TEDMED 2015
Russ Altman: What really happens when you mix medications?
Ras Altman (Russ Altman): Šta se zaista dešava kada mešate lekove?
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1,766,922 views
Ako uzimate dva različita leka iz dva različita razloga, evo zamisli koja vam može otvoriti oči: vaš lekar možda baš i ne razume u potpunosti šta se dešava kada se oni kombinuju, jer je interakcije lekova neverovatno teško proučavati. U ovom fascinantnom i pristupačnom govoru, Ras Altman pokazuje kako lekari proučavaju neočekivane interakcije lekova pomoću iznenađujućeg izvora: upita za pretragu pretraživača interneta.
Russ Altman - Big data techno-optimist and internist
Russ Altman uses machine learning to better understand adverse effects of medication. Full bio
Russ Altman uses machine learning to better understand adverse effects of medication. Full bio
Double-click the English transcript below to play the video.
Odete kod doktora i obavite neke analize.
00:12
So you go to the doctor
and get some tests.
and get some tests.
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00:16
The doctor determines
that you have high cholesterol
that you have high cholesterol
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Doktor utvrdi da imate visok holesterol
00:19
and you would benefit
from medication to treat it.
from medication to treat it.
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i da bi bilo dobro da uzimate lekove
kako biste to lečili,
kako biste to lečili,
00:22
So you get a pillbox.
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pa uzmete pilule.
Verujete, vaš lekar veruje
da će to da pomogne.
da će to da pomogne.
00:25
You have some confidence,
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00:26
your physician has some confidence
that this is going to work.
that this is going to work.
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Kompanija koja je izumela lek
je obavila dosta ispitivanja,
je obavila dosta ispitivanja,
00:29
The company that invented it did
a lot of studies, submitted it to the FDA.
a lot of studies, submitted it to the FDA.
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podnela ga na odobrenje
Upravi za hranu i lekove.
Upravi za hranu i lekove.
00:33
They studied it very carefully,
skeptically, they approved it.
skeptically, they approved it.
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Ispitali su ga veoma pažljivo,
skeptično i odobrili ga.
skeptično i odobrili ga.
00:36
They have a rough idea of how it works,
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Imaju izvesnu predstavu
o tome kako deluje,
o tome kako deluje,
00:38
they have a rough idea
of what the side effects are.
of what the side effects are.
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o tome koje su nuspojave.
Trebalo bi da bude u redu.
00:40
It should be OK.
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00:42
You have a little more
of a conversation with your physician
of a conversation with your physician
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Dodatno ste pričali sa svojim lekarom
00:45
and the physician is a little worried
because you've been blue,
because you've been blue,
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i on je malo zabrinut jer ste tužni,
niste baš svoji,
00:48
haven't felt like yourself,
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ne uživate u stvarima u životu
kao i obično.
kao i obično.
00:50
you haven't been able to enjoy things
in life quite as much as you usually do.
in life quite as much as you usually do.
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00:53
Your physician says, "You know,
I think you have some depression.
I think you have some depression.
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Lekar vam kaže: „Znate,
mislim da imate depresiju.
mislim da imate depresiju.
00:57
I'm going to have to give
you another pill."
you another pill."
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Moraću da vam dam druge pilule.“
01:00
So now we're talking
about two medications.
about two medications.
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Tako sad govorimo o dva leka.
01:03
This pill also -- millions
of people have taken it,
of people have taken it,
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Sa tim pilulama je isto -
milioni su ih uzimali,
milioni su ih uzimali,
01:06
the company did studies,
the FDA looked at it -- all good.
the FDA looked at it -- all good.
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kompanija je ispitivala,
Uprava za hranu i lekove
je pregledala, sve je u redu.
je pregledala, sve je u redu.
01:10
Think things should go OK.
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Mislite da će sa ovim da bude sve u redu.
01:12
Think things should go OK.
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Mislite da će i sa ovim
da bude sve u redu.
da bude sve u redu.
01:15
Well, wait a minute.
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Ipak, sačekajte malo.
01:16
How much have we studied
these two together?
these two together?
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Koliko smo ova dva leka izučavali zajedno?
01:20
Well, it's very hard to do that.
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Pa, to je vrlo teško uraditi.
01:22
In fact, it's not traditionally done.
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Zapravo, to se po običaju ne radi.
01:25
We totally depend on what we call
"post-marketing surveillance,"
"post-marketing surveillance,"
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Potpuno zavisimo od onoga što zovemo
„postmarketinški nadzor“,
„postmarketinški nadzor“,
01:30
after the drugs hit the market.
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nakon što lek bude pušten na tržište.
01:32
How can we figure out
if bad things are happening
if bad things are happening
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Kako možemo da otkrijemo
da li se nešto loše dešava
da li se nešto loše dešava
01:35
between two medications?
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između dva leka?
01:37
Three? Five? Seven?
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Tri? Pet? Sedam?
01:39
Ask your favorite person
who has several diagnoses
who has several diagnoses
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Pitajte svoju omiljenu osobu
sa nekoliko dijagnoza
sa nekoliko dijagnoza
01:42
how many medications they're on.
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koliko lekova uzima.
Zašto je meni stalo do ovog problema?
Jako mi je stalo do toga.
Jako mi je stalo do toga.
01:44
Why do I care about this problem?
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01:46
I care about it deeply.
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01:47
I'm an informatics and data science guy
and really, in my opinion,
and really, in my opinion,
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Ja sam tip koji se bavi informatikom
i naukom o podacima,
i naukom o podacima,
i prema mom mišljenju, jedina nada
da razumemo ove interakcije
da razumemo ove interakcije
01:51
the only hope -- only hope --
to understand these interactions
to understand these interactions
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01:55
is to leverage lots
of different sources of data
of different sources of data
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je da usaglasimo
mnogo različitih izvora podataka
mnogo različitih izvora podataka
01:58
in order to figure out
when drugs can be used together safely
when drugs can be used together safely
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kako bismo otkrili kada se lekovi
mogu bezbedno koristiti zajedno,
mogu bezbedno koristiti zajedno,
02:02
and when it's not so safe.
