기본징후인 발열과 발적, 부종, 통증 및 기능 상실이 특징적이며, 현미경적 소견으로는 혈관성 변화와 삼출물 형성이 주 작용이므로 일명 삼출성 염증이라고 한다. 만성 염증 (chronic inflammation)은, 급성 염증으로부터 이행되거나 만성으로 시작된다. 염증지속 기간은 보통 4주 이상 장기화 된다. 보통 염증의 경우에는 염증 생성 cytokine인 Th1 cytokine (IL-2, interferone [IFN]-$\gamma$ 및 TNF-$\alpha$ 등)의 생성 후, 거의 즉각적으로 항 염증성 cytokine인 Th2 cytokine(IL-4, IL-6, IL-10 및 transforming growth factor [TGF]-$\beta$ 등)이 생성되어 정상반응으로 회복된다. 그러나, 어떤 원인에서든 면역세포에 의한 염증원 제거 반응이 문제가 되면, 만성염증으로 진행된다. 이 반응에 주로 작용을 하는 염증세포로는 단핵구와 대식세포, 림프구, 형질세포 등이 있다. 암은 전세계적으로 사망률 1위의 원인이 되는 면역질환의 하나이다. 산화적 스트레스나 자외선 조사 혹은 암유발 물질들에 의해 염색체내 protooncogene, tumor-suppressor gene 혹은 DNA repairing gene의 일부 DNA의 돌연변이 혹은 결손 등이 발행되면 정상세포는 암화과정을 시작하게 된다. 양성세포 수준에서 약 5에서 10여년 후 악성수준의 암세포가 생성되게 되면 이들 세포는 새로운 환경을 찾아 전이하게 되는데 이를 통해 암환자들은 다양한 장기에 동인 오리진의 암세포들이 생성한 종양들을 가지게 된다. 이들 종양세포는 정상 장기의 기능을 손상시켜며 결국 생명을 잃게 만든다. 이들 염색체 수준에서의 돌연변이 유래 암세포는 거의 대부분이 체내 면역시스템에 의해 사멸되는 것으로 알려져 있다. 그러나 계속되는 스트레스나 암유발 물질의 노출은 체내 면역체계를 파괴하면서 최후의 방어선을 무너뜨리면서 암발생에 무방비 상태를 만들게 된다. 이런 이유로 체내 면역시스템의 정상적 가동 및 증강을 유도하게 하는 전략이 암예방시 매우 중요한 표적으로 인식되면서 다양한 형태의 면역증강 물질 개발을 시도하고 있다. 인삼은 두릅나무과의 여러해살이 풀로써, 오랜동안 한방 및 민간에서 원기를 회복시키고, 각종 질병을 치료할 수단으로 사용되고 있는 대표적인 전통생약이다. 예로부터 불로(不老), 장생(長生), 익기(益氣), 경신(經身)의 명약으로 구전되어졌는데, 이는 약 2천년 전 중국의 신농본초경(神農本草經)에서 "인삼은 오장(五腸)을 보하고, 정신을 안정시키고, 혼백을 고정하며 경계를 멈추게 하고, 외부로부터 침입하는 병사를 제거하여주며, 눈을 밝게 하고 마음을 열어 더욱 지혜롭게 하고 오랫동안 복용하면 몸이 가벼워지고 장수한다" 라고 기술되어있는 데에서 유래한 것이다. 다양한 연구를 통해 우리나라에서 생산되는 고려인삼 (Panax ginseng)이 효능 면에서 가장 탁월한 것으로 알려져 있으며 특별이 고려인삼으로부터 제조된 고려홍삼은 전세계적으로도 그 효능이 우수한 것으로 보고되어 있다. 대부분의 홍삼 약효는 dammarane계열의 triterpenoid인 ginsenosides라고 불리는 인삼 saponin에 의해 기인된 것으로 알려져 있다. 이들 화합물군의 기본 골격에 따라, protopanaxadiol (PD)계 (22종) 및 protopanaxatriol (PT)계 (10종)으로 구분되고 있다 (표 1). 실험적 접근을 통해 인삼의 약리작용 이해를 위한 다양한 노력들이 경주되고 있으나, 여전히 많은 부분에서 충분히 이해되고 있지 않다. 그러나, 현재까지 연구된 인삼의 약리작용 관련 연구들은 심혈관, 당뇨, 항암 및 항스트레스 등과 같은 분야에서 인삼효능이 우수한 것으로 보고하고 있다. 그러나 면역조절 및 염증현상과 관련된 최근 연구결과들은 많지 않으나, 향후 다양하게 연구될 효능부분으로 인식되고 있다.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.143-163
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• 2016

The demographics of Internet users are the most basic and important sources for target marketing or personalized advertisements on the digital marketing channels which include email, mobile, and social media. However, it gradually has become difficult to collect the demographics of Internet users because their activities are anonymous in many cases. Although the marketing department is able to get the demographics using online or offline surveys, these approaches are very expensive, long processes, and likely to include false statements. Clickstream data is the recording an Internet user leaves behind while visiting websites. As the user clicks anywhere in the webpage, the activity is logged in semi-structured website log files. Such data allows us to see what pages users visited, how long they stayed there, how often they visited, when they usually visited, which site they prefer, what keywords they used to find the site, whether they purchased any, and so forth. For such a reason, some researchers tried to guess the demographics of Internet users by using their clickstream data. They derived various independent variables likely to be correlated to the demographics. The variables include search keyword, frequency and intensity for time, day and month, variety of websites visited, text information for web pages visited, etc. The demographic attributes to predict are also diverse according to the paper, and cover gender, age, job, location, income, education, marital status, presence of children. A variety of data mining methods, such as LSA, SVM, decision tree, neural network, logistic regression, and k-nearest neighbors, were used for prediction model building. However, this research has not yet identified which data mining method is appropriate to predict each demographic variable. Moreover, it is required to review independent variables studied so far and combine them as needed, and evaluate them for building the best prediction model. The objective of this study is to choose clickstream attributes mostly likely to be correlated to the demographics from the results of previous research, and then to identify which data mining method is fitting to predict each demographic attribute. Among the demographic attributes, this paper focus on predicting gender, age, marital status, residence, and job. And from the results of previous research, 64 clickstream attributes are applied to predict the demographic attributes. The overall process of predictive model building is compose of 4 steps. In the first step, we create user profiles which include 64 clickstream attributes and 5 demographic attributes. The second step performs the dimension reduction of clickstream variables to solve the curse of dimensionality and overfitting problem. We utilize three approaches which are based on decision tree, PCA, and cluster analysis. We build alternative predictive models for each demographic variable in the third step. SVM, neural