• Save
How a Jeopardy-winning machine makes the World a Smarter Place
Upcoming SlideShare
Loading in...5

Like this? Share it with your network


How a Jeopardy-winning machine makes the World a Smarter Place

Uploaded on

Presentation from a lecture at the IT-university, University of Copenhagen, sept 2013. Covers trends, where is data coming from, what is cognitive computing, what is Watson, how does it work and......

Presentation from a lecture at the IT-university, University of Copenhagen, sept 2013. Covers trends, where is data coming from, what is cognitive computing, what is Watson, how does it work and how to apply to real-world issues.

More in: Technology , Business
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Be the first to comment
No Downloads


Total Views
On Slideshare
From Embeds
Number of Embeds



Embeds 0

No embeds

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

    No notes for slide
  • What is bringing about the need for a new era of computing. In large part it is because of the explosion of data. And not just the typical structured data we find in computer databases, but through voice, social media, and sensors throughout the world. Up to 80 percent of this data is projected to be unstructured data by 2015.As you can see, data is just beginning its rapid growth. We’re sill on the blade part of the hockey stick.
  • Main point: Data is growing at an astounding rate. It is growing so fast that we often lack the ability to use it to its full potential. The highly unstructured nature of this data makes the challenge that much more difficult. This is a real problem for business. It makes informed decisions more difficult to make. Business leaders need a way to find hidden patterns and isolate the valuable nuggets that they need to make business decisions.Further speaking points: Yet, the rewards for finding a way to harness the data into useful information are great; 54% of companies in this year’s study with MIT/Sloan are using analytics for competitive advantage… and that number has surged 57% in just the past 12 months. “Dying of thirst in an ocean of data”… It’s an apt analogy. Data is everywhere. 90% of it didn't exist just two years ago. The vast majority of it is totally useless for any given goal and therefore amounts to noise and a hindrance to finding the key useful information needed in a specific time and place. Additional information: See information and stats
  • As this chart shows, the issue with data isn’t just the volume of data. IT systems need to be able to deal with the speed of the data, the various forms of data and the growing awareness that much of our data is uncertain.On Jeopardy, Watson competed well across all of these attributes.Arvind Krishna will talk more about these aspects of big data tomorrow.
  • In order to know we are making progress on scientific problems like open-domain QA well-defined challenges help demonstrate we can solve concrete & difficult tasks. As you might know Jeopardy! Is a long-standing, well-regarded and highly challenging Television quiz show in the US that demands human contestants to quickly understand and answer richly expressed natural language questions over a staggering array of topics.The Jeopardy! Challenge uniquely provides a palpable, compelling and notable way to drive the technology of Question Answering along key dimensionsIf you are familiar with the quiz show it asks an I incredibly broad range of questions over a huge variety of topics.In a single round there is a grid of 6 Categories and for each category 5 rows with increasing $ values. Once a cell is chosen by 1 of three players, A question, or what is often called a Clue is revealed.Here you see some example questions.<read some of the questions> Jeopardy uses complex and often subtle language to describe what is being asked. To win you have to be extraordinarily precise. You must deliver the exact answer – no more and no less – it is not good enough for it be somewhere in the top 2, 10 or 20 documents – you must know it exactly and get it in first place – otherwise no credit – in fact you loose points. You must demonstrate Accurate Confidences -- That is -- you must know what you know – if you “buzz –in” and then get it wrong you lose the $$ value of the question. And you have to do this all very quickly – deeply analyze huge volumes of content, consider many possible answers, compute your confidence and buzz in – all in just seconds.As we shall see compete with human champions at this game represents a Grand Challenge in Automatic Open-Domain Question Answering.<STOP><NEXT SLIDE>
