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Knowledge graph construction for research & medicine
1) Elsevier aims to build knowledge graphs to help address challenges in research and medicine like high drug development costs and medical errors. 2) Knowledge graphs link entities like people, concepts, and events to provide answers by going beyond traditional bibliographic descriptions. 3) Elsevier constructs knowledge graphs using techniques like information extraction from text, integrating data sources, and predictive modeling of large patient datasets to identify statistical correlations.
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Knowledge graph construction for research & medicine
- 1. KNOWLEDGE GRAPH CONSTRUCTION FORRESEARCH & MEDICINE Paul Groth (@pgroth) pgroth.com Disruptive Technology Director Elsevier Labs (@elsevierlabs) Connected Data London 2017 Contributions: Brad Allen, Pascal Coupet, Sujit Pal, Craig Stanley, Ron Daniel, Alex de Jong
- 2. Our customers arefacing challenges in science and health 1. Industrial Research Institute 2. The Lancet 3. Tufts 4. World Health Organization Elsevier is in a unique position to make a contribution towards solving these challenges Life-saving drugs are expensive to develop.3 Global research spend is growing every year.1 3.4% from 2015 Predicted spend $1.9TN research in 2016 Studies: 70-80% of research asks the wrong questions or cannot be reproduced Researchers lack the tools they need to be effective.2 Preventable medicalerrors: Third largest cause of death in theUS Health providers cannot save lives without the best information.4 $2.5BN median pharmaceutical spend per drug 1/20 successrate of drugs Heart Disease 611k Cancer 585k Medical Error 225k 149k Respiratory Illness
- 3. ELSEVIER’S BUSINESS: PROVIDINGANSWERS FOR RESEARCHERS, DOCTORS AND NURSES My work is moving towards a new field; what should I know? • Journal articles, reference works, profiles of researchers, funders & institutions • Recommendations of people to connect with, reading lists, topic pages How should I treat my patient given her condition & history? • Journal articles, reference works, medical guidelines, electronic health records • Treatment plan with alternatives personalized for the patient How can I master the subject matter of the course I am taking? • Course syllabus, reference works, course objectives, student history • Quiz plan based on the student’s history and course objectives
- 4. THE ROLE OFMETADATA IN THE SECOND MACHINE AGE – DC-2016 / KØBENHAVN / 13 OCTOBER ANSWERS ARE ABOUT THINGS, NOT JUST WORKS Why shouldn’t a search on an author return information about the author, including the author’s works? Where was the author born, when did she live, what is she known for? … All of this is possible, but only if we can make some fundamental changes in our approach to bibliographic description. ... The challenge for us lies in transforming what we can of our data into interrelated “things” without overindulging that metaphor. Coyle, K. (2016). FRBR, before and after: a look at our bibliographical models. Chicago: ALA Editions.
- 5. THE ROLE OFMETADATA IN THE SECOND MACHINE AGE – DC-2016 / KØBENHAVN / 13 OCTOBER KNOWLEDGE GRAPHS DEFINED • Knowledge graphs are "graph structured knowledge bases (KBs) which store factual information in form of relationships between entities” (Nickel, M., Murphy, K., Tresp, V. and Gabrilovich, E. (2015). A review of relational machine learning for knowledge graphs. arXiv:1503.00759v3) • Knowledge graphs are metadata evolved beyond the focus on the work, linking people, concepts, things and events • Knowledge Graphs are focused on things to provide answers
- 9. THE ROLE OFMETADATA IN THE SECOND MACHINE AGE – DC-2016 / KØBENHAVN / 13 OCTOBER ELSEVIER’S KNOWLEDGE PLATFORM Products Data & Content Sources Knowledge Graphs Platforms & Shared Services Entity Hubs Usage logs Pathways EHRsArticles Authors Institutions SyllabiCitations ChemicalsBooks DrugsFunders Funder Hub Article HubProfile Hub Journal Hub Institution Hub Research HealthcareLife Sciences Content Life Sciences Search IdentityResearch Reaxys CK SherpathScopus SD ROS
- 10. THE ROLE OFMETADATA IN THE SECOND MACHINE AGE – DC-2016 / KØBENHAVN / 13 OCTOBER THE GROWTH OF SCIENCE COMPLICATES OUR EFFORTS
- 11. MORE DOMAINS &MORE SPECIFICITY Gregory, K., Groth, P., Cousijn, H., Scharnhorst, A., & Wyatt, S. (2017). Searching Data: A Review of Observational Data Retrieval Practices. arXiv preprint arXiv:1707.06937. Some observations from @gregory_km survey: 1. The needs and behaviours of specific user groups (e.g. early career researchers, policy makers, students) are not well documented. 2. Background uses of observational data are better documented than foreground uses. 3. Reconstructing data tables from journal articles, using general search engines, and making direct data requests are common.
