class: title-slide, left, bottom # AI 기반 체외진단 응용 ---- ## **데이터 과학과 AI** ### 이광춘 ### 2021-07-16 --- class: inverse, middle name: data-science-case # 발표 개요 ---- .pull-left[ 알파고가 2016년 이세돌 9단을 가볍게 승리하면서 **AI 디지털 경제전환**이 촉발되었다면 코로나19는 .red[**디지털 경제전환**]을 가속화를 넘어 디지털 불평등을 심화시키고 있다. AI 디지털 경제전환을 이해하는데 꼭 필요한 .black[**데이터 과학**]과 .green[**인공지능**] 기술적 토대를 살펴보고 이를 .warmyellow[**헬스케어**] 영역에 응용하여 활용방안을 모색해 보자. ] .pull-right[ .left[ 1\. **[.red[디지털 경제전환 (DT)]](#digital-transformation)** 2\. [데이터 과학 사례](#digital-data-science-case-study) 3\. [데이터 과학 (Data Science)](#data-science) 4\. [인공지능 (AI)](#digital-ai) 5\. [헬스케어 응용](#digital-healthcare) 6\. 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esg-digital-country-start # 디지털 강대국 지표 .center[  ] - [디지털 정부 순위](https://www.oecd-ilibrary.org/deliver/4de9f5bb-en.pdf?itemId=%2Fcontent%2Fpaper%2F4de9f5bb-en&mimeType=pdf) - [정부 인공지능 준비지수](https://www.oxfordinsights.com/government-ai-readiness-index-2020) - [데이터 경제 순위](https://hbr.org/2019/01/which-countries-are-leading-the-data-economy) - [산업용 로봇 밀도](https://ifr.org/downloads/press2018/Robot_Density_by_country_WorldRobotics2020_graph.jpg) - [세계 경쟁력 보고서](https://reports.weforum.org/global-competitiveness-report-2019/competitiveness-rankings/) - [IMD 세계 경쟁력 연감](https://www.imd.org/wcc/world-competitiveness-center-rankings/world-digital-competitiveness-rankings-2020/) --- name: esg-digital-government # 디지털 강대국 지표 - 디지털 정부 순위 <div id="htmlwidget-6695be83c6c04ab558b5" class="reactable html-widget" style="width:auto;height:auto;"></div> <script type="application/json" data-for="htmlwidget-6695be83c6c04ab558b5">{"x":{"tag":{"name":"Reactable","attribs":{"data":{"country":["Korea, 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AI 디지털 경제전환을 이해하는데 꼭 필요한 .black[**데이터 과학**]과 .green[**인공지능**] 기술적 토대를 살펴보고 이를 .warmyellow[**헬스케어**] 영역에 응용하여 활용방안을 모색해 보자. ] .pull-right[ .left[ 1\. [디지털 경제전환 (DT)](#digital-transformation) 2\. **[.black[데이터 과학 사례]](#digital-data-science-case-study)** 3\. [데이터 과학 (Data Science)](#data-science) 4\. [인공지능 (AI)](#digital-ai) 5\. [헬스케어 응용](#digital-healthcare) 6\. [마무리](#digital-goodbye) ] ] <!--end of right-column--> --- name: case-study-worldcup ## 러시아 월드컵 (독일전) .center[ <img src="fig/worldcup.gif" alt="월드컵" width="80%" /> ] .footnote[ [러시아 월드컵(2018) - 한국 vs 독일](https://statkclee.github.io/viz/viz-worldcup-germany.html) ] --- name: case-study-earthquake ## 지진해일 대피소 .center[ <img src="fig/earthquake.png" alt="월드컵" width="80%" /> ] .footnote[ [xwMOOC (2018-01-17), "지진해일 대피소 - `crosstalk`"](https://statkclee.github.io/viz/viz-earthquake-shelter.html) ] --- name: case-study-congress ## 국회의원 프로필 .center[ <img src="fig/trelliscopejs.png" alt="국회의원 사진" width="80%" /> ] .footnote[ [국회의원 사진 - `trelliscope`](https://statkclee.github.io/viz/viz-congressman.html) ] --- name: case-study-avi ## 동영상 감성 분석 .center[ <img src="fig/kbs-video.jpg" alt="슬픔은 어디에" width="80%" /> ] .footnote[ - [xwMOOC 딥러닝 - 동영상 