  • Main Point: At the core of what makes Watson different are three powerful technologies - natural language, hypothesis generation, and evidence based learning. But Watson is more than the sum of its individual parts. Watson is about bringing these capabilities together in a way that’s never been done before resulting in a fundamental change in the way businesses look at quickly solving problemsSolutions that learn with each iterationCapable of navigating human communicationDynamically evaluating hypothesis to questions askedResponses optimized based on relevant dataIngesting and analyzing Big DataDiscovering new patterns and insights in secondsFurther speaking points:. Looking at these one by one, understanding natural language and the way we speak breaks down the communication barrier that has stood in the way between people and their machines for so long. Hypothesis generation bypasses the historic deterministic way that computers function and recognizes that there are various probabilities of various outcomes rather than a single definitive ‘right’ response. And adaptation and learning helps Watson continuously improve in the same way that humans learn….it keeps track of which of its selections were selected by users and which responses got positive feedback thus improving future response generationAdditional information: The result is a machine that functions along side of us as an assistant rather than something we wrestle with to get an adequate outcome
  • Here we see the same question, the same parse, but on the other side we see that there exists a passage containing the RIGHT answer BUT with only one key word in common. <read the green passage> The system must consider in parallel and in detail a huge amount of content just to get a SHOT at this evidence and then must find and weigh the right inferences that will allow it to match and score with an accurate confidence, for example in this case  <click> Date Math, Statistical Paraphrasing and Geospatial reasoning. And its still not 100% certain What if, for example, the passage said “considered landing in” rather than “landed in” or what if there was just a preponderance of weaker evidence for another answer. Question Answering Technology tries to understand what the user is really asking for and to deliver precise and correct responses. But Natural language is hard. Meaning can be expressed in so many different ways and to achieve high levels of precision and confidence you must consider much more information and analyze it much more deeply. We is needed is a radically different approach that explores many different plaussive interpretations in parallel and collects and evaluates all sorts of evidence in support or in refutation of those possibilities.
  • Main point: Healthcare is a great example of how these challenges come to life. Physicians can not keep up with the explosive growth of medical information which is doubling every five years. Reading journals is the primary way new medical information is delivered yet the vast majority of physicians don’t spend anywhere near enough time to keep up with it.. Meanwhile, diagnosis, treatments, and preventable deaths leave huge room for improvementFurther speaking points: Imagine you’re in a hospital waiting room with 9 others waiting to seen. Chances are, two of you are going to be misdiagnosed. Preventable medical errors kill 44K-98K Americans every year. That’s enough to fill a big college football stadium. Imagine what the total would be world wide. As Steven Shapiro, Chief medical and scientific officer at UPMC says “Medicine has become too complex (and only) about 20% of the knowledge clinicians use today is evidence based”. We just spoke about the gap between today’s IT needs and traditional IT. Surely, a new approach to IT can help address some of the healthcare difficulties described here. Additional information: statistics on right.
  • Main point: Watson is evolving to meet the unique challenges of Healthcare. But it already has the core capabilities to handle a variety of healthcare-specific needs. Further speaking points:. It understands natural language. While this is valuable in any industry, healthcare is especially qualitative and verbal in its operations and stands to benefit strongly from Watson’s conversational approachAnalyzes large volumes of unstructured data which is especially prevalent in medicine such as Physician Notes, Medical Journals, Clinical Trials, Pathology Results, Blogs, WikipediaGenerates and evaluates hypothesis and presents responses with confidence. Just like healthcare professionals already do. Unlike some other fields, medicine has always looked at diagnosis and treatment options as probabilistic rather than ever identifying Supports iterative dialogue to refine results so as more information is made available, different hypotheses gain and shrink in probabilistic likelihood. Learns from results over time. During Jeopardy! trials, Watson was trained iteratively so it could learn from its experience. Same thing in healthcare.