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a kada baš i nije bezbedno.
Dopustite da vam ispričam
priču o nauci o podacima.
priču o nauci o podacima.
02:04
So let me tell you a data science story.
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02:06
And it begins with my student Nick.
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Počinje sa mojim studentom Nikom.
02:08
Let's call him "Nick,"
because that's his name.
because that's his name.
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Zvaćemo ga „Nik“, jer mu je to ime.
02:11
(Laughter)
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(Smeh)
02:12
Nick was a young student.
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Nik je bio mladi student.
02:14
I said, "You know, Nick, we have
to understand how drugs work
to understand how drugs work
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Rekao sam: „Znaš, Nik,
moramo da razumemo kako lekovi deluju,
moramo da razumemo kako lekovi deluju,
kako deluju zajedno
i kako deluju zasebno,
i kako deluju zasebno,
02:17
and how they work together
and how they work separately,
and how they work separately,
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a ne razumemo mnogo o tome.
02:19
and we don't have a great understanding.
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Međutim, Uprava za hranu i lekove
je objavila neverovatnu bazu podataka.
je objavila neverovatnu bazu podataka.
02:21
But the FDA has made available
an amazing database.
an amazing database.
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02:24
It's a database of adverse events.
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To je baza podataka
o neželjenim događajima.
o neželjenim događajima.
02:26
They literally put on the web --
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Bukvalno su je postavili na mrežu -
02:27
publicly available, you could all
download it right now --
download it right now --
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javno je dostupna,
svi možete da je sada skinete -
svi možete da je sada skinete -
02:31
hundreds of thousands
of adverse event reports
of adverse event reports
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stotine hiljada izveštaja
o neželjenim događajijma
o neželjenim događajijma
02:34
from patients, doctors,
companies, pharmacists.
companies, pharmacists.
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od pacijenata, doktora,
kompanija, farmaceuta.
kompanija, farmaceuta.
Ti izveštaji su prilično jednostavni.
02:38
And these reports are pretty simple:
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02:40
it has all the diseases
that the patient has,
that the patient has,
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Tu su sve bolesti koje pacijent ima,
02:43
all the drugs that they're on,
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svi lekovi koje uzima
02:44
and all the adverse events,
or side effects, that they experience.
or side effects, that they experience.
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i svi neželjeni događaji
ili nuspojave koje doživljavaju.
ili nuspojave koje doživljavaju.
02:48
It is not all of the adverse events
that are occurring in America today,
that are occurring in America today,
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To nisu svi neželjeni događaji
koji se danas javljaju u Americi,
koji se danas javljaju u Americi,
02:52
but it's hundreds and hundreds
of thousands of drugs.
of thousands of drugs.
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ali to su stotine i stotine
hiljada lekova.
hiljada lekova.
02:54
So I said to Nick,
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Rekao sam Niku:
02:56
"Let's think about glucose.
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„Razmotrimo glukozu.
02:57
Glucose is very important,
and we know it's involved with diabetes.
and we know it's involved with diabetes.
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Glukoza je veoma važna
i znamo da je u vezi sa dijabetesom.
i znamo da je u vezi sa dijabetesom.
03:01
Let's see if we can understand
glucose response.
glucose response.
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Hajde da vidimo da li možemo
da razumemo reakciju glukoze.“
da razumemo reakciju glukoze.“
03:05
I sent Nick off. Nick came back.
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Ispratio sam Nika. Vratio se.
03:08
"Russ," he said,
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„Ras“, rekao je,
03:10
"I've created a classifier that can
look at the side effects of a drug
look at the side effects of a drug
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„Napravio sam klasifikator koji može
da sagleda neželjene efekte leka
da sagleda neželjene efekte leka
03:15
based on looking at this database,
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na osnovu pregleda baze podataka
03:17
and can tell you whether that drug
is likely to change glucose or not."
is likely to change glucose or not."
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i može nam reći da li postoji šansa
da će taj lek promeniti
nivo glukoze ili ne.“
nivo glukoze ili ne.“
Uspeo je. Bilo je prosto, na neki način.
03:21
He did it. It was very simple, in a way.
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Uzeo je sve lekove za koje se zna
da menjaju nivo glukoze
da menjaju nivo glukoze
03:23
He took all the drugs
that were known to change glucose
that were known to change glucose
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i gomilu lekova
koji ne menjaju nivo glukoze
koji ne menjaju nivo glukoze
03:26
and a bunch of drugs
that don't change glucose,
that don't change glucose,
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03:28
and said, "What's the difference
in their side effects?
in their side effects?
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i zapitao se: „U čemu je razlika
između njihovih nuspojava?
između njihovih nuspojava?
03:31
Differences in fatigue? In appetite?
In urination habits?"
In urination habits?"
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Razlike u osećaju premora? Apetitu?
U pogledu vršenja mokrenja?“
U pogledu vršenja mokrenja?“
Sve to u sklopu mu je dalo
veoma dobro sredstvo predviđanja.
veoma dobro sredstvo predviđanja.
03:36
All those things conspired
to give him a really good predictor.
to give him a really good predictor.
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Rekao je: „Ras, mogu da predvidim
sa 93 posto verovatnoće tačnosti
sa 93 posto verovatnoće tačnosti
03:39
He said, "Russ, I can predict
with 93 percent accuracy
with 93 percent accuracy
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03:42
when a drug will change glucose."
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kada će lek menjati nivo glukoze.“
03:43
I said, "Nick, that's great."
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Rekao sam: „Nik, to je sjajno.“
03:45
He's a young student,
you have to build his confidence.
you have to build his confidence.
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On je mladi student,
morate da mu podignete samopouzdanje.
morate da mu podignete samopouzdanje.
03:48
"But Nick, there's a problem.
