This document describes a study that used a type of deep learning called a convolutional neural network to create an algorithm for detecting diabetic retinopathy and macular edema in retinal fundus photographs. The algorithm was trained on a dataset of over 128,000 retinal images graded by ophthalmologists. It was then validated on two separate datasets, achieving high sensitivity and specificity for detecting referable diabetic retinopathy. The results demonstrate the potential for deep learning algorithms to accurately analyze medical images and help screen for diabetic eye disease.
'인공지능은 의료를 어떻게 혁신하는가' 주제의 2017년 11월 버전입니다.
'How Artificial Intelligence would Innovate the medicine of the future'
최윤섭 소장 (최윤섭 디지털 헬스케어 연구소)
Yoon Sup Choi, PhD (Director/Founder, Digital Healthcare Institute)
yoonsup.choi@gmail.com
'인공지능은 의료를 어떻게 혁신하는가' 주제의 2017년 11월 버전입니다.
'How Artificial Intelligence would Innovate the medicine of the future'
최윤섭 소장 (최윤섭 디지털 헬스케어 연구소)
Yoon Sup Choi, PhD (Director/Founder, Digital Healthcare Institute)
yoonsup.choi@gmail.com
디지털 헬스케어 기반의 능동적, 선제적 보험
수동적, 사후적 대응에서 능동적, 선제적 관리로의 변화
- 디지털 헬스케어 기반의 가입자 데이터의 측정
- 데이터 분석을 통한 가입자 관리: 질병 위험군 분류, 계리
- 질병 관리 및 치료에 대한 능동적 개입: 관리 방안 및 인센티브
Is the increasing availability of automated image analysis a possibility to strengthen the application of diffusion-MRI as a biometric parameter, and to enhance the future of image biobanks? Or is this evolution threatening the position of radiologists as medical doctors. Is a redefinition of radiologist as computer technicians inevitable?
디지털 헬스케어 기반의 능동적, 선제적 보험
수동적, 사후적 대응에서 능동적, 선제적 관리로의 변화
- 디지털 헬스케어 기반의 가입자 데이터의 측정
- 데이터 분석을 통한 가입자 관리: 질병 위험군 분류, 계리
- 질병 관리 및 치료에 대한 능동적 개입: 관리 방안 및 인센티브
Is the increasing availability of automated image analysis a possibility to strengthen the application of diffusion-MRI as a biometric parameter, and to enhance the future of image biobanks? Or is this evolution threatening the position of radiologists as medical doctors. Is a redefinition of radiologist as computer technicians inevitable?
Digitalisation Of Healthcare - Towards A Better Future - Free Download E bookkevin brown
Digital health has been around for quite some
time. Advancements in technology, rising
demand for better care, and governments' focus
on improved health economy have contributed
to the digital transformation in the healthcare
sector. Healthcare providers and professionals
are continuously challenged to come up with
innovative and cost-effective ways of providing
effective care and better patient outcomes.
In the past few years, digital technologies
have changed the healthcare landscape into
becoming more patient-centric, with care givers
focusing on engaging patients and improving
their experiences.
According a Deloitte report, global healthcare
spending is estimated to cross US$10 trillion by
2022. As the global healthcare market embraces
digitalisation, innovation has a major role to
play. Healthcare companies have been investing
heavily in digital technologies to drive innovation
and value-based care, while making care giving
more accessible and efficient. Digitalisation results
in better usage of patient data by care givers
enabling them to offer personalised healthcare
to the patients.
Presented at the Healthcare CEO50 Certificate Program, School of Hospital Management, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand on October 4, 2021
Europe's Top 5 Effective Leaders in Healthcare Edition.pdfinsightscare
Insights Care’s team carried out its own research and found the ‘Europe's Top 5 Effective Leaders in Healthcare,’ and depicted their revolutionary tales
The Future of mHealth - Jay Srini - March 2011LifeWIRE Corp
Jay Srini's presentation of her take on the Future of mHealth, presented at the 3rd mHealth Networking Conference, March 30, 2011. Aside from being one of the preeminent thought leader in the area of innovation and mhealth, she holds a number of positions including Assistant Professor at the University of Pittsburgh and CIO for LifeWIRE Corp.
The 10 most trusted diagnostics and pathology center.Merry D'souza
Inquisitiveness is a primary trait of human beings. The human brain which processes millions of thoughts in a flash of time needs to know about everything and anything around it. Human well being or fitness is one such area that has grown by leaps and bound over centuries given to the human quest to stay healthy. Specific to it is the study of diseases, their cause and effect which comes under the broad spectrum of Pathology.
Recomendações da OMS sobre cuidados maternos e neonatais para uma experiência pós-natal positiva.
Em consonância com os ODS – Objetivos do Desenvolvimento Sustentável e a Estratégia Global para a Saúde das Mulheres, Crianças e Adolescentes, e aplicando uma abordagem baseada nos direitos humanos, os esforços de cuidados pós-natais devem expandir-se para além da cobertura e da simples sobrevivência, de modo a incluir cuidados de qualidade.
Estas diretrizes visam melhorar a qualidade dos cuidados pós-natais essenciais e de rotina prestados às mulheres e aos recém-nascidos, com o objetivo final de melhorar a saúde e o bem-estar materno e neonatal.
Uma “experiência pós-natal positiva” é um resultado importante para todas as mulheres que dão à luz e para os seus recém-nascidos, estabelecendo as bases para a melhoria da saúde e do bem-estar a curto e longo prazo. Uma experiência pós-natal positiva é definida como aquela em que as mulheres, pessoas que gestam, os recém-nascidos, os casais, os pais, os cuidadores e as famílias recebem informação consistente, garantia e apoio de profissionais de saúde motivados; e onde um sistema de saúde flexível e com recursos reconheça as necessidades das mulheres e dos bebês e respeite o seu contexto cultural.
Estas diretrizes consolidadas apresentam algumas recomendações novas e já bem fundamentadas sobre cuidados pós-natais de rotina para mulheres e neonatos que recebem cuidados no pós-parto em unidades de saúde ou na comunidade, independentemente dos recursos disponíveis.