  • Main point: A great way to understand how Watson works is to through a simulation. In this case, we can see how an incoming patient is diagnosed with increasing precision as more information is made available. Further speaking points:. A woman describes her symptoms to her healthcare professional. It can handle alternate meanings, misspellings (i.e. –her voice is ‘horse’ rather than ‘hoarse”). Based on the symptoms alone, Watson considers five possible diagnosis and scores probabilities for each based on the evidence… in the case of present symptoms, influenza seems most likely.It then considers explicitly absent symptoms (no abdominal pain, no cough, no shortness of breath, etc.) and now considers diabetes the most probable diagnosis.It correlates the various symptoms and evaluates co-relationships between them. So based on the symptoms alone, a UTI is most likely with Diabetes close behind. A family history shows strong Diabetes likelihood… enough to outweigh the symptoms-driven UTI likelihood but not by much. Her patient history brings UTI back up above diabetes. Looking at medications she is using brings another hypothesis into the mix but does not alter the balance of the two most likely diagnoses. So tests are done for both. Tests confirm the presence of a UTI. Additional information: Note that the iterative process matches the process the physician would use in an unaided diagnosis. And the process extends beyond healthcare. You can think about this iterative process in other situations beyond healthcare.


  • 1. 1 Total Pages 16 Subject precert Memo Ref# Jwa010m66715 Urgent request procedure is 12/27 Please call xxxxx at nnnn- nnn-nnn ..y G LI F 10 0513Z Er 12/20/2012 12:54 5733646493 PAGE 01/15 Th, r DateDEC 0 52012 Time: / I e0 IL,' DATE OF BIRTH: 5- tall ^ SR PRINT PARIEDO" NAME: Patient Name: xx Referring Physician: xxx . Ht .41 '' Temp qi. 1 FutEcehArvi <, St.-mi.d. PCP:3 r . eheit,Liv„ V. Wt 350 Pulse gp Location: Li RR - Well developed, well nourished , Quality: 2 N - Voice quality normal limits 1k Medication her; Allergies: -I- Lek eri--e- (- Severity: Duration: if/10 ks.,0 eAtAp„z,../vais /.....,,4 r,..,,..,„ . fr.-, w.,,,-,-_,- C' Thr Sse-v-e.tat-9 €74,4-•-' Medications: a 63',,,,- ION if Timing: CT Sfirytd."-Is't 11(a-etta. -r-cm-t4 NNS td.4.,n--i A110-440.P NI, I aa:-.• /11,4402",nX DO Context: t• C ... st.m.., rheleni e...n.-- dm? SaGICAL 0 HISTORY: 6030 Modifying ice- soiN Factors:Warr, Lir, 6,,,,liccX . 1- nil ctan- -)c, (8g--raA, Revi Cawcter A , owed _Try..,,, Y N laett fit Atan.4r, Impressions/Plan Signs & SYmPtoths:SA-,--0-- eAdra-. of Care efts; ea_ "ArStirr. -Ionuse .1-1 A, e cylLetr- 5 utee.ict441rdi PAST ILLNESSES: Reviewnsi Y • e•-• • , _,,, Cc/ _CMArna rrie, kikt$T c44,2-4.,/aner Avvya • greis- efrsolineptiO Reviewed FAMILY HISTORY: Y N SOCIAL HISTORY: Marital Status: S9 W D Living Status; Nursing Home / Other Father: ) I Cath Minor Living Status: Mother / Father / Other Mother: sibiingso,,. C lo r ca 6 .w.t0 b 1- L-7,- V VV-A Chi ots„ 1 Habits: If Child: Caffeine Cigarettes/Tobacco Alcottoi Breast fed 6) Bottle Y YO &Haw N fed how How Much Much Much s3rPc othe years_ .a41 „. . al- 4.„ ..