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Will Yu of Lumiata provides an overview of using real-time big analytics with ever-learning graph combining hundreds of healthcare data sets. Presented at YTH Live 2014 plenary session "Mapping Big ...

Will Yu of Lumiata provides an overview of using real-time big analytics with ever-learning graph combining hundreds of healthcare data sets. Presented at YTH Live 2014 plenary session "Mapping Big Data, Infographics and other Good Stuff."

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    Lumiata Lumiata Presentation Transcript

    • helping you revolutionize human health management by combining the best of Data Science + Medical Science Wil Yu – wil@lumiata.com
    • what: ensuring the best of medical science is applied in every patient interaction (for both acute and chronic care) how: using real-time big analytics with ever-learning graph combining hundreds of healthcare data sets and over 22,000+ physician hours of curation raw big data processed into curated, usable real-time big knowledge 2
    • To realize this vision we combined graph analytics & medical science 5 The Medical Graph allows Lumiata to apply the same transformational techniques to health analytics that Google applied to web search
    • Raw Data Demographic s Previous Diagnoses Labs Meds Step 2a Apply the Graph of Medicine (160M+ data points to connect and apply medical science to health data) Step 2b Generate GapDx by lab, med, criteria, and Graph Step 1 Convert raw data into longitudinal per patient data How we conduct our analyses… Step 3 HCC Analysis Step 4 Patient Story & Opportunity Report 6
    • Graph analytics + medical science symptom diagnosis Labs Imaging Tx ProcsMeds Envir. Factors, Seasons lifestyle Types of Nodes symptom diagnosis Labs ∫(age, gender, duration, ethnicity, …) diagnosis ∫(age, gender, sensitivity, specificity,,…) Types of Edges Enabling us to create a per patient micro-graph and its extension enabling a personalized markov model analytics for example. 7
    • 8 Key Diagnoses via Graph Analytics depression alcoholism schizophrenia bipolar disorder general anxiety disorder diabetes mellitus type 2 dysthymic disorder anxiety post traumatic stress disorder myocardial infarction major depressive disorder obsessive compulsive disorder obesity nicotine addiction metabolic syndrome hypothyroidism hypertension obstructive sleep apnea mania chronic obstructive pulmonary disease hypercholesterolemia Zooming into a part of Lumiata’s Medical Graph
    • Patient Story Per Patient From raw data to a conscience current and probable future patient story Multiple raw data files with scattered patient data Lumiata generates a per patient JSON object Using Lumiata Medical Graph a per patient story is generated 9
    • Health Systems & Risk Bearers Emerging, HIT Develprs delivered through API: machine to machine interface to simplify use – application programmers interface App: web and mobile software for specific purposes 10 Apps & APIs … all of which for individuals and populations are
    • helping you revolutionize human health management by combining the best of Data Science + Medical Science Wil Yu – wil@lumiata.com