Using Machine Learning in Healthcare

Nigam Shah, MBBS, PhDnigam@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 -11 0 -1

1 0 -11 0 -1

1 0 -1

Pe

rson

s

Features

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

Claims Quantified self

Answering Clinical Questions

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 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”?

Generating Insights

250 million patientsHripcsak 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!

Deceased131,006 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 death3-12 months

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)

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 actionReg, the need for interpretabilityReg, 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

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