P ocesamien o del Lenguaje Na u al pa a la
es uc u ación de his o iales clínicos de cánce
de mama
S uc u ing elec onic heal h eco ds o b eas
cance wi h Na u al Language P ocessing
T abajo de Fin de G ado
Cu so 2022–2023
Au o
Ál a o Ga cía Ba agán
Di ec o
Víc o Robles Fo cada
Tu o UCM: José Ignacio Hidalgo Pé ez
Colabo ado
E nes ina Menasal as
G ado en Ingenie ía In o má ica
Facul ad de In o má ica
Uni e sidad Complu ense de Mad id
P ocesamien o del Lenguaje Na u al pa a
la es uc u ación de his o iales clínicos de
cánce de mama
S uc u ing elec onic heal h eco ds o
b eas cance wi h Na u al Language
P ocessing
T abajo de Fin de G ado en Ingenie ía In o má ica
Au o
Ál a o Ga cía Ba agán
Di ec o
Víc o Robles Fo cada
Tu o UCM: José Ignacio Hidalgo Pé ez
Colabo ado
E nes ina Menasal as
Con oca o ia: Junio 2023
G ado en Ingenie ía In o má ica
Facul ad de In o má ica
Uni e sidad Complu ense de Mad id
29 de mayo de 2023
Dedica o ia
A mi mad e, po sabe de p ime a mano que
es el cánce de mama y supe a lo

Ag adecimien os
A odo el g upo de in es igación del labo a o io MEDAL, comenzando po Víc o
y E nes, quienes desde el p ime día me han hecho sen i como en casa, y e minando
con odos los es udian es con quienes he enido la opo unidad de abaja y c ece
an o p o esionalmen e como pe sonalmen e.
ii
Resumen
P ocesamien o del Lenguaje Na u al pa a la es uc-
u ación de his o iales clínicos de cánce de mama
Los his o iales clínicos es án esc i os en lenguaje na u al y, po an o, es in-
o mación no es uc u ada. El obje i o del p oyec o es es uc u a la in o mación
de his o iales clínicos de pacien es con cánce de mama de un hospi al público de
Mad id con el in de consegui in o mación u il pa a los medicos. De es a o ma,
se p opone ealiza la es uc u ación a a és del uso de edes neu onales p o undas
pa a la clasi icacion de las en idades: NER (Named En i y Recogni ion), en conjun o
con o as écnicas de NLP. Finalmen e se gene a á una base de da os semies uc-
u ada en o ma o json con los his o iales, que pod á se p ocesada pos e io men e
con di e en es in enciones.
Palab as cla e
P ocesamien o del Lenguaje Na u al (PNL), Ap endizaje P o undo, T ans o -
me s, Reconocimien o de en idades nomb adas, Ex acción de in o mación, Ex ac-
ción del diagnós ico del cánce , Cánce de mama
ix

Lis o igu es
2.1. Spacy okeniza ion [1] . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.2. Wo d embbedings ep esen ed in 3D [2] . . . . . . . . . . . . . . . . . 11
2.3. Rep esen a ion o an RNN . . . . . . . . . . . . . . . . . . . . . . . 12
2.4. LSTM cell a chi ec u e [3] . . . . . . . . . . . . . . . . . . . . . . . . 13
2.5. BILSTMlaye o NER.......................... 14
2.6. La ge Language Models: A New Moo e’s Law ? [4] . . . . . . . . . . 15
2.7. Compa ing CNN (padding okens a e omi ed), RNN, and sel -a en ion
a chi ec u es[5].............................. 16
2.8. BERT s.GPT[6] ............................ 17
2.9. Ex ac ing cance concep s using BERT. . . . . . . . . . . . . . . . . 17
2.10.BERT ine uning[6] ........................... 18
2.11.SpaCypipeline[1]............................. 21
2.12. Ins ance o spaCy syn ac ic ee . . . . . . . . . . . . . . . . . . . . . 22
2.13. Example o IOB o ma . . . . . . . . . . . . . . . . . . . . . . . . . . 26
2.14. Example o CONLL o ma . . . . . . . . . . . . . . . . . . . . . . . 26
2.15. 5- old c oss alida ion . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
4.1. T ans o ming Clinical No es in o S uc u ed JSON . . . . . . . . . . 33
4.2. Co puscollec ion ............................. 34
4.3. Modelsc ea ion.............................. 34
4.4. NCP: NLP Cance Pipeline . . . . . . . . . . . . . . . . . . . . . . . 35
4.5. Technology pipeline . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
4.6. Au oma ed pipeline in ba ches . . . . . . . . . . . . . . . . . . . . . . 37
4.7. P e-p ocessing o he no es . . . . . . . . . . . . . . . . . . . . . . . . 38
4.8. Example o an anno a ed clinical no e . . . . . . . . . . . . . . . . . . 39
4.9. Nega ion and Unce ain y Anno a ions . . . . . . . . . . . . . . . . . 41
4.10. JSON o ma o s uc u ing b eas cance in o ma ion . . . . . . . . 42
x ii
5.1. G aphical ep esen a ion o Neg Unce Co pus coun ing . . . . . . . 45
5.2. G aphical ep esen a ion o Clinical co pus coun ing . . . . . . . . . . 46
Lis o ables
2.1. Numbe o no es pe se ice and ype . . . . . . . . . . . . . . . . . . 8
2.2. P e ained models de ails [7] . . . . . . . . . . . . . . . . . . . . . . . 19
2.3. spaCyNLPPipeline ........................... 22
2.4. spaCy Command Line In e ace . . . . . . . . . . . . . . . . . . . . . 23
4.1. De ini ion o he en i ies . . . . . . . . . . . . . . . . . . . . . . . . . 40
4.2. No maliza ion............................... 42
5.1. Model hype -pa ame e s . . . . . . . . . . . . . . . . . . . . . . . . . 44
5.2. In o ma ion abou NER co pus . . . . . . . . . . . . . . . . . . . . . 44
5.3. Resul s om Nega ion and Unce ainly Co pus pe en i y ype . . . . 47
5.4. Gene al esul s om Clinical Co pus . . . . . . . . . . . . . . . . . . 47
5.5. Resul s om Clinical co pus pe en i y ype . . . . . . . . . . . . . . 47
5.6. Discussion o Clinical NER model . . . . . . . . . . . . . . . . . . . . 49
xix
Chap e 1
In oduc ion
“Science is no only a discipline o eason, bu also o
omance and passion”
— S ephen Hawking
Cance emains one o he main public heal h p oblems, anked as he leading
cause o dea h globally [8]. Acco ding o he Wo ld Heal h O ganiza ion (WHO)1,
cance caused nea ly 10 million dea hs wo ldwide in 2020. In pa icula , b eas
cance is cu en ly he mos common cance globally, accoun ing o 12.5% o all new
annual cance cases wo ldwide2. In 2020, he e we e 2.3 million women diagnosed
wi h b eas cance and 685,000 dea hs a ound he wo ld.
The p ocess o diagnosing and ea ing cance pa ien s gene a es a huge amoun
o in o ma ion ha desc ibes symp oms, he cance diagnosis, amily his o y, ea -
men s, and he e olu ion o he pa ien a he ime. Physicians egis e his in o -
ma ion in Elec onic Heal h Reco ds (EHR) using clinical no es w i en in na a i e
o m. Ex ac ing and mining his in o ma ion is c ucial o suppo oncology esea ch,
design ea men plans, and imp o e pa ien ou comes [9].
Howe e , ex ac ing in o ma ion om clinical na a i es is a challenge due o he
complexi y o na u al language [10]. Mo eo e , clinical ex s a e w i en by highly
skilled physicians and nu ses using domain-speci ic e ms, unde ime p essu e, wi h
ich and complex ja gon, which makes hese ex s di e om hose o o he domains.
In ecen yea s he use o Na u al Language P ocessing (NLP) in he biomedical
domain has inc eased he possibili y o au oma ically ex ac ing in o ma ion om
clinical na a i es. NLP is a sub- ield o Compu e Science (CS), and A i icial In-
elligence (AI) ha enables compu e s o unde s and he meaning o human na u al
language [11]. The applica ion o NLP and AI echniques o p ocessing medical
eco ds plays an inc easingly signi ican ole in ad ancing clinical decision suppo .
The use o EHR o pe o m s udies in he cance ield has also inc eased in he las
ew yea s.
1h ps://www.who.in /news- oom/ ac -shee s/de ail/cance
2h ps://www.b eas cance .o g/ ac s-s a is ics
1

2Chap e 1. In oduc ion
Cu en ly, we a e expe iencing a signi ican boom in he ield o AI, wi h new
and e olu iona y sys ems eme ging on a egula basis. One a ea ha is pa icula ly
g owing is NLP, hanks o he elease o he esea ch pape "A en ion is all you
need" [12], which in oduced he inno a i e T ans o me s a chi ec u e. This has led
o he c ea ion o popula language models such as Cha GPT [13], which a e causing
a buzz in he indus y. Tha ’s why his a chi ec u e has been chosen o he ask
desc ibed in his p ojec .
This p ojec is hoped o b ing some imp o emen s o he e iciency and quali y
o heal hca e, as well as making some p og ess in medical esea ch and public heal h
policy planning ela ed o b eas cance .
1.1. Mo i a ion
Medical eco ds ha e exis ed o a long ime, bu he o maliza ion o elec onic
heal h eco ds began in he ea ly 20 h cen u y. P io o his, doc o s eco ded
no es in pape books and physical iles, bu hese eco ds we e o en incomple e,
diso ganized, and di icul o access.
The o maliza ion o medical eco ds began in 1928 when he Ame ican Associa-
ion o Medical Reco d Lib a ians and Heal h In o ma ion Managemen (AHIMA)3
es ablished a commi ee o de elop a uni o m sys em o medical eco ds. This com-
mi ee de eloped he i s edi ion o he In e na ional Lis o Medical Te ms in 1932,
which was designed o be a guide o consis en and uni o m medical eco d keeping.
In he ollowing yea s, medical eco ds became inc easingly impo an o heal h-
ca e. In he 1950s, in o ma ion echnology began o be used in heal hca e, and he
use o compu e ized sys ems o medical eco d keeping became mo e common. In
he 1970s, au oma ed medical eco d keeping sys ems we e in oduced, which al-
lowed doc o s o inpu da a di ec ly in o a compu e .
