Watson for Healthcare

Transcription

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/ Mobil: ++43/

2 Agenda What is IBM Watson and why is it important? Examples of Watson in Healthcare solutions What can we implement today in DACH? 2

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 Search Deterministic Enterprise data Machine language Simple outputs Discovery Probabilistic Big Data Natural language Intelligent options

4 Businesses on a Smarter Planet are dying of thirst in an ocean of data 90% of the world s data was created in the past two years 80% of the world s data today is unstructured 20% is the amount of available data traditional systems leverage 4 1 in 2 business leaders don t have access to data they need 2x/5y Medical information is doubling every 5 years, much of which is unstructured Source: GigaOM, Software Group, IBM Institute for Business Value" Source: International Journal of Circumpolar Health, DoctorDirectory.com, Institute for Medicine" 5h/mon 81% of physicians report spending 5 hours or less per month reading medical journals

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 5

6 IBM Watson brings together a set of transformational technologies to drive optimized outcomes 1 Understands natural language and human speech 2 Generates and evaluates hypothesis for better outcomes 99% 60% 10% 3 Adapts and learns from user selections and responses built on a massively parallel probabilistic evidence-based architecture optimized for POWER7 6

7 How Watson Works: parse request, generate hypotheses, evaluate evidence, and respond with confidence Question Balance & Combine Analyze question Generate hypotheses Collect and evaluate evidence Weigh and combine for final confidences Multiple Interpretations 100 s sources 100 s Possible Answers 1000 s of Pieces of Evidence 100,000 s Scores from many Deep Analysis Algorithms Answer & Confidence 7

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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). 9

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14 IBM Watson brings together a set of transformational technologies to drive optimized outcomes 1 Understands natural language and human speech 2 Generates and evaluates hypothesis for better outcomes 99% 60% 10% 14 3 Adapts and Learns from user selections and responses IBM Content Analytics with Enterprise Search Enterprise Search Search and Analyze Content Content Analytics Secure, robust and scalable Natural Language Processing Context-driven using NLP Fact and Relationship Extraction (Annotation) Content Classification Content Classification

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 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 Facets Time Series Connections Dashboard Enterprise Search Deviations / Trends 15 NLP* Natural Language Processing 15

16 Attribute extraction in Watson and IBM Content Analytics Diseases Symptoms Medications Modifiers 16

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. Patient Alter: 42 Geschlecht: männlich Leistung Leistung Hemikolektomie Diagnose: Adenokarzinom Anatomische Lage: Ileum Appendektomie Diagnose: keine Anatomische Lage: Appendix 17

18 Overview workflow intelligent text-analysis Spracherkennung Segmentierung Normalisierung Anreicherung Wörterbücher Regeln deutsch Einzelne Wörter: z.b. Herr Sätze: z.b. Die Untersuchung ergab eine koronare Dreigefäßerkrankung. ergab = ergeben Koronare=koronar Untersuchung = Nomen ergeben = Verb Fachwörter: z.b. koronar, 10.Jän.2013= koronare = Adv. Dreigefäßerkrankung 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... 18

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) Literature analysis, Combination of unstructured data and literature Find relevant literature that fits to unstructured information 19

20 Learn more at: (Tweet #ibmwatson ) 20