TedTest

From the Ted Talk by Peter van Manen: Better baby care -- thanks to Formula 1

Unscramble the Blue Letters

Motor racing is a funny old bisenuss. We make a new car every year, and then we spend the rest of the season trying to understand what it is we've bliut to make it better, to make it faster. And then the next year, we start again. Now, the car you see in front of you is quite complicated. The chassis is made up of about 11,000 components, the engine another 6,000, the electronics about eight and a half thsnoaud. So there's about 25,000 things there that can go wrong. So motor racing is very much about atotneitn to detail. The other thing about Formula 1 in particular is we're always changing the car. We're always trying to make it faster. So every two weeks, we will be making about 5,000 new components to fit to the car. Five to 10 percent of the race car will be different every two weeks of the year. So how do we do that? Well, we start our life with the racing car. We have a lot of sensors on the car to mresaue things. On the race car in fnrot of you here there are about 120 srensos when it goes into a race. It's measuring all sorts of things around the car. That data is logged. We're logging about 500 different parameters within the data systems, about 13,000 health parameters and events to say when things are not working the way they should do, and we're sending that data back to the garage using telemetry at a rate of two to four migabtes per second. So during a two-hour race, each car will be sending 750 million nrmbeus. That's twice as many numbers as words that each of us skepas in a lifetime. It's a huge amount of data. But it's not enough just to have data and measure it. You need to be able to do something with it. So we've spent a lot of time and effort in turning the data into stories to be able to tell, what's the state of the engine, how are the treis degrading, what's the situation with fuel cpotosmunin? So all of this is taking data and turning it into knowledge that we can act upon. Okay, so let's have a look at a little bit of data. Let's pick a bit of data from another three-month-old patient. This is a clihd, and what you're seeing here is real data, and on the far right-hand side, where everything starts getting a little bit catastrophic, that is the patient going into cardiac aresrt. It was deemed to be an unpredictable evnet. This was a heart attack that no one could see coming. But when we look at the information there, we can see that things are starting to become a little fuzzy about five mientus or so before the cardiac arrest. We can see small changes in things like the haret rate moving. These were all undetected by normal thsredhlos which would be applied to data. So the question is, why couldn't we see it? Was this a predictable event? Can we look more at the patterns in the data to be able to do things better? So this is a child, about the same age as the racing car on stgae, three months old. It's a patient with a heart problem. Now, when you look at some of the data on the screen above, things like heart rate, psule, oxygen, respiration rates, they're all unusual for a normal child, but they're quite normal for the child there, and so one of the challenges you have in health care is, how can I look at the patient in front of me, have something which is specific for her, and be able to detect when things start to change, when things start to deteriorate? Because like a racing car, any patient, when things start to go bad, you have a short time to make a dfereifnce. So what we did is we took a data system which we run every two weeks of the year in Formula 1 and we installed it on the hastipol copemturs at Birmingham Children's Hospital. We streamed data from the bdeside irmntnetuss in their pediatric intensive care so that we could both look at the data in real time and, more importantly, to store the data so that we could start to learn from it. And then, we applied an application on top which would allow us to tease out the patterns in the data in real time so we could see what was happening, so we could dtiermnee when things started to change. Now, in motor racing, we're all a little bit ambitious, audacious, a little bit arrogant sometimes, so we dideecd we would also look at the children as they were being transported to intensive care. Why should we wait until they arrived in the hospital before we started to look? And so we installed a real-time link between the ambulance and the hospital, just using normal 3G telephony to send that data so that the ambulance became an extra bed in intensive care. And then we started looking at the data. So the wlggiy lines at the top, all the colors, this is the normal sort of data you would see on a monitor — heart rate, pulse, oxygen within the blood, and respiration. The lnies on the bottom, the blue and the red, these are the inrtinseteg ones. The red line is showing an automated version of the ealry wirnnag score that Birmingham Children's Hospital were already rnuinng. They'd been running that since 2008, and already have stopped cardiac arrests and dtsierss within the hospital. The blue line is an indication of when patterns start to change, and immediately, before we even started puittng in cliniacl interpretation, we can see that the data is speaking to us. It's telling us that something is going wnorg. The plot with the red and the green blobs, this is pltointg different components of the data against each other. The green is us learning what is normal for that child. We call it the cloud of normality. And when things start to chngae, when conditions sartt to deteriorate, we move into the red line. There's no rekoct sceicne here. It is displaying data that exists already in a different way, to amplify it, to provide cues to the doctors, to the nserus, so they can see what's happening. In the same way that a good ranicg dveirr relies on cues to decide when to aplpy the brakes, when to turn into a corner, we need to help our physicians and our nurses to see when things are starting to go wrong. So we have a very ambitious pgorarm. We think that the race is on to do something differently. We are thinking big. It's the right thing to do. We have an approach which, if it's successful, there's no reason why it should stay within a hospital. It can go beyond the walls. With wireless cinettnivocy these days, there is no reason why patients, doctors and nurses always have to be in the same place at the same time. And meanwhile, we'll take our little three-month-old baby, keep taking it to the trcak, kneepig it safe, and mkaing it faster and better. Thank you very much. (Applause)