In he 1990s, in e ne echnology began o be used o medical eco d keeping.
This allowed doc o s o sha e in o ma ion mo e easily and quickly, and also allowed
pa ien s o access hei medical eco ds online.
EHRs ha e e ol ed in di e en gene a ions as echnology has ad anced. The
di e en gene a ions a e as ollows [14]:
Fi s gene a ion: This gene a ion eme ged in he 1970s and was based on
au oma ed medical eco d sys ems. These sys ems we e limi ed in hei unc-
ionali y and we e designed p ima ily o s o e basic pa ien in o ma ion, such
as diagnoses, ea men s, and es esul s.
Second gene a ion: In he 1980s, EHRs second-gene a ion we e de eloped,
which we e based on compu e ized medical eco d sys ems and o e ed mo e
unc ionali y. These sys ems allowed doc o s o inpu de ailed in o ma ion
abou pa ien heal hca e, including eco ds o pas isi s, p esc ibed medica-
ions, and labo a o y es esul s.
3h ps://www.ahima.o g/
1.1. Mo i a ion 3
Thi d gene a ion: The hi d gene a ion was de eloped in he 1990s and o-
cused on in e connec i i y and da a exchange be ween di e en heal hca e
sys ems. These sys ems allowed doc o s o access medical in o ma ion om
pa ien s o o he heal hca e p o ide s and we e designed o imp o e heal hca e
coo dina ion.
Fou h gene a ion: EHRs ou h-gene a ion eme ged in he ea ly 2000s and
we e based on web echnology. These sys ems allowed doc o s o access pa ien
medical eco ds online and also allowed pa ien s o access hei own medical
in o ma ion and communica e wi h hei doc o s ia he web.
Fi h gene a ion4: Cu en ly, i h-gene a ion is being de eloped. This gene -
a ion will use ad anced echnologies such as machine lea ning and a i icial
in elligence o imp o e diagnos ic accu acy and clinical decision-making.
In his way, an EHR speci ically e e s o he elec onic eco d o a pa ien ’s med-
ical in o ma ion. The e o e, an EHR is a compu e sys em ha allows heal hca e
p o essionals o cap u e, s o e, and access a pa ien ’s medical in o ma ion elec on-
ically. This includes he pa ien ’s medical and heal h in o ma ion, such as medical
his o y, diagnoses, ea men s, es esul s, and p esc ibed medica ions.
On he o he hand, Heal h Ca e In o ma ion Sys em (HCIS) e e o clinical in-
o ma ion sys ems used in hospi als o manage pa ien in o ma ion, s a , and hospi-
al esou ces. These sys ems ypically include modules o appoin men scheduling,
pa ien admission, clinical da a eco ding, pa ien acking, pha macy managemen ,
billing managemen , and human esou ces managemen . The e o e, an HCIS usually
includes a module o EHR egis a ion.
The Mad id Heal h Se ice (SERMAS) has a unique and cen alized Elec onic
Heal h Reco d sys em in he p ima y ca e sys em. Howe e , in he specialized
ca e sys em (hospi als), he e a e di e en HCIS, which a e speci ic o each hospi al
cen e . Some examples o hese a e: HCIS include Heal hca e P o ide - Heal h Ca e
In o ma ion Sys em (HP-HCIS), Elec onic Medical Reco d Sys em (SELENE) and
Medical and Heal h In o ma ion Exchange (IMDH).
In HCIS, in o ma ion can be classi ied in o wo ca ego ies, s uc u ed and un-
s uc u ed:
S uc u ed in o ma ion e e s o in o ma ion ha is s o ed in da a ields wi h
de ined o ma s, making i easie o compu e sys ems o p ocess and analyze.
Examples o s uc u ed in o ma ion include pa ien demog aphics, diagnoses,
es esul s, p esc ibed medica ions, among o he s.
Uns uc u ed in o ma ion e e s o in o ma ion ha does no ollow a de ined
o ma and can be mo e di icul o p ocess and analyze au oma ically. Ex-
amples o uns uc u ed in o ma ion include medical epo s, p og ess no es,
clinical obse a ions, medical images, among o he s.
4h ps://www2.deloi e.com/us/en/insigh s/indus y/heal h-ca e/eh -sys ems- he- u u e-o -
elec onic-heal h- eco ds.h ml
4Chap e 1. In oduc ion
The s a ing hypo hesis is ha i is possible o ex ac use ul in o ma ion om
uns uc u ed da a in HCIS and con e i in o s uc u ed in o ma ion ha can be
u ilized by compu e sys ems.
Thus, he main mo i a ion behind his wo k is he de elopmen o NLP ools o
ex ac use ul in o ma ion om uns uc u ed da a in HCIS. This uns uc u ed da a
may include medical epo s, p og ess no es and clinical obse a ions.
1.2. Objec i es
The main objec i es o his wo k a e:
To de elop and e alua e NLP ools o ex ac ing use ul in o ma ion om
uns uc u ed da a in HCIS in spanish.
To con e he ex ac ed in o ma ion in o s uc u ed da a ha can be used by
compu e sys ems in HCIS.
De elop an au oma ed NLP pipeline ha can ex ac he mos impo an
in o ma ion om a gi en clinical no e.
These objec i es en ail hese implica ions:
To imp o e he e iciency and quali y o heal hca e by making de ailed and
accu a e pa ien in o ma ion eadily a ailable o heal hca e p o essionals.
To enhance he abili y o compu e sys ems o analyze and gene a e accu a e
and de ailed epo s on he heal h and well-being o pa ien s.
To con ibu e o medical esea ch and public heal h policy planning by p o-
iding accu a e and de ailed pa ien in o ma ion.
To ca y ou his ask o de eloping NLP ools o ex ac use ul in o ma ion om
uns uc u ed da a in HCIS, b eas cance da a om a enowned hospi al will be used.
These da a will include medical epo s, p og ess no es, and clinical obse a ions
ela ed o b eas cance .
Addi ionally, we will wo k in collabo a ion wi h heal hca e p o essionals and
b eas cance specialis s o ensu e he ele ance and use ulness o he ex ac ed and
con e ed s uc u ed in o ma ion o use in HCIS.
1.3. Wo k Plan
In o de o ul ill he goals o he p ojec he ollowing asks ha e been de ined:
Iden i y he speci ic ypes o uns uc u ed da a ha a e mos ele an o he
p ojec and he ypes o in o ma ion ha need o be ex ac ed.
1.3. Wo k Plan 5
Conduc a ho ough li e a u e e iew on NLP ools and echniques o ex-
ac ing in o ma ion om uns uc u ed da a in HCIS.
Ga he and p ep ocess he uns uc u ed da a om he HCIS, cleaning and
s anda dizing i o u he analysis.
De elop and ain NLP models o ex ac he desi ed in o ma ion om he
uns uc u ed da a, such as named en i y ecogni ion o ela ion ex ac ion.
E alua e he pe o mance o he NLP models using app op ia e me ics and
adjus he models as needed.
12 Chap e 2. Ma e ials and Techniques
uns uc u ed ex da a, and o label hem wi h hei co esponding ca ego ies. This
is a challenging ask, as named en i ies can be exp essed in many di e en o ms
and can appea in a ious con ex s wi hin he ex . Fu he mo e, people o en make
spelling e o s, which can u he complica e his ask.
Techniques o NER include: egex, RNNs, LSTM and BiLSTM ne wo ks. LMs
like BERT ha e achie ed s a e-o - he-a pe o mance on NER asks, hanks o he
T ans o me s a chi ec u e. Models a e p e ained on la ge da ase s in an unsu-
pe ised manne , and hen ine- uned on smalle , ask-speci ic da ase s. All hese
echniques, o ganized by he quali y o hei ou comes, a e de ailed in his sec ion.
2.4.1. REGEX
Regula exp ession, also known as egex o egexp, is a pa e n made up o
a sequence o cha ac e s ha is used o sea ch o o ma ch speci ic pa e ns o
ex . I is a powe ul ool ha allows you o speci y a se o ules o ma ching
and manipula ing ex . Regula exp essions a e widely used in p og amming, da a
p ocessing, and ex edi ing applica ions o sea ch, eplace, and ex ac ex based
on speci ic pa e ns.
In he ield o NLP, i is common o use egex o asks such as ex cleaning,
no maliza ion o NER. These echniques a e widely used o iden i y and ex ac
impo an in o ma ion om ex , as well as o p epa e he ex o u he analysis
o machine lea ning asks.
2.4.2. RNN and LSTM
Recu en Neu al Ne wo ks (RNNs) ha e been widely used in NLP o NER.
RNNs a e a ype o neu al ne wo k a chi ec u e ha can handle sequen ial da a,
making hem well-sui ed o NER asks.
In NER, RNNs p ocess ex inpu s one oken a a ime, and hey use he con ex
o he p e ious okens o classi y each oken as a named en i y o no . The key
ad an age o RNNs is hei abili y o cap u e dependencies be ween he cu en
oken and p e ious okens, which can be essen ial o accu a e NER.
Figu e 2.3: Rep esen a ion o an RNN

2.4. NER 13
The anishing g adien p oblem is a common issue ha a ises when aining
Recu en Neu al Ne wo ks (RNNs) on long sequences o da a. The p oblem a ises
when g adien s, which a e used o upda e he ne wo k weigh s du ing aining,
become ex emely small as hey a e backp opaga ed h ough he ne wo k om he
ou pu o he inpu laye . When his happens, he ne wo k may no be able o lea n
long- e m dependencies e ec i ely and may pe o m poo ly on long sequences.
The anishing g adien p oblem is pa icula ly p e alen in RNNs because hey
p ocess inpu sequences one elemen a a ime, and he g adien s mus low back
h ough he same se o weigh s o each elemen in he sequence. As a esul , e o s
can accumula e and cause g adien s o become e y small o e en anish o e ime.
Unlike adi ional RNNs, which su e om he anishing g adien p oblem and
ha e di icul y lea ning long- e m dependencies, LSTMs a e designed o cap u e
long- e m dependencies by using a mo e complex ga ing mechanism ha selec i ely
emembe s o o ge s in o ma ion o e ime.
Long Sho -Te m Memo y (LSTM) was in oduced in 1997 by Hoch ei e and
Schmidhube [21] and ha e since become one o he mos popula and e ec i e
a chi ec u es o sequen ial da a p ocessing. They consis o memo y cells ha
s o e in o ma ion o e ime, inpu ga es ha con ol he low o in o ma ion in o
he cells, ou pu ga es ha con ol he low o in o ma ion ou o he cells, and o ge
ga es ha selec i ely disca d in o ma ion om he cells.