É fornecido um conjunto abrangente de recomendações para cuidados durante o período puerperal, com ênfase nos cuidados essenciais que todas as mulheres e recém-nascidos devem receber, e com a devida atenção à qualidade dos cuidados; isto é, a entrega e a experiência do cuidado recebido. Estas diretrizes atualizam e ampliam as recomendações da OMS de 2014 sobre cuidados pós-natais da mãe e do recém-nascido e complementam as atuais diretrizes da OMS sobre a gestão de complicações pós-natais.
O estabelecimento da amamentação e o manejo das principais intercorrências é contemplada.
Recomendamos muito.
Vamos discutir essas recomendações no nosso curso de pós-graduação em Aleitamento no Instituto Ciclos.
Esta publicação só está disponível em inglês até o momento.
Prof. Marcus Renato de Carvalho
www.agostodourado.com
- Video recording of this lecture in English language: https://youtu.be/lK81BzxMqdo
- Video recording of this lecture in Arabic language: https://youtu.be/Ve4P0COk9OI
- Link to download the book free: https://nephrotube.blogspot.com/p/nephrotube-nephrology-books.html
- Link to NephroTube website: www.NephroTube.com
- Link to NephroTube social media accounts: https://nephrotube.blogspot.com/p/join-nephrotube-on-social-media.html
micro teaching on communication m.sc nursing.pdfAnurag Sharma
Microteaching is a unique model of practice teaching. It is a viable instrument for the. desired change in the teaching behavior or the behavior potential which, in specified types of real. classroom situations, tends to facilitate the achievement of specified types of objectives.
Tom Selleck Health: A Comprehensive Look at the Iconic Actor’s Wellness Journeygreendigital
Tom Selleck, an enduring figure in Hollywood. has captivated audiences for decades with his rugged charm, iconic moustache. and memorable roles in television and film. From his breakout role as Thomas Magnum in Magnum P.I. to his current portrayal of Frank Reagan in Blue Bloods. Selleck's career has spanned over 50 years. But beyond his professional achievements. fans have often been curious about Tom Selleck Health. especially as he has aged in the public eye.
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Introduction
Many have been interested in Tom Selleck health. not only because of his enduring presence on screen but also because of the challenges. and lifestyle choices he has faced and made over the years. This article delves into the various aspects of Tom Selleck health. exploring his fitness regimen, diet, mental health. and the challenges he has encountered as he ages. We'll look at how he maintains his well-being. the health issues he has faced, and his approach to ageing .
Early Life and Career
Childhood and Athletic Beginnings
Tom Selleck was born on January 29, 1945, in Detroit, Michigan, and grew up in Sherman Oaks, California. From an early age, he was involved in sports, particularly basketball. which played a significant role in his physical development. His athletic pursuits continued into college. where he attended the University of Southern California (USC) on a basketball scholarship. This early involvement in sports laid a strong foundation for his physical health and disciplined lifestyle.
Transition to Acting
Selleck's transition from an athlete to an actor came with its physical demands. His first significant role in "Magnum P.I." required him to perform various stunts and maintain a fit appearance. This role, which he played from 1980 to 1988. necessitated a rigorous fitness routine to meet the show's demands. setting the stage for his long-term commitment to health and wellness.
Fitness Regimen
Workout Routine
Tom Selleck health and fitness regimen has evolved. adapting to his changing roles and age. During his "Magnum, P.I." days. Selleck's workouts were intense and focused on building and maintaining muscle mass. His routine included weightlifting, cardiovascular exercises. and specific training for the stunts he performed on the show.
Selleck adjusted his fitness routine as he aged to suit his body's needs. Today, his workouts focus on maintaining flexibility, strength, and cardiovascular health. He incorporates low-impact exercises such as swimming, walking, and light weightlifting. This balanced approach helps him stay fit without putting undue strain on his joints and muscles.
Importance of Flexibility and Mobility
In recent years, Selleck has emphasized the importance of flexibility and mobility in his fitness regimen. Understanding the natural decline in muscle mass and joint flexibility with age. he includes stretching and yoga in his routine. These practices help prevent injuries, improve posture, and maintain mobilit
Title: Sense of Smell
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the primary categories of smells and the concept of odor blindness.
Explain the structure and location of the olfactory membrane and mucosa, including the types and roles of cells involved in olfaction.
Describe the pathway and mechanisms of olfactory signal transmission from the olfactory receptors to the brain.
Illustrate the biochemical cascade triggered by odorant binding to olfactory receptors, including the role of G-proteins and second messengers in generating an action potential.
Identify different types of olfactory disorders such as anosmia, hyposmia, hyperosmia, and dysosmia, including their potential causes.
Key Topics:
Olfactory Genes:
3% of the human genome accounts for olfactory genes.
400 genes for odorant receptors.
Olfactory Membrane:
Located in the superior part of the nasal cavity.
Medially: Folds downward along the superior septum.
Laterally: Folds over the superior turbinate and upper surface of the middle turbinate.
Total surface area: 5-10 square centimeters.
Olfactory Mucosa:
Olfactory Cells: Bipolar nerve cells derived from the CNS (100 million), with 4-25 olfactory cilia per cell.
Sustentacular Cells: Produce mucus and maintain ionic and molecular environment.
Basal Cells: Replace worn-out olfactory cells with an average lifespan of 1-2 months.
Bowman’s Gland: Secretes mucus.
Stimulation of Olfactory Cells:
Odorant dissolves in mucus and attaches to receptors on olfactory cilia.
Involves a cascade effect through G-proteins and second messengers, leading to depolarization and action potential generation in the olfactory nerve.
Quality of a Good Odorant:
Small (3-20 Carbon atoms), volatile, water-soluble, and lipid-soluble.
Facilitated by odorant-binding proteins in mucus.
Membrane Potential and Action Potential:
Resting membrane potential: -55mV.
Action potential frequency in the olfactory nerve increases with odorant strength.
Adaptation Towards the Sense of Smell:
Rapid adaptation within the first second, with further slow adaptation.
Psychological adaptation greater than receptor adaptation, involving feedback inhibition from the central nervous system.
Primary Sensations of Smell:
Camphoraceous, Musky, Floral, Pepperminty, Ethereal, Pungent, Putrid.
Odor Detection Threshold:
Examples: Hydrogen sulfide (0.0005 ppm), Methyl-mercaptan (0.002 ppm).
Some toxic substances are odorless at lethal concentrations.
Characteristics of Smell:
Odor blindness for single substances due to lack of appropriate receptor protein.