Open Cloze

Motor racing is a funny old ________. We make a new car every year, and then we spend the rest of the season trying to understand what it is we've _____ to make it better, to make it faster. And then the next year, we start again. Now, the car you see in front of you is quite complicated. The chassis is made up of about 11,000 components, the engine another 6,000, the electronics about eight and a half ________. So there's about 25,000 things there that can go wrong. So motor racing is very much about _________ to detail. The other thing about Formula 1 in particular is we're always changing the car. We're always trying to make it faster. So every two weeks, we will be making about 5,000 new components to fit to the car. Five to 10 percent of the race car will be different every two weeks of the year. So how do we do that? Well, we start our life with the racing car. We have a lot of sensors on the car to _______ things. On the race car in _____ of you here there are about 120 _______ when it goes into a race. It's measuring all sorts of things around the car. That data is logged. We're logging about 500 different parameters within the data systems, about 13,000 health parameters and events to say when things are not working the way they should do, and we're sending that data back to the garage using telemetry at a rate of two to four ________ per second. So during a two-hour race, each car will be sending 750 million _______. That's twice as many numbers as words that each of us ______ in a lifetime. It's a huge amount of data. But it's not enough just to have data and measure it. You need to be able to do something with it. So we've spent a lot of time and effort in turning the data into stories to be able to tell, what's the state of the engine, how are the _____ degrading, what's the situation with fuel ___________? So all of this is taking data and turning it into knowledge that we can act upon. Okay, so let's have a look at a little bit of data. Let's pick a bit of data from another three-month-old patient. This is a _____, and what you're seeing here is real data, and on the far right-hand side, where everything starts getting a little bit catastrophic, that is the patient going into cardiac ______. It was deemed to be an unpredictable _____. This was a heart attack that no one could see coming. But when we look at the information there, we can see that things are starting to become a little fuzzy about five _______ or so before the cardiac arrest. We can see small changes in things like the _____ rate moving. These were all undetected by normal __________ which would be applied to data. So the question is, why couldn't we see it? Was this a predictable event? Can we look more at the patterns in the data to be able to do things better? So this is a child, about the same age as the racing car on _____, three months old. It's a patient with a heart problem. Now, when you look at some of the data on the screen above, things like heart rate, _____, oxygen, respiration rates, they're all unusual for a normal child, but they're quite normal for the child there, and so one of the challenges you have in health care is, how can I look at the patient in front of me, have something which is specific for her, and be able to detect when things start to change, when things start to deteriorate? Because like a racing car, any patient, when things start to go bad, you have a short time to make a __________. So what we did is we took a data system which we run every two weeks of the year in Formula 1 and we installed it on the ________ _________ at Birmingham Children's Hospital. We streamed data from the _______ ___________ in their pediatric intensive care so that we could both look at the data in real time and, more importantly, to store the data so that we could start to learn from it. And then, we applied an application on top which would allow us to tease out the patterns in the data in real time so we could see what was happening, so we could _________ when things started to change. Now, in motor racing, we're all a little bit ambitious, audacious, a little bit arrogant sometimes, so we _______ we would also look at the children as they were being transported to intensive care. Why should we wait until they arrived in the hospital before we started to look? And so we installed a real-time link between the ambulance and the hospital, just using normal 3G telephony to send that data so that the ambulance became an extra bed in intensive care. And then we started looking at the data. So the ______ lines at the top, all the colors, this is the normal sort of data you would see on a monitor — heart rate, pulse, oxygen within the blood, and respiration. The _____ on the bottom, the blue and the red, these are the ___________ ones. The red line is showing an automated version of the _____ _______ score that Birmingham Children's Hospital were already _______. They'd been running that since 2008, and already have stopped cardiac arrests and ________ within the hospital. The blue line is an indication of when patterns start to change, and immediately, before we even started _______ in ________ interpretation, we can see that the data is speaking to us. It's telling us that something is going _____. The plot with the red and the green blobs, this is ________ different components of the data against each other. The green is us learning what is normal for that child. We call it the cloud of normality. And when things start to ______, when conditions _____ to deteriorate, we move into the red line. There's no ______ _______ here. It is displaying data that exists already in a different way, to amplify it, to provide cues to the doctors, to the ______, so they can see what's happening. In the same way that a good ______ ______ relies on cues to decide when to _____ the brakes, when to turn into a corner, we need to help our physicians and our nurses to see when things are starting to go wrong. So we have a very ambitious _______. We think that the race is on to do something differently. We are thinking big. It's the right thing to do. We have an approach which, if it's successful, there's no reason why it should stay within a hospital. It can go beyond the walls. With wireless ____________ these days, there is no reason why patients, doctors and nurses always have to be in the same place at the same time. And meanwhile, we'll take our little three-month-old baby, keep taking it to the _____, _______ it safe, and ______ it faster and better. Thank you very much. (Applause)