network, and logistic regression are used for modeling. The last step evaluates the alternative models in view of model accuracy and selects the best model. For the experiments, we used clickstream data which represents 5 demographics and 16,962,705 online activities for 5,000 Internet users. IBM SPSS Modeler 17.0 was used for our prediction process, and the 5-fold cross validation was conducted to enhance the reliability of our experiments. As the experimental results, we can verify that there are a specific data mining method well-suited for each demographic variable. For example, age prediction is best performed when using the decision tree based dimension reduction and neural network whereas the prediction of gender and marital status is the most accurate by applying SVM without dimension reduction. We conclude that the online behaviors of the Internet users, captured from the clickstream data analysis, could be well used to predict their demographics, thereby being utilized to the digital marketing.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.95-108
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• 2017

Recently, AlphaGo which is Bakuk (Go) artificial intelligence program by Google DeepMind, had a huge victory against Lee Sedol. Many people thought that machines would not be able to win a man in Go games because the number of paths to make a one move is more than the number of atoms in the universe unlike chess, but the result was the opposite to what people predicted. After the match, artificial intelligence technology was focused as a core technology of the fourth industrial revolution and attracted attentions from various application domains. Especially, deep learning technique have been attracted as a core artificial intelligence technology used in the AlphaGo algorithm. The deep learning technique is already being applied to many problems. Especially, it shows good performance in image recognition field. In addition, it shows good performance in high dimensional data area such as voice, image and natural language, which was difficult to get good performance using existing machine learning techniques. However, in contrast, it is difficult to find deep leaning researches on traditional business data and structured data analysis. In this study, we tried to find out whether the deep learning techniques have been studied so far can be used not only for the recognition of high dimensional data but also for the binary classification problem of traditional business data analysis such as customer churn analysis, marketing response prediction, and default prediction. And we compare the performance of the deep learning techniques with that of traditional artificial neural network models. The experimental data in the paper is the telemarketing response data of a bank in Portugal. It has input variables such as age, occupation, loan status, and the number of previous telemarketing and has a binary target variable that records whether the customer intends to open an account or not. In this study, to evaluate the possibility of utilization of deep learning algorithms and techniques in binary classification problem, we compared the performance of various models using CNN, LSTM algorithm and dropout, which are widely used algorithms and techniques in deep learning, with that of MLP models which is a traditional artificial neural network model. However, since all the network design alternatives can not be tested due to the nature of the artificial neural network, the experiment was conducted based on restricted settings on the number of hidden layers, the number of neurons in the hidden layer, the number of output data (filters), and the application conditions of the dropout technique. The F1 Score was used to evaluate the performance of models to show how well the models work to classify the interesting class instead of the overall accuracy. The detail methods for applying each deep learning technique in the experiment is as follows. The CNN algorithm is a method that reads adjacent values from a specific value and recognizes the features, but it does not matter how close the distance of each business data field is because each field is usually independent. In this experiment, we set the filter size of the CNN algorithm as the number of fields to learn the whole characteristics of the data at once, and added a hidden layer to make decision based on the additional features. For the model having two LSTM layers, the input direction of the second layer is put in reversed position with first layer in order to reduce the influence from the position of each field. In the case of the dropout technique, we set the neurons to disappear with a probability of 0.5 for each hidden layer. The experimental results show that the predicted model with the highest F1 score was the CNN model using the dropout technique, and the next best model was the MLP model with two hidden layers using the dropout technique. In this study, we were able to