Heal h Recommende Sys em design in he con ex o
CAREGIVERSPRO-MMD P ojec
Luis Oli a-Felipe
Uni e si a Poli ècnica de Ca alunya -
Ba celonaTech
Ba celona, Ca alunya, Spain
[email p o ec ed]
C is ian Ba ué
Uni e si a Poli ècnica de Ca alunya -
Ba celonaTech
Ba celona, Ca alunya, Spain
[email p o ec ed]
A ia Co és
Uni e si a Poli ècnica de Ca alunya -
Ba celonaTech
Ba celona, Ca alunya, Spain
[email p o ec ed]
Emma Wol e son
Uni e si y o Hull
Hull, Uni ed Kingdom
e.w[email p o ec ed]
Ma co An oma ini
COOSS Ma che ONLUS
Ancona, I aly
[email p o ec ed]
Isabelle Land in
Cen e Hospi alie Uni e si ai e de
Rouen
Rouen, F ance
Isabelle.Land in@chu- ouen.
Kons an inos Vo is
In o ma ion Technologies Ins i u e
Cen e o Resea ch and Technology
Hellas
Thessaloniki, G eece
[email p o ec ed]
Ioannis Paliokas
In o ma ion Technologies Ins i u e
Cen e o Resea ch and Technology
Hellas
Thessaloniki, G eece
[email p o ec ed]
Ulises Co és
Uni e si a Poli ècnica de Ca alunya -
Ba celonaTech
Ba celona, Ca alunya, Spain
[email p o ec ed]
ABSTRACT
CAREGIVERSPRO-MMD an EU H2020 unded p ojec aims o build
a digi al pla o m ocusing on people li ing wi h demen ia and hei
ca egi e s, o e ing a selec ion o ad anced, indi idually ailo ed
se ices enabling hem o li e well in he communi y o as long as
possible. This pape p o ides an ou line o a heal h ecommende
sys em designed in he con ex o he p ojec o p o ide ailo ed
in e en ions o ca egi e s and people li ing wi h demen ia.
CCS CONCEPTS
•Human-cen e ed compu ing →Human compu e in e ac-
ion (HCI);Sys ems and ools o in e ac ion design;
KEYWORDS
Recommende sys ems, Social Ne wo ks, Demen ia, Ca egi e
ACM Re e ence Fo ma :
Luis Oli a-Felipe, C is ian Ba ué, A ia Co és, Emma Wol e son, Ma co An-
oma ini, Isabelle Land in, Kons an inos Vo is, Ioannis Paliokas, and Ulises
Co és. 2018. Heal h Recommende Sys em design in he con ex o CAREGIVERSPRO-
MMD P ojec . In PETRA ’18: The 11 h PE asi e Technologies Rela ed o As-
sis i e En i onmen s Con e ence, June 26–29, 2018, Co u, G eece. ACM, New
Yo k, NY, USA, 8 pages. h ps://doi.o g/10.1145/3197768.3201558
ACM acknowledges ha his con ibu ion was au ho ed o co-au ho ed by an employee,
con ac o o a ilia e o a na ional go e nmen . As such, he Go e nmen e ains a
nonexclusi e, oyal y- ee igh o publish o ep oduce his a icle, o o allow o he s
o do so, o Go e nmen pu poses only.
PETRA ’18, June 26–29, 2018, Co u, G eece
©2018 Associa ion o Compu ing Machine y.
ACM ISBN 978-1-4503-6390-7/18/06...$15.00
h ps://doi.o g/10.1145/3197768.3201558
1 INTRODUCTION
Acco ding o he Wo ld Heal h O ganisa ion [
31
], 47M people
a ound he wo ld ha e some o m o demen ia, o which he e is
no e ec i e in e en ion, o hal o e e se he p og essi e cogni-
i e impai men . As Eu ope’s popula ion is ageing, long- e m ca e
o elde ly ci izens will become an inc easing cos o socie y. To
manage his ansi ion, heal hca e policies in he EU and indi idual
Membe S a es a e hea ily ocused on ex ending he independen
li e o he elde ly, wi h he dual aim o inc easing hei quali y o
li e and educing he cos s o ca e.
Heal h deli e y p ac ices a e shi ing owa ds home ca e. The
easons a e he be e possibili ies o managing ch onic ca e, con-
olling heal h deli e y cos s, inc easing quali y o li e and quali y
o heal h se ices and he dis inc possibili y o p edic ing and hus
a oiding se ious complica ions [
17
]. The concep o social ne wo ks
using mobile de ices o suppo heal communi ies has eme ged as
a new and iable eali y in he ield o IT o heal h and elemedicine
unde he unding o he EU p og ams. The so-called mHeal h has
he “po en ial o ans o m he ace o heal h se ice deli e y ac oss
he globe", and he e is inc easing media and consume in e es in
mHeal h, illus a ed o example by a ecen panel a he US Con-
sume Elec onics Show on The Digi al Heal h Re olu ion [
19
],[
29
],
[30].