Behavioral and emotional influences of smell.
Transmission of Olfactory Signals:
From olfactory cells to glomeruli in the olfactory bulb, involving lateral inhibition.
Primitive, less old, and new olfactory systems with different path
Ozempic: Preoperative Management of Patients on GLP-1 Receptor Agonists Saeid Safari
Preoperative Management of Patients on GLP-1 Receptor Agonists like Ozempic and Semiglutide
ASA GUIDELINE
NYSORA Guideline
2 Case Reports of Gastric Ultrasound
Basavarajeeyam is an important text for ayurvedic physician belonging to andhra pradehs. It is a popular compendium in various parts of our country as well as in andhra pradesh. The content of the text was presented in sanskrit and telugu language (Bilingual). One of the most famous book in ayurvedic pharmaceutics and therapeutics. This book contains 25 chapters called as prakaranas. Many rasaoushadis were explained, pioneer of dhatu druti, nadi pareeksha, mutra pareeksha etc. Belongs to the period of 15-16 century. New diseases like upadamsha, phiranga rogas are explained.
5. 최윤섭 지음
의료인공지능
표지디자인•최승협
컴퓨터
털 헬
치를 만드는 것을 화두로
기업가, 엔젤투자가, 에반
의 대표적인 전문가로, 활
이 분야를 처음 소개한 장
포항공과대학교에서 컴
동 대학원 시스템생명공
취득하였다. 스탠퍼드대
조교수, KT 종합기술원 컨
구원 연구조교수 등을 거
저널에 10여 편의 논문을
국내 최초로 디지털 헬스
윤섭 디지털 헬스케어 연
국내 유일의 헬스케어 스
어 파트너스’의 공동 창업
스타트업을 의료 전문가
관대학교 디지털헬스학과
뷰노, 직토, 3billion, 서지
소울링, 메디히어, 모바일
자문을 맡아 한국에서도
고 있다. 국내 최초의 디
케어 이노베이션』에 활발
을 연재하고 있다. 저서로
와 『그렇게 나는 스스로
•블로그_ http://www
•페이스북_ https://w
•이메일_ yoonsup.c
최윤섭
의료 인공지능은 보수적인 의료 시스템을 재편할 혁신을 일으키고 있다. 의료 인공지능의 빠른 발전과
광범위한 영향은 전문화, 세분화되며 발전해 온 현대 의료 전문가들이 이해하기가 어려우며, 어디서부
터 공부해야 할지도 막연하다. 이런 상황에서 의료 인공지능의 개념과 적용, 그리고 의사와의 관계를 쉽
게 풀어내는 이 책은 좋은 길라잡이가 될 것이다. 특히 미래의 주역이 될 의학도와 젊은 의료인에게 유용
한 소개서이다.
━ 서준범, 서울아산병원 영상의학과 교수, 의료영상인공지능사업단장
인공지능이 의료의 패러다임을 크게 바꿀 것이라는 것에 동의하지 않는 사람은 거의 없다. 하지만 인공
지능이 처리해야 할 의료의 난제는 많으며 그 해결 방안도 천차만별이다. 흔히 생각하는 만병통치약 같
은 의료 인공지능은 존재하지 않는다. 이 책은 다양한 의료 인공지능의 개발, 활용 및 가능성을 균형 있
게 분석하고 있다. 인공지능을 도입하려는 의료인, 생소한 의료 영역에 도전할 인공지능 연구자 모두에
게 일독을 권한다.
━ 정지훈, 경희사이버대 미디어커뮤니케이션학과 선임강의교수, 의사
서울의대 기초의학교육을 책임지고 있는 교수의 입장에서, 산업화 이후 변하지 않은 현재의 의학 교육
으로는 격변하는 인공지능 시대에 의대생을 대비시키지 못한다는 한계를 절실히 느낀다. 저와 함께 의
대 인공지능 교육을 개척하고 있는 최윤섭 소장의 전문적 분석과 미래 지향적 안목이 담긴 책이다. 인공
지능이라는 미래를 대비할 의대생과 교수, 그리고 의대 진학을 고민하는 학생과 학부모에게 추천한다.
━ 최형진, 서울대학교 의과대학 해부학교실 교수, 내과 전문의
최근 의료 인공지능의 도입에 대해서 극단적인 시각과 태도가 공존하고 있다. 이 책은 다양한 사례와 깊
은 통찰을 통해 의료 인공지능의 현황과 미래에 대해 균형적인 시각을 제공하여, 인공지능이 의료에 본
격적으로 도입되기 위한 토론의 장을 마련한다. 의료 인공지능이 일상화된 10년 후 돌아보았을 때, 이 책
이 그런 시대를 이끄는 길라잡이 역할을 하였음을 확인할 수 있기를 기대한다.
━ 정규환, 뷰노 CTO
의료 인공지능은 다른 분야 인공지능보다 더 본질적인 이해가 필요하다. 단순히 인간의 일을 대신하는
수준을 넘어 의학의 패러다임을 데이터 기반으로 변화시키기 때문이다. 따라서 인공지능을 균형있게 이
해하고, 어떻게 의사와 환자에게 도움을 줄 수 있을지 깊은 고민이 필요하다. 세계적으로 일어나고 있는
이러한 노력의 결과물을 집대성한 이 책이 반가운 이유다.
━ 백승욱, 루닛 대표
의료 인공지능의 최신 동향뿐만 아니라, 의의와 한계, 전망, 그리고 다양한 생각거리까지 주는 책이다.
논쟁이 되는 여러 이슈에 대해서도 저자는 자신의 시각을 명확한 근거에 기반하여 설득력 있게 제시하
고 있다. 개인적으로는 이 책을 대학원 수업 교재로 활용하려 한다.
━ 신수용, 성균관대학교 디지털헬스학과 교수
최윤섭지음
의료인공지능
값 20,000원
ISBN 979-11-86269-99-2
최초의 책!
계 안팎에서 제기
고 있다. 현재 의
분 커버했다고 자
것인가, 어느 진료
제하고 효용과 안
누가 지는가, 의학
쉬운 언어로 깊이
들이 의료 인공지
적인 용어를 최대
서 다른 곳에서 접
를 접하게 될 것
너무나 빨리 발전
책에서 제시하는
술을 공부하며, 앞
란다.
