Mankind KvD

8/18/2019 Mankind KvD

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Talking about ScienceA lecture in the 6th Century course

“Mankind in the Universe”

by Kees van DeemterCom!uting Science de!t" University o#

Aberdeen


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$b%ectivity

• a ma%or theme in “Mankind in the Universe” – Can !eo!le kno& the universe'

(e"g" the )ig )ang man*made global &arming+ – Can !eo!le kno& ob%ectively &hat,s right'

(e"g" stem*cell research+• -hiloso!hical !ositions include

– .ealism – Anti*realism –

Constructivism• This lecture/ the e0!ression o# scienti1c

data and theories in language


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-lan o# the lecture

2" -ublishing scienti1c results

3" Using com!uters/ #rom data to te0t

4" (Science in daily li#e and !olitics+


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2" -ublishing Scienti1c.esults

Peer review: the main mechanism #or deciding&hether a result is &orth !ublishing (e"g" as a

journal article+

(2+Authors submit article(3+ 5ditors select e0!ert revie&ers (“!eers”+

(4+ .evie&ers assess article

(+ 5ditors decide/ acce!t7re%ect7revise

8# revise then authors may go back to (2+

Submissions as conference papers lack “revise”o!tion


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-eer revie& is no guaranteeagainst 9a&s

2" :uman #railty/ – Maybe the e0!erts lack in e0!ertise – -eers may disagree &ith each other –

(Maybe they don,t like the authors+ – (A dishonest !eer may re%ect then “steal”results+

-ossible solutions – Anonimity o# revie&er and7or revie&ee – Declaring con9icts o# interest

;o silver bullet" Much de!ends on the editor"


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-eer revie& is no guaranteeagainst 9a&s

3" -ublication bias

•

.evie&ers and editors are keen on“interesting” results"

• 8nteresting results are read eagerly

are o#ten


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So ho& about disa!!ointingresults'

• .esearch hy!othesis/ “activity 0 makesyou more likely to get cancer”

• 2=== !atients tested" >== do 0 >== don,t

do 0"0/ >= get cancernot 0/ >4 get cancer

• ?our hy!othesis is not con1rmed (the

trend even goes in the o!!osite direction+• ?our %ournal submission may be re%ected

because it,s not interesting enough"


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• ?our “negative” 1ndings may never get!ublished

• ?et they tell us something o# !otential value/ – Maybe 0 is unrelated to cancer – Maybe 0 makes you less likely to get cancer

• ;ote/ ?our e0!eriment does not sho&convincingly that 0 makes you less likely toget cancer" (>=7>4 is too small a di@erence+ –

Statisticians say/ the result is not signifcant• )ut others may have #ound similar negative

results


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Meta*analysis

• A stats analysis that tries to dra&conclusions #rom a set o#e0!eriments" (Meta/ “about”+

• Cham!ioned among others by theCochrane collaboration

• 8nstructive logo/


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The Cochrane logoe0!lained

• A landmark 2BB analysis o# the use o#steroids on !rematurely born babies/

– 3 studies had #ound a !ositive e@ect (signi1cant+

–

> studies had #ound no signi1cant e@ect – Doctors did not believe the e@ect until a meta*

analysis o# all studies together sho&ed a!ositive e@ect

•

)ack to our imaginary study o# cancer/A meta*analysis might have sho&n that0 makes you less likely to get cancer


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)ut

• those negative results &ill not be countedin the meta*analysis because they &erenever !ublished

• $mission o# “disa!!ointing” results couldeven result in the erroneous conclusionthat 0 makes you more likely to get cancer

EFoldacre 3==BG Bad Science" :ar!er-erennial


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4" Cheating

• Dishonesty about authorshi!/

!lagiarism

• Dishonesty about data and statistics


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-lagiarism

“taking someone else’s work and passing it oas one’s own”

• There is a grey area" 8 got my de1nition #romthe Mac,s Dictionary a!!lication" Do 8 have to

ackno&ledge this'• 8# you take someone else,s ideas then (try to+

say&ho had them 1rst

• 8# you also take someone else,s words

verbatim(#or more than %ust a #e& &ords+ then!ut


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-lagiarism and !eer revie&

• -eer revie& contains im!ortant sa#eguardsagainst !lagiarism" – $ne o# your revie&ers may have read that

earlier article

• )ut !eer revie& is no guarantee" – Hhat i# the article &as !ublished in Ia!anese'

