Calling Dr Watson To Radiology - RSNA Presentation


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  • Background on technology and Watson/Jeopardy and the data Tsunami we face in h/cHow Deep QA WorksDeep QA applied to HealthcareCurrent Example of Medical Intelligence (CTRM)Future Use Cases
  • Brent James, MD, MStat, Chief Quality Officer, Intermountain Health Care; subject of The New York Times article “If Health Care is Going to Change, Dr. Brent James Will Lead the Way”
  • Massively Parallel Probabilistic Evidence-Based Architecture  This is like looking inside the brain of the DeepQA system from about 30,000 feet high.
  • Notes: This is easy if you know that Charles Dickens wrote Victorian literature. This is not part of medical inference, though, so we do not cover that, and an incorrect answer is preferred because its passage matched the query better. Without knowing about Victorian literature, there is not enough other information in the question to reliably find the correct answer.
  • Incidentalomas
  • doi: 10.4065/ 81.3.338Mayo ClinicProceedings March 2006 vol. 81 no. 3 338-344 ClinProc. 2006;81(3):338-344
  • Calling Dr Watson To Radiology - RSNA Presentation

    1. 1. Calling Dr Watson to RadiologyWatson in Healthcare from NuanceNick van Terheyden, M.D.Chief Medical Information Officer – Clinical Language UnderstandingNuance
    2. 2. Medicine used to be simple, ineffective and relatively safe. Now it is complex, effective and potentially dangerous Sir Cyril Chantler, Kings Fund Chantler C. The role and education of doctors in the delivery of health care. Lancet 1999;353:1178-81u
    3. 3. Information Overload – Big Data Medical information is doubling approximately every 5 years - Brent James, MD, MStat, Chief Quality Officer, Intermountain Health Care 1.8 zetabytes of information created this year – majority of it unstructured – 57 Billion 32Gb iPods (Source: IDC) - That’s enough information to fill 57 billion 32GB Apple iPads (which could build a mountain of iPads 25 times higher than Mt Fuji Watson can sift through 200 million pages in 3 secs
    4. 4. Watson DeepQAHOW DOES IT WORK
    5. 5. DeepQA: The Technology Behind Watson Learned Models help combine and weigh the Evidence Evidence Balance Sources & Combine Answer Models Models Sources DeepQuestion Answer Evidence Models Models Evidence Candidate Scoring Retrieval 100,000’s Scores from Primary 1000’s of Scoring many Deep Analysis Answer Models Models Search Pieces of Evidence Algorithms Generation 100’s Possible Answers Multiple 100’s Interpretations sourcesQuestion & Final Confidence Question Hypothesis Hypothesis and Evidence Topic Synthesis Merging & Decomposition Generation Scoring Analysis Ranking Hypothesis Hypothesis and Evidence Answer & Generation Scoring Confidence ...
    6. 6. ArchitectureUser ExperienceBy Nuance and Partners….. … of consumers – large and small CLU…… Cloud to Cloud DeepQA Solutions for ….community of HealthcareEMRs Content Publishers LargeInstitutiona … of l Providers CASE Content Partners
    7. 7. How Does Watson Perform inHealthcare
    8. 8. Question and Answer SetsSuccess Question: This hormone deficiency is associated with Kallmanns syndrome. - Passage: Isolated deficiency of GnRH or its receptor causes failure of normal pubertal development and amenorrhea in women. This disorder is termed Kallmann syndrome when it is accompanied by anosmia and has also been termed idiopathic hypogonadotropic hypogonadism (IHH).” Answer: GnRH Notes: We know that “GnRH” is a hormone (from the ontology) so that lets us choose it as the most likely answer.
    9. 9. Question and Answer SetsMiss Question: Eponym from Victorian literature for obesity hypoventilation syndrome. - Correct passage: Obesity-hypoventilation syndrome is also known as pickwickian syndrome, in reference to Charles Dickens’… - Correct answer: Pickiwickian Syndrome - Wrong passage: Other clinical features associated with obesity- hypoventilation syndrome are daytime hypersomnolence and cor pulmonale. - Wrong answer: cor pulmonale
    10. 10. Radiology Use Case Radiologists identifies radiological findings in a report - Hepatic Lesion in Woman with History of Breast CA - Too Small to Classify (TSTC) - Offer follow up and alternative assessment criteria helping classify more accurately for interval follow up or imaging work up Ask Watson - discrimination points, appropriate follow up, critical results etc Watson Consumes the report and patient context information - Offers next steps, differential list, new/recent information & links What possible next steps/studies would be appropriate to rule in/out diagnosis and/or determine best course of treatment
    11. 11. Generic Use Cases Medical Diagnosis - Consumption of medical records, results etc offering differential diagnosis and probability analysis with links to underlying literature sources - True personalization of medicine based on large cohort historical data analysis Interactive Model - Requires real time access to patient data from multiple sources including provider/patient interaction - Ongoing refinement based on dynamic interaction and learning
    12. 12. Challenges Ambiguous human language Integration with existing systems – extract of complete data set for history, results etc - Often in disparate systems - Non standard interfaces - Non standard format - Unstructured narrative Medical Data Sources and Ongoing Access and Updating Patient interaction with technology vs humans - Telemedicine and consumer trend towards home based care
    13. 13. Replacing the Doctor? Study done by the Mayo Clinic in 2006 identified the most important characteristics patients feel a good doctor must possess The Ideal clinician is - confident, - empathetic, - humane, - personal, - forthright, - respectful, and - thorough These facets are entirely human and will be hard for technology to replace Mayo Clin Proc. 2006;81(3):338-344
    14. 14. Calling Dr Watson to RadiologyWatson in Healthcare from NuanceNick van Terheyden, M.D.Chief Medical Information Officer – Clinical Language UnderstandingNuance