Figu e 2.4: LSTM cell a chi ec u e [3]
O e all, LSTMs a e a powe ul and lexible ool o modeling sequen ial da a
and ha e enabled signi ican ad ances in a wide ange o applica ions.
2.4.3. BiLSTM
Bidi ec ional LSTM [22] (BiLSTM) is a a ian o he Long Sho -Te m Memo y
(LSTM) a chi ec u e ha has he abili y o p ocess inpu sequences in bo h o wa d
and backwa d di ec ions. This allows he ne wo k o cap u e in o ma ion om bo h
pas and u u e con ex s, making i pa icula ly use ul o sequence labeling asks
such as NER.
In con as , s anda d LSTMs p ocess inpu sequences in only one di ec ion, yp-
ically om pas o u u e. While LSTMs a e also e ec i e a cap u ing long- e m
14 Chap e 2. Ma e ials and Techniques
Figu e 2.5: BILSTM laye o NER
dependencies in sequences, hey may no be as e ec i e as BiLSTMs o asks ha
equi e bidi ec ional con ex .
In p ac ice, BiLSTMs o en ou pe o m LSTMs in asks ha equi e bidi ec ional
con ex , pa icula ly when dealing wi h longe sequences o da a. Howe e , hey can
also be mo e compu a ionally expensi e and equi e mo e memo y han LSTMs,
due o he need o s o e and p ocess in o ma ion in bo h o wa d and backwa d
di ec ions.
2.4.4. LMs
A Language Model (LM) is a s a is ical model ha is ained on a co pus o ex
da a o p edic he likelihood o a sequence o wo ds occu ing in a language. The
model assigns a p obabili y sco e o each possible sequence o wo ds in he language,
based on he equency o occu ence o hose wo ds in he aining da a.
In o he wo ds, an LM is a machine lea ning algo i hm ha is capable o gen-
e a ing ex ha is simila o human language by modeling he s a is ical pa e ns
and s uc u es o language. The goal o an LM is o gene a e ex ha is cohe en ,
g amma ical, and meaning ul, gi en a sequence o inpu wo ds o cha ac e s.
Language Models (LMs) ha e a wide ange o applica ions in na u al language
p ocessing, including bu no limi ed o he ollowing:
Tex Comple ion: LMs can be used o p edic he nex wo d o ph ase in a
gi en ex , which is use ul o applica ions such as au o-comple ion in ex
2.4. NER 15
edi o s o sea ch engines.
Machine T ansla ion: LMs can be used o ansla e ex om one language o
ano he by modeling he p obabili y dis ibu ion o wo ds in bo h languages.
Speech Recogni ion: LMs can be used o con e spoken language o ex by
modeling he p obabili y dis ibu ion o wo ds in he spoken language.
Sen imen Analysis: LMs can be used o analyze he sen imen o a piece o
ex , such as de e mining whe he a e iew is posi i e o nega i e.
Ques ion Answe ing (Q&A): LMs can be used o answe ques ions posed in
na u al language by gene a ing an answe based on he inpu ex and he
con ex o he ques ion.
Named En i y Recogni ion(NER): LMs can be used o lea n associa ions be-
ween wo ds and speci ic named en i ies, like o ganiza ions o loca ions.
Some examples o LMs used o NER include BERT (see subsec ion 2.4.6) [6]
and Gene a i e P e- ained T ans o me (GPT) [23] models. These models ha e
achie ed s a e-o - he-a pe o mance on NER asks and ha e been used in a ious
na u al language p ocessing applica ions, such as in o ma ion ex ac ion and ex
classi ica ion.
Figu e 2.6: La ge Language Models: A New Moo e’s Law ? [4]
16 Chap e 2. Ma e ials and Techniques
2.4.5. T ans o me s
T ans o me s is a new a chi ec u e de elop by google enginee s in 2017 [12],
which has become he s a e o he a hanks o wo concep s:
Posi ional encoding is a echnique used o inco po a e in o ma ion abou
he posi ion o he okens in a sequence in o he inpu ep esen a ion. In
con as o RNNs, which use he o de o he wo ds implici ly h ough hei
sequen ial p ocessing, ans o me s a e no designed o p ocess inpu s sequen-
ially. Ins ead, hey p ocess all he wo ds in a sequence in pa allel. To inco -
po a e posi ion in o ma ion in o he inpu , ans o me s use a ixed unc ion
ha maps each posi ion in he sequence o a unique ec o ep esen a ion.
This ec o ep esen a ion is added o he embedding o each wo d, allowing
he ans o me o di e en ia e be ween wo ds based on hei posi ion in he
sequence.
Sel a en ion mechanism is used o cap u e he ela ionships be ween wo ds
in a sequence, allows he model o ocus on di e en pa s o he inpu du ing
p ocessing. The a en ion ma ix de e mines how much a en ion o gi e o
each inpu ec o when compu ing he weigh ed sum. By paying a en ion o
he mos impo an pa s o he inpu , he sel -a en ion mechanism helps he
model o be e cap u e he ela ionships and dependencies be ween di e en
pa s o he ex , which can ul ima ely lead o mo e accu a e p edic ions.
Figu e 2.7: Compa ing CNN (padding okens a e omi ed), RNN, and sel -a en ion
a chi ec u es [5]
Fo mo e in o ma ion abou his sec ion, please e e o my p esen a ion a ailable
a : Link p esen a ion. I ha e c ea ed his p esen a ion o unde s and he echnology
behind ans o me s.
2.4. NER 17
2.4.6. BERT
Bidi ec ional Encode Rep esen a ions om T ans o me s (BERT) is based on
a deep neu al ne wo k composed o se e al laye s o bidi ec ional ans o me en-
code s. The encode s used a e simila o hose ound in he o iginal ans o me
a chi ec u e, bu di e in hei bidi ec ional app oach. Ins ead o p ocessing ex
om le o igh o igh o le , i uses in o ma ion om bo h di ec ions o gene a e
con ex ual ep esen a ions o wo ds. On he o he hand, GPT T ans o me [24] uses
sel -a en ion ha limi s each symbol’s a en ion o only he con ex o i s le .
Figu e 2.8: BERT s. GPT [6]
The igu e depic ed in Figu e 2.9 illus a es he u iliza ion o BERT o he
pu pose o ex ac ing named en i ies om clinical eco ds.
Figu e 2.9: Ex ac ing cance concep s using BERT.

18 Chap e 2. Ma e ials and Techniques
2.4.7. P e aining and Fine Tuning
P e aining e e s o he p ocess o aining a model on a la ge da ase in an
unsupe ised manne . The main objec i es a e:
Lea ning con ex ualized wo d embeddings: lea n wo d embeddings ha cap-
u e he meaning o wo ds based on hei su ounding con ex .
T ans e lea ning: model can lea n gene al linguis ic ea u es ha can be use ul
o a wide ange o asks.
E iciency: a model can lea n use ul ea u es ha can educe he amoun o
da a needed o ine- uning, and can also speed up aining and in e ence imes.
BERT has been p e- ained in wo unsupe ised asks:
Masked Language Modeling (MLM): ce ain okens in a sen ence a e andomly
masked and he model is ained o p edic he o iginal wo ds. The pe cen age
o okens ha a e masked in BERT du ing aining is ypically se o 15% o
he inpu okens.
Nex sen ence p edic ion (NSP): The aim o NSP is o each he model o
unde s and he ela ionship be ween wo consecu i e sen ences in a ex .
Fine- unning is he p ocess o adjus ing a p e- ained LM o i on a smalle
labeled da ase o a speci ic ask h ough a supe ised aining p ocess. Due o
p e aining, he da a se equi ed is small and leads o equi es less lea ning ime.
Fi ing o a speci ic ask, o en equi es adding a laye a he end o he model.
As depic ed in Figu e 2.10, BERT is capable o pe o ming asks such as SQuAD5,
NER and MNLI6.
Figu e 2.10: BERT ine uning [6]
5S an o d Ques ion Answe ing Da ase [25]
6Mul i-Gen e Na u al Language In e ence [26]
2.4. NER 19
2.4.8. BERT p e ained se ups
BERT models ha e been p e ained wi h di e en s con igu a ions ha a e a ail-
able in Hugging Face Hub7. These o e a ange o choices wi h di e en sizes
and p e- aining cha ac e is ics enabling esea che s o selec he mos app op ia e
model o hei speci ic NLP asks and language equi emen s. The able p o ided
in Table 2.2 showcases a ious p e- ained models wi h di e en con igu a ions. I
is impo an o no e ha he able ep esen s only a subse o he ull ange o
a ailable models. These speci ic con igu a ions a e p ima ily designed o p ocessing
Spanish o English ex s. He e a e some conside a ions o selec ing a BERT model:
Model Size and Capaci y: Models base a e smalle and ha e ewe pa ame e s,
making hem sui able when compu a ional esou ces a e limi ed o o smalle -
scale asks. On he o he hand, models la ge a e la ge models wi h mo e
capaci y, which can be bene icial o complex asks ha equi e cap u ing
ine-g ained language pa e ns.
Case Sensi i i y: I p ese ing he o iginal casing o wo ds is impo an o you
ask, you should choose he cased a ian s. These models e ain he o iginal
casing in he p e- aining phase and can be use ul when he dis inc ion be ween
uppe case and lowe case is seman ically signi ican .
Language: I you NLP ask in ol es mul iple languages, you can conside us-
ing he mul ilingual models. These models a e ained on ex om mul iple
languages and can handle a ious languages simul aneously, making hem sui -
able o mul ilingual applica ions. Howe e , i you ask speci ically in ol es
he Spanish language, he be o models a e a good op ion.
Table 2.2: P e ained models de ails [7]
Model Laye s Hidden Heads Pa ams T aining Da a
be -base-uncased 12 768 12 110M LC English ex
be -la ge-uncased 24 1024 16 340M LC English ex
be -base-cased 12 768 12 110M Cased English ex
be -la ge-cased 24 1024 16 340M Cased English ex
be -base-mul ilingual-uncased 12 768 12 110M LC ex in op
102 languages
wi h la ges Wikipedias
be -base-mul ilingual-cased 12 768 12 110M Cased ex in op
104 languages
wi h la ges Wikipedias
be o-base-uncased 12 1024 16 110M LC Spanish ex
be o-base-cased 12 1024 16 110M LC Spanish ex
*LC : Lowe -Cased
7h ps://hugging ace.co/
20 Chap e 2. Ma e ials and Techniques
2.5. NLP ool: spaCy
Al hough he e a e nume ous NLP ools a ailable such as NLTK8, Spa kNLP9,
and S an o d Co eNLP10, we op ed o spaCy due o i is de eloped wi h Py hon,
i s in ui i e and use - iendly in e ace, and i s wide ange o powe ul ea u es.
