• 501(c)(3) nonprofit organization• Many strategic partners (SFASA, BAES, etc.) • A young organization founded 3 years ago• Membership is FREE• <1% administrative cost. All funding goes to serve the

community• Provide a virtual community for people interested in

data science to exchange ideas and help each other• Organize

• International scientific conferences• Monthly journal clubs• Technical seminars• Career development events• Educational lectures such as legal series

• Actively seeking members, volunteers and new ideas

Any questions, please contact email: dahshuinfo@gmail.com

November's Onsite EventKeynote Speakers:Dr. Lisa LaVange

Dr. Lisa LaVange•Professor and Associate Chair, Department of

Biostatistics; Director, Collaborative Coordinating

Center; University of North Carolina

Dr. Steven Shakr. Steven Shak

Co-Founder, Chief Scientific Officer, and Chief Medical

Officer, Genomic Health

Dr. Mingxiu Hu. Mingxiu Hu

Senior Vice President, Nektar Therapeutics

:

Featured Sessions:• Innovative Trial Design • Innovative Technology and Applications in Clinical Trials • Reflection on Recent Regulatory Guidance • Successful Examples in Statistical Innovation and Leadership • Machine Learning, Al, Big Data, and Applications in Clinical Trials • Panel Discussion on Innovation, Impact, and Leadership

Featured Sessions:

July ’s Speaker and Topic

Speaker: Dr. Li Zhou, MD, PhD, FACMI

Dr. Zhou is an Associate Professor at Harvard Medical School (HMS) and a Lead Investigator at the Division of General Internal Medicine and Primary Care of the Brigham and Women’s Hospital. Dr. Zhou’s primary research areas include natural language processing (NLP), temporal reasoning, knowledge representation, clinical decision support, and health information systems. Dr. Zhou served as a Senior Medical Informatician at Partners HealthCare Systems for more than 10 years. She has served as Principal Investigator and co-Investigator on many research programs funded by AHRQ, NIH, PCORI, CRICO, etc. Dr. Zhou directs the MTERMS Lab (http://mterms.bwh.harvard.edu/) and has led the design and development of multiple NLP systems.

4

Big Data and AI

in Healthcare

Li Zhou, MD, PhD, FACMI

Associate Professor, Division of General Internal Medicine and Primary Care,

Brigham and Women’s Hospital, Harvard Medical School

Big Data & Artificial Intelligence

in Healthcare

5

Big Data and AI

in Healthcare

How much data is generated every minute?

Source: http://www.iflscience.com/technology/how-much-data-does-the-world-generate-every-minute/

Source: https://www.forbes.com/sites/andrewcave/2017/04/13/what-will-we-do-when-the-worlds-data-hits-163-

zettabytes-in-2025/#5a7fc9f349ab

90% of the data in the world

today has been created in the

past few years.

16.3 zettabytes of data per year

163 zettabytes per year by 2025

(a zettabytes = one trillion

gigabytes)

5 V’ s

Volume

Velocity

Variety

Veracity

Value

6

Big Data and AI

in Healthcare

Big Data in Healthcare

Sources: The “big data” revolution in healthcare”. The Mckinsey & Company. 2013.

https://www.medgadget.com/2018/04/big-data-in-healthcare-market-value-share-of-20-69-with-

cerner-co-cognizant-dell-philips-siemens-and-business-forecast-to-2022.html

“Pools” of healthcare data

Clinical data/genetic data

Claims and cost data

Pharmaceutical research data

Patient behavior and sentiment data

7

Big Data and AI

in Healthcare

Electronic Health Record (EHR)

Source: https://dashboard.healthit.gov/evaluations/data-briefs/non-federal-acute-care-hospital-ehr-

adoption-2008-2015.php#figure1

8

Big Data and AI

in Healthcare

Precision Medicine

200 terabytes = 16 million file cabinets filled with text = 30,000 DVDs

Publicly available on the Amazon Web Services cloud since 2012

Allows any researcher to access and analyze the data at a fraction

of the cost and analyze the data much more quickly

Sources: https://www.nih.gov/news-events/news-releases/1000-genomes-project-data-available-amazon-cloud

http://www.1000genomes.org/; https://allofus.nih.gov/

1000 Genomes Project (2008-2015) – a large freely

accessible public catalogue of human variation and

genotype data

Genomes of 2,504 people across 5 continental regions

All of US (2016- ) - $215 million in funding aimed to collect genetic and health data

from one million subjects.