Ageing is one o he g ea es social and economic challenges o
he 21
s
cen u y o Wo ld socie ies. I will a ec all EU coun ies
and mos policy a eas. The Ageing o Eu ope is cha ac e ised by a
dec ease in e ili y, a dec ease in mo ali y a e, and a highe li e
expec ancy among Eu opean popula ions, oge he wi h con inued
bu decele a ing inwa d ne mig a ion o he EU. In all Eu ope,
li e expec ancy is inc easing in an almos con inuous and uni o m
end a he a e o 2-3 mon hs e e y yea and is he main d i e
behind he popula ion ageing. By 2025 mo e han 20% o Eu opeans
PETRA ’18, June 26–29, 2018, Co u, G eece L. Oli a e al.
will be 65 o o e , wi h a pa icula ly apid inc ease in numbe s o
o e -80s.
Due o an ageing popula ion, he public p o ision o long- e m
ca e poses an inc easing challenge o he sus ainabili y o public
inances in he EU. The ageing o he popula ion is expec ed o pu
p essu e on go e nmen s o p o ide long- e m ca e se ices as old
people o en de elop mul i-mo bidi y condi ions, which equi e
long- e m medical ca e and assis ance wi h a numbe o daily asks.
Heal h ends among he elde ly a e mixed: se e e disabili y is
declining in some coun ies bu inc easing in o he s, while mild dis-
abili y and ch onic disease a e gene ally inc easing. This si ua ion
is compa able in many socie ies a ound he wo ld.
CAREGIVERSPRO-MMD (C-MMD) is an EU unded p ojec unde
he H2020 p og amme de o ed o building a mHeal h applica ion
ha is speci ically a ge ed o ca egi e s and people wi h cogni i e
impai men o mild o mode a e demen ia. The no el y o C-MMD
consis s in o in eg a ing a b oade diagnos ic app oach, inco po a -
ing he li e-in amily Ca egi e -Pe son li ing wi h demen ia dyad
and conside ing his dyad as he uni o ca e.
CAREGIVERSPRO-MMD is ocused on people li ing wi h Mild
Cogni i e Impai men o Mild o Mode a e Demen ia (PLWD om
now on) and hei ca egi e s. Mild Cogni i e Impai men (MCI)
is a condi ion ha alls somewhe e be ween no mal age- ela ed
memo y loss and Alzheime ’s disease o a simila impai men . No
e e yone wi h MCI de elops demen ia. And like demen ia, MCI
is no an illness, bu a clus e o symp oms ha desc ibe changes
in how you hink o p ocess in o ma ion. Memo y p oblems a e
he mos common indica o s o MCI. A pe son wi h MCI may also
expe ience di icul ies wi h judgemen , o ien a ion, hinking and
language beyond wha one migh expec wi h no mal ageing. Fo
unknown easons, MCI appea s o a ec men mo e han women.
The p ojec comp ises h ee phases: i s , o design and de elop
he i s p o o ype o he mHeal h applica ion conside ing p e i-
ous wo k on demen ia and psychia ic co-mo bidi y symp oms,
sc eening and in e en ion s a egies o be implemen ed by he
pla o m.
In he second phase, o conduc a use -cen ic analysis o e-
design he exis ing p o o ype o PLWDs. The de elopmen was
s ee ed by PLWDs, ca egi e s and doc o s, h ough use -cen ic
design: eedback is being collec ed on each new e sion o he
applica ion un il he design is adap ed o he use s’ condi ions.
In he hi d phase, he op imised applica ion is being pilo ed wi h
600 dyads (PLWDs and hei espec i e ca egi e s) and 600 con-
ols. This will show he clinical and social bene i s o PLWDs and
ca egi e s, as well as inancial bene i s o he heal hca e sys em.
C-MMD is an in elligen suppo pla o m p omo ing Quali y
o Li e (QoL), well-being and medica ion compliance o PLWD
and Ca egi e s in he communi y a he poin o ca e. I will be
a ailable on sma phones and able compu e and web b owse s.
I s in e ace is being designed o sui use s wi h low IT amilia i y
[
20
]. Addi ionally, C-MMD will be complian wi h in e nal secu i y
p o ocols and policies as well as indus y egula o y policies, i is
designed o only collec and p ocess da a conce ning heal h o
speci ic and legi ima e pu poses.