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- 15. HOW - OVERVIEW Content Books,Articles, Ontologies ... • Identification of concepts • Disambiguation • Domain/sub-domain identification • Abbreviations, variants • Gazeteering • Identification and classification of text snippets around concepts • Features building for concept/snippet pairs • Lexical, syntactic, semantic, doc structure … • Ranking concept snippet pairs • Machine learning • Hand made rules • Similarities • Deduplication Technologies NLP, ML • Curation • White list driven • Black list • Corrections/improve ments • Evaluation • Gold set by domain • Random set by domain • By SMEs (Subject Matter Experts) • Automation • Content Enrichment Framework • Taxonomy coverage extension Knowledge Graph Concepts, snippets, meta data, …
- 16. | 16 OmniScience Neuros cience Extension vocabulariesby domains to provide coverage Number of Concepts Number of Labels OmniScience 01.16.11 45969 47421 OmniScience Neuroscience branch 21/11/2016 2356 2455 OmniScience Extension Neuroscience branch 21/11/2016 23932 101276
- 17. | 17 Concept BadGood Inferior Colliculus By comparing activation obtained in an equivalent standard ( non-cardiac-gated ) fMRI experiment , Guimaraes and colleagues found that cardiac- gated activation maps yielded much greater activation in subcortical nuclei , such as the inferior colliculus . The inferior colliculus (IC) is part of the tectum of the midbrain (mesencephalon) comprising the quadrigeminal plate (Lamina quadrigemina). It is located caudal to the superior colliculus on the dorsal surface of the mesencephalon ( Figure 36.7 FIGURE 36.7Overview of the human brainstem; view from dorsal. The superior and inferior colliculi form the quadrigeminal plate. Parts of the cerebellum are removed.). The ventral border is formed by the lateral lemniscus. The inferior colliculus is the largest nucleus of the human auditory system. … Purkinje cells It is felt that the aminopyridines are likely to increase the excitability of the potassium channel- rich cerebellar Purkinje cells in the flocculus ( Etzion and Grossman , 2001 ) . Purkinje cells are the most salient cellular elements of the cerebellar cortex. They are arranged in a single row throughout the entire cerebellar cortex between the molecular (outer) layer and the granular (inner) layer. They are among the largest neurons and have a round perikaryon, classically described as shaped “like a chianti bottle,” with a highly branched dendritic tree shaped like a candelabrum and extending into the molecular layer where they are contacted by incoming systems of afferent fibers from granule neurons and the brainstem… Olfactory Bulb The most common sites used for induction of kindling include the amygdala, perforant path , dorsal hippocampus , olfactory bulb , and perirhinal cortex. The olfactory bulb is the first relay station of the central olfactory system in the vertebrate brain and contains in its superficial layer a few thousand glomeruli, spherical neuropils with sharp borders ( Figure 1 Figure 1Axonal projection pattern of olfactory sensory neurons to the glomeruli of the rodent olfactory bulb. The olfactory epithelium in rats and mice is divided into four zones (zones 1–4). A given odorant receptor is expressed by sensory neurons located within one zone of the epithelium. Individual olfactory sensory neurons express a single odorant receptor… Examples of good and bad snippets
- 19. 19 One Weird Trickfrom Natural Language Processing (NLP) • Knowledge bases are populated by scanning text and doing Information Extraction • Most information extraction systems are looking for very specific things, like drug-drug interactions • Best accuracy for that one kind of data, but misses out on all the other concepts and relations in the text • For broad knowledge base, use Open Information Extraction that only uses some knowledge of grammar • The weird trick for open information extraction … a simple algorithm, known as ReVerb*: 1. Find “relation phrases” starting with a verb and ending with a verb or preposition 2. Find noun phrases before and after the relation phrase 3. Discard relation phrases not used with multiple combinations of arguments. In addition, brain scans were performed to exclude other causes of dementia. * Fader et al. Identifying Relations for Open Information Extraction