• Still o@enders get caught" Moreover i# thedishonesty only concerned the authorshi!the im!lications #or science are limited – A victimless crime'


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8m!ro!er use o# data

8n science (as o!!osed to teaching+

this is a bigger !roblem than!lagiarism

(2+Conscious cheating

(3+Unconscious cheating


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Conscious cheating ('+

• Some notorious cases &here ita!!ears that data &ere intentionally#aked or distorted – Andre& Hake1eld,s &ork linking the

MM. vaccine to autism

– -arts o# the University o# 5ast Anglia,s

&ork on global &arming – :&ang Hoo*suk,s &ork on stem*cell

research and human cloning


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))C ;e&s 2> Dec 3==>

(+ Stem cell success J#aked,A South Korean cloning !ioneer has admitted#abricating results in key stem cell research acolleague claims" At least nine o# 22 stem cell

colonies used in a landmark research !a!er by Dr:&ang Hoo*suk &ere #aked said .oh Sung*il &hocollaborated on the !a!er" Dr :&ang &ants the US

%ournal Science to &ithdra& his !a!er on stem cellcloning Mr .oh said" Dr :&ang &ho is re!orted tobe receiving hos!ital treatment #or stress &as not

available #or comment" Science could not con1rm&hether it had received a re


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Unconscious cheating/observer bias

$ne e0!eriment/ Some !atients got a medicineagainst multi!le sclerosis others got a placebo

• >= o# trained observers (A+ kne& &ho got the

!lacebo• >= o# trained observers ()+ did not kno&• $bservers (A+ observed an im!rovement in the

condition o# !atients &ho &ere given the medicine• $bservers ()+ did not observe an im!rovement

;ose&orthy et al" The im!act o# blinding on the resultso# a randomiLed !lacebo*controlled multi!le sclerosisclinical trial" Neurology. 200!"#$S% S%".


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Unconscious cheating

• Rosenthal eect" -artici!ants &ere given!hotogra!hs o# !eo!le and ask to say &hetherthese &ere “success#ul in li#e”" – Some (A+ e0!erimenters &ere told that !artici!ants

%udge most !hotogra!hs as successul – $ther e0!erimenter ()+ &ere told that !artici!ants %udge most !hotogra!hs as unsuccessul

• -artici!ants su!ervised by A %udged

!hotogra!hs much more !ositively than thosesu!ervised by )

• Su!ervisors could only read out a set s!eech


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Unconscious cheating

• .osenthal e@ect (conclusion+/ by believing in agiven behaviour you can make this result comeabout

.osenthal ." 8nter!ersonal e0!ectations/ e@ects o# the

e0!erimenterJs hy!othesis" 8n/ .osenthal N .osno&(eds"+ &rtifact in Be'a(ioral )esearc'. New *ork+ N*$ &cademic ,ress! --$/2##

• .osenthal e@ect concerns e0!eriments &ith!eo!leO observations in !hysics can be haLardous

as &ell(e"g" &hen do you sto! running an e0!eriment'+

• $bserver bias N .osenthal e@ect are reasons #ormaking studies &ith human sub%ects double-blind


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• Cheating is not something done by a#e& criminals but something &e allneed to constantly be on guard against

– in science

– in daily li#e

• The science behind these !henomena

is interesting in itsel# • E)en Foldacre 3==G (again+


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Dubious uses o# statistics

• “There are lies damn lies andstatistics”(author unkno&n+

• This not an indictment o# numbers orstatistics

– Statistics is sa#e &hen !er#ormedcom!etentlybut errors are easy to make

– These can be conscious or unconscious


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$ne common abuse o#statistics

• Pailing to declare your research hy!othesis inadvance

• .ecall the “disa!!ointing” cancer study study – ?our research hy!othesis/ x makes cancer more

likely – ?ou #ound &eak indications #or the o!!osite/

x makes cancer less likely (>=7>4 not signi1cant+

– Su!!ose you had #ound strong indications #or this(e"g" =764 signi1cant+

– .e!orting this as a con1rmed hy!othesis &ould be&rong

– Stats is #or testing a !re*e0isting sus!icion

– Anything else is “data 1shing”


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$n to our ne0t to!ic


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3" Com!uters as authors/#rom data to te0t