Some o he needs ha PLWD and hei in o mal ca egi e s
cu en ly pe cei e as insu icien ly me by egula ca e and suppo
se ices migh be alle ia ed, o e en be me wi h he help o he
mu ual assis ance communi ies, using C-MMD. These needs can be
summa ized as (i) he need o gene al and pe sonalized in o ma-
ion; (ii) he need o suppo wi h ega d o symp oms o demen ia;
(iii) he need o social con ac and company; and (i ) he need o
heal h moni o ing and pe cei ed sa e y [14].
The he apeu ic educa ional in e en ions is ano he C-MMD’s
impo an se ice. This se ice o e s pe sonalized educa ional con-
en s o hei PLWDs and ca egi e s h ough he social ne wo k
eed ailo ed o hei speci ic p o ile. The educa ional ocus is wo-
old: i s , i o e s gene ic con en s ocused on demen ia disease,
demen ia and psychia ic co-mo bidi y symp oms and a ailable
esou ces on he communi y and secondly, i p oposes use - ailo ed
con en igge ed on he basis o use ’s p o ile. In his pape , we
will ocus on his se ice p o ision.
1.1 Plan o he wo k
The plan o his pape is he ollowing, in §2 we will explain he
concep o in e en ion and he speci ic s a egy selec ed in C-MMD.
Sec ion 3 p o ides backg ound in ecommende sys ems. In §4 we
explain he ecommende sys em app oach de eloped in C-MMD. In
§5 we will discuss ou conclusions and commen he u u e wo k.
2 PSYCHOSOCIAL INTERVENTIONS
A heal h in e en ion is an ac pe o med o , wi h o on behal o a
pe son o a popula ion whose pu pose is o assess, imp o e, main-
ain, p omo e o modi y heal h, unc ioning o heal h condi ions
[
22
]. A b oad ange o p o ide s can ca y ou in e en ions ac oss
he ull scope o heal h and social sys ems including acu e ca e,
p ima y ca e, ehabili a ion, and assis ance wi h unc ioning, p e-
en ion and public heal h. Fu he de ini ions conside Educa ional
In e en ion as an in e en ion ha aims o educa e, in o m and
shape unde s anding o demen ia and ca egi ing p ac ice [
9
] and
Psychosocial in e en ion as a b oad e m used o desc ibe di e en
ways o suppo people o o e come challenges and main ain good
men al heal h [
7
,
8
,
10
,
23
]. In he li e a u e, o en he e ms psy-
chosocial and nonpha macological a e used synonymously o e e
in e en ions. Psychosocial in e en ions a e o en adminis e ed
by a ained pe son, e.g. a GP, a psychologis o psycho he apis ,
occupa ional he apis s o nu ses, and a e usually pe son-cen e ed.
These so o in e en ions a e expec ed o help PLWD and o en
hei ca egi e s as well, wi h [10]:
•coming o e ms wi h a diagnosis o demen ia
•main aining social li e and ela ionships a e diagnosis
•
educing s ess and imp o ing mood, anxie y o dep ession
• educing o adap ing beha iou diso de s
•
imp o ing cogni i e unc ions, such as hinking and memo y
•li ing independen ly
•
main aining and imp o ing quali y o li e - main aining
heal h and happiness, and con ol o e one’s li e
•suppo ing he pa ne and amily
His o ically, mos psychosocial in e en ions ha e been adminis-
e ed in a ace- o- ace o ma be ween a p o ide (a ained pe son)
and a ca e ecei e (a PLWD, a Ca egi e o bo h). Mo e ecen ly,
hese ha e also included he use o elephones o o he digi al de-
ices, ideo con e ences, sel -guided books o In e ne ideos [
21
].
Some in e en ions combine one o mo e o hese op ions. In he
Heal h Recommende Sys em design in he con ex o CAREGIVERSPRO-MMD P ojec PETRA ’18, June 26–29, 2018, Co u, G eece
scope o C-MMD pla o m, he ocus is on non-pha macological
educa ional in e en ions o PLWD and hei ca egi e s ha can
be p o ided h ough digi al means.
Tailo ed in e en ions, which a e pe sonalized o he indi idual
and assessmen based, ake he concep one s ep u he by adap ing
he in e en ion o speci ically ma ch he indi idual pa ien p o ile.
This wo k desc ibes he echnical app oach aken o implemen he
pe sonaliza ion o he con en s p esen ed o he C-MMD pla o m
use s.