감정 분석](https://statkclee.github.io/deep-learning/ms-oxford-video.html) - [KBS NEWS, 인공지능으로 분석한 대통령의 마음…슬픔은 어디에?](http://news.kbs.co.kr/news/view.do?ncd=3390429&ref=D) ] --- name: case-study-regression ## 회귀분석 - MLB 투수 유전자는 유전이 될까? .center[ <img src="fig/major-league.png" alt="MLB 투수 유전자" width="77%" /> ] .footnote[ [회귀분석 - MLB 투수 유전자는 유전이 될까?](https://statkclee.github.io/politics/pe-baseball-era.html) ] --- name: case-study-birthrate ## 출산장려 예산은 출산율을 높일까? .center[ <img src="fig/budget-birthrate.png" width="57%" /> ] .footnote[ [저출산과 소요예산 - 상관관계](https://statkclee.github.io/viz/viz-low-birth-rate.html) ] --- name: case-study-tax ## 증가된 정부예산: 누가 세금을 많이 냈나? .center[ <img src="fig/tax-burden.png"width="77%" /> ] .footnote[ [국세 세금 데이터 분석](https://statkclee.github.io/election/election_tax_EDA.html) ] --- name: case-study-covid ## 코로나19 정부 정책 평가 .center[ <img src="fig/covid19-deaths.png"width="77%" /> ] .footnote[ [코로나19 사망자와 경제성장률](https://statkclee.github.io/viz/viz-mortality.html) ] --- class: inverse, middle name: data-science # 발표 개요 ---- .pull-left[ 알파고가 2016년 이세돌 9단을 가볍게 승리하면서 **AI 디지털 경제전환**이 촉발되었다면 코로나19는 .red[**디지털 경제전환**]을 가속화를 넘어 디지털 불평등을 심화시키고 있다. AI 디지털 경제전환을 이해하는데 꼭 필요한 .black[**데이터 과학**]과 .green[**인공지능**] 기술적 토대를 살펴보고 이를 .warmyellow[**헬스케어**] 영역에 응용하여 활용방안을 모색해 보자. ] .pull-right[ .left[ 1\. [디지털 경제전환 (DT)](#digital-transformation) 2\. **[.black[데이터 과학 사례]](#digital-data-science-case-study)** 3\. **[.black[데이터 과학 (Data Science)]](#data-science)** 4\. [인공지능 (AI)](#digital-ai) 5\. [헬스케어 응용](#digital-healthcare) 6\. [마무리](#digital-goodbye) ] ] <!--end of right-column--> --- name: data-science-definition # 데이터 과학 데이터 사이언스(Data Science)는 데이터를 다루는 과학이다. 따라서 자연법칙을 다루는 자연과학과 맥이 닿아있다. 자연법칙을 측정하여 데이터프레임(`DataFrame`)으로 나타내면 **변수(Variable)**, **관측점(Observation)**, **값(Value)**으로 표현된다. <img src="fig/data-science-overview.png" alt="데이터 사이언스 개요" width="100%" /> .footnote[ [Google Search - "데이터 과학"](https://www.google.com/search?q=%EB%8D%B0%EC%9D%B4%ED%84%B0+%EA%B3%BC%ED%95%99) ] --- name: tidyverse # AI/ML/DL vs 데이터 과학 <br> .center[ <img src="fig/tidyvese_os.png" width="77%" /> ] .footnote[ [한국통계학회 소식지 2019년 10월호 ](https://statkclee.github.io/ds-authoring/ds-stat-tidyverse.html) ] --- name: tidyverse-definition # `tidyverse` <br> .center[ <img src="fig/maso_tidyverse.png" width="77%" /> ] .pull-left[ - 기존 자료구조를 재사용 - 파이프 연산자로 간단한 함수를 조합 - 함수형 프로그래밍을 적극 사용 - 기계가 아닌 인간을 위한 설계 ] .pull-right[ - Reuse existing data structures. - Compose simple functions with the pipe. - Embrace functional programming. - Design for humans. ] --- name: y-fx # 30년의 변화를 그림으로 표현 .center[ <img src="fig/intree-xy-f.png" alt="XY" width="77%" /> ] .footnote[ [이광춘(2019-01-26), "Data Scientist 가 바라본 제조업의 발전 전략 및 방안", Intree 4.0 World Forum](https://statkclee.github.io/ds-authoring/intree-seminar-2019-01.html) ] --- name: data-science-graph ## Big Data/AI/ML/Data Science <br> .pull-left[ ### Google Trends: 미국  ] .pull-right[ ### Google Trends: 대한민국  ] .footnote[ [이광춘 (2020-07-21), "기업운영혁신을 위한 데이터 과학: 기업의 활용방안", 포항산업과학연구원(RIST)](https://statkclee.github.io/ds-authoring/ds-rist.html) ] --- name: data-science-resource # 데이터 경제와 산업 .pull-left[ ### 데이터 자원  ] .pull-right[ ### 파이프라인  ] .footnote[ [이광춘 (2020-07-21), "기업운영혁신을 위한 데이터 과학: 기업의 활용방안", 포항산업과학연구원(RIST)](https://statkclee.github.io/ds-authoring/ds-rist.html) ] --- name: data-sciece-value ## 가치 <small> <ul> <li>기술 분석(Descriptive Analytics): 무슨 일이 있었나? (What happened?)</li> <li>진단 분석(Diagnostic Analytics): 왜 일어났나? (Why did it happen?)</li> <li>예측 분석(Predictive Analytics): 무슨 일이 일어날까? (What will happen?)</li> <li>처방 분석(Prescriptive Analytics): 어떻게 그런 일을 일으킬까? (How can we make it happen?)</li> </ul> </small> .center[ <img src="fig/ds-gartner.png" width = "80%" /> ] --- name: data-sciece-cost # 패러다임의 변화 .pull-left[ ### 비용  ] .pull-right[ ### 자동화  ] <!-------------------------- 인공지능 -----------------------------------------> --- class: inverse, middle name: healthcare-ai # 발표 개요 ---- .pull-left[ 알파고가 2016년 이세돌 9단을 가볍게 승리하면서 **AI 디지털 경제전환**이 촉발되었다면 코로나19는 .red[**디지털 경제전환**]을 가속화를 넘어 디지털 불평등을 심화시키고 있다. AI 디지털 경제전환을 이해하는데 꼭 필요한 .black[**데이터 과학**]과 .green[**인공지능**] 기술적 토대를 살펴보고 이를 .warmyellow[**헬스케어**] 영역에 응용하여 활용방안을 모색해 보자. ] .pull-right[ .left[ 1\. [디지털 경제전환 (DT)](#digital-transformation) 2\. [데이터 과학 사례](#digital-data-science-case-study) 3\. [데이터 과학 (Data Science)](#data-science) 4\. **[.green[인공지능 (AI)]](#digital-ai)** 5\. [헬스케어 응용](#digital-healthcare) 6\. [마무리](#digital-goodbye) ] ] <!--end of right-column--> --- name: ml-overview # AI / ML / 통계 <br> .center[ <img src="fig/ai-ml-statistics.png" alt="AI > ML > Statistics" alt="100%" /> ] --- name: ml-sofware # 소프트웨어 1.0과 2.0 - 소프트웨어 1.0: 사람이 직접 규칙(Rule)을 작성하여 똑똑한 시스템 개발 - 소프트웨어 2.0: 데이터에서 딥러닝 알고리즘을 활용 기계가 알고리즘(Algorithm) 제작 .center[ <img src="fig/programming_machine_learning_comparison.png" width="77%"> ] .footnote[ [궁극의 주인이 될 알고리즘(Master Algorithm)](https://statkclee.github.io/ai-lab/master-algorithm.html) ] --- name: ml-bigdata-ml # 빅데이터와 기계학습 .center[ <img src="fig/intree-xy-f.png" alt="정형데이터와 비정형 데이터, 그리고 기계학습" width="90%"> ] .footnote[ - [데이터 가져오기](https://statkclee.github.io/ingest-data/) - [직사각형 데이터](https://statkclee.github.io/data-science/) - [텍스트 데이터](https://statkclee.github.io/text/) - [네트워크 데이터](https://statkclee.github.io/network/) - [지리정보 데이터](https://statkclee.github.io/spatial/) - [이미지 데이터](https://statkclee.github.io/trilobite/) - 오디오/소리 데이터 - ... ] --- name: ml-deep-learning # 딥러닝(Deep Learning) .center[ <img src="fig/three-pillars.png" width="90%"> ] .footnote[ [xwMOOC 딥러닝과 `\(H_2 O\)`](http://statkclee.github.io/deep-learning/) ] <!