• Measurement can give rise to a hugeamount o# numerical in#ormation e"g" – Monitoring !atients in intensive care – Climate !redictions/ 3 !etabyte (3 Q 2=2> bytes+

• -eo!le are bad at making sense o# this so&e use ;atural Ranguage Fenerationto let com!uters !roduce readable te0t

• At Aberdeen/ .eiter Turner Sri!ada Davy"

50am!le/ Turner,s !ollen level #orecastsdemo/

htt!/77&&&"csd"abdn"ac"uk7rturner7cgibin7!ollen"html

;eonatal 8CU ()abytalk

http://www.csd.abdn.ac.uk/~rturner/cgi_bin/pollen.htmlhttp://www.csd.abdn.ac.uk/~rturner/cgi_bin/pollen.html


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;eonatal 8CU ()abytalk!ro%ect+


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)aby Monitoring

S!$3 (S$:S+

5CF (:.+

Core Tem!erature (TC+

Arterial Rine

()lood -ressure+

-eri!heral Tem!erature(T-+

Transcutaneous -robe(C$$+


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8n!ut/ Sensor Data (> min,s+


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Some medical %argon

• Bradycardia/ &hen the heart rate is tooslo&

• ntubation/ !lacing a tube in the &ind!i!e

(e"g" #or o0ygen or drugs+• !i"#/ a metric o# o0ygen 9o&

• $ats/ o0ygen saturation levels

• %&& suction: “sucking” a&ay contaminated

secretions (&hich might cause !neumonia+• BP/ )lood -ressure

• 'R/ :eart rate


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Hritten by nurse

• 8n !re!aration #or re*intubation a bolus o# >=ug o# mor!hine isgiven at 2=4B &hen the Pi$3 V 4>" There is a momentarybradycardia and then the mean )- increases to =" The sats godo&n to B and take 3 mins to come back u!" The toe7coretem!erature ga! increases to 2"6 degrees"

• At 2=6 the baby is turned #or re*intubation and re*intubation is

com!lete by 22== the baby being bagged &ith 6= o0ygenbet&een tubes" During the re*intubation there have been somesigni1cant bradycardias do&n to 6=7min but the sats haveremained "( " The mean )- has varied bet&een 34 and >6 buthas no& settled at 4=" The central tem!erature has #allen to46"2WC and the !eri!heral tem!erature to 44"WC" The baby hasneeded u! to = o0ygen to kee! the sats u!"

• $ver the ne0t 2= mins the :. decreases to 2= and the mean)- V 4=*=" The sats #all &ith 5TT suction so the Pi$3 isincreased to = but by 2223 the Pi$3 is do&n to B"


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Fenerated by )abytalksystem

?ou sa& the baby bet&een 2=/4= and 22/23" :eart .ate (:.+ V 2" Core Tem!erature (T2+ V 4">" -eri!heral Tem!erature (T3+ V 46"4" Mean )lood-ressure (mean )-+ V 3" $0ygen Saturation (Sa$3+ V B6"

The tcm sensor &as re*sited"

By )*:+* $a"# had decreased to ," As a result Praction o# 8ns!ired$0ygen (P8$3+ &as set to 46" $a"# increased to ./" There had been a

bradycardia do&n to B=" -reviously >="= mics7min o# mor!hine had beenadministered" $ver the ne0t 2 minutes mean )- gradually increased to4"

)y 22/== the baby had been hand*bagged a number o# times causing 3successive bradycardias" She &as success#ully re*intubated a#ter 3attem!ts" The baby &as sucked out t&ice"

At 22/=3 P8$3 &as raised to B"

)y 22/=6 the baby had been sucked out a number o# times" -reviously T3had increased to 4"4" $ver the ne0t 2 minutes :. decreased to 2="

P8$3 &as lo&ered to 62"

:o& the com!uter generates


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:o& the com!uter generatesthe te0t

1four stages+ just a sketc' 3

A kind o# data mining/ using com!utersto analyse N summarise data

2" Signal analysis

3" Data abstraction

4" Content Determination" Saying it in 5nglish

(alternative/ gra!hs7diagrams+


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2" Signal Analysis

5ssentially a collection o# mathematical tools

• Detect trends !atterns events etc in the

data – ()lood o0ygen levels+ increasing

– Do&n&ard s!ike (in heart rate+

– 4tc"

