IBM Health Innovation Forum 2013 - Watson for Healthcare

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IBM Health Innovation Forum 2013, Rueschlikon, CH

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IBM Health Innovation Forum 2013 - Watson for Healthcare

  1. 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. 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 © 2013 International Business Machines Corporation
  3. 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 © 2013 International Business Machines Corporation
  4. 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" © 2013 International Business Machines Corporation
  5. 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 © 2013 International Business Machines Corporation
  6. 6. IBM Watson brings together a set of transformational technologies to drive optimized outcomes 2 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 6 …built on a massively parallel probabilistic evidence-based architecture optimized for POWER7 © 2013 International Business Machines Corporation
  7. 7. How Watson Works: parse request, generate hypotheses, evaluate evidence, and respond with confidence Question Analyze question 100’s Multiple sources Interpretations 7 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 © 2013 International Business Machines Corporation
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  9. 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 © 2013 International Business Machines Corporation
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  14. 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 14 Search and Analyze Content © 2013 International Business Machines Corporation
  15. 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 15 NLP* Natural Language Processing © 2013 International Business Machines Corporation 15
  16. 16. Attribute extraction in Watson and IBM Content Analytics Diseases Medications 16 Symptoms Modifiers © 2013 International Business Machines Corporation
  17. 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 …. 17 Patient Alter: 42 Geschlecht: männlich Leistung Hemikolektomie Diagnose: Adenokarzinom Anatomische Lage: Ileum Leistung Appendektomie Diagnose: keine Anatomische Lage: Appendix © 2013 International Business Machines Corporation
  18. 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 . . . 18 © 2013 International Business Machines Corporation
  19. 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) ‒ ‒ 19 Literature analysis, Combination of unstructured data and literature Find relevant literature that fits to unstructured information © 2013 International Business Machines Corporation
  20. 20. Learn more at: www.ibmwatson.com. www.facebook.com/ibmwatson. www.twitter.com/ibmwatson (Tweet #ibmwatson ) www.youtube.com/ibm 20 © 2013 International Business Machines Corporation

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