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Towards Personalized Medicine in The Netherlands Clinical Decision Support and Cognitive Computing in Oncology © 2016 E-HEALTH WEEK AMSTERDAM 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 © 2016 E-HEALTH WEEK AMSTERDAM © 2016 E-HEALTH WEEK AMSTERDAM 3 Introduction speakers Dr Nicky Hekster IBM Netherlands Technical Leader Healthcare & LifeSciences Watson Ambassador © 2016 E-HEALTH WEEK AMSTERDAM Prof. Dr Sabine Linn Netherlands Cancer Institute Medical oncologist, specialized in Breast Cancer Prof. Dr Gerrit Meijer Netherlands Cancer Institute Diagnostic oncologist, specialized in Translational Gastrointestinal Oncology Data is growing exponentially 44 zettabytes It demands new approaches in both technology and strategy Non-standardized data and numbers, free text, speech, video, images, pictures, … 80% unstructured data Numbers, spreadsheets, standardized data models (semantic models), … We are here 20% structured data 2010 © 2016 E-HEALTH WEEK AMSTERDAM 2016 2020 Four industrial revolutions Medicine is here! © 2016 E-HEALTH WEEK AMSTERDAM A new computer era is coming of age – cognitive computing From automating the world to understanding the world 1900 1950 >2010 Tabulating systems Programmable Era Cognitive Era © 2016 E-HEALTH WEEK AMSTERDAM 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 © 2016 E-HEALTH WEEK AMSTERDAM 10 How cognition works To become an expert Observation Interpretation Decision Evaluation © 2016 E-HEALTH WEEK AMSTERDAM 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. © 2016 E-HEALTH WEEK AMSTERDAM History of AI 1990s: AI on www AI-based extraction programs prevalent on www 2005: Autonomous car Stanford-built autonomous car wins DARPA Grand Challenge 1956: “Birth” of AI John McCarthy coins term artificial intelligence (AI) at Dartmouth Conference 1950s 1960s 1970s 1950: Turing Test Turing introduces way to test for intelligent behavior 1965: First Expert System Stanford team led by Ed Feigenbaum creates DENDRAL and MYCIN © 2016 E-HEALTH WEEK AMSTERDAM 2014: Market changes 1974- 1980: 1st AI “Winter” 1980s IBM formation of Watson Group and Google acquisition of Nest Labs 1990s 2000s 2010… 2011: Watson 1987- 1993: 2nd AI “Winter” IBM’s Watson competes and wins on Jeopardy! 1997: Deep Blue IBM Deep Blue defeats World Chess Champion 2016: Google DeepMind AlphaGo wins Go 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 © 2016 E-HEALTH WEEK AMSTERDAM Thomas J. Watson (1874 – 1956) © 2016 E-HEALTH WEEK AMSTERDAM 14 February 2011 © 2016 E-HEALTH WEEK AMSTERDAM IBM Watson is based on Big Data & Analytics Artificial Intelligence Cognitive Experience Cognitive Knowledge Computing Infrastructure Data Mining, Optimization, Text Analytics Machine Learning, Natural Language Processing, Algorithms & Theory HCI, Speech, Translation, Machine Vision, Visualization Knowledge Representation, Ontologies, Semantics, Context High Performance Computing, Distributed Systems, Programming Models & Tools © 2016 E-HEALTH WEEK AMSTERDAM Watson is an example of a cognitive system Intelligence Amplification 1 Understands 2 Reasons, generates and evaluates hypothesis for better outcomes natural language and human speech 3 Adapts and learns from user selections and responses Watson does not predict! Watson only explains from a very large set of data and helps human beings taking complex decisions. © 2016 E-HEALTH WEEK AMSTERDAM Brief history of IBM Watson IBM Research Project Jeopardy! Grand Challenge (2006 – ) (Feb 2011) Watson for Healthcare Watson for Financial Services (Aug 2011 –) (Mar 2012 – ) Expansion Commercialization Demonstration R&D © 2016 E-HEALTH WEEK AMSTERDAM Internal start-up division Watson Group (Jan 2014 – ) Cross-industry Applications Watson Health Group Watson IoT Group (April 2015 – ) (Jan 2016 – ) Health and Lifesciences Applications Internet of Things Applications IBM Bluemix PaaS platform © 2016 E-HEALTH