2.1 C-MMD In e en ions
In e en ions a e well desc ibed in he li e a u e bu he e is no
widely accep ed s anda d classi ica ion sys em ha would allow
o a mo e comp ehensi e unde s anding o he di e en ypes o
ea men s and ca e p ac ices, he cha ac e is ics o people who
bene i om hem, o he kinds o p oblems each one has been
shown o educe o esol e [
2
]. Howe e , many o ganisa ions p o-
ide hei own p oposals unde di e en c i e ia. The NICE-SCIE,
o ins ance, ca ego izes in e en ions acco ding o hei use: cogni-
i e symp oms, non-cogni i e symp oms and beha iou , como bid
emo ional condi ions and ca egi e suppo . On he o he hand,
o he classi ica ions can also iden i y in e en ions o he p ob-
lem hey a ge (e.g. sleep p oblems). In he C-MMD ecosys em
wo co e g oups o s akeholde s can be ound, di ided hei knowl-
edge backg ound: (i) Heal h and Social P o essionals (HSP) and
(ii) Ca egi e s/PLWD. The impo ance o de eloping and using
language and e minology ha is unde s andable and meaning ul
o hese s akeholde g oups in all e o s o expand he a ailabil-
i y and use o non-pha macological ea men s and ca e p ac ices
mus be emphasized. The C-MMD pla o m mus allow he HSP o
classi y co ec ly he in e en ions and con en s hey c ea e o he
PLWD and Ca egi e s while hese mus be able o iden i y hei
p oblems and needs in he ca ego isa ion. The C-MMD Conso ium
has explo ed se e al in e en ion axonomies om us ed sou ces
like:
•
In e dem, a pan-Eu opean ne wo k o esea che s collabo-
a ing in esea ch on and dissemina ion o Ea ly, Timely and
Quali y Psychosocial In e en ions in Demen ia [18]
•
The Na ional Ins i u e o Heal h and Clinical Excellence -
Social Ca e Ins i u e o Excellence Guideline on Suppo ing
People Wi h Demen ia and Thei Ca e s in Heal h and Social
Ca e [9]
•
The B i ish Psychology Socie y Guide o Psychosocial In e -
en ions in Ea ly S ages o Demen ia [10]
•
Demen ia Ca e si e de eloped wi h and by amily membe s
who look a e someone who has demen ia [28]
The C-MMD Conso ium has iden i ied he domains o he De-
men ia Ca e p oposal as he mo e signi ican , as hey e lec he
mos he poin o iew o dyad daily li e bu i mus be no ed ha
he e is a consis en pa allelism in he di e en p oposals. Based
on li e a u e su ey, he C-MMD Conso ium depic ed a lis o 26
in e en ions ca ego ized by in e en ion a ea om which 12 we e
selec ed (see Table 1) o be as candida es o be in eg a ed in o he C-
MMD pla o m upon he easibili y o be echnically implemen ed.
2.2 In e en ion P o ision
Acco ding o NICE-SCIE, ea men and ca e should ake in o accoun
pa ien s’ needs and p e e ences (o heal hca e goals). People wi h de-
men ia should ha e he oppo uni y o make in o med decisions abou
hei ca e and ea men , in pa ne ship wi h hei heal hca e p o-
essionals. One o he main cha ac e is ics o he C-MMD pla o m
is he possibili y o empowe bo h he PLWD and he Ca egi e s
imp o ing he abili ies o he sel -managemen o hei heal h and
aking in o accoun hei a i udes and inclina ion. Fo such eason,
he s a egy o in e en ion p o ision is also es ablished acco ding
o he use ’s p e e ences. One o he C-MMD p ima y se ices is
o p o ide use - ailo ed in e en ions, anked acco ding o use s’
needs and p e e ences and sc eening esul s. In o de o p o ide
ailo ed in e en ions, i has o be aken in o accoun hese wo im-
po an dimensions: use p e e ences and he use s’ clinical/social
p o ile. Pe son-de ined heal hca e goals and p e e ences align p i-
ma y and speciali y ca e ocusing on wha ma e s mos o he
people. PLWDs and Ca egi e s should be a mo e ac i a ed and
engaged in hei ca e when i ocuses on achie ing wha ma e s
mos o hem.
The main goal o he con en ecommenda ion sys em will be o
combine he use p e e ences (based on hei in e es s), he heal h-
ca e p o essional ecommenda ions (based on hei p o essional
c i e ia) and he Ca egi e s’ opinion (conside ing hei ca e expe i-
ence):
•A PLWD can selec i s opic p e e ences when egis e ing
•
A Ca egi e can de ine i s opic p e e ences when egis e ing
and also sugges opics o i s ca ed one conside ing his/he
speci ic needs
•
A heal hca e p o ide (medical/social) can de ine opic goals
o his managed PLWD/Ca egi e conside ing his/he spe-
ci ic needs
•
O he heal hca e me ada a ela ed o he use p o ile is used
o ecommend in e en ions
All his p e e ence selec ion is dynamic and can be modi ied in he
sys em a any ime, allowing a lexible pe sonaliza ion.
