using machine learning in healthcare · construct a profile . of the disease by state. meta...
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Make predictions
Answer clinical questions
Generate insights
To bring AI to the clinic, safely and ethically, in 3-5 years
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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
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Timeless data frames
1 0 -11 0 -1
1 0 -11 0 -1
1 0 -1
Pe
rson
s
Features
ProceduresDevicesDiseasesDrugs Sequence, Expression (gene, protein), Metabolites …
Claims Quantified self
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Answering Clinical Questions
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The Green Button vision
Consult Service
Analysis + Report• The question as posed• How we asked the question• Our interpretation• Research walkthrough
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http://greenbutton.stanford.edu
www.tinyurl.com/search-ehr
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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?
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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 research1. Phenotyping (how do I know the patient had X)2. Representation learning3. Matching, and population level inference4. Personalized effect estimates
• Financial viability – who would want to pay for this “test”?
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Generating Insights
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250 million patientsHripcsak et al, PNAS 2016
What are doctors doing?
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Does it work?
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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
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160 x 160 pairs of odds ratios
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Predictive Modeling
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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.
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Current “pull” model for Palliative Care
Can you please see this patient?
Sure!
Deceased131,006 6.51%
with V66.7 4,657 3.55%with V66.7 at least
6 mon prior to death 105 0.08%
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How Palliative Care could work
Good catch! I agree.
I might be able to help this patient; what do you think?
𝑓𝑓(�⃑�𝑋)
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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
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A predictive model for mortality
Patient’s Medical Record
Prediction date
Observation Window
Time of death3-12 months
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Information flow
SoM (R-IT) SHC (CBA)
EPICCaché
Daily refresh
Model Development with retrospective data
Model Deployment with “live” data
Pull data onrecent admits
Write resultsback to EDW
CBA EDW
Send recommendations to Palliative Care Team
STRIDE
𝑓𝑓(�⃑�𝑋)
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Utility, Safety, Ethics
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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 actionReg, the need for interpretabilityReg, the incentives & ability to take an action
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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
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To bring AI to the clinic, safely and ethically, in 3-5 years
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AcknowledgementsGroup 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), PaeaLePendu (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