의사 면허를 취득
저가 도움되면 좋
를 불러일으킬 것
화를 일으킬 수도
슈에 제대로 대응
분은 의학 교육의
예비 의사들은 샌
지능과 함께하는
레이닝 방식도 이
전에 진료실과 수
겠지만, 여러분들
도생하는 수밖에
미래의료학자 최윤섭 박사가 제시하는
의료 인공지능의 현재와 미래
의료 딥러닝과 IBM 왓슨의 현주소
인공지능은 의사를 대체하는가
값 20,000원
ISBN 979-11-86269-99-2
레이닝 방식도 이
전에 진료실과 수
겠지만, 여러분들
도생하는 수밖에
소울링, 메디히어, 모바일
자문을 맡아 한국에서도
고 있다. 국내 최초의 디
케어 이노베이션』에 활발
을 연재하고 있다. 저서로
와 『그렇게 나는 스스로
•블로그_ http://www
•페이스북_ https://w
•이메일_ yoonsup.c
20. • 1978
• As part of the obscure task of “discovery” —
providing documents relevant to a lawsuit — the
studios examined six million documents at a
cost of more than $2.2 million, much of it to pay
for a platoon of lawyers and paralegals who
worked for months at high hourly rates.
• 2011
• Now, thanks to advances in artificial intelligence,
“e-discovery” software can analyze documents
in a fraction of the time for a fraction of the
cost.
• In January, for example, Blackstone Discovery of
Palo Alto, Calif., helped analyze 1.5 million
documents for less than $100,000.
21. “At its height back in 2000, the U.S. cash equities trading desk at
Goldman Sachs’s New York headquarters employed 600 traders,
buying and selling stock on the orders of the investment bank’s
large clients. Today there are just two equity traders left”
28. l 4 URUOUM AM c Z Q USQZOQ
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29.
30. 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100
90%
50%
10%
PT-AI
AGI
EETNTOP100 Combined
m o w m3
Philosophy and Theory of AI (2011)
Artificial General Intelligence (2012)
Greek Association for Artificial Intelligence
Survey of most frequently cited 100 authors (2013)
Combined
응답자
누적 비율
Superintelligence, Nick Bostrom (2014)
31. Superintelligence: Science of fiction?
Panelists: Elon Musk (Tesla, SpaceX), Bart Selman (Cornell), Ray Kurzweil (Google),
David Chalmers (NYU), Nick Bostrom(FHI), Demis Hassabis (Deep Mind), Stuart
Russell (Berkeley), Sam Harris, and Jaan Tallinn (CSER/FLI)
January 6-8, 2017, Asilomar, CA
https://brunch.co.kr/@kakao-it/49
https://www.youtube.com/watch?v=h0962biiZa4
32. Superintelligence: Science of fiction?
Panelists: Elon Musk (Tesla, SpaceX), Bart Selman (Cornell), Ray Kurzweil (Google),
David Chalmers (NYU), Nick Bostrom(FHI), Demis Hassabis (Deep Mind), Stuart
Russell (Berkeley), Sam Harris, and Jaan Tallinn (CSER/FLI)
January 6-8, 2017, Asilomar, CA
D0 m w m3
D0 m s m w n m3
Table 1
Elon Musk Start Russell Bart Selman Ray Kurzweil David Chalmers Nick Bostrom DemisHassabis Sam Harris Jaan Tallinn
YES YES YES YES YES YES YES YES YES
Table 1-1
Elon Musk Start Russell Bart Selman Ray Kurzweil David Chalmers Nick Bostrom DemisHassabis Sam Harris Jaan Tallinn
YES YES YES YES YES YES YES YES YES
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Table 1-1-1
Elon Musk Start Russell Bart Selman Ray Kurzweil David Chalmers Nick Bostrom DemisHassabis Sam Harris Jaan Tallinn
Complicated Complicated Complicated YES Complicated YES YES Complicated Complicated
https://brunch.co.kr/@kakao-it/49
https://www.youtube.com/watch?v=h0962biiZa4
49. 600,000 pieces of medical evidence
2 million pages of text from 42 medical journals and clinical trials
69 guidelines, 61,540 clinical trials
IBM Watson on Medicine
Watson learned...
+
1,500 lung cancer cases
physician notes, lab results and clinical research
+
14,700 hours of hands-on training
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56. Annals of Oncology (2016) 27 (suppl_9): ix179-ix180. 10.1093/annonc/mdw601
Validation study to assess performance of IBM cognitive
computing system Watson for oncology with Manipal
multidisciplinary tumour board for 1000 consecutive cases:
An Indian experience
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57. San Antonio Breast Cancer Symposium—December 6-10, 2016
Concordance WFO (@T2) and MMDT (@T1* v. T2**)
(N= 638 Breast Cancer Cases)
Time Point
/Concordance
REC REC + FC
n % n %
T1* 296 46 463 73
T2** 381 60 574 90
This presentation is the intellectual property of the author/presenter.Contact somusp@yahoo.com for permission to reprint and/or distribute.26
* T1 Time of original treatment decision by MMDT in the past (last 1-3 years)
** T2 Time (2016) of WFO’s treatment advice and of MMDT’s treatment decision upon blinded re-review of non-concordant
cases
58. WFO in ASCO 2017
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2017 ASCO annual Meeting, J Clin Oncol 35, 2017 (suppl; abstr 8527)
59. WFO in ASCO 2017
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60. ORIGINAL ARTICLE
Watson for Oncology and breast cancer treatment
recommendations: agreement with an expert
multidisciplinary tumor board
S. P. Somashekhar1*, M.-J. Sepu´lveda2
, S. Puglielli3
, A. D. Norden3
, E. H. Shortliffe4
, C. Rohit Kumar1
,
A. Rauthan1
, N. Arun Kumar1
, P. Patil1
, K. Rhee3
Y. Ramya1
1
Manipal Comprehensive Cancer Centre, Manipal Hospital, Bangalore, India; 2
IBM Research (Retired), Yorktown Heights; 3
Watson Health, IBM Corporation,
Cambridge; 4
Department of Surgical Oncology, College of Health Solutions, Arizona State University, Phoenix, USA
*Correspondence to: Prof. Sampige Prasannakumar Somashekhar, Manipal Comprehensive Cancer Centre, Manipal Hospital, Old Airport Road, Bangalore 560017, Karnataka,
India. Tel: þ91-9845712012; Fax: þ91-80-2502-3759; E-mail: somashekhar.sp@manipalhospitals.com
Background: Breast cancer oncologists are challenged to personalize care with rapidly changing scientific evidence, drug
approvals, and treatment guidelines. Artificial intelligence (AI) clinical decision-support systems (CDSSs) have the potential to
help address this challenge. We report here the results of examining the level of agreement (concordance) between treatment
recommendations made by the AI CDSS Watson for Oncology (WFO) and a multidisciplinary tumor board for breast cancer.