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„Ipak, Nik, postoji problem.
03:49
It's that every physician in the world
knows all the drugs that change glucose,
knows all the drugs that change glucose,
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Činjenica je da svaki lekar na svetu
zna sve lekove koji menjaju nivo glukoze
zna sve lekove koji menjaju nivo glukoze
03:53
because it's core to our practice.
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jer je to u suštini naše prakse.
03:55
So it's great, good job,
but not really that interesting,
but not really that interesting,
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Tako da je to sjajno, odlično obavljeno,
ali nije baš naročito zanimljivo,
ali nije baš naročito zanimljivo,
definitivno ne nešto
što se može objaviti.“
što se može objaviti.“
03:59
definitely not publishable."
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04:01
(Laughter)
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(Smeh)
Rekao je: „Znam, Ras.
Pretpostavio sam da ćeš to reći.“
Pretpostavio sam da ćeš to reći.“
04:02
He said, "I know, Russ.
I thought you might say that."
I thought you might say that."
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04:04
Nick is smart.
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Nik je pametan.
„Pretpostavio sam da ćeš to reći,
pa sam sproveo još jedan eksperiment.
pa sam sproveo još jedan eksperiment.
04:06
"I thought you might say that,
so I did one other experiment.
so I did one other experiment.
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04:09
I looked at people in this database
who were on two drugs,
who were on two drugs,
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Posmatrao sam ljude u ovoj bazi podataka
koji uzimaju dva leka
koji uzimaju dva leka
04:11
and I looked for signals similar,
glucose-changing signals,
glucose-changing signals,
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i tražio sam slične signale,
signale za promenu nivoa glukoze,
signale za promenu nivoa glukoze,
04:16
for people taking two drugs,
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za osobe koje uzimaju dva leka,
04:18
where each drug alone
did not change glucose,
did not change glucose,
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pri čemu svaki lek
sam po sebi ne menja glukozu,
sam po sebi ne menja glukozu,
04:23
but together I saw a strong signal."
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ali vidim da zajednički daju jak signal.“
04:26
And I said, "Oh! You're clever.
Good idea. Show me the list."
Good idea. Show me the list."
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Rekao sam: „O, pametan si.
Dobra ideja. Pokaži mi spisak.“
Dobra ideja. Pokaži mi spisak.“
Tu je bila gomila lekova,
ne naročito zanimljivo.
ne naročito zanimljivo.
04:29
And there's a bunch of drugs,
not very exciting.
not very exciting.
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04:31
But what caught my eye
was, on the list there were two drugs:
was, on the list there were two drugs:
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Ono što mi je privuklo pažnju
je da su na spisku bila dva leka:
je da su na spisku bila dva leka:
04:35
paroxetine, or Paxil, an antidepressant;
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paroksetin ili Paksil, antidepresiv,
04:39
and pravastatin, or Pravachol,
a cholesterol medication.
a cholesterol medication.
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i pravastatin ili Pravakol,
lek protiv holesterola.
lek protiv holesterola.
04:43
And I said, "Huh. There are millions
of Americans on those two drugs."
of Americans on those two drugs."
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Rekoh: „Ha! Milioni Amerikanaca
koriste ova dva leka.“
koriste ova dva leka.“
Zapravo, kako smo kasnije saznali,
04:48
In fact, we learned later,
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04:49
15 million Americans on paroxetine
at the time, 15 million on pravastatin,
at the time, 15 million on pravastatin,
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15 miliona Amerikanaca uzimalo je
paroksetin u to vreme
paroksetin u to vreme
i 15 miliona pravastatin, a milion,
prema našoj proceni, uzimalo je oba.
prema našoj proceni, uzimalo je oba.
04:55
and a million, we estimated, on both.
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04:58
So that's a million people
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Dakle, to je milion ljudi
05:00
who might be having some problems
with their glucose
with their glucose
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koji možda imaju probleme sa glukozom
05:02
if this machine-learning mumbo jumbo
that he did in the FDA database
that he did in the FDA database
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ako ovo čudo od mašinskog učenja
koje je on sproveo u bazi Uprave
koje je on sproveo u bazi Uprave
05:05
actually holds up.
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zaista drži vodu.
Ipak, rekao sam:
„I dalje nije za objavljivanje,
„I dalje nije za objavljivanje,
05:07
But I said, "It's still not publishable,
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05:08
because I love what you did
with the mumbo jumbo,
with the mumbo jumbo,
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mada mi se dopada to što si uradio
sa tim čudesima, sa mašinskim učenjem,
sa tim čudesima, sa mašinskim učenjem,
05:11
with the machine learning,
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ali to što imamo baš i nije
odgovarajuća vrsta dokaza.“
odgovarajuća vrsta dokaza.“
05:12
but it's not really standard-of-proof
evidence that we have."
evidence that we have."
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05:17
So we have to do something else.
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Moramo da uradimo nešto drugo.
05:19
Let's go into the Stanford
electronic medical record.
electronic medical record.
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Hajde da uđemo u elektronski
medicinski zapis Stenforda.
medicinski zapis Stenforda.
Imamo njegovu kopiju
koja je u redu za istraživanje,
koja je u redu za istraživanje,
05:22
We have a copy of it
that's OK for research,
that's OK for research,
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05:24
we removed identifying information.
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uklonili smo informacije
za identifikaciju,
za identifikaciju,
i rekao sam: „Hajde da vidimo da li osobe
koje uzimaju ova dva leka
koje uzimaju ova dva leka
05:26
And I said, "Let's see if people
on these two drugs
on these two drugs
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05:29
have problems with their glucose."
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imaju probleme sa glukozom.“
05:31
Now there are thousands
and thousands of people
and thousands of people
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Postoje hiljade i hiljade ljudi
u stenfordskim medicinskim podacima
koji uzimaju parokesetin i pravastatin.
koji uzimaju parokesetin i pravastatin.