Solution

business
stage
running
clinical
decided
wiggly
event
lines
start
thresholds
apply
measure
sensors
putting
built
difference
pulse
track
change
numbers
attention
bedside
connectivity
wrong
keeping
early
consumption
nurses
thousand
plotting
computers
driver
instruments
racing
distress
determine
arrest
tires
megabits
child
program
heart
front
interesting
speaks
making
hospital
science
rocket
minutes
warning

Original Text

Motor racing is a funny old business. We make a new car every year, and then we spend the rest of the season trying to understand what it is we've built to make it better, to make it faster. And then the next year, we start again. Now, the car you see in front of you is quite complicated. The chassis is made up of about 11,000 components, the engine another 6,000, the electronics about eight and a half thousand. So there's about 25,000 things there that can go wrong. So motor racing is very much about attention to detail. The other thing about Formula 1 in particular is we're always changing the car. We're always trying to make it faster. So every two weeks, we will be making about 5,000 new components to fit to the car. Five to 10 percent of the race car will be different every two weeks of the year. So how do we do that? Well, we start our life with the racing car. We have a lot of sensors on the car to measure things. On the race car in front of you here there are about 120 sensors when it goes into a race. It's measuring all sorts of things around the car. That data is logged. We're logging about 500 different parameters within the data systems, about 13,000 health parameters and events to say when things are not working the way they should do, and we're sending that data back to the garage using telemetry at a rate of two to four megabits per second. So during a two-hour race, each car will be sending 750 million numbers. That's twice as many numbers as words that each of us speaks in a lifetime. It's a huge amount of data. But it's not enough just to have data and measure it. You need to be able to do something with it. So we've spent a lot of time and effort in turning the data into stories to be able to tell, what's the state of the engine, how are the tires degrading, what's the situation with fuel consumption? So all of this is taking data and turning it into knowledge that we can act upon. Okay, so let's have a look at a little bit of data. Let's pick a bit of data from another three-month-old patient. This is a child, and what you're seeing here is real data, and on the far right-hand side, where everything starts getting a little bit catastrophic, that is the patient going into cardiac arrest. It was deemed to be an unpredictable event. This was a heart attack that no one could see coming. But when we look at the information there, we can see that things are starting to become a little fuzzy about five minutes or so before the cardiac arrest. We can see small changes in things like the heart rate moving. These were all undetected by normal thresholds which would be applied to data. So the question is, why couldn't we see it? Was this a predictable event? Can we look more at the patterns in the data to be able to do things better? So this is a child, about the same age as the racing car on stage, three months old. It's a patient with a heart problem. Now, when you look at some of the data on the screen above, things like heart rate, pulse, oxygen, respiration rates, they're all unusual for a normal child, but they're quite normal for the child there, and so one of the challenges you have in health care is, how can I look at the patient in front of me, have something which is specific for her, and be able to detect when things start to change, when things start to deteriorate? Because like a racing car, any patient, when things start to go bad, you have a short time to make a difference. So what we did is we took a data system which we run every two weeks of the year in Formula 1 and we installed it on the hospital computers at Birmingham Children's Hospital. We streamed data from the bedside instruments in their pediatric intensive care so that we could both look at the data in real time and, more importantly, to store the data so that we could start to learn from it. And then, we applied an application on top which would allow us to tease out the patterns in the data in real time so we could see what was happening, so we could determine when things started to change. Now, in motor racing, we're all a little bit ambitious, audacious, a little bit arrogant sometimes, so we decided we would also look at the children as they were being transported to intensive care. Why should we wait until they arrived in the hospital before we started to look? And so we installed a real-time link between the ambulance and the hospital, just using normal 3G telephony to send that data so that the ambulance became an extra bed in intensive care. And then we started looking at the data. So the wiggly lines at the top, all the colors, this is the normal sort of data you would see on a monitor — heart rate, pulse, oxygen within the blood, and respiration. The lines on the bottom, the blue and the red, these are the interesting ones. The red line is showing an automated version of the early warning score that Birmingham Children's Hospital were already running. They'd been running that since 2008, and already have stopped cardiac arrests and distress within the hospital. The blue line is an indication of when patterns start to change, and immediately, before we even started putting in clinical interpretation, we can see that the data is speaking to us. It's telling us that something is going wrong. The plot with the red and the green blobs, this is plotting different components of the data against each other. The green is us learning what is normal for that child. We call it the cloud of normality. And when things start to change, when conditions start to deteriorate, we move into the red line. There's no rocket science here. It is displaying data that exists already in a different way, to amplify it, to provide cues to the doctors, to the nurses, so they can see what's happening. In the same way that a good racing driver relies on cues to decide when to apply the brakes, when to turn into a corner, we need to help our physicians and our nurses to see when things are starting to go wrong. So we have a very ambitious program. We think that the race is on to do something differently. We are thinking big. It's the right thing to do. We have an approach which, if it's successful, there's no reason why it should stay within a hospital. It can go beyond the walls. With wireless connectivity these days, there is no reason why patients, doctors and nurses always have to be in the same place at the same time. And meanwhile, we'll take our little three-month-old baby, keep taking it to the track, keeping it safe, and making it faster and better. Thank you very much. (Applause)

