Towards Personalized Medicine in The Netherlands - etouches · © 2016 E-HEALTH WEEK AMSTERDAM CONFIDENTIAL CONFIDENTIAL CONFIDENTIAL CONFIDENTIAL Towards Personalized Medicine in

© 2016 E-HEALTH WEEK AMSTERDAM

CONFIDENTIAL

CONFIDENTIAL

CONFIDENTIAL

CONFIDENTIAL

Towards Personalized Medicine in The Netherlands Clinical Decision Support and Cognitive Computing in Oncology


The Dutch Health Deal – CDSS in Oncology (June 8TH 2016)

Stimulating innovation between government and (private) partners

Impactful innovations improving quality of life, efficiency, outcomes

Initiated by ‘market’ entities

Government removes bottlenecks for the parties involved


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Introduction speakers

Prof. Dr Gerrit Meijer Netherlands Cancer Institute Diagnostic oncologist, specialized in Translational Gastrointestinal Oncology

Dr Nicky Hekster IBM Netherlands Technical Leader Healthcare & LifeSciences Watson Ambassador

Prof. Dr Sabine Linn Netherlands Cancer Institute Medical oncologist, specialized in Breast Cancer


Data is growing exponentially It demands new approaches in both technology and strategy

We are here

44 zettabytes

80% unstructured data

2016 2020

20% structured data

2010

Non-standardized data and numbers, free text, speech, video, images, pictures, …

Numbers, spreadsheets, standardized data models (semantic models), …


Four industrial revolutions Medicine is here!


Tabulating systems

1900 Programmable Era

1950 Cognitive Era

>2010

A new computer era is coming of age – cognitive computing From automating the world to understanding the world


Definition of Cognition The mental action of acquiring knowledge and understanding through thought, experience, and our senses

Knowledge

Ability to understand

Ideation, conviction

Sensation, observation

Imagination

Store in and retrieve from memory

Problem solving capabilities

Think

Language

10


How cognition works To become an expert

Evaluation

Observation

Decision Interpretation


How our biran wkros?

I cdn'uolt blveiee taht I cluod aulaclty uesdnatnrd waht I was rdanieg: the

phaonmneel pweor of the hmuan mnid. Aoccdrnig to a rseearch taem at

Cmabrigde Uinervtisy, it deosn't mttaer in waht oredr the ltteers in a wrod are,

the olny iprmoatnt tihng is taht the frist and lsat ltteer be in the rghit pclae. The

rset can be a taotl mses and you can sitll raed it wouthit a porbelm. Tihs is

bcuseae the huamn mnid deos not raed ervey lteter by istlef, but the wrod as a

wlohe.


History of AI

1974- 1980: 1st AI “Winter”

1970s 1980s 1990s

1956: “Birth” of AI John McCarthy coins term artificial intelligence (AI) at Dartmouth Conference

1965: First Expert System Stanford team led by Ed Feigenbaum creates DENDRAL and MYCIN

1987- 1993: 2nd AI “Winter” 1950: Turing Test

Turing introduces way to test for intelligent behavior

1990s: AI on www AI-based extraction programs prevalent on www

1997: Deep Blue IBM Deep Blue defeats World Chess Champion

2016: Google DeepMind AlphaGo wins Go

2005: Autonomous car

Stanford-built autonomous car wins DARPA Grand Challenge

2014: Market changes IBM formation of Watson Group and Google acquisition of Nest Labs

2011: Watson IBM’s Watson competes and wins on Jeopardy!