-.....s PGF/GM: ca--)-14 rr-d-ei 611) '1 119 ' '' ReligiGps Preference; MGFIGM: e , Genera/ Health: weigh v /perdfaitpti Eyes: yr itching, dry. bluffed, CV: MI, palpiteoKysnosis, erturrifies, cp.di/ sweats / chills, ala241sturbance I rednessA rupii-d..t.,, rheumatic feeer, angina, cyanosis w/ feeding , cnyironyetnal/food Allergic/immunologic 9.1egies, immuniratians: tendency, adenopathy pew eausatatons, 1,04est, HereatopeicticfLymphatictdeenkeiThleoillig and reactions, END: colds, infectionttsorslbseC ts litffingluss ,ttto 7 rtdX -"ear - gt, ea di to dental cliff:imam vote Respirstogaatt- chronic Muscuioskclatakpenc3welling, muscular inteknentatry: r f, itri4g, lesions, "Kees pneurnonige rcl wcanass, sloop d ce 01: diarrhea, dyspleagia tiftetla, constipation, abdominal-an, hearibriitn, hepatitis incligegron, hcmatemeais therapy, Endocrine; theenaSe intolerance to air heat changes. or cold, hormone with Neurological: syncope, memory, mm5floss, speech, are, special paralysis, senses trepalt, incootdinatio<difficulties GU: .."5 Other: to Psychtstrie: sperly, do on, nerv,sicss xxxxxDate of Service: December 5, 2012 Level of Exam PerfoCm and Document Problem Focused One to five element% identified by a bullet Expanded Problem Focused At look six elements identified by a bullet Detailed At ieht twelve elements identified by a but let Comprehensive Oerform all elements identified by a bullet; document every element in every heavy box and at least on dement in every regular box HEAD AND FACE NECK Normocephalic • Atraumatic .[•••""" I _— SuppleY Trache N N Full range of M *Lesions: Palpation Y it or percussion Masses: sinus tendern Masses: Laryngeal Y s ton is without crepitutrON *Facial asymmetry or weakness noted: -Parotid tender & Y s Obvious dibular nodularity glands: metric masses __left asymmetric right *Enlarged Tender: Y thyr sea: .,- 1 /igeht left both lobes EARS, NOSE, MOUTH at THROAT EARS: NOSE: *External External Tympanic Effusion: Infection: Foreign Dearing: Examined be. , auditory Y a m under ted microscope: canals es in: Ye intact: 01 Tuning Lesions left left left left ar Y rit right right tight N right sw/Fingc Masses _oebilaterally bilaterally _Lbilaterally ear Scrtum Turbirtates Mucosa Dnamage: Foreign Nose: Masses: Scars intact bod is appear ' Y and N and ey describe midline: hypertro moist: in Lesions ibe left flare Y deviated N ET Masses right nate We) .... copy: movement good / / none left right blisters? TMJ Tubes Cen Removed MOUTH/THROAT: Tongue is mobile: se in Y Lips are symmetrical: N Lesions az. Nasopharynx Eustachian Drainage: Posterior Defenod Buccal Other: Masses/lesion describe Posterior Tonsils mucosa are: chrome phaea/ tubes with N tont is are: pink Unable Manes: adenoids mirror floor and I? to except drains moist: are: of Lock-- detenni mouth, for :hildren); e ?Tani/soft Y Lesions: Y pais / / N N Y-62 *Larynx Masses: Nodules, Interarytenoid Pyriform Vocal Epiglottis Vallecula Deferred: Teeth Healthy cords in (with YIN gums: erythema, good Sinuses: clear/trio with Y approx. mirror / cobblestoning: N repair sharp describe Masses masses: Unable edema except well borders: with to N Y for N Y determine / IN good N dentures children): other mobility: CPIN Y Y Y Y / / I / N N N N Nasopharyngoscopy: Rea Cardiovascular CTA Resp Effort Bilatera Lungs Chest Abeam wa symmetric Sounds AVID 'nevem -Exam Temp, Peripheral pulse, edema, Vascular tenderness by ObservatioaN *Mood %Cranial -Alert Ocular arid and mobtility nerves oriented affect: 2-12 goo0 x intact 3. and N no Eyes -, se Infantrfoddler a ory alignment deficits noterlaPN no alert Neurological/Psychiatric Y / N Other: *Lymph *PAR, Finger-to-nose: Gait Heel- Dlisdiadoehokinesia: no nodes toe murmurs, maneuver: neck: N clicks, bettor, Level of Service PR How a Jeopardy-winning machine makes the World a Smarter Place Kim Escherich, Executive Innovation Architect
  • 2. © 2013 International Business Machines Corporation2 What are the trends, requirements and challenges? What is Watson and how does it work? How is Watson solving real-world problems?