get some findings as the experiment had proceeded. First, models using dropout techniques have a slightly more conservative prediction than those without dropout techniques, and it generally shows better performance in classification. Second, CNN models show better classification performance than MLP models. This is interesting because it has shown good performance in binary classification problems which it rarely have been applied to, as well as in the fields where it's effectiveness has been proven. Third, the LSTM algorithm seems to be unsuitable for binary classification problems because the training time is too long compared to the performance improvement. From these results, we can confirm that some of the deep learning algorithms can be applied to solve business binary classification problems.

• The Journal of Korean Society for Radiation Therapy
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• v.25 no.1
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• pp.1-8
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• 2013

Purpose: A selection of proper energy in treatment planning is very important because of having different dose distribution in body as photon energy. In generally, the low energy photon has been used in intensity-modulated radiation therapy (IMRT) for head and neck (H&N) cancer. The aim of this study was to evaluate the effect of partially used high energy photon at posterior oblique fields on IMRT plan for H&N cancer. Materials and Methods: The study was carried out on 10 patients (nasopharyngeal cancer 5, tonsilar cancer 5) treated with IMRT in Seoul National University Bundang Hospital. CT images were acquired 3 mm of thickness in the same condition and the treatment plan was performed by Eclipse (Ver.7.1, Varian, Palo Alto, USA). Two plans were generated under same planing objectives, dose volume constraints, and eight fields setting: (1) The low energy plan (LEP) created using 6 MV beam alone, (2) the partially used high energy plan (PHEP) created partially using 15 MV beam at two posterior oblique fields with deeper penetration depths, while 6 MV beam was used at the rest of fields. The plans for LEP and PHEP were compared in terms of coverage, conformity index (CI) and homogeneity index (HI) for planning target volume (PTV). For organs at risk (OARs), $D_{mean}$ and $D_{50%}$ were analyzed on both parotid glands and $D_{max}$, $D_{1%}$ for spinal cord were analyzed. Integral dose (ID) and total monitor unit (MU) were compared as addition parameters. For the comparing dose to normal tissue of posterior neck, the posterior-normal tissue volume (P-NTV) was set on the patients respectively. The $D_{mean}$, $V_{20Gy}$ and $V_{25Gy}$ for P-NTV were evaluated by using dose volume histogram (DVH). Results: The dose distributions were similar with regard to coverage, CI and HI for PTV between the LEP and PHEP. No evident difference was observed in the spinal cord. However, the $D_{mean}$, $D_{50%}$ for both parotid gland were slightly reduced by 0.6%, 0.7% in PHEP. The ID was reduced by 1.1% in PHEP, and total MU for PHEP was 1.8% lower than that for LEP. In the P-NTV, the $D_{mean}$, $V_{20Gy}$ and $V_{25Gy}$ of the PHEP were 1.6%, 1.8% and 2.9% lower than those of LEP. Conclusion: Dose to some OARs and a normal tissue, total monitor unit were reduced in IMRT plan with partially used high energy photon. Although these reduction are unclear how have a clinical benefit to patient, application of the partially used high energy photon could improve the overall plan quality of IMRT for head and neck cancer.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.117-127
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• 2012

Due to the recent expansion of the Web 2.0 -based services, along with the widespread of smartphones, online social network services are being popularized among users. Online social network services are the online community services which enable users to communicate each other, share information and expand human relationships. In the social network services, each relation between users is represented by a graph consisting of nodes and links. As the users of online social network services are increasing rapidly, the SNS are actively utilized in enterprise marketing, analysis of social phenomenon and so on. Social Network Analysis (SNA) is the systematic way to analyze social relationships among the members of the social network using the network theory. In general social network theory consists of nodes and arcs, and it is often depicted in a social network diagram. In a social network diagram, nodes represent individual actors within the network and arcs represent relationships between the nodes. With SNA, we can measure relationships among the people such as degree of intimacy, intensity of connection and classification of the groups. Ever since Social Networking Services (SNS) have drawn increasing attention from millions of users, numerous researches have made to analyze their