1960s 1950s 2010… 2000s

2014: Facebook Recognize individuals DeepText


The Grand Challenges

Chess – Deep Blue (1997)

• A finite, mathematically well-defined search space (10120)

• Limited number of moves and states on an 8 x 8 board

• Grounded in explicit, unambiguous mathematical rules

Human Language – Watson (2011)

• Ambiguous, contextual and implicit

• Grounded only in human cognition

• Seemingly infinite number of ways to express the same meaning

Go – DeepMind (2016) • A finite, mathematically well-defined but very large search space (10761)

• Limited number of positions and states on a 19 x 19 board

• Based on explicit, unambiguous logical rules


Thomas J. Watson (1874 – 1956)


14 February 2011

https://www.youtube.com/watch?v=lI-M7O_bRNg


Big Data & Analytics

Data Mining, Optimization, Text Analytics

Artificial Intelligence

Machine Learning, Natural Language Processing, Algorithms & Theory

Cognitive Experience

HCI, Speech, Translation, Machine Vision, Visualization

Cognitive Knowledge

Knowledge Representation, Ontologies, Semantics, Context

Computing Infrastructure

High Performance Computing, Distributed Systems, Programming Models & Tools

IBM Watson is based on


Understands natural

language and human speech

Adapts and learns from

user selections and responses

Reasons, generates and evaluates hypothesis for

better outcomes

3

2

1

Watson is an example of a cognitive system Intelligence Amplification

Watson does not predict! Watson only explains from a very large set of data and helps human beings taking complex decisions.


R&D

Demonstration

Commercialization

Health and Lifesciences Applications

IBM Research Project

(2006 – )

Jeopardy! Grand Challenge

(Feb 2011)

Watson for

Healthcare (Aug 2011 –)

Watson Health Group (April 2015 – )

Watson for Financial

Services (Mar 2012 – )

Expansion

Internal start-up division

Watson IoT Group

(Jan 2016 – )

Internet of Things

Applications

Brief history of IBM Watson

Watson Group

(Jan 2014 – )

Cross-industry Applications


IBM Bluemix PaaS platform


Relationship Extraction

Questions &

Answers

Language Detection

Personality Insights

Keyword Extraction

Image Link

Extraction

Feed Detection

Visual Recognition

Concept Expansion

Concept Insights

Dialog Sentime

nt Analysis

Text to Speech

Tradeoff Analytics

Natural Language Classifier

Author Extraction

Speech to

Text

Retrieve &

Rank

Watson News

Language Translation

Entity Extraction

Tone Analyze

r

Concept Tagging

Taxonomy

Text Extraction

Message Resonance

Image Tagging

Face Detection

Answer Generation

Usage Insights

Fusion Q&A

Video Augmentation

Decision Optimization

Knowledge Graph

Risk Stratification

Policy Identification

Emotion Analysis

Decision Support

Criteria Classification

Knowledge Canvas

Easy Adaptati

on

Knowledge Studio Service

Statistical

Dialog

Q&A Qualification

Factoid Pipeline

Case Evaluation

The Watson that competed on Jeopardy! in 2011 comprised what is now a single API—Q&A—built on five underlying technologies.

Since then, Watson has grown to a family of 28 APIs.

By the end of 2016, there will be nearly 50 Watson APIs— with more added every year.

Natural Language Processing

Machine Learning

Question Analysis

Feature Engineering

Ontology

Analysis

Catalog will grow from 28 to 50 APIs (2016)


American Cancer Society creates a Virtual Cancer Health Advisor with IBM Watson The advisor will anticipate the needs of people with different types of cancers, at different

stages of disease, and at various points in treatment.

It will become increasingly personalized as individuals engage with it, effectively getting

“smarter” each time it is used. The advisor will use ACS's cancer.org 14.000 pages of

detailed information on more than 70 cancer topics.

ACS and IBM also envision incorporating Watson’s voice recognition and natural language

processing technology, enabling users to ask questions and receive audible responses.


Current diagnosis based on imaging alone or with limited context

Clinical Records

Knowledge

IBM Cognitive Capabilities

Imaging

Providing evidenced based options

IBM combines multiple data sources and cognitive capabilities to assist the physician

+ +

Applying cognitive tools to medical imaging will help assist medical experts


Raw image Reference Highlighted anatomy Segmented arteries Arterial features

Learn from databases Annotated reference Before registration After registration Anomaly (stenosis)

Anomaly detection involves complex analytics


Medical information doubles every 5 years. By 2020 it is expected to double every quarter.

80% of the healthcare professionals spends at most 5 hrs/month to keep abreast of his/her domain

80% of the information is unstructured

Only 20% of the knowledge doctors use is evidence based: 1 out of 5 diagnoses are wrong or incomplete.