9

Big Data and AI

in Healthcare

Health Data Networks

Source: Curtis LH, et al. Four health data networks illustrate the potential for a shared national multipurpose big-

data network. Health Affairs July 2014: 33:7.

10

Big Data and AI

in Healthcare

Emerging Technology Breakthroughs

Fourth Industrial Revolution

(Industry 4.0)

Artificial Intelligence

“AI is the new electricity”

Robotics

the Internet of Things

3D printing

Quantum Computing

Nanotechnology

Autonomous Vehicles

Sources: http://www.dbta.com/Editorial/Trends-and-Applications/Powering-the-Internet-of-Things-with-Real-Time-Hadoop-103469.aspx

http://thefutureofthings.com/8973-7-major-advancements-3d-printing-is-making-in-the-medical-field/

https://en.wikipedia.org/wiki/Industry_4.0

13

Big Data and AI

in Healthcare

Pathways to Revolutionize Healthcare

Right living: prevention; informed lifestyle choice

Right care: evidence-based care; identification of

patients at high-risk; personalized medicine

Right provider: most appropriate provider and setting

Right value: increased quality and reduced costs

Right innovation: new knowledge discovery

Source: The “big data” revolution in healthcare”. The Mckinsey & Company. 2013.

14

Big Data and AI

in Healthcare

“Disruptive Dozen: 12 AI technologies

that will reinvent care”

Sources: World Medical Innovation Forum - AI 2018.

https://www.youtube.com/channel/UCauKpbsS_hUqQaPp8EVGYOg

AI at the bedside

A picture is worth a thousand words

Can personal devices improve your health?

Risky business: using EHRs to predict disease risk

Reading the tea leaves of cancer immunotherapy

Bringing “smart” machines to medicine

Harnessing the power of digital pathology

Minimizing the treats of antimicrobial resistance and infections associated

with antibiotic use

Getting back to face time: AI tools that help reduce physicians’ computer use

Disseminating medical expertise to areas that need it most

Next-gen radiology

Melding mind and machine

15

Big Data and AI

in Healthcare

Source: Kalis B, et al. Harvard Business Review. May 2018. https://hbr.org/2018/05/10-promising-ai-

applications-in-health-care#comment-section

16

Big Data and AI

in Healthcare

AI, Machine Learning and Deep Learning

Source: http://houseofbots.com/news-detail/2754-1-a-take-on-deep-learning

18

Big Data and AI

in Healthcare

An artificial intelligence

trained to classify images of

skin lesions as benign

lesions or malignant skin

cancers achieves the

accuracy of board-certified

dermatologists.

Esteva A, Kuprel B, Novoa RA, Ko J,

Swetter SM, Blau HM, Thrun S.

Dermatologist-level classification of skin

cancer with deep neural networks.

Nature. 2017;542(7639):115-8.

21

Big Data and AI

in Healthcare

Deep Convolution Neural Network

(CNN)

22

Big Data and AI

in Healthcare

CNN vs. Dermatologists

23

Big Data and AI

in Healthcare

Viz AI: detecting potential stroke

https://www.viz.ai/

24

Big Data and AI

in Healthcare

IDx-DR: detecting diabetic retinopathy

25

Big Data and AI

in Healthcare

26

Big Data and AI

in Healthcare

Natural Language Processing (NLP)

NLP lies at the intersection of artificial intelligence and linguistics

NLP aims to create intelligent agents to understand and manipulate human

languages

Any system that analyzes or synthesizes text or speech (e.g., voice to text;

text to structured data, translations, text analytics)

NLP is in High Demand

The global NLP market will be worth $13.4 Billion by 2020.

The global healthcare NLP market is estimated to be worth $2.7 billion by 2020 and

$4.3 billion by 2024, growing significantly from the $936 million reported in 2015.