  • 3. Minneapolis Interactive Macro-Mood Installation Placemeter: Real-World Analytics Gaian: Dynamic Distributed Federated Database What happpens in 60 seconds? University of Washington: Brain-to-brain communications
  • 4. © 2013 International Business Machines Corporation4 Big Data: We are still at the beginning 2010 VolumeinExabytes 9000 8000 7000 6000 5000 4000 3000 2015 Percentage of uncertain data Percentofuncertaindata 100 80 60 40 20 0 Sensors & Devices VoIP Enterprise Data Social Media Source: IBM Global Technology Outlook - 2012 You are here
  • 5. © 2013 International Business Machines Corporation5 Businesses are “dying of thirst in an ocean of data” 1 in 2 business leaders don’t have access to data they need 83% of CIOs cited BI and analytics as part of their visionary plan 2.2X more likely that top performers use business analytics 80% of the world‟s data today is unstructured 90% of the world‟s data was created in the last two years 1 Trillion connected devices generate 2.5 quintillion bytes data / day
  • 6. © 2013 International Business Machines Corporation6 Big data changes everything: From forecasting to nowcasting Volume Terabytes to exabytes of existing data to process Velocity Streaming data, milliseconds to seconds to respond Variety Structured, unstructured, text and multimedia Veracity Uncertainty from inconsistency, ambiguities, etc.
  • 7. Hello, Dave. You're looking well today.
  • 8. © 2013 International Business Machines Corporation8 Reason Learn Never forget A cognitive system is built upon brain-inspired technologies that… 1 It serves to improve discovery and decision-making… By augmenting human ability with systems that embody deep domain knowledge 2 It interacts naturally in partnership with humans on human terms Using conversational natural language using textual, audible, visual and haptic interfaces 3
  • 9. 9 “Can we design a computing system that rivals a human‟s ability to answer questions posed in natural language, interpreting meaning and context and retrieving, analyzing and understanding vast amounts of information in real-time?”
  • 10. ‟ http://www.youtube.com/watch?v=WFR3lOm_xhE
  • 11. © 2013 International Business Machines Corporation11 The Jeopardy! Challenge Broad/Open Domain Complex Language High Precision Accurate Confidence High Speed $600 In cell division, mitosis splits the nucleus & cytokinesis splits this liquid cushioning the nucleus $200 If you're standing, it's the direction you should look to check out the wainscoting. $2000 Of the 4 countries in the world that the U.S. does not have diplomatic relations with, the one that’s farthest north $1000 The first person mentioned by name in ‘The Man in the Iron Mask’ is this hero of a previous book by the same author.
  • 12. © 2013 International Business Machines Corporation12 Understands natural language and human communication Adapts and learns from user selections and responses Generates and evaluates evidence-based hypothesis IBM Watson combines transformational technologies 1 2 3
  • 13. © 2013 International Business Machines Corporation13 Informed decision making: search vs. Watson Decision Maker Search Engine Finds Documents Containing Keywords Delivers Documents Based on Popularity Has Question Distills to 2-3 Keywords Reads Documents, Finds Answers Finds & Analyzes Evidence Watson Understands Question Produces Possible Answers & Evidence Delivers Response, Evidence & Confidence Analyzes Evidence, Computes Confidence Asks NL Question Considers Answer & Evidence Decision Maker
  • 14. © 2013 International Business Machines Corporation14 Jeopardy! covers a very broad domain IBM Confidential
  • 15. © 2013 International Business Machines Corporation15 Different Types of Evidence: Keyword Evidence celebrated India In May 1898 400th anniversary arrival in Portugal India In May Garyexplorer celebrated anniversary in Portugal Keyword Matching Keyword Matching Keyword Matching Keyword Matching Keyword Matching 15 arrived in In May, Gary arrived in India after he celebrated his anniversary in Portugal. In May 1898 Portugal celebrated the 400th anniversary of this explorer’s arrival in India. Evidence suggests “Gary” is the answer BUT the system must learn that keyword matching may be weak relative to other types of evidence