3 RECOMMENDER SYSTEMS BACKGROUND
C-MMD pla o m will ake ad an age o ecommende sys em ech-
nologies in o de o p o ide PLWD and Ca egi e s wi h a pe son-
alized lis o psychosocial in e en ions. This sec ion p o ides an
o e iew o he ela ed wo k and me hods ha ha e been in es i-
ga ed o de elop he Heal h Recommende Sys em o he C-MMD
p ojec .
In he mid-nine ies o las cen u y, Recommende Sys ems eme ged
as an independen esea ch ield o In o ma ion Re ie al and A -
i icial In elligence o add ess he in o ma ion o e load p oblem
by using a speci ic ype o in o ma ion il e ing echniques ha
a emp s o ecommend in o ma ion i ems (e.g., mo ies, TV, ideos
on demand, music, books, news, images, Web pages, esea ch pa-
pe s) ha a e likely o be o in e es o he use . In public heal h
sys ems, no only he e is an o e load p oblem bu also in o ma ion
comes om di e en sou ces and o ma s. Pe sonal heal h eco d
sys ems a e mean o cen alise and s anda dise an indi idual’s
heal h da a and enhance he da a sha ing among au ho ised p o es-
sionals o en i ies. The e o e, ecen ends in esea ch ha e ocused
PETRA ’18, June 26–29, 2018, Co u, G eece L. Oli a e al.
Table 1: C-MMD In e en ion Types
Domain In e en ion
In o ma ion and adjus men o diagnosis
Demen ia Ad iso s
Pos Diagnos ic G oups
Signpos ing
S ess, anxie y o dep ession managemen
Pee Suppo G oups
S ess/Anxie y managemen
Reminiscence
Imp o ing and main aining cogni i e unc ioning Assis i e Technology: ad ice and suppo
Cogni i e T aining
Heal h and quali y o li e
Physical Exe cise he apy
Home modi ica ion
Fall p e en ion
Music The apy
on heal h in o ma ion e ie ing and di e en app oaches o Heal h
Recommende Sys ems (HRS) can be ound in he li e a u e.
An HRS is a specialisa ion o a ecommende sys em, whe e a
ecommendable i em o in e es is "a piece o non-con iden ial, sci-
en i ically p o en o a leas gene ally accep ed medical in o ma ion,
which in i sel is no linked o an indi idual’s medical his o y" [
32
].
Possible examples o a ecommended i em a e ood/nu i ion in o -
ma ion, a physical ac i i y, a diagnosis, a he apy o a medica ion
[
5
]. Au ho s in [
32
] ha e iden i ied HRS designed o wo a ge
publics and pu poses :
•
diagnos ic o educa ional ool o assis physicians in he
decision-making p ocess when ea ing a pa ien
•
pe sonal heal h ad ising ool o use s, especially ocused
on heal hy beha iou al change, engaging use s in o physical
ac i i ies o nu i ion based ecommende sys ems
The inal objec i e is o empowe pa ien s by educa ing hem abou
hei heal h and o e ing means o sel -diagnosis, bu also o p o ide
clinicians wi h mo e accu a e in o ma ion abou his/he pa ien s.
Finally, heal h ca e p o ide s o insu ances would bene i om his
e icien , ailo ed and cos -sa ing se ices [
27
]. One o he main
challenges o HRS, in compa ison o he ones aced by adi ional
ecommende sys ems, is he implica ion o e hical issues when
collec ing o p ocessing pe sonal da a and he heal h in o ma ion
deli e ed o he end-use . People migh p esen di e en , mul iple
condi ions, and each o hem will ha e di e en needs and in e es s.
HRS ough o be able o de e mine hese heal h condi ions and
which is he meaning ul da a om he pa ien s’ heal h eco d, in
o de o p o ide ailo ed, con ex - ela ed, high quali y and us ed
ecommenda ions.
Nowadays, HRS a e becoming popula due o he inc easing de-
mand o nu i ion-based and heal hie li es yle beha iou al ecom-
mende s. The In ape sonal Re ospec i e Recommenda ion (IRR,
[
16
]) is a li es yle change ecommende ha only uses pe sonal
his o y and he goal o achie emen . This app oach migh su e
om he new use p oblem since ecommenda ions a e based on
he e ospec i e his o y and beha iou al pa e ns o an indi idual.