Patients and methods: Treatment recommendations were provided for 638 breast cancers between 2014 and 2016 at the
Manipal Comprehensive Cancer Center, Bengaluru, India. WFO provided treatment recommendations for the identical cases in
2016. A blinded second review was carried out by the center’s tumor board in 2016 for all cases in which there was not
agreement, to account for treatments and guidelines not available before 2016. Treatment recommendations were considered
concordant if the tumor board recommendations were designated ‘recommended’ or ‘for consideration’ by WFO.
Results: Treatment concordance between WFO and the multidisciplinary tumor board occurred in 93% of breast cancer cases.
Subgroup analysis found that patients with stage I or IV disease were less likely to be concordant than patients with stage II or III
disease. Increasing age was found to have a major impact on concordance. Concordance declined significantly (P 0.02;
P 0.001) in all age groups compared with patients 45 years of age, except for the age group 55–64 years. Receptor status
was not found to affect concordance.
Conclusion: Treatment recommendations made by WFO and the tumor board were highly concordant for breast cancer cases
examined. Breast cancer stage and patient age had significant influence on concordance, while receptor status alone did not.
This study demonstrates that the AI clinical decision-support system WFO may be a helpful tool for breast cancer treatment
decision making, especially at centers where expert breast cancer resources are limited.
Key words: Watson for Oncology, artificial intelligence, cognitive clinical decision-support systems, breast cancer,
concordance, multidisciplinary tumor board
Introduction
Oncologists who treat breast cancer are challenged by a large and
rapidly expanding knowledge base [1, 2]. As of October 2017, for
example, there were 69 FDA-approved drugs for the treatment of
breast cancer, not including combination treatment regimens
[3]. The growth of massive genetic and clinical databases, along
with computing systems to exploit them, will accelerate the speed
of breast cancer treatment advances and shorten the cycle time
for changes to breast cancer treatment guidelines [4, 5]. In add-
ition, these information management challenges in cancer care
are occurring in a practice environment where there is little time
available for tracking and accessing relevant information at the
point of care [6]. For example, a study that surveyed 1117 oncolo-
gists reported that on average 4.6 h per week were spent keeping
VC The Author(s) 2018. Published by Oxford University Press on behalf of the European Society for Medical Oncology.
All rights reserved. For permissions, please email: journals.permissions@oup.com.
Annals of Oncology 29: 418–423, 2018
doi:10.1093/annonc/mdx781
Published online 9 January 2018
Downloaded from https://academic.oup.com/annonc/article-abstract/29/2/418/4781689
by guest
61. ORIGINAL ARTICLE
Watson for Oncology and breast cancer treatment
recommendations: agreement with an expert
multidisciplinary tumor board
S. P. Somashekhar1*, M.-J. Sepu´lveda2
, S. Puglielli3
, A. D. Norden3
, E. H. Shortliffe4
, C. Rohit Kumar1
,
A. Rauthan1
, N. Arun Kumar1
, P. Patil1
, K. Rhee3
Y. Ramya1
1
Manipal Comprehensive Cancer Centre, Manipal Hospital, Bangalore, India; 2
IBM Research (Retired), Yorktown Heights; 3
Watson Health, IBM Corporation,
Cambridge; 4
Department of Surgical Oncology, College of Health Solutions, Arizona State University, Phoenix, USA
*Correspondence to: Prof. Sampige Prasannakumar Somashekhar, Manipal Comprehensive Cancer Centre, Manipal Hospital, Old Airport Road, Bangalore 560017, Karnataka,
India. Tel: þ91-9845712012; Fax: þ91-80-2502-3759; E-mail: somashekhar.sp@manipalhospitals.com
Background: Breast cancer oncologists are challenged to personalize care with rapidly changing scientific evidence, drug
approvals, and treatment guidelines. Artificial intelligence (AI) clinical decision-support systems (CDSSs) have the potential to
help address this challenge. We report here the results of examining the level of agreement (concordance) between treatment
recommendations made by the AI CDSS Watson for Oncology (WFO) and a multidisciplinary tumor board for breast cancer.
Patients and methods: Treatment recommendations were provided for 638 breast cancers between 2014 and 2016 at the
Manipal Comprehensive Cancer Center, Bengaluru, India. WFO provided treatment recommendations for the identical cases in
2016. A blinded second review was carried out by the center’s tumor board in 2016 for all cases in which there was not
agreement, to account for treatments and guidelines not available before 2016. Treatment recommendations were considered
concordant if the tumor board recommendations were designated ‘recommended’ or ‘for consideration’ by WFO.
Results: Treatment concordance between WFO and the multidisciplinary tumor board occurred in 93% of breast cancer cases.
Subgroup analysis found that patients with stage I or IV disease were less likely to be concordant than patients with stage II or III
disease. Increasing age was found to have a major impact on concordance. Concordance declined significantly (P 0.02;
P 0.001) in all age groups compared with patients 45 years of age, except for the age group 55–64 years. Receptor status
was not found to affect concordance.
Conclusion: Treatment recommendations made by WFO and the tumor board were highly concordant for breast cancer cases
examined. Breast cancer stage and patient age had significant influence on concordance, while receptor status alone did not.
This study demonstrates that the AI clinical decision-support system WFO may be a helpful tool for breast cancer treatment
decision making, especially at centers where expert breast cancer resources are limited.