- 20. 20 ReVerb output # SDDocuments Scanned 14,000,000 Extracted ReVerb Triples 473,350,566
- 21. 21 Universal schemas –Predict ‘missing‘ KG facts • Make a matrix: • columns for the relation phrases from ReVerb or the semantic relations from EMMeT • rows are the pairs of concepts linked by a relation • A ‘1.0’ in a cell if those concepts were linked by that relation • Outlined cells in diagram are the ones initialized to 1. • Factorize matrix to ExK and KxR, then recombine. • “Learns” the correlations between text relations and EMMeT relations, in the context of pairs of objects. • Cells going from 0 to > 0 indicates potential. • Find new triples to go into EMMeT e.g., (glaucoma, has_alternativeProcedure, biofeedback)
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- 23. 23 Medical Graph –Statistical correlations at scale I65 Occlusion and stenosis of precerebral arteries G40 Epilepsy has_successor I61 C71 Malignant neoplasm of brain odds ratio: 1.12 intracerebral hemorrhage has_successor criteria1: • Correlation selected by preditive modeling algorithmus • No. of relations is higher than in mirrored relation • p-value < 0,05 • Odds ratios balanced over all covariates. 1 Criteria based on: Jensen et.al.: Temporal disease trajectories condensed from population-wide registry data covering 6.2 million patients. Nature Communications, 2014 Jun 24 ;5:4022. doi: 10.1038/ncomms5022. Other covariates Primary care Secondary care Drug prescriptions 5m patients each 6 years longitudinality
- 24. 24 Medical Graph inpractice, patient 35: risk of depression • 49 year old man • Dx: overweight, diabetes, hypertension, anxiety disorder has an absolute risk of 36% to develop a depression within the next 4 years
- 25. 25 … and rationaleof why model thinks this
- 26. 26 • Targets forprediction: ICD-coded diagnoses • Only incident patients per diagnose considered, i.e. diagnosis-free 2009 – 2010 • if these patients remain diagnosis-free 2011 - 2014 (observation period), then 0 else 1 • Covariates: all ICD-/ATC-codes, age and sex measured in 2010 Example: Model to predict „I50 – Heart Failure“ 26 Analysis Design Predict 4 year long-term effects, balanced for all co-variables I50 - I50 free patients 2009 2010 time I50 - (coded as 0) I50 + (coded as1) 2011 2014 Covariates Remaining I50 free patients/ newly I50 diagnosed patients
- 27. 27 27 (A) integrate &clean Research on anonymized claims data Primary care Secondary care Drug prescriptions Other data Visits & diagnoses Visits, diagnoses & procedures Drug presciptions Further cooperations just started Will enable analysis of vital and laboratory parameters Data integration & cleaning • Data cleaning • Longitudinally linked & integrated for analytics • Anonymized 6 Mio patients 6 years > 1.5b events Billing data flow 60+ sickness funds
- 28. 28 Technology stack feature extraction For 3.8m patients: •age, gender • all diagnoses: ICD10-coded, 3 digits, i.e. 2054 codes • all medications: ATC-coded, 5 digits, i.e. 906 codes • death, hospitalization Results in: 6277 features • 1623 targets, 2011-2014 • 2320 covariates, 2010 • 2334 filter-columns, 2009-2010 data mining Calculate prevalence, incidence, mean age for all covariates (i.e. diseases and medications) machine learning Predictive modelling for ~1600 targets • Linear classification model, resulting in odds ratios • Calculation of p-values (B) mine & learn Calculate statistics & build prediction models for ~1600 targets
- 29. KNOWLEDGE GRAPHS AS ABACKBONE 29
- 31. • Total concepts= 540,632 • 100+ person years of clinical expert knowledge EMMeT Ontology
- 32. EXPANDING TO INCLUDEIMAGE SNIPPETS
- 33. 33 Enrichment though integrationusing linked data
- 34. CONCLUSION • Knowledge graphsare critical components for delivering customer value • AI techniques such as machine learning and predictive modelling from data are key parts of knowledge graph construction • This is particularly the case as the amount, speed and specificity of data and requirements accelerates • Leveraging existing assets such as ontologies, data, and controlled (i.e. connected data) have been key assets for Elsevier in the build out of knowledge graphs • Another talk is how all this enables intelligent based solutions • Oh and we are hiring • Paul Groth p.groth@elsevier.com
Editor's Notes
- #12 Work with dans Reviewed 400 papers deep dive 114