Thus, SpaCy11 is an open-sou ce Py hon lib a y ha is widely used o NLP
asks. I has been de eloped by Explosion AI a company ha specializes in NLP
echnologies. I has a numbe o impo an ea u es. Some o he mos ou s anding
ones a e:
E iciency: is a e y as and e icien ool o p ocessing la ge amoun s o ex ,
because i is p og ammed in Cy hon12.
Ease o use: is easy o ins all and use, e en o hose who ha e no expe ience
in na u al language p ocessing.
Accu acy: uses machine lea ning models o analyse he ex , which allows i
o ha e a high accu acy in he analysis.
Cus omisa ion: allows you o cus omise machine lea ning models o sui spe-
ci ic needs and imp o e pa sing accu acy.
Mul ilingual suppo : suppo s mul iple languages, making i a use ul ool o
analysing ex in di e en languages.
In eg a ion: in eg a es easily wi h o he na u al language p ocessing ools and
amewo ks as Tenso low 13, making i a popula choice in he de elopmen
o ex analysis applica ions and se ices.
Suppo : Suppo o mul iple NLP asks, including NER, POS agging, de-
pendency pa sing, and ex classi ica ion
Fu he mo e, can con e wo d ec o s om popula ools such as Fas Tex 14 and
Gensim15, o load hem in o any p e- ained ans o me model. I is also possible
o pe o m one’s own p e- aining o he language.
SpaCy’s p ocessing pipeline in ol es h ee main componen s: language p ocess-
ing, linguis ic analysis, and machine lea ning. The ool also comes wi h a CLI
(see subsec ion 2.5.2) o simpli ied model de elopmen . SpaCy employs an "ea ly
s opping" echnique du ing aining. In his sec ion, hese ea u es a e explained.
8h ps://www.nl k.o g/
9h ps://spa knlp.o g
10h ps://s an o dnlp.gi hub.io/Co eNLP/
11h ps://spacy.io/
12h ps://cy hon.o g/
13h ps://www. enso low.o g/
14h ps:// as ex .cc/
15h ps:// adim ehu ek.com/gensim/
2.5. NLP ool: spaCy 21
2.5.1. P ocessing pipelines
The spaCy pipeline is a sequence o componen s ha a e sequen ially applied o
a ex o pe o m di e en NLP asks as shown in he Figu e 2.11.
In spaCy, he pipeline can be cus omized o include only he componen s needed
o a speci ic NLP ask. The componen s in he pipeline a e execu ed in a p ede ined
o de , bu i is possible o modi y he o de o add new componen s as needed.
The componen s o he spaCy pipeline can be di ided in o h ee main ca ego ies:
Language p ocessing componen s: pe o m p ep ocessing asks, such as o-
keniza ion and lemma iza ion, which con e ex in o a mo e manageable
s uc u e o downs eam componen s.
Linguis ic analysis componen s: pe o m mo e complex asks, such as pa sing
and named en i y iden i ica ion, o ex ac seman ic and s uc u al in o ma ion
om he ex .
Machine lea ning componen s: use machine lea ning models o pe o m NLP
asks, such as ex classi ica ion o POS labeling. These models a e p e- ained
on la ge da ase s o lea n linguis ic pa e ns, and hen uned o speci ic asks
using smalle da ase s.
Table 2.3 shows he main componen s o spacy. Howe e , new componen s can
be c ea ed and added acco ding o he needs o he applica ion.
Figu e 2.11: SpaCy pipeline [1]
28 Chap e 2. Ma e ials and Techniques
is used as he es se while he emaining olds a e used o aining. This p ocess
helps p o ide a mo e obus es ima e o he model’s pe o mance by using all he
da a o bo h aining and es ing. The a e age pe o mance ac oss all i e a ions is
calcula ed o assess he model’s gene aliza ion abili y and de ec any issues such as
o e i ing o unde i ing. Common alues o k include 5 and 10.
k- old CV =1
k
k
X
i=1
e alua e(Mi)(2.5)
Figu e 2.15: 5- old c oss alida ion
2.8. Wo ds simila i y
In NLP i is no uncommon o wo ds o con ain spelling e o s. Howe e ,
when i comes o s uc u ed da abases, i is impe a i e ha he wo ds a e in hei
co ec o m. To add ess his issue, i is essen ial o ha e a s uc u ed da abase ha
includes a comp ehensi e ocabula y. This allows o a compa ison o each wo d o
de e mine i s alidi y. By employing his app oach, spelling and ocabula y e o s
can be iden i ied and co ec ed e ec i ely.

2.8. Wo ds simila i y 29
The e a e mul iple ways o compa e wo ds in a compu e , and i ’s impo an
o dis inguish be ween compa ing embeddings, which is used when he wo ds a e
co ec ly spelled. The p ima y me hods include:
Le ensh ein Dis ance [28]: used o calcula e he minimum numbe o single-
cha ac e edi s (inse ions, dele ions, o subs i u ions) equi ed o ans o m
one s ing in o ano he . I was named a e he So ie ma hema ician Vladimi
Le ensh ein, who in oduced i in 1965.
le (s 1, s 2) =



















leng h(s 2) i leng h(s 1) = 0,
leng h(s 1) i leng h(s 2) = 0,
le ( ail(s 1), ail(s 2)) i s 1[0] = s 2[0],
1 + min 




le ( ail(s 1), s 2)
le (s 1, ail(s 2))
le ( ail(s 1), ail(s 2))





o he wise
(2.6)
whe e he ail o a s ing, deno ed as ail(x), e e s o he subs ing ob ained
by excluding he i s cha ac e om he s ing x.
Ja o-Winkle Dis ance [29]: This measu e calcula es he simila i y be ween
wo wo ds by aking in o accoun cha ac e ansposi ions and p e ix simila i y.
To compa e embeddings, he Cosine dis ance [30] is commonly used. I measu es
he simila i y be ween wo nume ical ec o s.
cosine_simila i y(A,B) = A·B
∥A∥∥B∥(2.7)
In o de o lea n how o calcula e he simila i y be ween wo s ings in Py hon,
you can e e o my Gi Hub eposi o y a ailable a : h ps://gi hub.com/Al a o8gb/
S ings-simila i y
Chap e 3
S a e o he A
In ecen yea s he use o NLP in he biomedical domain has inc eased he
possibili y o au oma ically ex ac ing in o ma ion om clinical na a i es [31; 32;
33]. The i s challenge o be add essed when ex ac ing in o ma ion om clinical
ex s is he iden i ica ion o medical-named en i ies. Ex ac ing named en i ies is
one o he mos impo an asks in he medical domain since pe o ming clinical
s udies commonly equi es de ailed pa ien in o ma ion eco ded in clinical no es
[34; 10]. Clinical Named En i y Recogni ion (Clinical NER) is he ask ha aims
o iden i y medical concep s om clinical ex [32; 35].
Recen ly, deep lea ning-based app oaches ha e shown impo an ad ances and
imp o emen s in ex ac ing in o ma ion in he biomedical domain [36; 37; 38; 39].
Howe e , mos o hese p oposals ha e ocused on he English language [36; 40].
In ac , in o ma ion ex ac ion in he medical domain ep esen s i s own challenges
in languages o he han English [41; 42]. The main ad an age o deep lea ning ap-
p oaches is he abili y o au oma ically lea n high-le el ea u es om ex s, educing
he ime in he hand-c a ed ea u e enginee ing p ocess.
The use o deep-lea ning me hods has also encou aged he ex ac ion o mo e
de ailed in o ma ion ela ed o cance . Fo ins ance, in [43], he au ho s desc ibed a
deep-lea ning app oach o ex ac b eas cance concep s using BERT. The goal o
his p oposal is o ex ac a comp ehensi e se o b eas cance concep s om clinical
no es w i en in Chinese. The au ho s demons a e ha he BERT-based model
pe o ms be e han adi ional machine lea ning algo i hms a ex ac ing named
en i ies in he cance ield. In [44], he au ho s desc ibe a BiLSTM-based model
o clinical concep ex ac ion om oncological clinical no es w i en in Ge man.
This model suppo s ex ac ing se e al concep s such as diagnosis, ea men s, and
medica ions. Al hough deep lea ning-based app oaches ha e imp o ed he abili y o
ex ac medical concep s in he cance medical ield, mos o hese p oposals ha e
ocused on he English language [36; 40] and mos ecen ly, on Chinese [43; 45].
In he Spanish language case, in [46], he au ho s p opose Can emis , an anno-
a ed co pus o suppo umo mo phology ex ac ion. Se e al s udies [47; 48; 49]
ha e used his co pus o pe o m mo phology ex ac ion. Howe e , he main lim-
i a ion o hese p oposals is hey only suppo iden i ying one en i y ype ( umo
31
32 Chap e 3. S a e o he A
mo phology). Cance is a complex and specialized medical ield equi ing a comp e-
hensi e se o medical concep s o unde s anding i s e olu ion om clinical na a-
i es [43].
In conclusion, ex ac ing named en i ies om oncology clinical ex s w i en in
Spanish has no been explo ed deeply ye . The e is a lack o co po a o suppo
in o ma ion ex ac ion in he b eas cance domain in his language.
Chap e 4
NLP Cance Pipeline
4.1. P oblem s a emen
In Heal h Ca e In o ma ion Sys em, da a is p ima ily classi ied as s uc u ed o
uns uc u ed. S uc u ed da a, wi h i s de ined o ma s, is eadily p ocessable by
compu e sys ems. I encompasses pa ien demog aphics, diagnoses, es esul s,
and p esc ibed medica ions. On he o he hand, uns uc u ed da a lacks a de ined
o ma , complica ing au oma ic p ocessing and analysis. I ypically includes med-
ical epo s, p og ess no es, clinical obse a ions, and medical images.