Key words: Watson for Oncology, artificial intelligence, cognitive clinical decision-support systems, breast cancer,
concordance, multidisciplinary tumor board
Introduction
Oncologists who treat breast cancer are challenged by a large and
rapidly expanding knowledge base [1, 2]. As of October 2017, for
example, there were 69 FDA-approved drugs for the treatment of
breast cancer, not including combination treatment regimens
[3]. The growth of massive genetic and clinical databases, along
with computing systems to exploit them, will accelerate the speed
of breast cancer treatment advances and shorten the cycle time
for changes to breast cancer treatment guidelines [4, 5]. In add-
ition, these information management challenges in cancer care
are occurring in a practice environment where there is little time
available for tracking and accessing relevant information at the
point of care [6]. For example, a study that surveyed 1117 oncolo-
gists reported that on average 4.6 h per week were spent keeping
VC The Author(s) 2018. Published by Oxford University Press on behalf of the European Society for Medical Oncology.
All rights reserved. For permissions, please email: journals.permissions@oup.com.
Annals of Oncology 29: 418–423, 2018
doi:10.1093/annonc/mdx781
Published online 9 January 2018
Downloaded from https://academic.oup.com/annonc/article-abstract/29/2/418/4781689
by guest
Table 2. MMDT and WFO recommendations after the initial and blinded second reviews
Review of breast cancer cases (N 5 638) Concordant cases, n (%) Non-concordant cases, n (%)
Recommended For consideration Total Not recommended Not available Total
Initial review (T1MMDT versus T2WFO) 296 (46) 167 (26) 463 (73) 137 (21) 38 (6) 175 (27)
Second review (T2MMDT versus T2WFO) 397 (62) 194 (30) 591 (93) 36 (5) 11 (2) 47 (7)
T1MMDT, original MMDT recommendation from 2014 to 2016; T2WFO, WFO advisor treatment recommendation in 2016; T2MMDT, MMDT treatment recom-
mendation in 2016; MMDT, Manipal multidisciplinary tumor board; WFO, Watson for Oncology.
31%
18%
1% 2% 33%
5% 31%
6%
0% 10% 20%
Not available Not recommended RecommendedFor consideration
30% 40% 50% 60% 70% 80% 90% 100%
8% 25% 61%
64%
64%
29% 51%
62%
Concordance, 93%
Concordance, 80%
Concordance, 97%
Concordance, 95%
Concordance, 86%
2%
2%
Overall
(n=638)
Stage I
(n=61)
Stage II
(n=262)
Stage III
(n=191)
Stage IV
(n=124)
5%
Figure 1. Treatment concordance between WFO and the MMDT overall and by stage. MMDT, Manipal multidisciplinary tumor board; WFO,
Watson for Oncology.
5%Non-metastatic
HR(+)HER2/neu(+)Triple(–)
Metastatic
Non-metastatic
Metastatic
Non-metastatic
Metastatic
10%
1%
2%
1% 5% 20%
20%10%
0%
Not applicable Not recommended For consideration Recommended
20% 40% 60% 80% 100%
5%
74%
65%
34% 64%
5% 38% 56%
15% 20% 55%
36% 59%
Concordance, 95%
Concordance, 75%
Concordance, 94%
Concordance, 98%
Concordance, 94%
Concordance, 85%
Figure 2. Treatment concordance between WFO and the MMDT by stage and receptor status. HER2/neu, human epidermal growth factor
receptor 2; HR, hormone receptor; MMDT, Manipal multidisciplinary tumor board; WFO, Watson for Oncology.
Annals of Oncology Original article
62. ORIGINAL ARTICLE
Watson for Oncology and breast cancer treatment
recommendations: agreement with an expert
multidisciplinary tumor board
S. P. Somashekhar1*, M.-J. Sepu´lveda2
, S. Puglielli3
, A. D. Norden3
, E. H. Shortliffe4
, C. Rohit Kumar1
,
A. Rauthan1
, N. Arun Kumar1
, P. Patil1
, K. Rhee3
Y. Ramya1
1
Manipal Comprehensive Cancer Centre, Manipal Hospital, Bangalore, India; 2
IBM Research (Retired), Yorktown Heights; 3
Watson Health, IBM Corporation,
Cambridge; 4
Department of Surgical Oncology, College of Health Solutions, Arizona State University, Phoenix, USA
*Correspondence to: Prof. Sampige Prasannakumar Somashekhar, Manipal Comprehensive Cancer Centre, Manipal Hospital, Old Airport Road, Bangalore 560017, Karnataka,
India. Tel: þ91-9845712012; Fax: þ91-80-2502-3759; E-mail: somashekhar.sp@manipalhospitals.com
Background: Breast cancer oncologists are challenged to personalize care with rapidly changing scientific evidence, drug
approvals, and treatment guidelines. Artificial intelligence (AI) clinical decision-support systems (CDSSs) have the potential to
help address this challenge. We report here the results of examining the level of agreement (concordance) between treatment
recommendations made by the AI CDSS Watson for Oncology (WFO) and a multidisciplinary tumor board for breast cancer.
Patients and methods: Treatment recommendations were provided for 638 breast cancers between 2014 and 2016 at the
Manipal Comprehensive Cancer Center, Bengaluru, India. WFO provided treatment recommendations for the identical cases in
2016. A blinded second review was carried out by the center’s tumor board in 2016 for all cases in which there was not
agreement, to account for treatments and guidelines not available before 2016. Treatment recommendations were considered
concordant if the tumor board recommendations were designated ‘recommended’ or ‘for consideration’ by WFO.
Results: Treatment concordance between WFO and the multidisciplinary tumor board occurred in 93% of breast cancer cases.
Subgroup analysis found that patients with stage I or IV disease were less likely to be concordant than patients with stage II or III
disease. Increasing age was found to have a major impact on concordance. Concordance declined significantly (P 0.02;
P 0.001) in all age groups compared with patients 45 years of age, except for the age group 55–64 years. Receptor status
was not found to affect concordance.
Conclusion: Treatment recommendations made by WFO and the tumor board were highly concordant for breast cancer cases
examined. Breast cancer stage and patient age had significant influence on concordance, while receptor status alone did not.
This study demonstrates that the AI clinical decision-support system WFO may be a helpful tool for breast cancer treatment
decision making, especially at centers where expert breast cancer resources are limited.