05:33
in the Stanford medical records
that take paroxetine and pravastatin.
that take paroxetine and pravastatin.
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Međutim, bili su nam potrebni
posebni pacijenti.
posebni pacijenti.
05:36
But we needed special patients.
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05:38
We needed patients who were on one of them
and had a glucose measurement,
and had a glucose measurement,
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Bili su nam potrebni pacijenti
koji su uzimali jedan od tih lekova
i imali izmerenu glukozu,
i imali izmerenu glukozu,
05:43
then got the second one and had
another glucose measurement,
another glucose measurement,
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zatim dobili drugi lek
i imali drugu meru glukoze,
i imali drugu meru glukoze,
a sve to u okviru
prihvatljivog vremenskog perioda -
prihvatljivog vremenskog perioda -
05:46
all within a reasonable period of time --
something like two months.
something like two months.
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otprilike oko dva meseca.
05:50
And when we did that,
we found 10 patients.
we found 10 patients.
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Kada smo to uradili,
pronašli smo deset pacijenata.
pronašli smo deset pacijenata.
05:54
However, eight out of the 10
had a bump in their glucose
had a bump in their glucose
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Međutim, osmoro od tih deset
je imalo porast glukoze
je imalo porast glukoze
05:59
when they got the second P --
we call this P and P --
we call this P and P --
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kada su dobili drugi P -
nazivamo ih P i P -
nazivamo ih P i P -
06:01
when they got the second P.
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kada su dobili drugi P.
06:03
Either one could be first,
the second one comes up,
the second one comes up,
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Bilo koji može biti prvi,
zatim nastupa drugi,
zatim nastupa drugi,
06:05
glucose went up
20 milligrams per deciliter.
20 milligrams per deciliter.
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nivo glukoze raste za 1,1 mmol/l.
06:08
Just as a reminder,
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Samo da podsetim,
06:09
you walk around normally,
if you're not diabetic,
if you're not diabetic,
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normalno se krećete,
ako niste dijabetičar,
ako niste dijabetičar,
sa glukozom od oko 5.
06:12
with a glucose of around 90.
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Ako dostigne 6,6 - 6,9,
06:13
And if it gets up to 120, 125,
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06:15
your doctor begins to think
about a potential diagnosis of diabetes.
about a potential diagnosis of diabetes.
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vaš doktor će početi da pomišlja
na potencijalnu dijagnozu dijabetesa.
na potencijalnu dijagnozu dijabetesa.
06:19
So a 20 bump -- pretty significant.
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Tako da porast
od 1,1 prilično ima značaja.
od 1,1 prilično ima značaja.
06:22
I said, "Nick, this is very cool.
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Rekao sam: „Nik, ovo je veoma zanimljivo,
06:25
But, I'm sorry, we still
don't have a paper,
don't have a paper,
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ali, žao mi je, i dalje nemamo rad,
06:27
because this is 10 patients
and -- give me a break --
and -- give me a break --
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jer ovo je deset pacijenata i, ma daj,
to nije dovoljno pacijenata.“
06:30
it's not enough patients."
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Stoga smo se zapitali
šta možemo da uradimo.
šta možemo da uradimo.
06:31
So we said, what can we do?
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06:32
And we said, let's call our friends
at Harvard and Vanderbilt,
at Harvard and Vanderbilt,
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Rešili smo da pozovemo naše prijatelje
sa Harvarda i Vanderbilta -
sa Harvarda i Vanderbilta -
06:35
who also -- Harvard in Boston,
Vanderbilt in Nashville,
Vanderbilt in Nashville,
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Harvarda u Bostonu
i Vanderbilta u Nešvilu -
i Vanderbilta u Nešvilu -
06:38
who also have electronic
medical records similar to ours.
medical records similar to ours.
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koji imaju elektronske
medicinske podatke slične našim.
medicinske podatke slične našim.
Hajde da vidimo da li mogu da nađu
slične pacijente
slične pacijente
06:41
Let's see if they can find
similar patients
similar patients
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2020
06:43
with the one P, the other P,
the glucose measurements
the glucose measurements
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sa jednim P, drugim P, merama glukoze
06:46
in that range that we need.
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u opsegu koji nam je potreban.
06:48
God bless them, Vanderbilt
in one week found 40 such patients,
in one week found 40 such patients,
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Bog ih blagoslovio, Vanderbilt je pronašao
40 takvih pacijenata za nedelju dana,
40 takvih pacijenata za nedelju dana,
06:53
same trend.
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utvrđena je ista tendencija.
06:55
Harvard found 100 patients, same trend.
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Harvard je pronašao 100 pacijenata,
ista tendencija.
ista tendencija.
06:59
So at the end, we had 150 patients
from three diverse medical centers
from three diverse medical centers
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Tako smo na kraju imali 150 pacijenata
iz tri različita medicinska centra
iz tri različita medicinska centra
07:03
that were telling us that patients
getting these two drugs
getting these two drugs
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koji su nam ukazivali da pacijenti
koji uzimaju ova dva leka
koji uzimaju ova dva leka
07:07
were having their glucose bump
somewhat significantly.
somewhat significantly.
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imaju donekle značajan porast glukoze.
07:10
More interestingly,
we had left out diabetics,
we had left out diabetics,
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Što je još zanimljivije,
izostavili smo dijabetičare,
izostavili smo dijabetičare,
07:13
because diabetics already
have messed up glucose.
have messed up glucose.
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jer dijabetičari već imaju
poremećenu glukozu.
poremećenu glukozu.
Kada smo pogledali glukozu dijabetičara,
07:15
When we looked
at the glucose of diabetics,
at the glucose of diabetics,
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07:17
it was going up 60 milligrams
per deciliter, not just 20.
per deciliter, not just 20.
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bila je povišena
za 3,3 mmol/l, ne samo 1,1.
za 3,3 mmol/l, ne samo 1,1.
07:21
This was a big deal, and we said,
"We've got to publish this."
"We've got to publish this."