This p ojec is based on he hypo hesis ha uns uc u ed da a in HCIS holds
aluable in o ma ion ha , i ex ac ed and ans o med in o s uc u ed da a, could
signi ican ly bene i he heal h sys ems.
The cen al mo i a ion o his esea ch is o c ea e NLP pipeline ha can e ec-
i ely mine uns uc u ed da a in HCIS. The o e all p ocess is shown in Figu e 4.1.
Figu e 4.1: T ans o ming Clinical No es in o S uc u ed JSON
33

34 Chap e 4. NLP Cance Pipeline
4.2. S uc u ing b eas cance in o ma ion
To ob ain ob ain a module ha s uc u e a Clinical no e, we ha e o do o he
s eps ea lie . Fi s o all, as show in Figu e 4.2, we acqui ed wo anno a ed co pus,
clinical and Nega ion & Unce ainly.
Figu e 4.2: Co pus collec ion
Subsequen ly, we p oceeded o ain he models using he co pus we had acqui ed
ea lie and e alua ed hei pe o mance, as illus a ed in 4.3. This speci ic s ep is
de ailed in chap e 5, whe e we demons a e he de elopmen o wo NER models,
namely he Clinical and Nega ion & Unce ain y models.
Figu e 4.3: Models c ea ion
Finally, bo h models ha e been in eg a ed in o a comp ehensi e pipeline known
as NCP, which se es as he inal implemen a ion. This module has he capabili y
o ake a clinical no e in aw ex o ma and gene a e a JSON ile ha cap u es
he mos signi ican concep s wi hin he no e.
4.3. NCP S ages 35
Figu e 4.4: NCP: NLP Cance Pipeline
4.3. NCP S ages
In NLP, a s anda d wo k low in ol es mul iple s eps, each dedica ed o a unique
unc ion. The speci ics o hese s ages can a y based on he p oblem and ap-
plica ion a hand. Ou me hodology o s uc u ing clinical no es is depic ed in
Figu e 4.4 (showcasing he inal module) and Figu e 4.5 (highligh ing he echnolo-
gies employed). A specialized pipeline, e med NCP (NLP Cance Pipeline) has
been de eloped. Each clinical no e a e ses h ough e e y s age o he pipeline,
e en ually eme ging in a s uc u ed o ma :
Clinical No es: This s age in ol es collec ing and compiling he clinical no es
om a SQL Da abase. Clinical no es e e o he eco ds o pa ien s’ medical
his o ies, including hei symp oms, diagnoses, ea men s and ou comes.
P ep ocessing (Regex): In his s age, he clinical no es a e p ep ocessed o
p epa e hem o u he analysis. The p ep ocessing s ep in ol ed a ious
s eps, which a e p ima ily accomplished h ough he u iliza ion o egula ex-
p essions. The ollowing s eps a e in ol ed in his s age and desc ibed in de ail
in sec ion 4.4.
Co pus Anno a ion (P odigy): In his s age, he clinical no es a e anno a ed
wi h ele an medical en i ies desc ibed in Appendix A. I is impo an o
no e ha he anno a ed no es used in he anno a ion p ocess a e a subse o
he no es desc ibed in sec ion 2.2. This p ocess in desc ibed in sec ion 4.5.
T ans o me s Ne wo ks (BERT): This s age in ol es he use o ans o me s
36 Chap e 4. NLP Cance Pipeline
ne wo ks wi h mul ilingual uncased BERT1 o p ocess he anno a ed co pus.
This ne wo k use deep lea ning echniques o lea n om he anno a ed da a
and gene a e accu a e p edic ions.
Pos -p ocessing (Wo ds simila i y): In his s age, he p edic ions gene a ed by
he ans o me s ne wo ks a e pos -p ocessed o imp o e hei accu acy and
no malize he esul s. P ima ily, he employs wo d simila i y echniques o
accomplish his ask.
S uc u ed Clinical No es (mongoDB): In his inal s age, he p ocessed clinical
no es a e con e ed in o s uc u ed clinical no es. These no es a e o ganized
in o a s anda dized o ma ha can be easily sea ched and analyzed. S uc-
u ed clinical no es a e used o se e al applica ions such as clinical decision
suppo , popula ion heal h managemen , and quali y imp o emen .
Figu e 4.5: Technology pipeline
We de eloped a pipeline o e icien ly p ocess la ge olumes o no es by ope -
a ing on ba ches, as shown in Figu e 4.6. This app oach op imizes compu a ional
e iciency and educes o e head associa ed wi h deep lea ning models.
A p esen , he pipeline is only capable o s uc u ing no es in cases whe e he
diagnoses wi hin a gi en no e a e no in g amma ical ag eemen . As an example, he
clinical judgmen , "p esen a dos ca cinomas uno duc al y o o lobulilla " would no
be p ope ly ex ac ed by he pipeline due o he g amma ical ag eemen be ween
1h ps://hugging ace.co/be -base-mul ilingual-uncased
4.3. NCP S ages 37
he e ms "duc al" and "lobulilla ." Al hough he no e indica es ha he pa ien
has wo cance s, he pipeline only ecognizes one.
In ou assessmen , each diagnosis ep esen s a dis inc loca ion o he cance .
Diagnoses ha iden i y mul iple si es a e he e o e conside ed as sepa a e diagnoses.
As e idenced by he ollowing no e, he pipeline will ecognize he p esence o wo
dis inc diagnoses.
2 ocos:
- ca cinoma mucinoso in il an e de 17 mm, g1, e nega i o, p neha i o, he 2
posi i o (3+), ki67 5%
- ca cinoma mic opapila in il an e de 28mm, n+, g1, e 90%, p 90%, he 2
posi i o (3+), ki 67 10%
In o de o gain a comp ehensi e unde s anding o how he pipeline unc ions,
we ha e p o ided a de ailed desc ip ion in he ollowing sec ions.
1
2de ba ch(no es, no es_index):
3
4p ep ocess(no es, no es_index[" ex "])
5
6classi ica o (no es, no es_index[" ex "])
7
8s uc _no es =pos p ocess(no es, no es_index)
9
10 dump2mongo_db(s uc _no es) # o JSON db
11
12 i __name__ == "__main__" :
13
14 no es, no es_index =load_db() # om SQL db
15
16 ba chs =[ no es[i:i +ba ch_size]
17 o iin ange(0,len(no es), ba ch_size)]
18
19 h eads =[ Th ead( a ge =ba ch,
20 a gs=(chunk, no es_index))
21 o chunk in ba chs ]
22
23 o in h eads:
24 .s a ()
25
26 o in h eads:
27 .join()
Figu e 4.6: Au oma ed pipeline in ba ches
44 Chap e 5. Expe imen s and Resul s
Table 5.1: Model hype -pa ame e s
Pa ame e Value
Seed 8
Accumula e g adien s 3
D opou 0.1
Op imize Adam
GPU alloca o Py o ch
Ba ch size 2000
Ba ch size bu e 256
Ba ch size disca d o e size T ue
Lea n a e wa mup-linea
Wa m up s eps 250
Ini ial a e 0.00005
To al s eps 20000
Table 5.2: In o ma ion abou NER co pus
Co pus Nega ion and Unce ainly Clinical NER
Numbe o okens 324116 38378
Numbe o en i y okens 21091 10280
Pe cen age label okens 6.51% 26.79%
dispa i y be ween he numbe o anno a ions and he unique wo ds ( ocabula y
size) in igge labels (NegCue, Uce Cue) is mo e p onounced, which p o es ad-
an ageous o he model’s lea ning p ocess. This di e gence ensu es an abundan
supply o samples, allowing he model o e ec i ely comp ehend he ocabula y.
On he o he hand, he e is minimal di e ence obse ed in scope labels (NegScope,
Uce Scope), which is expec ed since nega ion and unce ain y can be exp essed in
ela ion o any wo d in na u al language. This p esen s a challenging ask o he
model, as i needs o accu a ely iden i y and scope wo ds ha i hasn’ encoun e ed
in he majo i y o cases.
In he Clinical co pus, we obse e signi ican a ia ion in he numbe o anno a ed
wo ds among di e en en i ies. The en i ies wi h he highes numbe o anno a ed
okens a e Molecula Ma ke and Cance Loca ion, bo h exceeding 1500. In con as ,
he e a e labels such as Cance In a ype o T ea men F equency, which ha e ewe
han 250 anno a ed okens. In addi ion, he Implici Da e label in he clinical co pus
is pa icula ly challenging o p edic , alongside he scope labels o Nega ion and
Unce ain y. The challenge s ems om he wide ange o g amma ical exp essions
u ilized o communica e empo al in o ma ion, o en inco po a ing a ious o ms o
empo al ad e bs. These ad e bs can be exp essed in nume ous ways, adding o he
complexi y o p edic ing he Implici Da e label in he clinical co pus. Fo example,
he occu ence o an e en ha happened yes e day can be exp essed in a ious
ways, such as "Aye " (Yes e day), "El día an e io " (The p e ious day), "Un día
a ás" (One day ago), "Hace un día" (A day ago) o "En la íspe a" (On he e e).

5.3. Expe imen s 45
Figu e 5.1: G aphical ep esen a ion o Neg Unce Co pus coun ing
5.3. Expe imen s
To assess he pe o mance o he p oposed app oach, we employed he well-
es ablished s anda d me ics, P ecision, Recall, and F-Sco e ( e e o subsec ion 2.7.1).
These me ics we e selec ed due o he i al impo ance o Recall in NER. Inco ec ly
iden i ying an en i y can esul in he p opaga ion o alse in o ma ion. The e o e,
i is p e e able o e ain om anno a ing an en i y when in doub , a he han
anno a ing i inco ec ly, i.e. a high Recall is be e han a high P ecision.
To assess he e ec i eness o he Clinical model, 10- old c oss alida ion (see
subsec ion 2.7.2) has been selec ed, due o he low numbe o samples om some
en i ies. The pe o mance was calcula ed as he a e age o all en olds execu ed
by he c oss- alida ion s a egy. To de e mine he ep esen a ion o he media, we
calcula e he s anda d de ia ion. The s anda d de ia ion is a measu e o he dis-
pe sion o a iabili y in a da a se . A lowe s anda d de ia ion indica es ha he
media is mo e e enly ep esen ed h oughou he da a se , sugges ing a balanced
dis ibu ion. On he o he hand, a highe s anda d de ia ion implies ha he occu -
ence o media ins ances is mo e a iable, indica ing po en ial biases o imbalances
in ep esen a ion.