Key words: Watson for Oncology, artificial intelligence, cognitive clinical decision-support systems, breast cancer,
concordance, multidisciplinary tumor board
Introduction
Oncologists who treat breast cancer are challenged by a large and
rapidly expanding knowledge base [1, 2]. As of October 2017, for
example, there were 69 FDA-approved drugs for the treatment of
breast cancer, not including combination treatment regimens
[3]. The growth of massive genetic and clinical databases, along
with computing systems to exploit them, will accelerate the speed
of breast cancer treatment advances and shorten the cycle time
for changes to breast cancer treatment guidelines [4, 5]. In add-
ition, these information management challenges in cancer care
are occurring in a practice environment where there is little time
available for tracking and accessing relevant information at the
point of care [6]. For example, a study that surveyed 1117 oncolo-
gists reported that on average 4.6 h per week were spent keeping
VC The Author(s) 2018. Published by Oxford University Press on behalf of the European Society for Medical Oncology.
All rights reserved. For permissions, please email: journals.permissions@oup.com.
Annals of Oncology 29: 418–423, 2018
doi:10.1093/annonc/mdx781
Published online 9 January 2018
Downloaded from https://academic.oup.com/annonc/article-abstract/29/2/418/4781689
by guest
Table 2. MMDT and WFO recommendations after the initial and blinded second reviews
Review of breast cancer cases (N 5 638) Concordant cases, n (%) Non-concordant cases, n (%)
Recommended For consideration Total Not recommended Not available Total
Initial review (T1MMDT versus T2WFO) 296 (46) 167 (26) 463 (73) 137 (21) 38 (6) 175 (27)
Second review (T2MMDT versus T2WFO) 397 (62) 194 (30) 591 (93) 36 (5) 11 (2) 47 (7)
T1MMDT, original MMDT recommendation from 2014 to 2016; T2WFO, WFO advisor treatment recommendation in 2016; T2MMDT, MMDT treatment recom-
mendation in 2016; MMDT, Manipal multidisciplinary tumor board; WFO, Watson for Oncology.
31%
18% 29% 51%
62%
Concordance, 93%
Concordance, 80%
Concordance, 97%
2%
2%
Overall
(n=638)
Stage I
(n=61)
5%
Annals of Oncology Original article
66. lJ9B x 3
gd
JM Z R BZO Se
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67. lJ9B x 3
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70. Empowering the Oncology Community for Cancer Care
Genomics
Oncology
Clinical
Trial
Matching
Watson Health’s oncology clients span more than 35 hospital systems
“Empowering the Oncology Community
for Cancer Care”
Andrew Norden, KOTRA Conference, March 2017, “The Future of Health is Cognitive”
77. qA R 1 deR TVd W cVec daVTe gV R R jd d$ O?9 W Ud RTe RS V d XYed R U
UV e W Vd a eV e R UcfXd W c T d UVcRe
qLYV Rfe ReVU XV VcRe W eYVdV d XYed d RTY VgVU Sj O?9 feVd
78. Kazimierz O.
Wrzeszczynski, PhD*
Mayu O. Frank, NP,
MS*
Takahiko Koyama, PhD*
Kahn Rhrissorrakrai, PhD*
Nicolas Robine, PhD
Filippo Utro, PhD
Anne-Katrin Emde, PhD
Bo-Juen Chen, PhD
Kanika Arora, MS
Minita Shah, MS
Vladimir Vacic, PhD
Raquel Norel, PhD
Erhan Bilal, PhD
Ewa A. Bergmann, MSc
Julia L. Moore Vogel,
PhD
Jeffrey N. Bruce, MD
Andrew B. Lassman, MD
Peter Canoll, MD, PhD
Christian Grommes, MD
Steve Harvey, BS
Laxmi Parida, PhD
Vanessa V. Michelini, BS
Michael C. Zody, PhD
Vaidehi Jobanputra, PhD
Ajay K. Royyuru, PhD
Robert B. Darnell, MD,
Comparing sequencing assays and
human-machine analyses in actionable
genomics for glioblastoma
ABSTRACT
Objective: To analyze a glioblastoma tumor specimen with 3 different platforms and compare
potentially actionable calls from each.
Methods: Tumor DNA was analyzed by a commercial targeted panel. In addition, tumor-normal
DNA was analyzed by whole-genome sequencing (WGS) and tumor RNA was analyzed by RNA
sequencing (RNA-seq). The WGS and RNA-seq data were analyzed by a team of bioinformaticians
and cancer oncologists, and separately by IBM Watson Genomic Analytics (WGA), an automated
system for prioritizing somatic variants and identifying drugs.
Results: More variants were identified by WGS/RNA analysis than by targeted panels. WGA com-
pleted a comparable analysis in a fraction of the time required by the human analysts.
Conclusions: The development of an effective human-machine interface in the analysis of deep
cancer genomic datasets may provide potentially clinically actionable calls for individual pa-
tients in a more timely and efficient manner than currently possible.
ClinicalTrials.gov identifier: NCT02725684. Neurol Genet 2017;3:e164; doi: 10.1212/
NXG.0000000000000164
GLOSSARY
CNV 5 copy number variant; EGFR 5 epidermal growth factor receptor; GATK 5 Genome Analysis Toolkit; GBM 5 glioblas-
toma; IRB 5 institutional review board; NLP 5 Natural Language Processing; NYGC 5 New York Genome Center; RNA-seq 5
RNA sequencing; SNV 5 single nucleotide variant; SV 5 structural variant; TCGA 5 The Cancer Genome Atlas; TPM 5
transcripts per million; VCF 5 variant call file; VUS 5 variants of uncertain significance; WGA 5 Watson Genomic Analytics;
WGS 5 whole-genome sequencing.