The market is projected to rise at a compound annual growth rate of 18.8%.

(EHR market is expected to reach $23.98 billion globally in 2020)

source: https://hitinfrastructure.com/news/healthcare-natural-language-processing-expects-steady-growth

27

Big Data and AI

in Healthcare

A significant portion of biomedical information is

stored in textual (unstructured) form

Electronic Health Records, such as clinic notes, progress notes, radiology reports, pathology reports, discharge summaries, free-text entries and comments

Patient Health Record/Gateway

Biomedical literature, such as journal articles and abstracts

Social Media

Others, such as emails, guidelines, books, and surveys

29

Big Data and AI

in Healthcare

Clinical Document-

ation

Info

Extraction Encoding

Data Mining

Knowledge Discovery

Clinical Decision Support

Research, Innovation, and others

• Information reconciliation

o medication, problem

and allergy

reconciliation

• Predictive models

o High risk

o Hospital readmission

o Mortality

• Diagnosis/treatment

• Quality measures

• Clinical information

o Problems, medications,

allergies, socio-

behavioral info,

functional status

• Contextual information

o Family histories,

temporal information,

negation

• Standard/ interoperability

o Terminology encoding

o Information modeling

• Document classification

• Clustering

• Relation identification

• Topic modeling

• Active learning

• Dictation: speech recognition

• Document quality: misspelling checker

• Template / Similarity

• Summarization• New methods development

• Application development

• Data and knowledge sharing

• Other areas: pharmacovigilance

Medical NLP - Ecosystem

NLP

(MTERMS)

http://mterms.bwh.harvard.edu/mterms/© 2018 Li Zhou

30

Big Data and AI

in Healthcare

Additional Information Extracted from

Notes using NLP

Navathe AS, Zhong F, Lei V, Chang F, Sordo Sanchez M, Navathe S, Topaz M, Rocha RA, Zhou L Hospital

Readmission and Social Risk Factors Identified from Physician Notes. Health Serv Res. 2017 Mar 13.

32

Big Data and AI

in Healthcare

Speech Recognition

SR becomes the bridge between human-machine interaction

33

Big Data and AI

in Healthcare

Ways to Document in EHR

Optical

Character

Recognition

Speech

RecognitionOnsite

ScribesTranscriptionistsTyping Remote

Scribes

© 2018 Li Zhou

Zhou L, Blackley SV, Kowalski L, Doan R, Acker WW, Landman AB, Kontrient E, Mack D, Meteer M, Bates DW, Goss

FR. Analysis of Errors in Dictated Clinical Documents Assisted by Speech Recognition Software and Professional

Transcriptionists. JAMA Network Open. 2018 Jul 6;1(3):e180530-.t

34

Big Data and AI

in Healthcare

Voice-enable Care34

36

Big Data and AI

in Healthcare

Language Processing & Diseases

Christie is the best-selling novelist of all time. Her works come third in the rankings

of the world's most-widely published books, behind only Shakespeare's works and

the Bible.

37

Big Data and AI

in Healthcare

Linguistic Analysis Claims Agatha

Christie Had Alzheimer’s

Compared a selection of Christie’s novels between the

ages of 28 and 82, counting numbers of different words,

indefinite nouns and phrases used in each

Statistically significant drops in vocabulary and increases in

repeated phrases and indefinite nouns

o A book she wrote aged 81 showed 30% fewer word

types than another book she wrote aged 63, 18% more

repeated phrases, and almost three times as many

indefinite words

These language effects are recognized as symptoms of

memory difficulties associated with Alzheimer's disease

38

Big Data and AI

in Healthcare

A.I. Could Help Diagnose Mental Disorders

39

Big Data and AI

in Healthcare

41

Big Data and AI

in Healthcare

AI vs. Clinician

Will some of our clinicians be out of jobs?

How to integrate AI solutions into clinical practice?

What are the legal liabilities if the machine makes mistake?

How much will we rely on AI?

For 2018 events:1. Speakers and topics are welcome2. Need support & help promoting

the events3. Journal collaborators are welcome 4. Sponsors are welcome

Dahshu Journal Club