IBM Health Innovation Forum 2013 - Watson for Healthcare

© 2013 International Business Machines Corporation

Watson for Healthcare

Vision, scope and possibilities in German speaking countries for healthcare-specific natural language processing

Dr. Eva Deutsch

GBS Healthcare Industry Leader Austria

Tel. ++43/0121145-2235 Mobil: ++43/06646185936

Email: [email protected]

© 2013 International Business Machines Corporation2

Agenda

What is IBM Watson and why is it important?

Examples of Watson in Healthcare solutions

What can we implement today in DACH?


SystemIntelligence

1900 1950

Learning systems are ushering a new era of computing

Tabulating System Era

Programmable Systems Era

Cognitive Systems Era

Punch cards

Time card readers

Discovery

Probabilistic

Big Data

Natural language

Intelligent options

2011

Search

Deterministic

Enterprise data

Machine language

Simple outputs


Businesses on a Smarter Planet are “dying of thirst in an ocean of data”

1 in 2business leaders don’t

have access to data they need

2x/5yMedical information is

doubling every 5 years,

much of which is

unstructured

5h/mon81% of physicians report spending 5 hours or less

per month reading medical journals

80%of the world’s data today is unstructured

90% of the world’s

data was created in the past two

years

20%is the amount of

available data traditional systems

leverage

Source: GigaOM, Software Group, IBM Institute for Business Value"

Source: International Journal of Circumpolar Health, DoctorDirectory.com, Institute for Medicine"


� Watson Wins!

� Largest Jeopardy! in 5 years

� 34.5M Jeopardy! Viewers

� 1.3B+ Impressions

� Over 10,000 Media Stories

� 11,000 attend watch events

� 2.5M+ Videos Views (top 10 only)

� 12,582 Twitter

� 25,763 Facebook Fans

On February 14, 2011, IBM Watson made history introducing a system that

rivaled a human’s ability to answer questions posed in natural language with speed, accuracy and confidence.


99%

60%

10%

Understandsnatural language and

human speech

Adapts and learns from user selections and responses

Generates and

evaluates hypothesis for

better outcomes

3

2

1

…built on a massively parallel probabilistic

evidence-based architecture optimized for POWER7

IBM Watson brings together a set of transformational technologies to drive optimized outcomes


Answer & Confidence

Question

How Watson Works: parse request, generate hypotheses, evaluate evidence, and respond with confidence

Analyze

question

Generate

hypotheses

Collect and

evaluate

evidence

Weigh and

combine for final

confidences

Balance& Combine

1000’s of Pieces of Evidence

Multiple Interpretations

100,000’s Scores from many Deep

Analysis Algorithms

100’s sources

100’s Possible Answers


Creating a Corpus of Knowledge for Cancer Care

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, including:

‒ 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).


99%

60%

10%

Understandsnatural language and

human speech

Adapts and Learns from user selections and responses

Generates and evaluates

hypothesis for better outcomes

3

2

1

IBM Watson brings together a set of transformational technologies to drive optimized outcomes

IBM Content Analytics with Enterprise Search

Enterprise Search

• Secure, robust and scalable

• Context-driven using NLP

• Content Classification

Content Analytics

• Natural Language Processing

• Fact and Relationship Extraction (Annotation)

• Content Classification

Search and AnalyzeContent


IBM Content Analytics for Healthcare is the first “Ready for Watson” solution …to complement and leverage IBM Watson

• NLP*-solution built on Watson

Unstructured Information Analysis

Architecture (UMIA) natural language

processing technology

• Trend, Pattern, Anomaly, Deviation

and Context Analysis

• Enterprise Search Capabilities

• Studio Workbench to Build

Annotators and Rules

• Add-on for Predictive Modeling and

Scoring for Probability and Outcome

Analysis

• Add-on for Patient Similarity Analytics

15

Facets

Dashboard

Enterprise Search

Deviations / Trends

Connections

Time Series

NLP* Natural Language Processing


Attribute extraction in Watson and IBM Content Analytics

Medications

SymptomsDiseases

Modifiers


Building a German Healthcare Domain knowledge in IBM Content Analytics

• Including first catalogues and rules for identification of diagnoses,

procedures, medication, anatomy

• Coding suggestions (ICD-10, MEL), first relationships and hierarchies

• Identification of sections inside the documents (anamnesis, discharge

diagnoses, recommended medication etc.)

• Normalization of selected information (dates, dosage, sizes etc.)

• Basic identification of Negations

Der 42 Jahre alte männliche

Patient wurde per Notaufnahme

aufgenommen. Er hatte vor

kurzem eine Hemikolektomie

aufgrund eines invasiv

wachsenden Adenokarzinoms

in der Ileum Region. Zur gleichen

Zeit erfolgte eine

Appendektomie. Der Appendix

zeigte keine Auffälligkeiten bei

der Diagnostik ….

Patient Alter: 42

Geschlecht: männlich

Leistung Hemikolektomie

Diagnose: Adenokarzinom

Anatomische Lage: Ileum

Leistung Appendektomie

Diagnose: keine

Anatomische Lage: Appendix


Overview workflow „intelligent“ text-analysis

Hinweis: Die Ableitung der Sätze bzw. Satzteile erfolgt automatisch . . .

Spracherkennung ► Segmentierung ► Normalisierung ► Anreicherung ► Wörterbücher ►Regeln

Herr Mustermann wurde nach akutem Koronarsyndrom aus dem Klinikum XX zur

Koronarangiografie übernommen. Die Untersuchung ergab eine koronare

Dreigefäßerkrankung. Zudem fiel eine höhergradige, symptomatische

Mitralinsuffizienz auf, so dass der Patient am 10.Jän.2013 sich einer

Bypass-Versorgung mit Mitralklappenersatz unterziehen wird.

� deutsch � Einzelne Wörter:

z.B. Herr

� Sätze:

z.B. Die Untersuchung

ergab eine koronare Dreigefäßerkrankung.

� ergab = ergeben

� Koronare=koronar

� 10.Jän.2013=10.01.2013

� Untersuchung = Nomen

� ergeben = Verb

� koronare = Adv.

� Dreigefäßerkrankung = Nomen

� Fachwörter:

z.B. koronar,

Dreigefäßerkrankung

� Diagnose:

z.B. Koronare

Dreigefäßerkrankung


IBM Content Analytics can be used in different settings in Healthcare

• Better overview for physicians in electronic health records / EMR Systems

(real-time)‒ Patient Summary

‒ Semantic Search

• Medical analytics based on patient records (retrospective)‒ Quality-Management

‒ Medical analysis of HIS/EMR documents or even archived documents

• Administrative analytics‒ Analyze DRG reimbursement based on clinical documents

‒ Analyze any other free text information like patient satisfaction

• Research analytics ‒ University hospitals, Pharma / Life Sciences, Payer

‒ Content Analytics, Predictive Analytics and Patient Similarity Analytics

• Literature search (physician, patient, researcher)‒ Literature analysis, Combination of unstructured data and literature

‒ Find relevant literature that fits to unstructured information


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www.facebook.com/ibmwatson.

www.twitter.com/ibmwatson(Tweet #ibmwatson )

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