In o de o e alua e he pe o mance o he nega ion and unce ain y model, we
employed an 80-20 spli . Due o he e enough ep esen a ion o each en i y. This
means ha he da a se was di ided in o wo pa s: 80% o aining and 20% o
es ing. I is impo an o no e ha spli s a e made in a ie ed manne o ensu e
su icien ep esen a ion o each en i y.
46 Chap e 5. Expe imen s and Resul s
Figu e 5.2: G aphical ep esen a ion o Clinical co pus coun ing
5.4. Resul s
This sec ion p esen s he esul s o he expe imen s desc ibed in he p e ious
sec ion. The esul s o he Nega ion and Unce ain y NER model, which ocuses on
he iden i ica ion o ou en i ies, a e shown in Table 5.3.
Besides, we p esen he esul s ob ained om he Clinical NER model ained on
he b eas cance co pus, which includes 21 en i ies. Table 5.4 displays he o e all
esul s achie ed by Clinical model. Following ha , we will p o ide he pe o mance
me ics o each indi idual en i y, as shown in Table 5.5.
5.4. Resul s 47
Table 5.3: Resul s om Nega ion and Unce ainly Co pus pe en i y ype
En i y Type P ecision Recall F-Sco e
Nega ion Concep 0.956 0.956 0.956
Nega ion Scope 0.836 0.823 0.829
Unce ainly Concep 0.874 0.846 0.860
Unce ainly Scope 0.748 0.708 0.728
O e all 0.873 0.859 0.866
Table 5.4: Gene al esul s om Clinical Co pus
Me ic Mean ±De ia ion
P ecision 0.9346 ±0.0123
Recall 0.9366 ±0.0083
F-sco e 0.9356 ±0.0099
Table 5.5: Resul s om Clinical co pus pe en i y ype
En i y Type P ecision Recall F-Sco e
Cance Concep 0.9806 ±0.0171 0.9732 ±0.0143 0.9767 ±0.0076
Cance Expansion 0.9801 ±0.0173 0.9796 ±0.0222 0.9798 ±0.0178
Cance G ade 0.9325 ±0.0309 0.9373 ±0.0281 0.9347 ±0.0261
Cance In a ype 0.963 ±0.1048 0.9798 ±0.0571 0.9667 ±0.0667
Cance Loca ion 0.9117 ±0.0324 0.9174 ±0.0268 0.9143 ±0.0255
Cance Me as asis 0.9055 ±0.0284 0.9014 ±0.0375 0.903 ±0.0275
Cance Recu ence 1.0±0.0 0.9815 ±0.0524 0.9899 ±0.0286
Cance S age 0.9576 ±0.0341 0.9442 ±0.0412 0.9501 ±0.0265
Cance Sub ype 0.7773 ±0.1789 0.7956 ±0.138 0.7675 ±0.1092
Cance Type 0.9789 ±0.0133 0.9822 ±0.0144 0.9804 ±0.0092
Da e 0.9596 ±0.029 0.969 ±0.0271 0.9641 ±0.0254
Implici Da e 0.7435 ±0.1084 0.7368 ±0.1123 0.7392 ±0.1064
Molec Ma ke 0.9343 ±0.0204 0.9506 ±0.0138 0.9423 ±0.016
Su ge y 0.9296 ±0.0551 0.9328 ±0.0386 0.931 ±0.0455
TNM 0.9019 ±0.0499 0.8916 ±0.0526 0.8965 ±0.0494
T ea men 0.9321 ±0.0336 0.9235 ±0.0547 0.9268 ±0.0356
T ea men D ug 0.9289 ±0.045 0.9397 ±0.0398 0.9335 ±0.0327
T ea men F equency 0.9841 ±0.0449 0.8889 ±0.1757 0.9221 ±0.1109
T ea men In e al 0.9348 ±0.0458 0.898 ±0.0481 0.9148 ±0.0319
T ea men Quan i y 0.9008 ±0.1648 0.8581 ±0.1692 0.8673 ±0.1463
T ea men Schema 0.868 ±0.1098 0.9196 ±0.0823 0.8886 ±0.0749
48 Chap e 5. Expe imen s and Resul s
5.5. Discussion
The esul s p esen ed in Table 5.3 o he Nega ion and Unce ain y Co pus
p o ide insigh s in o he pe o mance o he p oposed app oach o each en i y ype:
Fo Nega ion Concep , he model achie ed a p ecision o 0.956, ecall o 0.956,
and an F-sco e o 0.956. This indica es a high le el o accu acy and comple e-
ness in iden i ying nega ion concep s.
In e ms o Nega ion Scope, he p ecision was measu ed a 0.836, ecall a
0.823, and F-sco e a 0.829. Al hough sligh ly lowe han he p ecision and
ecall o Nega ion Concep , he model s ill exhibi ed easonably good pe o -
mance in ecognizing he scope o nega ion.
Mo ing on o Unce ain y Concep , he model ob ained a p ecision o 0.874,
ecall o 0.846, and an F-sco e o 0.860. These esul s sugges ha he e a e
ins ances whe e he model does no make accu a e p edic ions.
In he case o Unce ain y Scope, he p ecision sco e was 0.748, ecall was
0.708, and he F-sco e was 0.728. The p ecision and ecall alues o his
en i y ype a e he lowes among he o he s, causing equen con usion o
he model.
Conside ing he o e all pe o mance, he p oposed app oach alls sho wi h an
a e age p ecision o 0.873, ecall o 0.859, and an F-sco e o 0.866. This indica es ha
he e is oom o imp o emen in accu a ely iden i ying nega ion and unce ain y
concep s, as well as hei espec i e scopes, ac oss all en i y ypes in he Nega ion
and Unce ain y Co pus. The unde -pe o mance could po en ially be a ibu ed o
he model being ained on di e en ypes o no es. These esul s sugges ha he
model’s pe o mance in his a ea needs o be enhanced o ensu e a mo e e ec i e
implemen a ion o he p oposed app oach.
The esul s ob ained om he Clinical Co pus in Table 5.4 indica e a high le el
o pe o mance o he p oposed app oach wi h p ecision, ecall, and F-sco e sco es
consis en ly abo e 0.93. This sugges s ha he model is capable o accu a ely iden-
i ying en i ies while cap u ing a subs an ial po ion o he ele an en i ies p esen
in he da a.
The esul s ob ained om he Clinical Co pus, as displayed in Table 5.5, p o ide
aluable insigh s in o he pe o mance o he p oposed app oach o each en i y ype:
Fo he Cance Concep en i y ype, he model achie ed a high p ecision o
0.9806, ecall o 0.9732, and an F-sco e o 0.9767. These esul s sugges ha
he model demons a es excellen accu acy and comple eness in iden i ying
cance concep s wi hin he clinical da a.
Simila ly, o he Cance Expansion en i y ype, he model achie ed a p ecision
o 0.9801, ecall o 0.9796, and an F-sco e o 0.9798. These sco es indica e a
high le el o pe o mance in ecognizing expanded cance e minologies.
5.5. Discussion 49
Table 5.6: Discussion o Clinical NER model
En i y F-sco e IV Vocabula y size
Cance Concep 0.98 1.00 32
Cance Expansion 0.98 1.00 25
Cance G ade 0.93 0.99 49
Cance In a ype 0.97 0.98 6
Cance Loca ion 0.91 0.98 249
Cance Me as asis 0.90 0.99 31
Cance Recu ence 0.99 0.98 6
Cance S age 0.95 0.99 35
Cance Sub ype 0.77 0.88 26
Cance Type 0.98 1.00 24
Da e 0.96 0.45 540
Implici Da e 0.74 0.33 123
Molecula Ma ke 0.94 0.87 620
Su ge y 0.93 0.99 88
TNM 0.90 0.38 467
T ea men 0.93 0.97 58
T ea men D ug 0.93 0.95 110
T ea men F equency 0.92 0.92 12
T ea men In e al 0.91 0.88 81
T ea men Quan i y 0.87 0.59 36
T ea men Schema 0.89 0.96 19
The Cance G ade en i y ype achie ed a p ecision o 0.9325, ecall o 0.9373,
and an F-sco e o 0.9347. These esul s indica e sa is ac o y pe o mance in
iden i ying cance g ades wi hin he clinical da a.
Rega ding he Cance In a ype, Cance Loca ion, Cance Me as asis, Cance
Recu ence, Cance S age, Cance Sub ype, and Cance Type en i y ypes,
he model exhibi ed a ying le els o p ecision, ecall, and F-sco e. O e all,
he model achie ed ela i ely high sco es, sugges ing i s capabili y o cap u e
and iden i y hese cance - ela ed en i ies e ec i ely.
Fo en i ies such as Da e, Implici Da e, Molec Ma ke , Su ge y, TNM, T ea -
men , T ea men D ug, T ea men F equency, T ea men In e al, T ea men
Quan i y, and T ea men Schema, he model demons a ed easonable pe o -
mance, as indica ed by he p ecision, ecall, and F-sco e sco es.
Examining he pe o mance o di e en en i ies, we ound ha ags wi h low sup-
po , such as T ea men Schema, T ea men Quan i y, T ea men In e al, T ea -
men F equency, and Cance Sub ype, exhibi ed poo e esul s. Howe e , ags wi h
a limi ed ocabula y, such as T ea men o Cance Recu ence, demons a ed highe
accu acy, likely due o he model’s abili y o easily lea n hem. We p esen he co -
ela ion be ween he IV (Index o Va iabili y) and ocabula y size a iables wi h he

50 Chap e 5. Expe imen s and Resul s
F-sco e in Table 5.6. The esul s show ha , in mos cases, a highe IV co esponds
o be e classi ica ion pe o mance.
Fu he mo e, we no ed ha he Da e label consis en ly ou pe o med he Implici
Da e label. The Da e label, being sho e and mo e s uc u ed, ypically included
speci ic da es such as "23/02" o "24/03/2023," while he Implici Da e label o en
con ained ph ases like " omo ow in he mo ning". In p ac ice, egula exp essions
a e commonly employed o ex ac en i ies like Da e o TNM, le e aging hei sha ed
s uc u al pa e ns.
In o de o ackle he challenges and enhance he o e all pe o mance o he
models and hei unc ionali y wi hin he pipeline, we p opose he ollowing ecom-
menda ions:
Conduc addi ional anno a ion o en i ies ha exhibi signi ican de ia ions.