user relationships and messages. There are typical representative SNA methods: degree centrality, betweenness centrality and closeness centrality. In the degree of centrality analysis, the shortest path between nodes is not considered. However, it is used as a crucial factor in betweenness centrality, closeness centrality and other SNA methods. In previous researches in SNA, the computation time was not too expensive since the size of social network was small. Unfortunately, most SNA methods require significant time to process relevant data, and it makes difficult to apply the ever increasing SNS data in social network studies. For instance, if the number of nodes in online social network is n, the maximum number of link in social network is n(n-1)/2. It means that it is too expensive to analyze the social network, for example, if the number of nodes is 10,000 the number of links is 49,995,000. Therefore, we propose a heuristic-based method for finding the shortest path among users in the SNS user graph. Through the shortest path finding method, we will show how efficient our proposed approach may be by conducting betweenness centrality analysis and closeness centrality analysis, both of which are widely used in social network studies. Moreover, we devised an enhanced method with addition of best-first-search method and preprocessing step for the reduction of computation time and rapid search of the shortest paths in a huge size of online social network. Best-first-search method finds the shortest path heuristically, which generalizes human experiences. As large number of links is shared by only a few nodes in online social networks, most nods have relatively few connections. As a result, a node with multiple connections functions as a hub node. When searching for a particular node, looking for users with numerous links instead of searching all users indiscriminately has a better chance of finding the desired node more quickly. In this paper, we employ the degree of user node vn as heuristic evaluation function in a graph G = (N, E), where N is a set of vertices, and E is a set of links between two different nodes. As the heuristic evaluation function is used, the worst case could happen when the target node is situated in the bottom of skewed tree. In order to remove such a target node, the preprocessing step is conducted. Next, we find the shortest path between two nodes in social network efficiently and then analyze the social network. For the verification of the proposed method, we crawled 160,000 people from online and then constructed social network. Then we compared with previous methods, which are best-first-search and breath-first-search, in time for searching and analyzing. The suggested method takes 240 seconds to search nodes where breath-first-search based method takes 1,781 seconds (7.4 times faster). Moreover, for social network analysis, the suggested method is 6.8 times and 1.8 times faster than betweenness centrality analysis and closeness centrality analysis, respectively. The proposed method in this paper shows the possibility to analyze a large size of social network with the better performance in time. As a result, our method would improve the efficiency of social network analysis, making it particularly useful in studying social trends or phenomena.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.1-21
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• 2020

With the rapid development of artificial intelligence technology, various techniques have been developed to extract meaningful information from unstructured text data which constitutes a large portion of big data. Over the past decades, text mining technologies have been utilized in various industries for practical applications. In the field of business intelligence, it has been employed to discover new market and/or technology opportunities and support rational decision making of business participants. The market information such as market size, market growth rate, and market share is essential for setting companies' business strategies. There has been a continuous demand in various fields for specific product level-market information. However, the information has been generally provided at industry level or broad categories based on classification standards, making it difficult to obtain specific and proper information. In this regard, we propose a new methodology that can estimate the market sizes of product groups at more detailed levels than that of previously offered. We applied Word2Vec algorithm, a neural network based semantic word embedding model, to enable automatic market size estimation from individual companies' product information in a bottom-up manner. The overall process is as follows: First, the data related to product information is collected, refined, and restructured into suitable form for applying Word2Vec model. Next, the preprocessed data is embedded into vector space by Word2Vec and then the product groups are derived