How Machine Learning Enhances Healthcare

i’m going to talk to you a little bit

today

about machine learning for health and

as you have heard i do machine learning

i focus on artificial intelligence and

making these models

and you might be thinking why would you

do machine learning for health

how does that work isn’t machine

learning for self-driving cars why would

we want that as part of health

this is the far future that you’re

talking about

but actually machine learning and

algorithms are

a part of health care right now whether

we’re talking about the statistical

models that doctors currently use as

risk scores in the intensive care unit

or we’re talking about more advanced

high-capacity models that are being

trained

to understand what sort of risks are

relevant for patients and what sort of

treatments might be needed

machine learning and algorithms are a

part of health care

now you may be uh wondering well how are

you doing this

why do we have machine learning in

health and and how does it happen

generally how this works is we get

clinical data from

practice and from knowledge by practice

i mean

doctors they practice so if you could

look at clinical records from a hospital

from a clinic and see what sort of

treatments are given

how patients are interacting with a

healthcare team you could learn from

that practice

right but then there’s also knowledge

maybe we don’t just want to learn based

on how doctors are practicing

we may also want to look at the

knowledge that’s been generated

randomized controlled trials

rcts papers that are written textbooks

right we could learn from both of those

sources

once we have that data we can train

these simple statistical models

or more advanced high-capacity models

and then

we can predict things important clinical

events

forecasting treatments that a patient

might need those are really important

for

health care and progress so that’s what

could happen

in the future that’s what we’re working

on this is the goal of my group

what’s happening right now because the

data that we’re

getting to feed these models to learn

these rules

to train these algorithms that’s data

that we’re getting right now

and so the question that you should be

asking if we’re learning from

current practice and knowledge is how

much

do you trust your doctors because we’re

learning from clinical practice

we’re learning from doctors who are

burnt out

because we’re asking them to do a huge

set

of tasks not just provide clinical care

and burnt out doctors feel that they do

not have

the time to be empathetic

to their patients and that’s the kind of

practice that we’re learning from

and doctors who are burnt out and feel

that they don’t have time to be

empathetic

are also they’re just human humans

even humans with ethics training and the

best of intentions

are biased and so it’s been well

documented

that the biases of society are reflected

in the health care that we provide

so this is what we get from current

practice right now no technology

right what about knowledge

one of the big sources of knowledge is a

randomized controlled trial

where you give one set of people a

treatment and see how well

it works in that population however

randomized controlled trials are very

rare because they’re expensive

so only 10 to 20 percent of the

treatments that are given today

are based on randomized controlled

trials when we do have them

they are biased only six percent of

asthmatics

treated today would have been

eligible for the rct’s use to design

their treatments

not only is the knowledge that we have

rare and biased sometimes it’s just

wrong

so more than ten percent of the three

thousand plus

top journal papers from gemma the new

england journal the lancet

or what we call a medical reversal a

paper comes out that says that thing

you’ve been doing

common clinical practice the thing we

think is correct that’s wrong

we need to do something else

so neither practice nor knowledge are

perfect right now

without any machine learning without any

technology

and this is understandable because right

now think about what happens if you’re a

clinician

and a patient comes into your practice

sumana is having trouble breathing

how are you supposed to decide what kind

of care she needs

you’re going to think about prior cases

that you’ve treated

and then try to assign an appropriate

treatment but the issue is that humans

are actually reasonably unique and so if

i’m trying to assign a treatment for

sumana based on

patients who are like her i can go check

the medical record

but prior work has shown that even if

you take 250 million patient records

from

four countries and combine them a large

proportion of patients with common

conditions have unique

treatment pathways meaning nobody else

had a similar treatment history

and for common conditions like diabetes

depression and hypertension you would

not assume

this would be true but 10 percent of

diabetics

11 of those with depression and almost a

quarter of those with hypertension have

unique

treatment pathways what that means is

sumana comes into your clinic

and says can you give me a treatment

based on patients who are like me

and you would say no there is nobody

like you in 250 million patient records

so obviously we can’t use the practice

and the knowledge that we have now

perfectly and so my goal

is to use the complex data

that exists about a person to train

high-capacity models

and try to provide better indications

for doctors to provide

care to their patients and so if that’s

what’s happening now without technology

maybe we can use technology to improve

practice and knowledge so let’s go back

to sumana

she’s having trouble breathing which

goes into the intensive care unit what’s

the first thing we might want to do

we want to treat her there’s many

different interventions that we could

use

and so the first thing we might want to

do is plan our practice

right and so what we’ve done is we’ve

taken

the data from over 30 000 patients in

the hospital

and tried to learn what is the optimal

practice

how do we predict the interventions that

are needed for sumana

given all of this data so that we can

plan her

care and that’s important because when

you use these deep neural networks

to understand a patient’s history you

can combine their notes

their labs their vitals all of their

demographic information and every six

hours have a new

updated risk for what sort of

interventions might be necessary

and now that we’ve planned care right

tried to improve our practice another

thing you might think is

if sumana is still having trouble

breathing i need to send her down to

radiology

i think she might have a collapsed lung

a pneumothorax

but when you send a patient down to

radiology

what’s happening is maybe it’s late at

night

this radiologist has looked at hundreds

of images

they now have to try to generate this

knowledge make this note

but they’re tired and they’ve seen so

many of this

these kinds of images and it’s a very

tedious task

so another thing that we’ve worked on is

going from hundreds of

thousands of x-ray images

to the radiology reports that would have

been written about those

images and that’s important because if

we can

use high capacity models like

convolutional and recurrent neural

networks

to make clinical text that is both

accurate and human readable

we’re allowing a doctor to make better

decisions about care

while removing a tedious part

of their day another thing to think

about

after we’ve improved this practice and

we’ve provided more knowledge

is exactly how we’re doing that so

sumana is a minority

how do i ensure that the treatment that

she’s receiving

is fair and

remember our prior slide humans are

biased

right and we’re learning from human

generated data

so as an example we took some text

and asked a neural network to complete

the blank

and here the only thing we changed was

the patient’s race

so 71 year old caucasian patient patient

is in blank condition was dnr

do not resuscitate in the nursing home

if i change the race

in this sentence it shouldn’t change

what gets filled in the plank right but

it does

and it does because this model was

trained on real data

generated by people with real biases

and this is important to establish

because when we train high-capacity

models

we need to be able to audit them and

demonstrate for example

how well they do on patients of

different protected classes

different ethnicities different genders

and

importantly it could be that this model

performs better

than most doctors in your clinic

right the average performance even

though it varies across these protected

attributes

could be better but whether we’re

comfortable deploying an algorithm like

this

that could improve care but at different

levels

for different people this is an ethical

question

this is a societal question this is not

something that the machine learning

algorithm can answer

for you we have to make that call so my

group broadly focuses

on creating actionable insights in human

health we focus on creating models that

are healthy

so can we train models that work well in

a medical setting

what kind of healthcare is healthy once

you’ve trained a model that works well

can you then use it to interrogate

healthcare and establish what works best

for people

and finally what kind of behaviors are

healthy because ideally we don’t want to

just recommend optimal treatments

improving practice and knowledge we’d

like to keep you out

of the hospital entirely thank you

you