MyBeha iou [
24
] is a mobile applica ion which acks a use ’s
physical ac i i y and die ou ines and p o ides au oma ic heal h
eedback.
Educa ional HRS is he o he main ield o applica ion. An ex-
ample o i is Heal hRecSys [
26
], a seman ic-based ecommende
which gene a es Medline Plus links ex ac ed om me ada a o
selec ed You ube ideos. Au ho s ema k ha he quali y o ecom-
menda ions is a ec ed by he seman ic-gap be ween he laype son
language and he heal h p o essional.
In [
11
], au ho s p opose a ecommende sys em ha combines
bo h app oaches o de elop a sma phone-based pe sonalized ec-
ommende sys em o people wi h dep ession. The sys em will
bo h p opose ac i i ies o elie e nega i e emo ions, bu also o e s
expe knowledge on how o be awa e o such nega i e emo ions
and o help use s lea n how o con ain and change hem.
In gene al, HRS a e s ill in an imma u e phase al hough i is an
eme ging end in li e a u e. In [
5
], au ho s p oposed a amewo k
o HRS whe e all he componen s needed o c ea ing a success ul
and use ul ecommende sys em a e desc ibed: (i) domain de ini ion,
including i ems, con ex , s akeholde s, end-use s o da a a ailabili y,
(ii) HRS e alua ion, pa icula ly use accep ance and sa is ac ion,
us and p i acy, and communica ion quali y, (iii) beha iou al
e alua ions, o how e ec i e was he HRS, (i ) heal h impac , which
assesses how beha iou al changes lead o changes in heal h and ( )
e hical conside a ions o he HRS.
3.1 Recommenda ion s a egies
In he li e a u e [
25
], ecommenda ion s a egies a e classi ied ei-
he on he basis o hei knowledge sou ce o o he algo i hmic
echnique employed. When classi ied on he basis o he knowledge
sou ce, he ollowing wo main s a egies can be iden i ied: con en -
based il e ing (CB), which exp esses use in e es s as keywo d-
based use p o iles and consis s o ecommending i ems ma ching
use p e e ences and i em ea u es; and collabo a i e il e ing (CF),
which exp esses use p e e ences as i em a ings and whose ec-
ommenda ions a e based on ma ching use s o i ems wi h simila
a ing beha iou . Based on he algo i hmic echnique, wo main
app oaches can be iden i ied as well: heu is ic-based (also called
memo y-based), which employs some heu is ic o mula, such as
ec o -based simila i y and co ela ion measu es, o calcula e he
ecommenda ion; and model-based, which gene a es he ecom-
menda ion using a model lea n by applying some model-building
Heal h Recommende Sys em design in he con ex o CAREGIVERSPRO-MMD P ojec PETRA ’18, June 26–29, 2018, Co u, G eece
echnique o e he use -i em a ing ma ix. Typically, heu is ic-
based sys ems adap be e o changes in use ’s p e e ences bu scale
wo se han model-based ones. Heu is ic-based CB [
1
] is inspi ed
by In o ma ion Re ie al me hods and calcula es he use -i em
ma ching based on ec o measu es such as he cosine simila i y.
Model-based CB [
3
] calcula es he ma ching based on a model o
use ’s as es buil by applying Machine Lea ning echniques, such
as naï e Bayesian ne wo ks, o ex ca ego iza ion. Heu is ic-based
CF employs heu is ic echniques such as a ing co ela ion analysis
and is also known as he neighbou hood-based app oach (based
on he k-nea es -neighbou algo i hm). Depending on whe he a
subse o use s o i ems is chosen o compu e ecommenda ions,
wo classes o heu is ic-based CF can be iden i ied: use -based [
12
],
when he algo i hm ocuses on inding use s simila o he ac i e
one o ecommending; and i em-based [
15
], when he algo i hm
ocuses on inding i ems simila o hose he ac i e use likes. Model-
based CF uses he a ings o lea n a compac p edic i e model o
ep esen use -i em in e ac ions based on la en ac o s [
13
]. In
gene al, ecommende sys ems ha e a signi ican dependency on
da a. I is possible o dis inguish he ollowing p oblems:
(1)
Cold-s a p oblem: To p o ide good ecommenda ions i is
necessa y o ha e enough eedback om use s (e.g., a ings
o p e iously deli e ed in e en ions) o lea n which i ems
i hei needs. Fo his eason, new use s ep esen a blank
sla e and he sys em does no know how o p o ide pe son-
alised esul s since he e a e no elemen s o he decision o
do il e ing. This si ua ion is also known as he cold s a
p oblem and i also a ec s i ems. I can be usually ound
when using collabo a i e il e ing. In his si ua ion, a new
i em does no ha e any a ing.
