This document discusses IBM Watson and its potential applications in healthcare in German-speaking countries. It provides an overview of Watson's capabilities, including its ability to understand natural language, generate and evaluate hypotheses, and adapt and learn from interactions. The document also discusses how Watson has been applied to healthcare through projects like creating knowledge bases for cancer care and working with IBM Content Analytics to extract attributes from medical texts. Overall, the document presents Watson as a system that can help address the challenges of analyzing growing amounts of unstructured medical data through its advanced natural language processing and machine learning abilities.

1. 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: eva_deutsch@at.ibm.com
© 2013 International Business Machines Corporation

2. Agenda
What is IBM Watson and why is it important?
Examples of Watson in Healthcare solutions
What can we implement today in DACH?
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3. Learning systems are ushering a new era of computing
System
Intelligence
Cognitive Systems Era
Programmable Systems Era
Tabulating System Era
Punch cards
Time card readers
1900
3
Search
Deterministic
Enterprise data
Machine language
Simple outputs
1950
Discovery
Probabilistic
Big Data
Natural language
Intelligent options
2011
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4. Businesses on a Smarter Planet are “dying of thirst in
an ocean of data”
90%
80%
20%
of the world’s
data was created
in the past two
years
of the world’s
data today is
unstructured
is the amount of
available data
traditional systems
leverage
1 in 2
5h/mon
business leaders don’t
have access to data
they need
4
2x/5y
Medical information is
doubling every 5 years,
much of which is
unstructured
81% of physicians report
spending 5 hours or less
per month reading
medical journals
Source: GigaOM, Software Group, IBM Institute for Business Value"
Source: International Journal of Circumpolar Health, DoctorDirectory.com, Institute for Medicine"
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5. 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.
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
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6. IBM Watson brings together a set of transformational
technologies to drive optimized outcomes
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1 Understands
natural
language and
human speech
Generates and
evaluates
hypothesis for
better outcomes
99%
60%
10%
3 Adapts and
learns from user
selections and
responses
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…built on a massively parallel probabilistic
evidence-based architecture optimized for POWER7
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7. How Watson Works: parse request, generate hypotheses,
evaluate evidence, and respond with confidence
Question
Analyze
question
100’s
Multiple
sources
Interpretations
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Balance
& Combine
Generate
hypotheses
100’s
Possible
Answers
1000’s of
Pieces of
Evidence
Collect and
evaluate
evidence
Weigh and
combine for final
confidences
100,000’s Scores
from many Deep
Analysis Algorithms
Answer &
Confidence
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8. 8
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9. 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).
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14. IBM Watson brings together a set of transformational
technologies to drive optimized outcomes
2 Generates and
evaluates
hypothesis for
better outcomes
1 Understands
natural
language and
human speech
and
3 Adapts from
Learns
user selections
and responses
99%
60%
10%
IBM Content
Analytics with
Enterprise
Search
Enterprise Search
Content Analytics
• Secure, robust and scalable
• Natural Language
Processing
• Fact and Relationship
Extraction (Annotation)
• Content Classification
• Context-driven using NLP
• Content Classification
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Search and
Analyze
Content
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15. 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
Dashboard
Facets
Enterprise Search
• Trend, Pattern, Anomaly, Deviation
and Context Analysis
• Enterprise Search Capabilities
Time Series
Deviations / Trends
• Studio Workbench to Build
Annotators and Rules
• Add-on for Predictive Modeling and
Scoring for Probability and Outcome
Analysis
Connections
• Add-on for Patient Similarity Analytics
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NLP* Natural Language Processing
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16. Attribute extraction in Watson and IBM Content Analytics
Diseases
Medications
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Symptoms
Modifiers
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17. 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 ….
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Patient
Alter: 42
Geschlecht: männlich
Leistung
Hemikolektomie
Diagnose: Adenokarzinom
Anatomische Lage: Ileum
Leistung
Appendektomie
Diagnose: keine
Anatomische Lage: Appendix
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18. Overview workflow „intelligent“ text-analysis
Spracherkennung ► Segmentierung ► Normalisierung ► Anreicherung
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
► Wörterbücher ►Regeln
Fachwörter:
Untersuchung = Nomen
z.B. koronar,
ergeben = Verb
Dreigefäßerkrankung
koronare = Adv.
Dreigefäßerkrankung = Nomen
Diagnose:
z.B. Koronare
Dreigefäßerkrankung
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.
Hinweis: Die Ableitung der Sätze bzw. Satzteile erfolgt automatisch . . .
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19. 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)
‒
‒
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Literature analysis, Combination of unstructured data and literature
Find relevant literature that fits to unstructured information
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20. Learn more at:
www.ibmwatson.com.
www.facebook.com/ibmwatson.
www.twitter.com/ibmwatson
(Tweet #ibmwatson )
www.youtube.com/ibm
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