Healthcare and Lifesciences professionals are suffering from Infobesity



Creating a Corpus of Knowledge for Cancer Care Based on > 290 medical journals, > 200 textbooks and > 12 million pages free text

Ingestion of NCCN guidelines for breast cancer and lung cancer

• Roughly 500,000 unique combinations of breast cancer patient attributes.

• Roughly 50,000 unique combinations of lung cancer patient attributes.

Over 600,000 pieces of evidence ingested, from 42 different publications/publishers • The Breast Journal, National Comprehensive Cancer Network (Clinical Practice Guidelines, Drug and Biologics

compendium, et al.), American Journal Of Hematology, Annals Of Neurology, CA: A Cancer Journal For Clinicians, Cancer Journal, Cochrane, EBSCO, Hematological Oncology, Hepatology, International Journal Of Cancer, Journal Of Gene Medicine, Journal of Clinical Oncology, Journal of Oncology Practice, Massachusetts Medical Society Journal Watch, Massachusetts Medical Society New England Journal Of Medicine, Merck, Nephrology, UptoDate, Clinical Lung Cancer, Current Problems in Cancer, Cancer Treatment Reviews, Elsevier's Monographs in Cancer (multiple), Clinical Breast Cancer, European Journal of Cancer, Lung Cancer (the journal).

• YAGO, DBpedia, WordNet



Mayo Clinic

Selected Watson to analyze EMRs for Clinical Efficiency and

Effectiveness Program

Department of Veterans Affairs

Selected Watson to analyze EMRs in a demo project

Bumrungrad International Hospital

5 year agreement for Watson for Oncology

Watson for Oncology, trained by Memorial Sloan Kettering

available in clinical use in lung, breast, colon and rectal cancer

Baylor College of Medicine Published results of use with Watson Discovery Advisor – identified 7 targets for P53

activation within weeks

Watson Genomics Advisor Secured 13 Cancer and

academic medical centers for beta testing

MD Anderson Introduced proprietary

solution with Watson for clinical use for Leukemia and

Molecular Targeted Therapies

Mayo Clinic Completed testing with Clinical Trial Matching for lung, breast,

colon and rectal cancer

Manipal Hospitals Selected Watson for Oncology

to identify evidence-based treatment options among

200.000 patients/y

Manipal Hospitals Selected Watson for Oncology

to identify evidence-based treatment options among

200.000 patients per yearpatients/year.

Ongoing Training Partner

Watson Cognitive in de Gezondheidszorg

Metropolitan Health Uses Watson Engagement

Advisor to handle more that 12 million client interactions per

year/r.


Sabine Linn


Disclosures

Sabine Linn received institutional unrestricted research grants from:

•Amgen, AstraZeneca, Genentech, Roche, Sanofi

Sabine Linn is named inventor on a BRCAness signature patent

Sabine Linn was an advisory board member for Novartis, Pfizer, Roche, Sanofi,

AstraZeneca

Sabine Linn is a member (pro bono) of the scientific advisory boards of

Cergentis and Philips Health BV


Where are we heading for?


Begin with the end in mind (S. Covey)


Why?

Too much scientific knowledge to keep up with as a clinician

Desirable to have continuous medical education during outpatient clinics

Standardize quality of care

More patients in clinical trials

Population-based datasets for research


How? Case: test intelligence amplification system (IBM Watson)

Gap analysis – translation to Dutch situation

Assess cost-effectiveness

Arrange governance

Ethical, legal and social aspects

•E.g. Data ownership, intellectual property etc


Demo IBM Watson Health for Oncology

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Added value Free text extraction (saves time)

Check for completeness of diagnostic information

More treatment options

Suggestions for eligible studies

Treatment overview for the patient

Continuous medical education during outpatient clinics

Data mining for self learning decision support system


Risks?

Who is responsible?

Simplifying medical complexity

IT will never be able to capture all symptom combinations in models

It is a SUPPORT TOOL

Cost-effectiveness?


Gerrit Meijer

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