The clinical application of next-generation sequencing technology to cancer diagnosis and treat-
ment is in its early stages.1–3
An initial implementation of this technology has been in targeted
panels, where subsets of cancer-relevant and/or highly actionable genes are scrutinized for
potentially actionable mutations. This approach has been widely adopted, offering high redun-
dancy of sequence coverage for the small number of sites of known clinical utility at relatively
79. Table 3 List of variants identified as actionable by 3 different platforms
Gene Variant
Identified variant Identified associated drugs
NYGC WGA FO NYGC WGA FO
CDKN2A Deletion Yes Yes Yes Palbociclib, LY2835219
LEE001
Palbociclib LY2835219 Clinical trial
CDKN2B Deletion Yes Yes Yes Palbociclib, LY2835219
LEE002
Palbociclib LY2835219 Clinical trial
EGFR Gain (whole arm) Yes — — Cetuximab — —
ERG Missense P114Q Yes Yes — RI-EIP RI-EIP —
FGFR3 Missense L49V Yes VUS — TK-1258 — —
MET Amplification Yes Yes Yes INC280 Crizotinib, cabozantinib Crizotinib, cabozantinib
MET Frame shift R755fs Yes — — INC280 — —
MET Exon skipping Yes — — INC280 — —
NF1 Deletion Yes — — MEK162 — —
NF1 Nonsense R461* Yes Yes Yes MEK162 MEK162, cobimetinib,
trametinib, GDC-0994
Everolimus, temsirolimus,
trametinib
PIK3R1 Insertion
R562_M563insI
Yes Yes — BKM120 BKM120, LY3023414 —
PTEN Loss (whole arm) Yes — — Everolimus, AZD2014 — —
STAG2 Frame shift R1012 fs Yes Yes Yes Veliparib, clinical trial Olaparib —
DNMT3A Splice site 2083-1G.C — — Yes — — —
TERT Promoter-146C.T Yes — Yes — — —
ABL2 Missense D716N Germline NA VUS
mTOR Missense H1687R Germline NA VUS
NPM1 Missense E169D Germline NA VUS
NTRK1 Missense G18E Germline NA VUS
PTCH1 Missense P1250R Germline NA VUS
TSC1 Missense G1035S Germline NA VUS
Abbreviations: FO 5 FoundationOne; NYGC 5 New York Genome Center; RNA-seq 5 RNA sequencing; WGA 5 Watson Genomic Analytics; WGS 5 whole-
genome sequencing.
Genes, variant description, and, where appropriate, candidate clinically relevant drugs are listed. Variants identified by the FO as variants of uncertain
significance (VUS) were identified by the NYGC as germline variants.
• WGA analysis vastly accelerated the time to discovery of potentially actionable variants from the VCF files.
• WGA was able to provide reports of potentially clinically actionable insights within 10 minutes
• , while human analysis of this patient's VCF file took an estimated 160 hours of person-time
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81. Stephen F.Weng et al PLoS One 2017
Can machine-learning improve cardiovascular
risk prediction using routine clinical data?
in a sensitivity of 62.7% and PPV of 17.1%. The random forest algorithm resulted in a net
increase of 191 CVD cases from the baseline model, increasing the sensitivity to 65.3% and
PPV to 17.8% while logistic regression resulted in a net increase of 324 CVD cases (sensitivity
67.1%; PPV 18.3%). Gradient boosting machines and neural networks performed best, result-
ing in a net increase of 354 (sensitivity 67.5%; PPV 18.4%) and 355 CVD (sensitivity 67.5%;
PPV 18.4%) cases correctly predicted, respectively.
The ACC/AHA baseline model correctly predicted 53,106 non-cases from 75,585 total non-
cases, resulting in a specificity of 70.3% and NPV of 95.1%. The net increase in non-cases
Table 3. Top 10 risk factor variables for CVD algorithms listed in descending order of coefficient effect size (ACC/AHA; logistic regression),
weighting (neural networks), or selection frequency (random forest, gradient boosting machines). Algorithms were derived from training cohort of
295,267 patients.
ACC/AHA Algorithm Machine-learning Algorithms
Men Women ML: Logistic
Regression
ML: Random Forest ML: Gradient Boosting
Machines
ML: Neural Networks
Age Age Ethnicity Age Age Atrial Fibrillation
Total Cholesterol HDL Cholesterol Age Gender Gender Ethnicity
HDL Cholesterol Total Cholesterol SES: Townsend
Deprivation Index
Ethnicity Ethnicity Oral Corticosteroid
Prescribed
Smoking Smoking Gender Smoking Smoking Age
Age x Total Cholesterol Age x HDL Cholesterol Smoking HDL cholesterol HDL cholesterol Severe Mental Illness
Treated Systolic Blood
Pressure
Age x Total Cholesterol Atrial Fibrillation HbA1c Triglycerides SES: Townsend
Deprivation Index
Age x Smoking Treated Systolic Blood
Pressure
Chronic Kidney Disease Triglycerides Total Cholesterol Chronic Kidney Disease
Age x HDL Cholesterol Untreated Systolic
Blood Pressure
Rheumatoid Arthritis SES: Townsend
Deprivation Index
HbA1c BMI missing
Untreated Systolic
Blood Pressure
Age x Smoking Family history of
premature CHD
BMI Systolic Blood Pressure Smoking
Diabetes Diabetes COPD Total Cholesterol SES: Townsend
Deprivation Index
Gender
Italics: Protective Factors
https://doi.org/10.1371/journal.pone.0174944.t003
PLOS ONE | https://doi.org/10.1371/journal.pone.0174944 April 4, 2017 8 / 14
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82. Stephen F.Weng et al PLoS One 2017
Can machine-learning improve cardiovascular
risk prediction using routine clinical data?
correctly predicted compared to the baseline ACC/AHA model ranged from 191 non-cases for
the random forest algorithm to 355 non-cases for the neural networks. Full details on classifi-
cation analysis can be found in S2 Table.
Discussion
Compared to an established AHA/ACC risk prediction algorithm, we found all machine-
learning algorithms tested were better at identifying individuals who will develop CVD and
those that will not. Unlike established approaches to risk prediction, the machine-learning
methods used were not limited to a small set of risk factors, and incorporated more pre-exist-
Table 4. Performance of the machine-learning (ML) algorithms predicting 10-year cardiovascular disease (CVD) risk derived from applying train-
ing algorithms on the validation cohort of 82,989 patients. Higher c-statistics results in better algorithm discrimination. The baseline (BL) ACC/AHA
10-year risk prediction algorithm is provided for comparative purposes.
Algorithms AUC c-statistic Standard Error* 95% Confidence
Interval
Absolute Change from Baseline
LCL UCL
BL: ACC/AHA 0.728 0.002 0.723 0.735 —
ML: Random Forest 0.745 0.003 0.739 0.750 +1.7%
ML: Logistic Regression 0.760 0.003 0.755 0.766 +3.2%
ML: Gradient Boosting Machines 0.761 0.002 0.755 0.766 +3.3%
ML: Neural Networks 0.764 0.002 0.759 0.769 +3.6%
*Standard error estimated by jack-knife procedure [30]
https://doi.org/10.1371/journal.pone.0174944.t004
Can machine-learning improve cardiovascular risk prediction using routine clinical data?
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