How technology can help people affected by Dementia

[Music]

dementia

is a near degenerative condition in

which

the brain cells die on the

on the right hand side you can see a

healthy brain tissue

and on on the left hand side you can see

a brain

tissue affected by dementia

currently there aren’t any

pharmaceutical or medical intervention

to cure or stop dementia

struggling to remember the current

events and

memory loss are the most common symptoms

of dementia other symptoms include

changing mood behavior and

being lost in familiar places

there are around 50 million people

around the world

affected with dementia it’s estimated by

2050 we will have over 130 million

people

with dementia in fact in the next

15 minutes over 200 people around the

globe will be

diagnosed with dementia that’s in

average something around

one every four seconds the

hospital admissions in people with

dementia is also

often higher in the uk for example

at any given time one in four

hospital beds are taken with by

someone with dementia

the alzheimer’s society in the uk did a

study and they reported

close to 20 of these hospital admissions

are due to preventable causes the most

common

uh reasons of people with dementia being

admitted to hospitals

are falls hip fractures

breathing problems stroke and urinary

tract infections

providing care for dementia is also a

very

resource intensive task my own

grandmother had dementia

and i’ve i’ve seen and noticed the

family members

usually shoulder a large part of this

burden

there are also disparities in how people

access to care depending on where they

live

in this map you see parts of the united

kingdom

and the darker shades of blue

shows more people had their care being

with dementia

had their care been reviewed over the

past 12 months the lighter you see the

shades are

less people their care had been reviewed

over the past 12 months

you can see in parts of the country

there were close to

50 percent of the of people affected

with dementia uh their care having been

reviewed uh over the past 12 months

often when people get diagnosed with

dementia they did they get

a diagnosis they meet the doctor and

they are sent home

and they may occasionally visit they see

their doctor

but often they the case is that

something happens they

decay their health deteriorates and

sometimes becomes serious and they’re

admitted to hospital or they need to go

and see their gp

also there is this assumption dementia

is a condition which affects people in

industrial countries but that’s not

necessarily true

actually there has been an increased

number of people being diagnosed with

dementia in lower income

countries as well especially in south

asia

and parts of the pacific

three years ago my colleagues in our

national

health services in the nhs and a group

of

clinicians we’re working with our

technical team we started a project

called

tim for dementia the idea was can we use

low-cost and connected technologies

these are devices which provide

environmental monitoring physiological

monitoring can be used as technology

and power of ai and machine learning to

provide

better care and support to people

affected with

dementia devices we work with several

companies and the devices we use

are in two categories devices that

monitor

environmental data like movement around

the house

if you open a fridge door cabinet tour

people go a number of times people go to

bathroom

sleep and use of home appliances like in

the uk people usually wake up and switch

on a kettle make a cup of tea probably

here they make a

coffee and the second category of

devices uh are

technologies which they use uh to

monitor

they basically use them to monitor

physiological symptoms like

uh heart rate blood pressure body

temperature

weight and hydration the

idea is if you use connected devices

user devices which are off the shelf

available and you can integrate if you

can integrate all this data you will

have more continuous information about

people and their day-to-day activities

first we needed to create a system to

which is

safe and secure because we are

collecting highly personalized

information we work with different

groups we have created a system which

allows to integrate

data from those devices and then we use

machine learning and ai

to analyze this information most of this

data are

numerical measurements they on the

single pieces of data information

usually they don’t make much sense

unless you combine them with other

information and you analyze them

over time one of the algorithms we have

analyzes people’s day-to-day activities

and their routine in this graph

the x-axis shows days of the week and

the y-axis shows time of the day and

each colored block shows one type of

activity for example you can imagine

uh red black shoes the sleeve green

shoes

having breakfast let’s say blue is

watching tv

and often if it’s someone it does the

same thing every day

the same time obviously you can see i’ve

made up that figure

uh on the right the horizontal line the

colors will look the same but

in reality no one will do the exactly

the same activity

exactly the same time of the day there

will be some randomness in people’s

activities

and you can see the other figures shows

a picture from a real

home of someone affected with dementia

but what we would

wanted to do uh we wanted to see how

much randomness is in this activities at

whether

within this randomness we can find some

patterns we have created an algorithm

which looks at the activities and looks

at the transitional probabilities

between them for example

if i wake up in the morning and i go and

make my cup of tea what is the

likelihood

i go and back to the bedroom i go and

sit on a chair

or go and watch tv we let the machines

to observe these activities and they’ll

learn this probabilities for example

let’s say if i make my cup of tea

eighty percent of the time i go back to

the bedroom ten percent i go and sit on

the sofa

ten percent of the time i go and switch

on the tv but

it’s highly unlikely i leave the house

now we let the machines to look at this

data over two months machines can

are good if you program them to learn

from experience then for two weeks we

let the machines to look at how much

people deviate from these activities

once we have learned that now we have a

personalized

model of each person’s activity routines

and what we are interested in