• Se!arate real data #rom arte#acts

– Sensors can mal#unction


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3" Data Abstraction (2+

• Detect higher*level events in the data

– )radycardia

– Sensor 9a!!ing against skin(in#erred #rom sha!es in data+

• ;ot %ust maths/ medical kno&ledgere


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3" Data Abstraction (3+

• Determine relative im!ortance o# events

• Rink related events – )lood $3 #alls there#ore $3 level in incubator

is increased 1reason for t'e action3

– :. u! because baby is being handled 1cause3

• -otentially a strong !oint o# te0tsummaries – Fra!hs7diagrams seldom sho& such links


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4" Content Determination

• Determine &hat,s im!ortant enoughto talk about"

• This – de!ends on !ur!ose N conte0t o# te0t

• :o& much s!ace7time is available'

• Saying A may #orce you to say ) as &ell

– uses im!ortance rating(#rom Data Abstraction (3++


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" Saying it in 5nglish

Rots o# di@erent issues" Por instance

• :o& to organise the te0t as a &hole' 1e.g.+5'ronologically6 7rganised in paragrap's63

• Hhat sentence !atterns to use' 1e.g.+ &cti(e mood6 7ne fact per sentence63

– “ have varied bet&een 34 and >6”

• :o& to re#er' 1e.g. refer to a time saying

“at $0"”+ or “after intu8ation”63• Hhat &ords to use

1e.g.+ a(oiding medical jargon63


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$b%ectivity issues

• 8n signal analysis/ Hhat,s an event' – 8magine three short do&n&ard s!ikes in :.

• Three events or one'

• 8n data abstraction/ – Conce!ts like “bradycardia” are theory

laden• 3= years #rom no& a di@erent de1nition'

– Causality is !roblematic• Has :. increase caused by handling'

– Many thresholds are a bit arbitrary


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$b%ectivity issues

8n Content Determination

• Su!!ose 4">C counts as a #ever"Su!!ose this lasts #or only 2=minutes – 8s this &orth saying' (Can it be relevant

#or clinical decisions'+


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• :o& long does your tem!erature needto be above threshold to call it a fe(er '

• :o& long be#ore &e call something a

8radycardia'• Hhat makes a momentary bradycardia

or signi9cant bradycardias'

• :o& long can a #ever last be#ore it is&orth re!orting'


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Using vague &ords

• Hhat does it take #or SATs to be “$K”' – As SATs decrease medical com!lications

become more likely

– This is not a ?es7;o thing but something gradual

– A!!lication o# vague &ords can be a matter o# %udgment• Should a !atient,s age be taken into account'

• :is7her medical condition' The nurse,s e0!ectations'

• Com!uters struggle using vague &ords

(“signi9cant”+ “momentary”+ “7:” + a!!ro!riately – $#ten avoided altogether (see earlier e0am!le+


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Using vague or cris! &ords

• Science o#ten re!laces vague conce!tsby cris! ones e"g" “obese V )M8 X 4=” – Such de1nitions make a value %udgment about

&hat,s good or bad #or one,s health (e"g"motivated by statistical data about li#ee0!ectancy+ – :ence they are theory laden

–

These value %udgments may not al&ays matchdoctors, assessment• There is more to morbide obesity than )M8


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• ;ot %ust in medical a@airs

•

Consider &eather #orecasting/


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T&o &eather #orecasters1;s t'e cup 'alf full or 'alf empty63

2" “$unny spells and mainly dry" Tem!eratures u! to 2>C this a#ternoonand &hen the sun is out it &ill #eel

!leasant enough in s!ite o# a moderatenortherly breeLe"”

3" “0loudy at times &ith a slight chanceo# rain" Tem!eratures only reaching 2>C

this a#ternoon and &ith any rain aroundand a moderate northerly breeLe it &ill#eel cooler"”

di i l


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.eading material on“Com!uters as Authors”

• E.eiter 3==G An architecture #or Data*to*te0t systems" 8n -roceedings o# 5;RF*="

(Con#erence !a!er on the ;RF challenges involved in ma!!ing data to te0t+

• Evan Deemter 3=2=G Not 4


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8n summary

• Com!lete ob%ectivity may not al&aysbe achievable


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8n summary

• Com!lete ob%ectivity may not al&aysbe achievable

• But we can keep trying1

4 S i i d il li# d


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4" Science in daily li#e and!olitics

• Too large a to!ic to s


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8nstead let,s have a brie#&ra!*u! o# the course

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