  • 16. © 2013 International Business Machines Corporation16 On 27th May 1498, Vasco da Gama landed in Kappad Beach On 27th May 1498, Vasco da Gama landed in Kappad Beach celebrated May 1898 400th anniversary arrival in In May 1898 Portugal celebrated the 400th anniversary of this explorer’s arrival in India. Portugal landed in 27th May 1498 Vasco da Gama Temporal Reasoning Statistical Paraphrasing GeoSpatial Reasoning explorer On 27th May 1498, Vasco da Gama landed in Kappad BeachOn the 27th of May 1498, Vasco da Gama landed in Kappad Beach Kappad Beach Para- phrase s Geo- KB Date Math 16 India Stronger evidence can be much harder to find and score. The evidence is still not 100% certain. Search Far and Wide Explore many hypotheses Find Judge Evidence Many inference algorithms Different Types of Evidence: Deeper Evidence
  • 17. © 2013 International Business Machines Corporation17 Inquiry Decomposition Answer Scoring Models Responses with Confidence Inquiry Evidence Sources Models Models Models Models ModelsPrimary Search Candidate Answer Generation Hypothesis Generation Hypothesis and Evidence Scoring Final Confidence Merging & Ranking Synthesis Answer Sources Inquiry/Topic Analysis Evidence Retrieval Deep Evidence Scoring Learned Models help combine and weigh the Evidence Hypothesis Generation Hypothesis and Evidence Scoring How Watson works: DeepQA Architecture 1000’s of Pieces of Evidence Multiple Interpretations of a question 100,000’s Scores from many Deep Analysis Algorithms 100’s sources 100’s Possible Answers Balance & Combine
  • 18. © 2013 International Business Machines Corporation18 Automatic Learning From “Reading” Officials Submit Resignations (.7) People earn degrees at schools (0.9) Inventors patent inventions (.8) Volumes of Text Syntactic Frames Semantic Frames Vessels Sink (0.7) People sink 8-balls (0.5) (in pool/0.8) Fluid is a liquid (.6) Liquid is a fluid (.5)
  • 19. © 2013 International Business Machines Corporation19 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Precision % Answered Baseline 12/06 v0.1 12/07 v0.3 08/08 v0.5 05/09 v0.6 10/09 v0.8 11/10 v0.4 12/08 v0.2 05/08 V0.7 04/10 Watson playing in the ”winners cloud” DeepQA: Incremental Progress in Answering Precision on the Jeopardy Challenge: 6/2007-11/2010
  • 20. © 2013 International Business Machines Corporation20 Brief History – and future - of IBM Watson R&D Demonstration Commercialization Cross-industry Applications IBM Research Project (2006 – ) Jeopardy! Grand Challenge (Feb 2011) Watson for Healthcare (Aug 2011 –) Watson Industry Solutions (2012 – ) Watson for Financial Services (Mar 2012 – ) Expansion
  • 21. 21 So…, What to use it for?
  • 22. © 2013 International Business Machines Corporation22 1 in 5 diagnosis that are estimated to be inaccurate or incomplete 1.5 million errors in the way medications are prescribed, delivered and taken in the U.S. every year 44,000 -98,000 # of Americans who die each year from preventable medical errors in hospitals alone 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: International Journal of Circumpolar Health, DoctorDirectory.com, Institute for Medicine" Healthcare Industry is beset with some of the most complex information challenges we collectively face
  • 23. © 2013 International Business Machines Corporation23 Understands natural language questions  What condition has red eye, pain, inflammation, blurred vision, floating spots and sensitivity to light?  Physician Notes, Medical Journals, Clinical Trials, Pathology Results, Blogs, Wikipedia Analyzes large volumes of unstructured data   Possible Diagnosis Confidence Uveitis 91% Iritis 48% Keratitis 29% Generates and evaluates hypothesis Presents responses with confidence  Family History, Patient Interview, Physical Exam, Current Medications Supports iterative dialogue to refine results What actions were taken? What treatments were prescribed? What was the outcome?Learns from results over time Why is Watson Technology ideal for healthcare?