(2)
Spa si y: Usually, he se o i ems g ows la ge han he se
o use s, which implies ha as he sys em ge s olde , he
a e age numbe o a ings by i em becomes lowe , since
use s end o a e a limi ed numbe o i ems. The e ec o
his si ua ion is ha use s become less simila since he e
a e less common a ings be ween hem.
(3)
Gene aliza ion: The gene aliza ion p oblem a ises when he
use model is oo gene ic, and i does no allow o make pe -
sonalized ecommenda ions bu jus in a gene ic way, hus
li le di e ences can be ound om wha is ecommended
be ween di e en use s
(4)
O e specializa ion: Opposed o gene aliza ion, his issue
occu s when he ecommende sys em collec s a signi ican
amoun o eedback oo ocused on a subse o i ems. In ha
case, he sys em may jus ecommend hose i ems ha a e
high likely o be al eady known by he use and igno ing
he es .
(5)
Insensi i eness o p e e ence changes: I he use model is
no lea n pe iodically, i can u n ou -da ed and do no o e
ecommenda ions aligned wi h cu en use ’s p e e ences.
In addi ion o his, i is possible o ind Demog aphic-based ap-
p oaches (DF), which build s e eo ypes acco ding o use s’ agg e-
ga ed pe sonal de ails, hus gene a ing use models om simila
use s’ g oups (wi h ega ds demog aphic c i e ia such as age o
gende ).
Table 2: T ade-o s in ecommenda ion s a egies
Limi a ions o Recommenda ion S a egies CB CF DF
New use cold-s a p oblem X X
New i em cold-s a p oblem X X
Spa si y p oblem X X
Gene aliza ion p oblem X X
O e specializa ion p oblem X
Insensi i e o p e e ence changes X X
Each algo i hmic app oach has p o ed o ob ain be e esul s
o sol e some, bu no all, o hem (see Table 2). In isola ion, CF
has p o en o be he mos accu a e app oach in mos domains.
Howe e , hese me hods su e om well-known limi a ions ha
conside ably a ec hei pe o mance. The mos common solu ion
o o e come hese limi a ions in eal applica ions consis s o ap-
plying a hyb id s a egy combining CB and CF me hods. Di e en
hyb idiza ion s a egies ha e been used in he li e a u e, such as: he
weigh ed s a egy, in which he sco e o di e en ecommenda ion
componen s a e combined nume ically; he ea u e augmen a ion
s a egy, in which he ecommenda ion echnique is used o com-
pu e a ea u e o se o ea u es, which is hen pa o he inpu o
ano he echnique; and he cascade s a egy, in which ecommen-
da ions made by one echnique a e e ined by ano he echnique.
Fo ins ance, using DF app oach o deal wi h cold-s a p oblems
o new use s, whe e no hing is known abou he use and, a leas ,
some app oxima ion can be pe o med o o e a use model mo e
use ul han jus andomly ecommending. A e wa ds, wi h mo e
knowledge abou use s, a mo e e ined use model can be lea n .
4 C-MMD RECOMMENDER COMPONENT
This sec ion p esen s an o e iew o he unde aken solu ion ap-
p oach o design he Heal h Recommende Sys em. This sys em
p o ides wo di e en se ices: a anked lis o in e en ions which
is ailo ed o use ’s p e e ences and equi emen s and a lis o po en-
ial acquain ances acco ding o hei simila i ies o he gi en use .
Al hough bo h unc ionali ies p o ided by he sys em sha e simila
componen s and in e aces owa ds da a sou ces, hei objec i es
and he na u e o he i ems ha each one handles, di e s. Fo his
eason, each pa has been designed di e en ly: hei aining and
ecommenda ion p ocesses, al hough basically simila , ha e some
sligh di e ences. I is wo h no ing ha he o me has al eady
been implemen ed while he la e has no been ye deployed a he
momen o w i ing his pape . Ne e heless, we will men ion his
second ecommende along he ex as i has a ec ed he o e all
design o he sys em.
Algo i hmic app oach
To de elop he C-MMD HRS we adop ed a hyb id il e ing app oach.