WEEK AMSTERDAM Catalog will grow from 28 to 50 APIs (2016) The Watson that competed on Jeopardy! in 2011 Since then, Watson has grown to comprised what a family of 28 APIs. is now a single API—Q&A—built on five underlying technologies. Questio ns & Answers Language Detection Visual Recognition Question Analysis Feature Engineering Ontology Natural Language Classifier Concept Tagging Language Translation Retrieve & Rank Text to Speech Tradeoff Analytics Speech to Text © 2016 E-HEALTH WEEK AMSTERDAM Text Extraction Watson News Entity Extraction Decision Support Risk Stratification Policy Identification Video Augmentation Face Detection Statistic al Dialog Criteria Classification Fusion Q&A Image Tagging Knowledg e Studio Service Knowledge Graph Message Resonance Tone Analyze r Easy Adaptati on Emotion Analysis Usage Insights Taxonom y Analysis Author Extraction Answer Generation Sentime nt Analysis Dialog Concept Insights Decision Optimization Image Link Extraction Feed Detection Relationship Extraction Concept Expansion Natural Language Processing Machine Learning Keyword Extraction Personality Insights By the end of 2016, there will be nearly 50 Watson APIs— with more added every year. Q&A Qualification Knowledge Canvas Factoid Pipeline Case Evaluation 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. © 2016 E-HEALTH WEEK AMSTERDAM Applying cognitive tools to medical imaging will help assist medical experts Current diagnosis based on imaging alone or with limited context IBM combines multiple data sources and cognitive capabilities to assist the physician + Imaging Clinical Records + Knowledge IBM Cognitive Capabilities Providing evidenced based options © 2016 E-HEALTH WEEK AMSTERDAM Anomaly detection involves complex analytics Reference Raw image Learn from databases Annotated reference © 2016 E-HEALTH WEEK AMSTERDAM Highlighted anatomy Before registration Segmented arteries Arterial features After registration Anomaly (stenosis) Healthcare and Lifesciences professionals are suffering from Infobesity 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. © 2016 E-HEALTH WEEK AMSTERDAM © 2016 E-HEALTH WEEK AMSTERDAM 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 © 2016 E-HEALTH WEEK AMSTERDAM © 2016 E-HEALTH WEEK AMSTERDAM Watson Cognitive in de Gezondheidszorg Ongoing Training Partner 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 © 2016 E-HEALTH WEEK AMSTERDAM Bumrungrad International Hospital 5 year agreement for Watson for Oncology 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 Department of Veterans Affairs Selected Watson to analyze EMRs in a demo project Metropolitan Health Manipal Hospitals Uses Watson Mayo Clinic Selected WatsonEngagement for Oncology to handle more that 12 Completed testing with Clinical Advisor to identify evidence-based client interactions per Trial Matching for lung, breast, million treatment options among year/r. colon and rectal cancer 200.000 patients/y Mayo Clinic Selected Watson to analyze EMRs for Clinical Efficiency and Effectiveness Program Manipal Hospitals Selected Watson for Oncology to identify evidence-based treatment options among 200.000 patients per yearpatients/year. Sabine Linn © 2016 E-HEALTH WEEK AMSTERDAM 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 © 2016 E-HEALTH WEEK AMSTERDAM Where are we heading for? © 2016 E-HEALTH WEEK AMSTERDAM Begin with the end in mind (S. Covey) © 2016 E-HEALTH WEEK AMSTERDAM 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 © 2016 E-HEALTH WEEK AMSTERDAM 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 © 2016 E-HEALTH WEEK AMSTERDAM Demo IBM Watson Health for Oncology © 2016 E-HEALTH WEEK AMSTERDAM 36 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 © 2016Data mining for self learning decision support system E-HEALTH WEEK AMSTERDAM 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? © 2016 E-HEALTH WEEK AMSTERDAM Gerrit Meijer © 2016 E-HEALTH WEEK AMSTERDAM