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Ovo je bila velika stvar i rekli smo:
„Moramo da objavimo ovo.“
„Moramo da objavimo ovo.“
07:25
We submitted the paper.
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Predali smo rad.
Sasvim je obuhvatao
dokaze zasnovane na podacima,
dokaze zasnovane na podacima,
07:26
It was all data evidence,
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podacima iz Uprave za hranu i lekove,
podacima iz Stenforda,
podacima iz Stenforda,
07:28
data from the FDA, data from Stanford,
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2483
07:31
data from Vanderbilt, data from Harvard.
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439081
1946
iz Vanderbilta i Harvarda.
07:33
We had not done a single real experiment.
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Nismo sproveli nijedan pravi eksperiment.
Ipak, bili smo nervozni.
07:36
But we were nervous.
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07:38
So Nick, while the paper
was in review, went to the lab.
was in review, went to the lab.
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Zato je Nik otišao u laboratoriju
dok je rad bio pod razmatranjem.
dok je rad bio pod razmatranjem.
Našli smo nekog ko se razumeo
u laboratorijske stvari.
u laboratorijske stvari.
07:41
We found somebody
who knew about lab stuff.
who knew about lab stuff.
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07:44
I don't do that.
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Ja to ne radim.
07:45
I take care of patients,
but I don't do pipettes.
but I don't do pipettes.
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Brinem se o pacijentima,
ali ne koristim pipete.
ali ne koristim pipete.
07:49
They taught us how to feed mice drugs.
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Naučili su nas kako da miševima
dajemo lekove.
dajemo lekove.
07:52
We took mice and we gave them
one P, paroxetine.
one P, paroxetine.
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Uzeli smo miševe
i dali im jedan P, paroksetin.
i dali im jedan P, paroksetin.
07:55
We gave some other mice pravastatin.
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Nekim drugim miševima
smo dali pravastatin,
smo dali pravastatin,
07:57
And we gave a third group
of mice both of them.
of mice both of them.
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3595
a trećoj grupi miševa smo dali oba.
08:01
And lo and behold, glucose went up
20 to 60 milligrams per deciliter
20 to 60 milligrams per deciliter
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I gle čuda, glukoza se popela
za 1,1 do 3,3 mmol/l kod miševa.
za 1,1 do 3,3 mmol/l kod miševa.
08:05
in the mice.
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Rad je prihvaćen samo na osnovu
informatičkih dokaza,
informatičkih dokaza,
08:07
So the paper was accepted
based on the informatics evidence alone,
based on the informatics evidence alone,
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3158
08:10
but we added a little note at the end,
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ali smo na kraju dodali malu belešku
u kojoj smo naveli da, uzgred,
ako ovo date miševima, poveća se.
ako ovo date miševima, poveća se.
08:12
saying, oh by the way,
if you give these to mice, it goes up.
if you give these to mice, it goes up.
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2899
08:15
That was great, and the story
could have ended there.
could have ended there.
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2508
To je bilo sjajno,
priča se mogla tu završiti.
priča se mogla tu završiti.
08:17
But I still have six and a half minutes.
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1997
Međutim, imam još šest i po minuta.
08:19
(Laughter)
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2807
(Smeh)
08:22
So we were sitting around
thinking about all of this,
thinking about all of this,
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Sedeli smo tako i razmišljali o ovome
i ne sećam se ko se setio toga,
ali neko je rekao:
ali neko je rekao:
08:25
and I don't remember who thought
of it, but somebody said,
of it, but somebody said,
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2735
„Pitam se da li pacijenti
koji uzimaju ova dva leka
koji uzimaju ova dva leka
08:28
"I wonder if patients
who are taking these two drugs
who are taking these two drugs
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3201
08:31
are noticing side effects
of hyperglycemia.
of hyperglycemia.
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primećuju nuspojave hiperglikemije.
08:34
They could and they should.
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1496
Mogli bi da osete i trebalo bi.
08:36
How would we ever determine that?"
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1877
Kako bismo uopšte to ustanovili?“
Rekli smo, pa, šta ćeš uraditi?
08:39
We said, well, what do you do?
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1443
08:41
You're taking a medication,
one new medication or two,
one new medication or two,
185
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2580
Uzimaš jedan lek, jedan ili dva nova leka
08:43
and you get a funny feeling.
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1538
i dobiješ čudan osećaj.
08:45
What do you do?
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1151
Šta onda radiš?
08:46
You go to Google
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1151
Odeš na Gugl
08:47
and type in the two drugs you're taking
or the one drug you're taking,
or the one drug you're taking,
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3349
i uneseš dva leka koja uzimaš
ili jedan lek koji uzimaš
ili jedan lek koji uzimaš
08:50
and you type in "side effects."
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i uneseš „nuspojave“.
08:52
What are you experiencing?
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520534
1356
Šta je to što doživljavate?
08:54
So we said OK,
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1151
Rekli smo, u redu,
08:55
let's ask Google if they will share
their search logs with us,
their search logs with us,
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hajde da pitamo Gugl da li hoće
da podele sa nama unose pretraga,
da podele sa nama unose pretraga,
08:58
so that we can look at the search logs
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tako da možemo da ih pogledamo
09:00
and see if patients are doing
these kinds of searches.
these kinds of searches.
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2565
i vidimo da li pacijenti
sprovode takve pretrage.
sprovode takve pretrage.
09:02
Google, I am sorry to say,
denied our request.
denied our request.
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Gugl je, nažalost, odbio naš zahtev.
Tako da sam se baš osećao loše.
09:06
So I was bummed.
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534819
1151
Bio sam na večeri sa kolegom
koji radi na istraživanjima u Majkrosoftu
koji radi na istraživanjima u Majkrosoftu
09:07
I was at a dinner with a colleague
who works at Microsoft Research
who works at Microsoft Research
198
535994
3166
09:11
and I said, "We wanted to do this study,
199
539184
1941
i rekao sam: „Hteli smo
da sprovedemo istraživanje
da sprovedemo istraživanje
09:13
Google said no, it's kind of a bummer."
