Using Machine Learning in Healthcare Construct a profile . of the disease by state. Meta analysis...

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Transcript of Using Machine Learning in Healthcare Construct a profile . of the disease by state. Meta analysis...

  • Using Machine Learning in Healthcare

    Nigam Shah, MBBS, PhD nigam@stanford.edu

  • Make predictions

    Answer clinical questions

    Generate insights

    To bring AI to the clinic, safely and ethically, in 3-5 years

  • Pi

    Healthcare happens over time

    Pi

    Disease x x

    Prescription x x x x x x x

    Procedures x x x x

    Imaging x x

    Billing (claims) x x x x x x x x

    Streaming data x x x x

    Genome x

    Pi

  • Timeless data frames

    1 0 -1 1 0 -1

    1 0 -1 1 0 -1

    1 0 -1

     Pe

    rs on

    s 

     Features 

    ProceduresDevicesDiseasesDrugs Sequence, Expression (gene, protein), Metabolites …

    Claims Quantified self

  • Answering Clinical Questions

  • http://greenbutton.stanford.edu

    http://greenbutton.stanford.edu/

  • The Green Button vision

    Consult Service

    Analysis + Report • The question as posed • How we asked the question • Our interpretation • Research walkthrough

  • http://greenbutton.stanford.edu

    www.tinyurl.com/search-ehr

  • Example consults

    Time to event rates

    Descriptive analyses

    Inferential analyses

    In adults with hypertrophic cardiomyopathy treated with beta blockers, or calcium channels blockers, is there a difference in time to atrial fibrillation, or heart failure?

    In patients with incidental interatrial septal aneurysms without other medical problems, what is the risk of thrombus (PE) or (CVA) with anticoagulation or antiplatelet med?

    In patients prescribed ibuprofen, is there any difference in peak blood glucose after treatment compared to patients prescribed acetaminophen?

  • Open questions • Other uses of the search engine • What is really useful?

    – Description of what happened – Estimation: Population or Individual level – Patient level prediction

    • Informatics research 1. Phenotyping (how do I know the patient had X) 2. Representation learning 3. Matching, and population level inference 4. Personalized effect estimates

    • Financial viability – who would want to pay for this “test”?

  • Generating Insights

  • 250 million patients Hripcsak et al, PNAS 2016

    What are doctors doing?

  • Does it work?

  • What diseases follow each other

    Patients with 2+ visits in 2009

    Construct a profile of the disease by state

    Meta analysis over all 50 states

  • 160 x 160 pairs of odds ratios

  • Predictive Modeling

  • Palliative care in the USA

    • 90 percent of hospitals (with > 300 beds) offer palliative care • 3.4 percent of admissions get palliative care. • 7.5 - 8.0 percent of admissions need palliative care.

  • Current “pull” model for Palliative Care

    Can you please see this patient?

    Sure!

    Deceased 131,006 6.51%

    with V66.7 4,657 3.55% with V66.7 at least

    6 mon prior to death 105 0.08%

  • How Palliative Care could work

    Good catch! I agree.

    I might be able to help this patient; what do you think?

    𝑓𝑓(�⃑�𝑋)

  • A predictive model for Palliative Care need

    1 year of: • Diagnoses • Medications • Encounters

    𝑓𝑓(�⃑�𝑋)

    For technical reasons, model: 𝑃𝑃 all causes mortality 1 year of medical records

  • A predictive model for mortality

    Patient’s Medical Record

    Prediction date

    Observation Window

    Time of death 3-12 months

  • Information flow

    SoM (R-IT) SHC (CBA)

    EPIC Caché

    Daily refresh

    Model Development with retrospective data

    Model Deployment with “live” data

    Pull data on recent admits

    Write results back to EDW

    CBA EDW

    Send recommendations to Palliative Care Team

    STRIDE

    𝑓𝑓(�⃑�𝑋)

  • Utility, Safety, Ethics

  • When you see a model, ask:

    Operational Medical

    Diagnostic

    Prognostic X

    Therapeutic

    • What is the kind of use case at hand?

    • Who will decide on the action to take?

    • What assumptions are being made?

    Reg, the existence of an alternative action Reg, the need for interpretability Reg, the incentives & ability to take an action

  • Training • AI in healthcare bootcamp • CI fellowship • BMI PhD program • CERC Fellowship

    Deployment • Palliative care • Length of stay

    Partnerships • Patient Guardian (Google) • Autoscribe (Google) • Elsevier (new)

    Designing for Utility • Cost of taking action • Logistics of taking action • Lead time of action • Frequency of action

    Let’s create the “how to” manual on incorporating AI technologies into clinical practice, safely and ethically

    Program for AI in healthcare

  • To bring AI to the clinic, safely and ethically, in 3-5 years

  • Acknowledgements Group Members:

    • Scientists: Ken Jung, Alison Callahan, Juan Banda, Jason Fries, Saurabh Gombar

    • Fellows: Rohit Vashisht, Azadeh Nikferjam, Katie Quinn • Engineer: Vladimir Polony • BMI Students: Sarah Poole, Alejandro Schuler, Vibhu

    Agarwal • Med Students: Mehr Kashyap, Tyler Bryant

    Alums: Anna Bauer-Mehren (Roche), Srini Iyer (Facebook), Amogh Vasekar (Citrix), Sandy Huang (Berkeley), Paea LePendu (Lexigram), Rave Harpaz (Oracle), Tyler Cole (Barrow Inst.), Sam Finlayson (Harvard), Will Chen (Yale), Yen Low (Netflix), Elsie Gyang (Fellowship in Surgery), Suzanne Tamang (Instructor)

    Collaborators: Purvesh Khatri, Tina Hernandez-Boussard, Winn Haynes, Kevin Nead, Nick Leeper, Madeleine Scott

    Funding: • NIH – NLM, NIGMS, NHGRI, NINDS, NCI, FDA • Stanford Internal – Dept. of Medicine, Population Health

    Sciences, Clinical Excellence Research Center, Dean’s office

    • Fellowships – Med Scholars, Siebel Scholars Foundation, Stanford Graduate Fellowship

    • Industry – Apixio, CollabRx, Healogics, Janssen R&D, Oracle, Baidu USA, Amgen

    IT: Alex Skrenchuk, SCCI team

    2017

    2014

    Using Machine Learning in Healthcare Slide Number 2 Healthcare happens over time Timeless data frames Answering Clinical Questions Slide Number 6 Slide Number 7 The Green Button vision Slide Number 9 Example consults Open questions Generating Insights Slide Number 13 Does it work? What diseases follow each other Slide Number 16 Slide Number 17 Predictive Modeling Palliative care in the USA Current “pull” model for Palliative Care How Palliative Care could work A predictive model for Palliative Care need A predictive model for mortality Information flow Utility, Safety, Ethics When you see a model, ask: Program for AI in healthcare Slide Number 28 Acknowledgements