by extracting similar products names based on cosine similarity calculation. Finally, the sales data on the extracted products is summated to estimate the market size of the product groups. As an experimental data, text data of product names from Statistics Korea's microdata (345,103 cases) were mapped in multidimensional vector space by Word2Vec training. We performed parameters optimization for training and then applied vector dimension of 300 and window size of 15 as optimized parameters for further experiments. We employed index words of Korean Standard Industry Classification (KSIC) as a product name dataset to more efficiently cluster product groups. The product names which are similar to KSIC indexes were extracted based on cosine similarity. The market size of extracted products as one product category was calculated from individual companies' sales data. The market sizes of 11,654 specific product lines were automatically estimated by the proposed model. For the performance verification, the results were compared with actual market size of some items. The Pearson's correlation coefficient was 0.513. Our approach has several advantages differing from the previous studies. First, text mining and machine learning techniques were applied for the first time on market size estimation, overcoming the limitations of traditional sampling based- or multiple assumption required-methods. In addition, the level of market category can be easily and efficiently adjusted according to the purpose of information use by changing cosine similarity threshold. Furthermore, it has a high potential of practical applications since it can resolve unmet needs for detailed market size information in public and private sectors. Specifically, it can be utilized in technology evaluation and technology commercialization support program conducted by governmental institutions, as well as business strategies consulting and market analysis report publishing by private firms. The limitation of our study is that the presented model needs to be improved in terms of accuracy and reliability. The semantic-based word embedding module can be advanced by giving a proper order in the preprocessed dataset or by combining another algorithm such as Jaccard similarity with Word2Vec. Also, the methods of product group clustering can be changed to other types of unsupervised machine learning algorithm. Our group is currently working on subsequent studies and we expect that it can further improve the performance of the conceptually proposed basic model in this study.

• Tuberculosis and Respiratory Diseases
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• v.54 no.2
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• pp.178-190
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• 2003

Background : The committee of tuberculosis(TB) survey planning for the year 2000 decided to construct the Korean Tuberculosis Surveillance System (KTBS), based on a doctor's routine reporting method. The successful keys of the KTBS rely on the precision of the recorded TB notification forms. The purpose of this study was to determine that the accuracy of the TB notification form written at a private general hospital given to the corresponding health center and to improve the comprehensiveness of these reporting systems. Materials and Methods : 291 adult TB patients who had been diagnosed from August 2000 to January 2001, were enrolled in this study. The lists of TB notification forms were compared with the medical records and the various laboratory results; case characteristics, history of previous treatment, examinations for diagnosis, site of the TB by the international classification of the disease, and treatment. Results : In the list of examinations for a diagnosis in 222 pulmonary TB patients, the concordance rate of the 'sputum smear exam' was 76% but that of the 'sputum culture exam' was only 23%. Among the 198 cases of the sputum culture exam labeled 'not examined', 43(21.7%) cases proved to be true 'not examined', 70 cases(35.4%) were proven to be 'culture positive', and 85(43.0%) cases were proven to be 'culture negative'. In the list of examinations for a diagnosis in 69 extrapulmonary TB patients, the concordance rate of the 'smear exam other than sputum' was 54%. In the list of treatments, the overall concordance rate of the 'type of registration' in the TB notification form was 85%. Among the 246 'new' cases on the TB notification form, 217(88%) cases were true 'new' cases and 13 were proven to be 'relapse', 2 were proven to be 'treatment after failure', one was proven to be 'treatment after default', 12 were proven to be 'transferred-in' and one was proven to be 'chronic'. Among the 204 HREZ prescribed regimen, 172(84.3%) patients were taking the HREZ regimen, and the others were prescribed other drug regimens. Conclusion : Correct recording of the TB notification form at the private sectors is necessary for supporting the effective TB surveillance system in Korea.