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1880
i Gugl je odbio, baš bezveze.“
09:15
He said, "Well, we have
the Bing searches."
the Bing searches."
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2086
Odgovorio je: „Pa, mi imamo
pretrage sa Binga.“
pretrage sa Binga.“
09:18
(Laughter)
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3483
(Smeh)
09:22
Yeah.
203
550805
1267
Da.
09:24
That's great.
204
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1151
To je sjajno.
Sada sam se osećao kao da -
09:25
Now I felt like I was --
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1151
09:26
(Laughter)
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1000
(Smeh)
09:27
I felt like I was talking to Nick again.
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2412
Kao da ponovo pričam sa Nikom.
09:30
He works for one of the largest
companies in the world,
companies in the world,
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2624
Radi u jednoj od najvećih
kompanija na svetu
kompanija na svetu
i već pokušavam da učinim
da se oseća bolje.
da se oseća bolje.
09:33
and I'm already trying
to make him feel better.
to make him feel better.
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2206
Međutim, rekao je: „Ne, Ras,
možda me nisi razumeo.
možda me nisi razumeo.
09:35
But he said, "No, Russ --
you might not understand.
you might not understand.
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2445
Ne samo da imamo pretrage sa Binga,
09:37
We not only have Bing searches,
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1500
već ako koristiš Internet Eksplorer
da bi pretraživao na Guglu,
da bi pretraživao na Guglu,
09:39
but if you use Internet Explorer
to do searches at Google,
to do searches at Google,
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3340
09:42
Yahoo, Bing, any ...
213
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1891
Jahuu, Bingu, gde god,
09:44
Then, for 18 months, we keep that data
for research purposes only."
for research purposes only."
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3643
tada čuvamo te podatke 18 meseci
samo u svrhe istraživanja.“
samo u svrhe istraživanja.“
Uzviknuo sam: „To je već druga priča!“
09:48
I said, "Now you're talking!"
215
576254
1936
To je bio Erik Horvic,
moj prijatelj sa Majkrosofta.
moj prijatelj sa Majkrosofta.
09:50
This was Eric Horvitz,
my friend at Microsoft.
my friend at Microsoft.
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2198
09:52
So we did a study
217
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1695
Tako smo sproveli istraživanje
09:54
where we defined 50 words
that a regular person might type in
that a regular person might type in
218
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4619
gde smo odredili 50 reči
koje bi bilo koja osoba mogla uneti
koje bi bilo koja osoba mogla uneti
09:58
if they're having hyperglycemia,
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1602
ako imaju hiperglikemiju,
10:00
like "fatigue," "loss of appetite,"
"urinating a lot," "peeing a lot" --
"urinating a lot," "peeing a lot" --
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588424
4762
kao što su „premor“, „gubitak apetita“,
„učestalo mokrenje“, „često piškanje“ -
„učestalo mokrenje“, „često piškanje“ -
oprostite, ali to je jedna od stvari
koje biste mogli uneti.
koje biste mogli uneti.
10:05
forgive me, but that's one
of the things you might type in.
of the things you might type in.
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2767
Dakle, imali smo 50 fraza
koje smo nazvali „dijabetskim rečima“.
koje smo nazvali „dijabetskim rečima“.
10:08
So we had 50 phrases
that we called the "diabetes words."
that we called the "diabetes words."
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2790
10:10
And we did first a baseline.
223
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2063
Prvo smo ustanovili polaznu liniju.
10:12
And it turns out
that about .5 to one percent
that about .5 to one percent
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2704
Ispostavilo se da oko 0,5 do 1 posto
10:15
of all searches on the Internet
involve one of those words.
involve one of those words.
225
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2982
svih pretraga na internetu
obuhvata jednu od ovih reči.
obuhvata jednu od ovih reči.
10:18
So that's our baseline rate.
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1742
To je naša osnova.
10:20
If people type in "paroxetine"
or "Paxil" -- those are synonyms --
or "Paxil" -- those are synonyms --
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4143
Ako ljudi unesu „paroksetin“
ili „Paksil“ - to su sinonimi -
ili „Paksil“ - to su sinonimi -
10:24
and one of those words,
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1215
i jednu od ovih reči,
10:25
the rate goes up to about two percent
of diabetes-type words,
of diabetes-type words,
229
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4890
dolazi do porasta od oko dva posto
za reči koje odgovaraju dijabetesu
za reči koje odgovaraju dijabetesu
10:30
if you already know
that there's that "paroxetine" word.
that there's that "paroxetine" word.
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3044
ako već znate da je prisutna
ta reč „paroksetin“.
ta reč „paroksetin“.
10:34
If it's "pravastatin," the rate goes up
to about three percent from the baseline.
to about three percent from the baseline.
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4547
Ako je u pitanju „pravastatin“, porast je
oko tri posto u odnosu na polaznu liniju.
oko tri posto u odnosu na polaznu liniju.
10:39
If both "paroxetine" and "pravastatin"
are present in the query,
are present in the query,
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4390
Ako su u upitu prisutni
i „paroksetin“ i „pravastatin“,
i „paroksetin“ i „pravastatin“,
10:43
it goes up to 10 percent,
233
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1669
porast je oko 10 posto,
10:45
a huge three- to four-fold increase
234
633278
3461
ogromno trostruko
do četvorostruko povećanje
do četvorostruko povećanje
10:48
in those searches with the two drugs
that we were interested in,
that we were interested in,
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636763
3389
u tim pretragama sa dva leka
koja su nas zanimala
koja su nas zanimala
10:52
and diabetes-type words
or hyperglycemia-type words.
or hyperglycemia-type words.
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3566
i reči vezanih za dijabetes
ili hiperglikemiju.
ili hiperglikemiju.
10:56
We published this,
237
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1265
Objavili smo ovo
10:57
and it got some attention.