Frequently Occurring Word Combinations

ngrams of length 2

collocation	frequency
intensive care	3
motor racing	2
race car	2
racing car	2
cardiac arrest	2
real time	2
red line	2

Important Words

act
age
ambitious
ambulance
amount
amplify
applause
application
applied
apply
approach
arrest
arrests
arrived
arrogant
attack
attention
audacious
automated
baby
bad
bed
bedside
big
birmingham
bit
blobs
blood
blue
bottom
brakes
built
business
call
car
cardiac
care
catastrophic
challenges
change
changing
chassis
child
children
clinical
cloud
colors
coming
complicated
components
computers
conditions
connectivity
consumption
corner
cues
data
days
decide
decided
deemed
degrading
detail
detect
deteriorate
determine
difference
differently
displaying
distress
doctors
driver
early
effort
electronics
engine
event
events
exists
extra
faster
fit
formula
front
fuel
funny
fuzzy
garage
good
green
happening
health
heart
hospital
huge
immediately
importantly
indication
information
installed
instruments
intensive
interesting
interpretation
keeping
knowledge
learn
learning
life
lifetime
line
lines
link
logged
logging
lot
making
measure
measuring
megabits
million
minutes
monitor
months
motor
move
moving
normal
normality
numbers
nurses
oxygen
parameters
patient
patients
patterns
pediatric
percent
physicians
pick
place
plot
plotting
predictable
problem
program
provide
pulse
putting
question
race
racing
rate
rates
real
reason
red
relies
respiration
rest
rocket
run
running
safe
science
score
screen
season
send
sending
sensors
short
showing
side
situation
small
sort
sorts
speaking
speaks
specific
spend
spent
stage
start
started
starting
starts
state
stay
stopped
store
stories
streamed
successful
system
systems
tease
telemetry
telephony
telling
thinking
thousand
thresholds
time
tires
top
track
transported
turn
turning
understand
undetected
unpredictable
unusual
version
wait
walls
warning
weeks
wiggly
wireless
words
working
wrong
year