This will in ol e ca e ully iden i ying and labeling ins ances whe e he models
s uggle o p oduce inaccu a e esul s.
Anno a e he Nega ion and Uce anly labels using he b eas cance co pus.
By le e aging a specialized da ase speci ic o b eas cance , we can ine- une
he model o be e unde s and and handle nega ion and unce ain y ela ed
o his pa icula domain. This a ge ed e aining will lead o imp o ed pe -
o mance and accu acy when dealing wi h b eas cance - ela ed in o ma ion.
Explo e he inco po a ion o egula exp ession o imp o e en i y ex ac ion in
s uc u ed pa e ns such as Da e o TNM, in conjunc ion wi h deep lea ning
models.
By implemen ing hese ecommenda ions, we can add ess he challenges aced by
he models, imp o e hei pe o mance, and enhance he o e all unc ionali y o he
pipeline. To es ablish he o de o p io i ies, i is ad isable o ollow he Amdahl’s
Law [51], which emphasizes imp o ing modules wi h b oade global impac . In ou
case, he Nega ion and Unce ain y model should ake p ecedence since i s e oneous
de ec ion leads o he ejec ion o clinical e ms p edic ed by he Clinical NER model.
Chap e 6
Conclusions and Fu u e Wo k
This p ojec in oduces a deep lea ning-cen ic me hodology o s uc u ing he
ex ac ion o b eas cance in o ma ion. Ou p oposal is an au oma ed pipeline,
called NCP -NLP Cance Pipeline-, capable o p ocessing clinical no es in Spanish
and p oducing a s uc u ed JSON ile. The esul s o his app oach unde sco e
he necessi y o clinical NER and he de ec ion o nega ion and unce ain y as he
main asks in in o ma ion s uc u ing wi hin he clinical domain. Le e aging deep
lea ning-based me hods o he ex ac ion o medical concep s yielded p omising
ou comes in p ocessing Spanish b eas cance clinical na a i es.
We p oposed an anno a ion scheme Table 4.1, which aims o ex ac de ailed and
speci ic concep s o cance diagnosis and ea men s. Thus, his anno a ion schema
is aimed a ex ac ing medical concep s and s uc u ing hem. The deep lea ning-
based model ained wi h mul ilingual BERT has also shown p omising esul s in
de ec ing nega ion and unce ain y in Spanish clinical ex s. Speci ically, we used
he scope o nega ion and unce ain y o ex ac ing medical concep s a ec ed by
hese linguis ic phenomena.
6.1. Deep Lea ning s. REGEX
Machine lea ning models a e conside ed be e o NER han egex, because hey
can lea n mo e complex pa e ns and ela ionships in ex da a, and can be e adap
o di e en con ex s and a ia ions in he da a. In con as , egula exp essions ely
on p ede ined ex pa e ns and a e less lexible in e ms o adap ing o di e en
a ia ions in he da a. Howe e , i is impo an o no e ha egula exp essions a e
mo e in e p e able1 han deep lea ning ne wo ks o his eason egex is s ill widely
used.
The p oblem wi h egula exp essions is ha i can be ime-consuming and
di icul o de ine pa e ns ha accu a ely ma ch all possible a ia ions o named
en i ies. Addi ionally, i may no be able o cap u e named en i ies ha do no
1A model is said o be in e p e able when i is easy o humans o unde s and and explain how
i makes p edic ions o decisions based on he inpu da a.
51
52 Chap e 6. Conclusions and Fu u e Wo k
ollow a speci ic pa e n o s uc u e. This p esen s a p oblem ha I e e o as
" he egex middle poin ". This issue a ises when he ex you’ e ying o ex ac
has nume ous a ia ions, and you need a egex ha migh ex ac wo ds you don’
wan . This p oblem e lec s he di icul y o making a egex ha i s exac ly wha
you wan .
I ’s ecommended o conside he sou ce o he ex be o e deciding on an ex-
ac ion me hod. I he ex is machine-gene a ed, egula exp essions ( egex) migh
o en be adequa e. Howe e , o mo e complex o na u al language ex , aining
a NER model based on deep lea ning could be mo e e ec i e. Fo u he in o -
ma ion and guidance on when o employ egex o a deep lea ning app oach, e-
e o he ollowing guide: h ps://gi hub.com/explosion/asse s/blob/main/
P odigy/P odigy_NER_ lowcha _ 2_0_0_ligh .pd .
6.2. Fu u e wo k
In u u e esea ch s udies, he e a e se e al main a enues o u he explo a ion
and in es iga ion in o a ious aspec s o he p oblem. These include:
Conside in es iga ing al e na i e language models designed o p ocessing
Spanish clinical ex , such as RoBERTa2o RoBERTa Clinical3. Ha nessing
he powe o models ha ha e a be e adap a ion o Spanish clinical no es
could po en ially enhance he accu acy and e ec i eness o he esul s.
Conside cons uc ing ou own language model. Le e aging a p e-exis ing
Spanish model as a ounda ion, we can engage in u he p e- aining u ilizing
a specialized medical co pus wi h an emphasis on oncology-speci ic language,
including con en om scien i ic esea ch jou nals wi hin his domain. This
model, once comple ed, could be eleased o he public, ma king a signi ican
miles one as he i s Spanish language model wi h a specializa ion in medical
cance ocabula y.
Enhancing he pe o mance o models ha handle nega ion and unce ain y
is c ucial in he ealm o clinical na a i e analysis in spanish.
The cu en pipeline is designed o s uc u e no es ha con ain a single diag-
nosis. To e ec i ely comp ehend and p ocess no es wi h mul iple diagnoses,
an e ec i e app oach could be o u ilize ano he Language Model (LM). This
LM could ini ially p ep ocess he no es, conduc ing a p ima y cleanup o he
da a. I mul iple diagnoses a e de ec ed, he LM could hen segmen he ex
in o dis inc pa ag aphs co esponding o each diagnosis.
2h ps://hugging ace.co/BSC-LT/ obe a-base-bne
3h ps://hugging ace.co/PlanTL-GOB-ES/bsc-bio-eh -es
Bibliog aphy
Imagina ion is mo e impo an han
knowledge. Knowledge is limi ed, while
imagina ion is no .
Albe Eins ein
[1] B. S ini asa-Desikan, Na u al Language P ocessing and Compu a ional Linguis-
ics: A p ac ical guide o ex analysis wi h Py hon, Gensim, spaCy, and Ke as.
Pack Publishing L d, 2018.
[2] C. Allen and T. Hospedales, “Analogies explained: Towa ds unde s anding wo d
embeddings,” in In e na ional Con e ence on Machine Lea ning. PMLR, 2019,
pp. 223–231.
[3] C. Olah. (2015) Unde s anding ls m ne wo ks. [Online]. A ailable: h ps:
//colah.gi hub.io/pos s/2015-08-Unde s anding-LSTMs/
[4] Hugging Face, “Hugging ace blog: La ge language model,” h ps://hugging ace.
co/blog/la ge-language-models, 2021.
[5] A. Zhang, Z. C. Lip on, M. Li, and A. J. Smola, “Di e in o deep lea ning,”
a Xi p ep in a Xi :2106.11342, 2021.
[6] J. De lin, M. W. Chang, K. Lee, and K. Tou ano a, “BERT: P e- aining
o deep bidi ec ional ans o me s o language unde s anding,” NAACL HLT
2019 - 2019 Con e ence o he No h Ame ican Chap e o he Associa ion o
Compu a ional Linguis ics: Human Language Technologies - P oceedings o he
Con e ence, ol. 1, no. Mlm, pp. 4171–4186, 2019.
[7] J. De lin, M.-W. Chang, K. Lee, and K. Tou ano a, “BERT: p e- aining
o deep bidi ec ional ans o me s o language unde s anding,” CoRR, ol.
abs/1810.04805, 2018. [Online]. A ailable: h p://a xi .o g/abs/1810.04805
[8] R. L. Siegel, K. D. Mille , H. E. Fuchs, and A. Jemal, “Cance s a is ics, 2022,”
CA: a cance jou nal o clinicians, 2022.
53
60 Appendix A. Co pus Anno a ion Guide
2. Implici Da e
This label is used o ep esen da es ha a e exp essed indi ec ly in he no es
h ough ime exp essions
Ing esado a u gencias hace 5 días.
T a ado con una ci ugía hace 3 años.
a amien o neodyud an e has a agos o 2015.
ing esada desde 17/08/2022 al 4/09/2020.
3. Cance Concep
This label is used o ep esen he di e en ways o e e ing o he e m cance
in clinical no es. Mainly e e s o he classi ica ion acco ding o he ype o
cell ha becomes cance ous. Mos commons examples:
Ca cinoma de mama de echa mul i ocal.
Diagnos icada de adenoca cinoma de pulmón de echo.
Neoplasia de mama .
p esen a nec osis umo al.
O he s e ms: ib oma, ib o ecoma, ib oadenoma, mioma, lin oma, sa -
coma, leucemia.
4. Cance Expansion
ca cinoma de mama de echa mul i ocal.
ca cinoma duc al in il an e mama de echa.
ca cinoma de mama lobulilla in si u.
O he s e ms: in asi o, mic oin il an e, mul icén ico, bi ocal o in il-
an e mul i ocal.
5. Cance Type
This label ep esen s he subca ego ies o cance acco ding o hei dis inc i e
ea u es and cha ac e is ics. Each sub ype may ha e di e en g ow h pa e ns,
clinical beha iou , esponse o ea men and p ognosis. The e a e he mos
impo an ypes o doc o s.
ca cinoma de mama lobulilla in si u.
ca cinoma de mama in aduc al.
ca cinoma in il an e ipo his ologico no especi ico.
O he s e ms: lobula , duc al, apoc ino, neo oendoc ino, endome ioide,
adenoide, inespecí ico, en e medad de page , cis oadenoma, iloides.
6. Cance Sub ype
This label ep esen s he o he subca ego ies o cance .

61
ca cinoma duc al con encional in il an e mama de echa.
ca cinoma mucinoso de mama de echa.
O he s e ms: clásico/con encional, medula , papila , ubula , mucosi-
no/coloide/mucinoso/mucoso/mucinosa, comedoniano, sólido, c ibi o me,
mic opapila , plano, pleomó ico/a, olicula .