238
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1466
i pridobilo je pažnju.
10:58
The reason it deserves attention
239
646995
1778
Razlog zbog kojeg zaslužuje pažnju
11:00
is that patients are telling us
their side effects indirectly
their side effects indirectly
240
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4312
je što nam pacijenti
indirektno govore o svojim nuspojavama
indirektno govore o svojim nuspojavama
11:05
through their searches.
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1156
kroz svoje pretrage.
11:06
We brought this
to the attention of the FDA.
to the attention of the FDA.
242
654313
2138
Izneli smo ovo
pred Upravu za hranu i lekove.
pred Upravu za hranu i lekove.
11:08
They were interested.
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1269
Bili su zainteresovani.
11:09
They have set up social media
surveillance programs
surveillance programs
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3606
Postavili su programe
za nadgledanje društvenih medija
za nadgledanje društvenih medija
kako bi sarađivali sa Majkrosoftom,
11:13
to collaborate with Microsoft,
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1751
koji je imao finu infrastrukturu
za sprovođenje ovog, i drugima,
za sprovođenje ovog, i drugima,
11:15
which had a nice infrastructure
for doing this, and others,
for doing this, and others,
246
663173
2794
11:17
to look at Twitter feeds,
247
665991
1282
da bi pregledali unose na Tviteru,
11:19
to look at Facebook feeds,
248
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1716
unose na Fejsbuku,
da bi pregledali unose pretraga,
11:21
to look at search logs,
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669037
1311
11:22
to try to see early signs that drugs,
either individually or together,
either individually or together,
250
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4909
da bi pokušali da uoče rane znake
da lekovi, bilo zasebno ili zajedno,
da lekovi, bilo zasebno ili zajedno,
11:27
are causing problems.
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stvaraju probleme.
Šta ovde smatram značajnim?
Zašto sam ispričao ovu priču?
Zašto sam ispričao ovu priču?
11:28
What do I take from this?
Why tell this story?
Why tell this story?
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2174
11:31
Well, first of all,
253
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1207
Pa, pre svega, sada imamo nadu
u podatke velikih i malih razmera
u podatke velikih i malih razmera
11:32
we have now the promise
of big data and medium-sized data
of big data and medium-sized data
254
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11:36
to help us understand drug interactions
255
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2918
koji će nam pomoći da razumemo
interakcije lekova
interakcije lekova
11:39
and really, fundamentally, drug actions.
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i ono što je zaista u osnovi,
dejstva lekova.
dejstva lekova.
11:41
How do drugs work?
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Kako lekovi deluju?
11:43
This will create and has created
a new ecosystem
a new ecosystem
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Ovo će stvoriti i stvorilo je
novi ekosistem
novi ekosistem
11:46
for understanding how drugs work
and to optimize their use.
and to optimize their use.
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za razumevanje dejstva lekova
i njihovo najoptimalno korišćenje.
i njihovo najoptimalno korišćenje.
11:50
Nick went on; he's a professor
at Columbia now.
at Columbia now.
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Nik je nastavio sa ovim;
danas je profesor na Kolumbiji.
danas je profesor na Kolumbiji.
Ovo je sproveo u svom doktoratu
na stotinama parova lekova.
na stotinama parova lekova.
11:52
He did this in his PhD
for hundreds of pairs of drugs.
for hundreds of pairs of drugs.
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11:57
He found several
very important interactions,
very important interactions,
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Otkrio je nekoliko
veoma važnih interakcija
veoma važnih interakcija
11:59
and so we replicated this
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i tako smo ovo ponovili
12:00
and we showed that this
is a way that really works
is a way that really works
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i pokazali da je ovaj način
zaista delotvoran
zaista delotvoran
12:03
for finding drug-drug interactions.
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u pronalaženju interakcija između lekova.
12:06
However, there's a couple of things.
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Međutim, u igri je još par stvari.
12:08
We don't just use pairs
of drugs at a time.
of drugs at a time.
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Ne koristimo samo parove lekova
u isto vreme.
u isto vreme.
12:11
As I said before, there are patients
on three, five, seven, nine drugs.
on three, five, seven, nine drugs.
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Kao što sam već rekao,
ima pacijenata koji uzimaju tri,
pet, sedam, devet lekova.
pet, sedam, devet lekova.
12:15
Have they been studied with respect
to their nine-way interaction?
to their nine-way interaction?
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Jesu li oni izučavani imajući u vidu
njihovu devetostruku interakciju?
njihovu devetostruku interakciju?
12:19
Yes, we can do pair-wise,
A and B, A and C, A and D,
A and B, A and C, A and D,
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Da, možemo da uzemo parove,
A i B, A i C, A i D,
A i B, A i C, A i D,
12:23
but what about A, B, C,
D, E, F, G all together,
D, E, F, G all together,
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ali šta ako A, B, C, D, E, F i G zajedno,
12:28
being taken by the same patient,
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ako ih uzima isti pacijent,
12:29
perhaps interacting with each other
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možda međusobno ulaze u interakciju
12:32
in ways that either makes them
more effective or less effective
more effective or less effective
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na načine koji ih čine
bilo više ili manje efikasnim
bilo više ili manje efikasnim
12:35
or causes side effects
that are unexpected?
that are unexpected?
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ili stvaraju neočekivane nuspojave?
12:38
We really have no idea.
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Zaista nemamo predstavu.
12:40
It's a blue sky, open field
for us to use data
for us to use data
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To je ogromno otvoreno polje
u kome možemo koristiti podatke
u kome možemo koristiti podatke
12:43
to try to understand
the interaction of drugs.
the interaction of drugs.
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da bismo pokušali da razumemo
interakciju lekova.
interakciju lekova.
12:46
Two more lessons:
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Još dve lekcije.
