Semantic Natural Language Understanding with Spark, UIMA & Machine Learned Ontologies
- 1. David Talby @davidtalby CTO, Atigeo SEMANTIC NATURAL LANGUAGE UNDERSTANDING WITH SPARK, UIMA & MACHINE-LEARNED ONTOLOGIES Claudiu Branzan @melcutz Principal Lead, Atigeo
- 2. 2 2 THE PROBLEM Who needs to be vaccinated? Who fits this clinical trial? Who is at risk for sepsis? Who is getting meds they’re allergic to? Who on this protocol did not have this side effect?
- 3. 3 AT THE BEGINNING, THERE WAS SEARCH Scalable & robust Indexing pipeline Tokenizers & analyzers Synonyms, spellers & Auto-suggest File formats & header boosting Rankers, link & reputation boosting
- 4. 4 THEN THERE WAS SEMANTIC SEARCH “cheap red prom dresses” “laptops under $500” “italian restaurants near me that deliver” “captain america civil war tonight” “nba scores” Dictionary Based Attribute Extraction Dell - XPS 15.6 4K Ultra HD Touch-Screen Laptop - Intel Core i5 - 8GB Memory - 256GB Solid State Drive - Silver Machine Learned Attribute Extraction If you go for the ambience, you'll be disappointed. If you go for good, inexpensive and authentic Mexican food, then you're in the right place.
- 5. 5 AND THEN, YOU NEED TO UNDERSTAND LANGUAGE Prescribing sick days due to diagnosis of influenza. Positive Jane complains about flu-like symptoms. Speculative Jane may be experiencing some sort of flu episode. Possible Jane’s RIDT came back negative for influenza. Negative Jane is at high risk for flu if she’s not vaccinated. Conditional Jane’s older brother had the flu last month. Family history Jane had a severe case of flu last year. Patient history
- 6. 6 LANGUAGE GETS COMPLEX & DOMAIN SPECIFIC Joe expressed concerns about the risks of bird flu. Nothing Joe shows no signs of stroke, except for numbness. Double Negative Nausea, vomiting and ankle swelling negative. Compound (it gets worse – in reality a lot of text isn’t valid English) Patient denies alcohol abuse. Speculative Allergies: Penicillin, Dust, Sneezing. Compound
- 7. 7 7 LET’S BUILD THIS! The input (patient records) The processing framework The output The query engines
- 8. 8 8 SENTENCE DETECTION SECTION DETECTION TOKENIZER LEMMATIZER STOPWORD REMOVAL NEGATION DETECTION CONDITIONAL SCOPE SPECULATIVE SCOPE DATE NUMBER UNIT QUANITITY CONCEPT EXTRACTION
- 9. 9 9 First Demo: Annotators & Assertions
- 10. 1 0 10 MACHINE LEARNED ANNOTATORS Grammatical Patterns If … then … Direct Inferences Age < 18 ==> Child Lookups RIDT (lab test) Under-diagnosed conditions Flu Depression Implied by Context relevant labs normal Sometimes, it’s easier to just code an annotation’s business logic But sometimes it’s easier to learn it from examples:
- 11. 1 1 11 Second Demo: Machine Learned Annotator
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- 13. 1 3 13 WHAT ABOUT EXPANDING & UPDATING ONTOLOGIES? Word2Vec
- 14. 1 4 14 LET’S BUILD THIS TOO!
- 15. 1 5 15 Third Demo: Ontology Enrichment
- 16. 1 6 16 SUMMARY & APPLICATIONS Who needs to be vaccinated? Who fits this clinical trial? Who is at risk for sepsis? Who is getting meds they’re allergic to? Who on this protocol did not have this side effect?
- 18. © 2015 Atigeo, Corporation. All rights reserved. Atigeo and the xPatterns logo are trademarks of Atigeo. The information herein is for informational purposes only and represents the current view of Atigeo as of the date of this presentation. Because Atigeo must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Atigeo, and Atigeo cannot guarantee the accuracy of any information provided after the date of this presentation. ATIGEO MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.
- 19. APPENDIX In case the live demo gets cold feet on stage 1 9
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