  • 24. © 2013 International Business Machines Corporation24 Putting the Pieces Together Can Be Life Changing Symptoms UTI Diabetes Influenza hypokalemia Renal failure no abdominal pain no back pain no cough no diarrhea (Thyroid Autoimmune) Esophagitis pravastatin Alendronate levothyroxine hydroxychloroquine Diagnosis Models frequent UTI cutaneous lupus hyperlipidemia osteoporosis hypothyroidism Confidence difficulty swallowing dizziness anorexia fever dry mouth thirst frequent urination Family History Graves’ Disease Oral cancer Bladder cancer Hemochromatosis Purpura Patient HistoryMedicationsFindings supine 120/80 mm HG urine dipstick: leukocyte esterase urine culture: E. Coli heart rate: 88 bpm Symptoms A 58-year-old woman complains of dizziness, anorexia, dry mouth, increased thirst, and frequent urination. She had also had a fever. She reported no pain in her abdomen, back, and no cough, or diarrhea. A 58-year-old woman presented to her primary care physician after several days of dizziness, anorexia, dry mouth, increased thirst, and frequent urination. She had also had a fever and reported that food would “get stuck” when she was swallowing. She reported no pain in her abdomen, back, or flank and no cough, shortness of breath, diarrhea, or dysuria Family History Her family history included oral and bladder cancer in her mother, Graves' disease in two sisters, hemochromatosis in one sister, and idiopathic thrombocytopenic purpura in one sister Patient History Her history was notable for cutaneous lupus, hyperlipidemia, osteoporosis, fr equent urinary tract infections, a left oophorectomy for a benign cyst, and primary hypothyroidism, diagnosed a year earlier Her medications were levothyroxine, hydroxychloroquine, pr avastatin, and alendronate. MedicationsFindings A urine dipstick was positive for leukocyte esterase and nitrites. The patient given a prescription fo ciprofloxacin for a urinary tract infection. 3 days later, patient reported weakness and dizziness. Her supine blood pressure was 120/80 mm Hg, and pulse was 88. • Extract Symptoms from record • Use paraphrasings mined from text to handle alternate phrasings and variants • Perform broad search for possible diagnoses • Score Confidence in each diagnosis based on evidence so far • Identify negative Symptoms • Reason with mined relations to explain away symptoms (thirst is consistent w/ UTI) • Extract Family History • Use Medical Taxonomies to generalize medical conditions to the granularity used by the models • Extract Patient History• Extract Medications • Use database of drug side-effects • Together, multiple diagnoses may best explain symptoms • Extract Findings: Confirms that UTI was present Most Confident Diagnosis: DiabetesMost Confident Diagnosis: UTIMost Confident Diagnosis: EsophagitisMost Confident Diagnosis: Influenza
  • 25. © 2013 International Business Machines Corporation25 Significant investments in cognitive computing across industries Self-help, customer contact center, multichannel • Converses with customer by speech, text, images • Diagnoses • Pulls feedback from docs, social media • Gets smarter SMARTER COMMERCE SMARTER HEALTHCARE SMARTER EDUCATION SMARTER FINANCIAL SERVICES Wellness, doctor-patient relation, outpatient medical home • Monitor devices and patient feedback for risk analysis • Evidence-based tips from probable outcomes • Engages patient • Builds insight into patient Next Gen University, personal coach, professional devt. • Recommend career based on background & demand • Display courses with evidence and social feedback • Monitor progress Market analysis, predict risk/return, supply new textual factors • Monitor market and textual indicators • Provide evidence-based recommendations • Explain factors
  • 26. Kim Escherich escherich@dk.ibm.com +45 2880 4733 internetofthings.dk escherich.biz @kescherich /escherich /in/escherich kescherich@gmail.com We have only just begun to build a new era of computing powered by cognitive systems  Transforming how organizations think, act, and operate  Learning through interactions  Delivering evidence based responses driving better outcomes