12:48
I want you to think about the power
that we were able to generate
that we were able to generate
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Želim da razmislite o moći
koji smo uspeli da proizvedemo podacima
koji smo uspeli da proizvedemo podacima
12:52
with the data from people who had
volunteered their adverse reactions
volunteered their adverse reactions
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od ljudi koji su dobrovoljno prijavili
svoje neželjene reakcije
svoje neželjene reakcije
12:57
through their pharmacists,
through themselves, through their doctors,
through themselves, through their doctors,
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preko njihovih farmaceuta,
njih samih, njihovih doktora,
njih samih, njihovih doktora,
ljudi koji su dozvolili
pristup bazama podataka
pristup bazama podataka
13:00
the people who allowed the databases
at Stanford, Harvard, Vanderbilt,
at Stanford, Harvard, Vanderbilt,
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na Stenfordu, Harvardu, Vanderbiltu
kako bi bile korišćene u istraživanju.
kako bi bile korišćene u istraživanju.
13:04
to be used for research.
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Ljudi su zabrinuti zbog podataka.
13:05
People are worried about data.
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13:07
They're worried about their privacy
and security -- they should be.
and security -- they should be.
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Zabrinuti su zbog svoje privatnosti
i bezbednosti i treba da budu.
i bezbednosti i treba da budu.
13:10
We need secure systems.
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Potrebni su nam bezbedni sistemi.
13:11
But we can't have a system
that closes that data off,
that closes that data off,
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Ipak, ne smemo imati sistem
koji blokira pristup tim podacima
koji blokira pristup tim podacima
13:15
because it is too rich of a source
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jer su previše bogat izvor
13:17
of inspiration, innovation and discovery
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inspiracije, inovacije i otkrića
13:21
for new things in medicine.
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za nove stvari u medicini.
13:24
And the final thing I want to say is,
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Poslednje što želim da kažem
13:26
in this case we found two drugs
and it was a little bit of a sad story.
and it was a little bit of a sad story.
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je da smo ovde otkrili dva leka
i to je bila pomalo tužna priča.
i to je bila pomalo tužna priča.
Dva leka su stvarala probleme.
13:29
The two drugs actually caused problems.
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13:31
They increased glucose.
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Povećavala su nivo glukoze.
13:33
They could throw somebody into diabetes
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Mogli su da prouzrokuju
dijabetes kod nekoga
dijabetes kod nekoga
13:35
who would otherwise not be in diabetes,
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ko inače ne bi imao dijabetes,
13:37
and so you would want to use
the two drugs very carefully together,
the two drugs very carefully together,
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tako da biste hteli da koristite
ta dva leka vrlo pažljivo zajedno,
ta dva leka vrlo pažljivo zajedno,
možda ne zajedno,
13:41
perhaps not together,
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13:42
make different choices
when you're prescribing.
when you're prescribing.
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doneti drugačije odluke
prilikom propisivanja lekova.
prilikom propisivanja lekova.
13:44
But there was another possibility.
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Međutim, postoji još jedna mogućnost.
13:46
We could have found
two drugs or three drugs
two drugs or three drugs
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Mogli smo da otkrijemo dva ili tri leka
13:48
that were interacting in a beneficial way.
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koji ulaze u interakciju
na povoljan način.
na povoljan način.
13:51
We could have found new effects of drugs
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Mogli smo naći nova dejstva lekova
13:54
that neither of them has alone,
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koje nijedan od njih nema zasebno,
13:56
but together, instead
of causing a side effect,
of causing a side effect,
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već zajedno, umesto da uzrokuju nuspojavu,
13:59
they could be a new and novel treatment
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mogu biti novi način lečenja
14:01
for diseases that don't have treatments
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za bolesti koje se ne leče
ili gde način lečenja nije delotvoran.
14:03
or where the treatments are not effective.
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Ako razmislite o lečenju lekovima danas,
14:05
If we think about drug treatment today,
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14:07
all the major breakthroughs --
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svim većim otkrićima -
14:09
for HIV, for tuberculosis,
for depression, for diabetes --
for depression, for diabetes --
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kod HIV-a, tuberkuloze,
depresije, dijabetesa -
depresije, dijabetesa -
14:13
it's always a cocktail of drugs.
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uvek je tu mešavina lekova.
14:16
And so the upside here,
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Dobra strana u ovome,
14:18
and the subject for a different
TED Talk on a different day,
TED Talk on a different day,
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kao i tema nekog drugog TED govora
nekog drugog dana
nekog drugog dana
14:21
is how can we use the same data sources
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je kako možemo da koristimo
iste izvore podataka
iste izvore podataka
14:24
to find good effects
of drugs in combination
of drugs in combination
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da bismo otkrili dobra dejstva
lekova u kombinaciji
lekova u kombinaciji
14:27
that will provide us new treatments,
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koji će nam pružiti nova lečenja,
14:29
new insights into how drugs work
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nove uvide u to kako lekovi deluju
14:31
and enable us to take care
of our patients even better?
of our patients even better?
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i omogućiti nam da se još bolje staramo
o našim pacijentima.
o našim pacijentima.
14:35
Thank you very much.
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Hvala vam mnogo.
14:36
(Applause)
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(Aplauz)
ABOUT THE SPEAKER
Russ Altman - Big data techno-optimist and internistRuss Altman uses machine learning to better understand adverse effects of medication.
Why you should listen
Professor of bioengineering, genetics, medicine and computer science at Stanford University, Russ Altman's primary research interests are in the application of computing and informatics technologies to problems relevant to medicine. He is particularly interested in methods for understanding drug actions at molecular, cellular, organism and population levels, including how genetic variation impacts drug response.
Altman received the U.S. Presidential Early Career Award for Scientists and Engineers, a National Science Foundation CAREER Award and Stanford Medical School's graduate teaching award. He has chaired the Science Board advising the FDA Commissioner and currently serves on the NIH Director’s Advisory Committee. He is a clinically active internist, the founder of the PharmGKB knowledge base, and advisor to pharmacogenomics companies.
Russ Altman | Speaker | TED.com