is level of surprise for example if one

day i woke up

had my cup of tea and i left the house

that’s something machine hadn’t seen

before

this level of surprise will increase and

what we do basically we measure this

level of surprise per day

and if we notice this passes over the

threshold we create

for example an alert that could be

related to someone’s health

declining they become less active they

become socially isolated depressed

or can be related to hyperactivity which

sometimes is related to mood changes

agitation and irritability one of the

other top reasons in people

with dementia being admitted to

hospitals is urinary tract infection and

urinary tract infection a bacteria gets

into the

bladder and that if that get detected

early

is very treatable you can treat them

with antibiotics

but in people with dementia because this

some of the symptoms gets also mixed up

with symptoms of dementia

it’s very difficult to sometimes to

detect that and if you don’t detect it

then can

the infection can spread in the blood

can become a really a serious health

problem

and often people are admitted to

hospital

uh the standard test the the medical

test is is using a dip test or blood

tests which happen

in the clinic but you do that only if

you know someone has a higher risk of

uti without that information how can we

actually start looking for the risk

factors

we worked with our clinical team and we

have created an algorithm it turns out

if someone has uti the number of times

they go to bathroom will increase

we put sensors and we count number of

times they go to bathroom

and what we are interested in the

increase someone having infection

could be possibly have a slight

temperature we ask people to

measure their body temperature twice uh

the sleep patterns change their movement

pattern change

because uti can combat like delirium and

then we have created a machine learning

model which has learned from examples we

have given

and basically detects the risk of uti

once we detect the risk

now the important part is how do we

communicate this information to

clinical teams we didn’t want to remove

human

from the clinical interactions we wanted

to help our clinicians to have better

information

to make more informed and efficient

decisions and a prioritized late task

to do that we have created a system

which we call it integrated view each

home will appear like a card

and these cards are dynamic depending on

the priority of the events we have if

something

serious happens they move always on top

left hand side of your screen you will

have the

highest priorities related to the cases

now our clinical team when they see a

system generates

the system generates an alert for

example say someone has

uti or hypertension they will click

and they will see a screen like this

this screen now will give them

uh all the information which we have

been collected for example

uh uh all the information we have been

collecting

uh for example they can click and see

the all the blood pressure for the past

two

two months plus two weeks depending on

the what they want they can look at all

the environmental information

but in reality what we want to do we

want to help machines to simplify this

task of decision making and make it much

more efficient

what happens is the machines when they

for example they detect someone has a

uti

or someone has hypertension they also

will give some explanation

like an algorithm will tell our

clinicians why i think this is a case of

uti and in that case they will have a

basically we have worked with our

clinicians to design clinical pass

pathways how to respond to these alerts

and in those cases sometimes a clinician

might need to

go and look at the the basically

background data the provenance

information to make uh

decisions when we started this project

uh most of the our work was like looking

for

identifying patterns and detecting the

cases but

more and more information we collect we

can now become more protective

because over time we have created

algorithms which we have seen the cases

before we are collecting more data

and we can train new ai and machine

learning models to extract his patterns

and to learn from the experiences

and examples we had before

also we can start personalizing this

model the model of activity detection

was an example that showed how you can

use

machines to learn something and

personalize it to

an individual we have for example models

looking at people’s vital signals

and then they learned what are the

people what are the

norms for an individual for example i

can be an upper boundary of blood

pressure

someone can be an uh a lower boundary uh

my doctor might think where i am is it

seems okay now for me

what we are interested in are changes if

my blood pressure

keeps changing enough from where i am

these are type of patterns we are

interested

in and training machines to be able to

pick up

part of the workouts that we have done

now we are creating a daily wellness

score because we are monitoring people’s

activities we are monitoring people’s

vital signals

if we create combine all this

information we can create like a daily

wellness

score and what happens is if you have

someone’s daily wellness

score over time you can now create also

models can become

protective you can see whether that that

trend is going towards

decline or towards improvement

uh in the next short video clip uh some

of my colleagues who

from our national health service have

been involved in designing and leading

this research they will talk about

their experience and my colleagues from

our technical team

and some of our user groups they will

speak about their

experience about this project it’s been

a fantastic opportunity it just

demonstrates

what can be achieved when sectors come

together in partnership

i think we will see a fully digitally

enabled nhs in the future

not only will it help us to understand

patients but it will move us towards

earlier diagnosis

and precision medicine techniques my

grandparents have dementia

although my my grandpa and father is not

involved in this project in a way i’m

thinking

and maybe what i do right now as a

research in future come up as a solution

that would also

help him it would be very nice to think

that this

is the norm for the future this is how

people

will be looked after in their own home

for as long as and for as much

time as possible

thank you

you