-------------------------- 헬스케어 -----------------------------------------> --- class: inverse, middle name: healthcare-biopsy # 발표 개요 ---- .pull-left[ 알파고가 2016년 이세돌 9단을 가볍게 승리하면서 **AI 디지털 경제전환**이 촉발되었다면 코로나19는 .red[**디지털 경제전환**]을 가속화를 넘어 디지털 불평등을 심화시키고 있다. AI 디지털 경제전환을 이해하는데 꼭 필요한 .black[**데이터 과학**]과 .green[**인공지능**] 기술적 토대를 살펴보고 이를 .warmyellow[**헬스케어**] 영역에 응용하여 활용방안을 모색해 보자. ] .pull-right[ .left[ 1\. [디지털 경제전환 (DT)](#digital-transformation) 2\. [데이터 과학 사례](#digital-data-science-case-study) 3\. [데이터 과학 (Data Science)](#data-science) 4\. [인공지능 (AI)](#digital-ai) 5\. **[.warmyellow[헬스케어 응용]](#digital-healthcare)** 6\. [마무리](#digital-goodbye) ] ] <!--end of right-column--> --- name: healthcare-data # 개인정보 .center[ <img src="https://raw.githubusercontent.com/statkclee/privacy/gh-pages/fig/personal-data.png" width = "87%" /> ] .footnote[ - [개인정보와 익명화](https://statkclee.github.io/privacy/anonymization.html) ] --- name: healthcare-respiration # 호흡 데이터 생성 .center[ <img src="fig/sensor-data-generation.png" width = "100%" /> ] .footnote[ Tiele, A., Wicaksono, A., Ayyala, S. K., & Covington, J. A. (2020). Development of a Compact, IoT-Enabled Electronic Nose for Breath Analysis. Electronics, 9(1), 84. doi:10.3390/electronics9010084 ] --- name: healthcare-respiration # 데이터 종류 <br> .center[ <img src="https://raw.githubusercontent.com/statkclee/data-science/gh-pages/fig/classify-data.png" width = "100%" /> ] .footnote[ - [Tidyverse Korea, "데이터 사이언스: 관계형 데이터베이스(RDBMS)"](https://statkclee.github.io/data-science/ds-rdbms.html) ] --- name: healthcare-respiration # RDBMS ERD - Chinook .center[ <img src="https://raw.githubusercontent.com/statkclee/data-science/gh-pages/fig/chinook-erd.png" width = "80%" /> ] .footnote[ - [Tidyverse Korea, "데이터 사이언스: 관계형 데이터베이스(RDBMS)"](https://statkclee.github.io/data-science/ds-rdbms.html) ] --- name: healthcare-respiration # 호흡 진단기 ERD .center[ <img src="fig/sensor-erd.png" width = "80%" /> ] .footnote[ - [Tidyverse Korea, "데이터 사이언스: 관계형 데이터베이스(RDBMS)"](https://statkclee.github.io/data-science/ds-rdbms.html) ] --- name: healthcare-respiration # Feature Engineering <br> <br> .center[ <img src="fig/feature-engineering.png" width = "80%" /> ] .footnote[ Yan, J., Guo, X., Duan, S., Jia, P., Wang, L., Peng, C., & Zhang, S. (2015). Electronic Nose Feature Extraction Methods: A Review. Sensors, 15(11), 27804–27831. doi:10.3390/s151127804 ] --- name: healthcare-respiration # AI 딥러닝 모델 개발 <br> <br> .center[ <img src="https://raw.githubusercontent.com/statkclee/model/gh-pages/fig/tidymodels-ecosystem.png" width = "100%" /> ] .footnote[ - [Tidyverse 모형 - tidymodels, "펭귄 성별예측모형: tidymodels"](https://statkclee.github.io/model/tidyverse-parsnip-penguin-101.html) ] --- name: healthcare-respiration # 설명가능한 기계학습 <iframe width="800" height="500" src="https://www.youtube.com/embed/K_TGdklC504" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe> <!-------------------------- 마무리 -----------------------------------------> --- name: digital-goodbye class: middle, inverse .pull-left[ # **경청해 주셔서 <br>감사합니다.** <br/> ## Tidyverse Korea ] .pull-right[ .right[ <img style="border-radius: 100%;" src="fig/Tidyverse_Korea_logo_transparent.png" width="300px"/> ] ]