7. Cance G ade
This label e e s o he g ade o he cance . I may be nuclea o No ingham
g ade. In addi ion, ei he he g ade numbe o he exp ession.
ca cinoma in il an e inespecí ico g ado his ológico 1 de no ingham po
pun uación de 5 (2+2+1).
ca cinoma de mama lobulilla in il an e, g2, e 100%.
con componen e in aduc al asociado de al o g ado nuclea .
8. Cance In a ype
The e a e 4 molecula sub ypes o b eas cance , de ined by he molecula
ma ke s.
luminal A
luminal B
HER2 sob eexp esado
T iple nega i o
9. Cance Loca ion
This label exp esses he o gan whe e he cance mani es s i sel .
Ca cinoma lobulilla in il an e en mama izquie da.
Ca de mama izquie da cuad an e supe io .
ca lobulilla o igen mama io.
ca cinoma lobulilla bila e al.
mas ec omía de mama de echa.
cánce de pulmón.
cánce de colon.
10. Cance Me as asis
This label includes e ms e e ing o he sp ead o cance o o he o gans.
ganglios lin a icos con me as asis.
p og ession nodula .
in asión pe ineu al.
62 Appendix A. Co pus Anno a ion Guide
in il ación lin o ascula .
11. Cance Recu ence
This label indica es whe he he pa ien has elapsed a e e adica ion o he
cance .
Recaída local de echa de ca cinoma lobulilla en no iemb e de 2016.
Recidi a local de ca cinoma duc al in il an e de mama de echa.
12. Cance S age
This label is used o ep esen he s age o he cance . Mos cance s ha e ou
s ages: s ages 0, I (1) o IV (4). S age I ep esen s he ea ly s age and s age
IV ep esen s he mos ad anced s age o he umou . These s ages can also
be combined wi h he le e s A, B o C as shown below.
Adenoca cinoma de mama es adio IV.
Ca cinoma escamoso es adio I-B.
Cánce de pulmón no mic ocí ico es adio IA.
Ca cinoma de mama al menos es adio iii.
13. TNM
This label is used o ep esen he s age o he cance using he malignan u-
mou classi ica ion: TNM no a ion. This no a ion ep esen s a cance concep
using h ee alphanume ic codes: Tumou (T) desc ibes he size o he umou .
The le e (N) desc ibes he lymph nodes ha a e a ec ed. The le e (M)
is used o ep esen whe he me as ases a e p esen . TNMs a e neoadju an
ea men s a wi h y.
Pacien e con cánce de pulmón cT3cN3cM1.
Ca cinoma de mama T3 N2 M0.
14. Molec Ma ke
This label e e s o molecula ma ke s ha a e measu ed in pa ien s, and
whose esul can be posi i e, nega i e o a pe cen age alue. Thei s udy
p o ides in o ma ion abou he cance and he ea men o be ollowed. This
will include:
Ho mone ecep o s: oes ogen ( e o e ), p oges e one ( p o p ) o bo h
ho mone ecep o s ( h o hh).
ki67. Shall be accompanied by a pe cen age.
he 2/Neu/E B2/Ce B2. Accompanied by he sign/pending/inconclusi e.
Examples:
63
ca cinoma duc al in il an e mama de echa e y p posi i os.
adenoca cinoma e 90%, p 95%, he 2 -, ki-67 1%.
15. T ea men
This label includes all ypes o ea men ha can be gi en o he pa ien ,
wi hou going in o any de ail, i.e. only gene ic ea men wo ds.
Inicia a amien o con quimio e apia.
T a amien o conse ado .
T a amien o con ho mona.
T a amien o adio e ápico.
Le han p opues o quimio e apia que la en e ma ha ehusado. Tiene p e-
is o ho mono e apia as la i adiación.
a ada con adio e apia adyu an e.
16. T ea men D ug
This label is used o iden i y speci ic d ug names used o ea cance pa ien s.
Pacien e diagnos icado con ca cinoma, a ado con cispla ino en julio de
2017.
Ho mono e apia con anas ozol.
En a amien o con he cep ín.
Inicia ensayo clínico con axol.
a amien o con inhibido es de la a oma asa.
17. T ea men F equency
This label e e s o he equency wi h which he ea men is applied.
Pacien e que ecibe QT CON Cispla ino 75 mg/m2 cada 21 días.
La dosis o al adminis ada ha sido de 50 gy día.
18. T ea men In e al
This label e e s o he o al in e al o e which a gi en ea men is admin-
is e ed.
iene pla ini icado adio e apia y gose elina, amoxi eno du an e 5 anos.
po 4 ciclos.
xol x4 semanal.
19. T ea men Quan i y
I is used o ep esen he dose o a medicine adminis e ed o a pa ien a a
gi en ime.
64 Appendix A. Co pus Anno a ion Guide
Pacien e que ecibe q con cispla ino 75 mg/m2 cada 21 días.
Recibe RT adical de 50 Gy el 15/12/2014.
Radio e apia de 60 gy.
20. T ea men Schema
This label e e s o egimens o schedules used in chemo he apy o he ea -
men o cance . Possible examples include:
EP (e oposide and cispla in)
FEC ( Fluo ou acil, epi ubicin hyd ochlo ide and cyclophosphamide)
AC ( Ad iamycin and cyclophosphamide)
ET (e oposide and ca bopla in)
ICE (i os amide, ca bopla in and e oposide)
CAV (cyclophosphamide, doxo ubicin o ad iamycin and inc is ine).
CMF (cyclophosphamide, me ho exa e, luo ou acil).
FOLFOX
•FOL ( olinic acid o leuco o in)
•F ( luo ou acil)
•OX (oxalipla in)
Examples:
a ada con quimio e apia cm .
21. Su ge y
This label is used o label cance - ela ed su gical p ocedu es.
Examples:
Lin adenec omía supe io izquie da.
Mas ec omía simple en mama de echa.
Realizada umo ec omía en mama de echa.
Recons ucción con do sal ancho y p o esis.
ci ugía conse ado a con lin adenec omía.
mas ec omía de econs ucción de la mama de echa.
Appendix B
Lis o medical ac onyms
This is he lis o ac onyms used in he p e-p ocessing o clinical no es. I is
impo an o no e ha hese ac onyms a e no uni e sal, bu a he ha a e speci ic
o he doc o s wo king in he hospi al. The lis is he ollowing:
ca cance
adio e apia
ec o oquimio e apia ex aco po ea
q quimio e apia
h ho mono e apia
cse cuad an e supe io ex e no
csi cuad an e supe io in e no
cie cuad an e in e io ex e no
cii cuad an e in e io in e no
lsi lobulo supe io izquie do
lsd lobulo supe io de echo
dcha de echa
de de echa
izq izquie da
ccee cuad an es ex e nos
ccss cuad an es supe io es
cis ca cinoma duc al in si u
65

66 Appendix B. Lis o medical ac onyms
cdi ca cinoma duc al in si u
cdis ca cinoma duc al in si u
nos inespeci ico
ns inespeci ico
bcg biopsia del ganglio cen inela
bsgc biopsia selec i a del ganglio cen inela
bgc biopsia ganglio cen inela
bscg biopsia selec i a del ganglio cen inela
il in il acion lin o ascula
ipn in asion pe ineu al
ihq inmunohis oquimica
op ope acion
gc ganglio cen inela
o a amien o
neoady neoadyu an e
ady adyu an e
ap ana omia pa ologica
mi econ uccion mama ia in e na
x adiog a ia
hh ecep o es ho monales
d x doce axel
p e pendien e
Lis o ac onyms
EHR: Elec onic Heal h Reco ds, His o ias Clínicas Elec ónicas
NLP: Na u al Language P ocessing, P ocesamien o del Lenguaje Na u al
NER: Named En i y Recogni ion, Reconocimien o de En idades Nomb adas
Clinical NER: Clinical Named En i y Recogni ion, Reconocimien o de En i-
dades Clínicas
POS: Pa e de la o ación, Pa -o -Speech
CLI: Command Line In e ace, In e az de Línea de Comandos
BRAT: B a Rapid Anno a ion Tool, He amien a de Ano ación Rápida B a
CONLL: Con e ence on Compu a ional Na u al Language Lea ning, Con e -
encia sob e Ap endizaje Compu acional del Lenguaje Na u al
WHO: Wo ld Heal h O ganiza ion, O ganización Mundial de la Salud
AHIMA: Ame ican Associa ion o Medical Reco d Lib a ians and Heal h In-
o ma ion Managemen , Asociación Ame icana de Biblio eca ios de Regis os
Médicos y Ges ión de In o mación de Salud
SERMAS: Se icio Mad ileño de Salud, Mad id Heal h Se ice
HCIS: Heal h Ca e In o ma ion Sys em, Sis ema de in o mación de a ención
médica
HP-HCIS: Heal hca e P o ide - Heal h Ca e In o ma ion Sys em, P o eedo
de se icios sani a ios - Sis ema de in o mación sani a ia
SELENE: Elec onic Medical Reco d Sys em, Sis ema Elec ónico de Expe-
dien e y Regis os Médicos
IMDH: IMDH, In e cambio de In o mación Médica y de Salud
LSTM: Long Sho -Te m Memo y, Memo ia a Co o Plazo
BiLSTM: Bidi ec ional Long Sho -Te m Memo y, Memo ia a Co o Plazo
Bidi eccional
67
BERT: Bidi ec ional Encode Rep esen a ions om T ans o me s, Rep esen a-
ciones de Codi icado Bidi eccional de T ans o mado es
BETO: BERT o Spanish, BERT pa a el Español
RoBERTa: Robus ly op imized BERT app oach, BERT Robus amen e Op-
imizado
GPT: Gene a i e P e- ained T ans o me , T ans o mado P e-en enado Gen-
e a i o
MLM: Masked Language Modeling, Modelado lingüís ico con másca a
NSP: Nex sen ence p edic ion, P edicción de la siguien e o ación
LM: Language Model, Modelo del Lenguaje
CS: Compu e Science, Ciencia de los Da os
AI: A i icial In elligence, In eligencia A i cial
CSV: Comma-Sepa a ed Values, Valo es sepa ados po comas
JSON: Ja aSc ip Objec No a ion, No ación de obje os de Ja aSc ip
Neu al ne wo ks a e a powe ul ool ha can help ans o m heal h ca e
and imp o e he quali y o li e o people a ound he wo ld
Jensen Huang, ounde and CEO o NVIDIA