I is well known ha hyb id sys ems p o ide be e esul s [
4
], con-
sequen ly, ha is he chosen app oach. Speci ically, ou app oach
consis s o a con en -based algo i hm and a ule-based il e ing
which uses a axonomical classi ica ion o in e en ions used by
heal h p o essionals ha c ea e hose in e en ions. In his way, we
aim o p o ide medical c i e ia o he p ocess o selec ing wha

PETRA ’18, June 26–29, 2018, Co u, G eece L. Oli a e al.
in e en ions should be e u ned o he use . Ideally, he e is a la ge
numbe o possible in e en ions, as many as he di e en kinds
o subjec s, a eas o in e es and so ha a use can indica e in i s
p o ile. I is also necessa y o no e ha gi en he na u e o he use s,
i may be possible ha no oo much eedback can be collec ed in
e ms o a ings. This implies ha any eedback e ie ed om
use s may be oo spa se, which makes di icul o ind simila use s
o p oceed wi h a collabo a i e- il e ing app oach. These easons
s eng hen ou app oach o applying a con en -based algo i hm o
p edic use ’s sa is ac ion along he use o a ule-based app oach
which akes in o conside a ion he opics o in e es chosen by
he PLWD, ca egi e and doc o s/social wo ke s. By means o ex-
ac ing ea u es ha de ine an in e en ion, i is possible o lea n
he p e e ence owa ds each ea u e om use ’s eedback gi en o
p e ious in e en ions. Fo ins ance, a use may p o ide posi i e
eedback owa ds in e en ions ha a e mainly ideos, explain-
ing ips on a speci ic opic o in e es , and nega i e eedback o
in e en ions ha a e long ex s. Thus, a con en -based algo i hm
can p edic be e sco e o in e en ions ha a e deli e ed in ideo
o ma . Besides his, he ecommende sys em also compu es a lis
o simila use s o a gi en use . This lis is unde s ood as po en ial
acquain ances o iends o ha use , which a e ele an because
o he sha ed in e es s hey ha e. Feedback is collec ed h ough he
C-MMD pla o m and i s g aphical in e ace. We can dis inguish
wo di e en kinds o eedback:
(1)
Explici : Feedback ha he use has explici ly p o ided, h ough
he C-MMD g aphical in e ace, and includes Use ’s p e e -
ences and pe sonal de ails. I also includes explici a ings
gi en o in e en ions. Explici eedback can become inac-
cu a e o ou -da ed since i depends on he use o ac i ely
ill in all in o ma ion as well as upda e i . This is especially
ue o PLWD use s; hus, we also include he p e e ences
sugges ed by ca egi e s and doc o s/social wo ke s wi h
ega ds o hei ela ed PLWD
(2)
Implici : Feedback ha use has no ac i ely p o ided, bu i
has been ob ained om he ac ions s/he has ca ied ou and i
is cap u ed au oma ically by he pla o m (e.g., in e en ions
iewed o sha ed)
Ou ini ial design is using bo h kinds o eedback. Speci ically, i is
using implici eedback in e ms o in e en ions ha ha e been
e ec i ely consumed by he use . This eedback is ob ained h ough
he eedback in e ace. Ou sys em is also using explici eedback
in e ms o likes/dislikes ha a use may ha e p o ided owa ds
in e en ions. I was also conside ed o ask use s o a e using a 1-5
Like scale bu i was disca ded since using his mo e ine-g ained
a ing could di icul use expe ience gi en he na u e o hei ill-
nesses.
Implemen a ion de ails
The HRS sys em is composed o he ollowing componen s (see
igu e 1):
(1)
The HRS manage , who akes ca e o o ches a ing he lea n-
ing p ocess, combining he esul s o he p edic ions made
by bo h ecommende sys ems, apply ha d cons ain s as p e
o pos il e ing ules
(2)
The ecommende sys ems, ha is, he app oaches we e
aken o lea n use models and p edic he a ings on non-
consumed in e en ions as well as simila use s
(3)
The adap o s, mean o decouple he in e nal da a models
o he ecommende om he da a sou ces models p o ided
by o he componen s in he CMMD pla o m, hus con ain-
ing po en ial p oblems om changes in hose da a model
schemas
(4) An endpoin o p o ide he ecommenda ions
Figu e 1: HRS a chi ec u e o e iew. Each elemen in he
con en -based ecommende s box can consume om di e -
en adap o s.
The p e ious componen s wo k o line; he use models equi e
being upda ed and e-lea n om ime o ime. The upda e e-
quency is cu en ly se in a daily basis, bu i may be changed de-
pending on he equency ha use s a e he i ems (in e en ions)
o change hei p e e ences. The sys em compu es p edic ions and
s o es hem o be accessed la e on h ough an API ha publishes
hem o each use . This HRS sys em has been implemen ed in Ja a
and Cloju e. The o me akes ca e o he engine in e nals while he
la e w aps he engine o ake ca e o he public in e ace and da a
p epa a ion. We ha e used wo di e en languages so we can use
Ja a o ge he ad an age o exis ing lib a ies (e.g., Apache Mahou )
and Cloju e. Using Ja a also acili a es eusing he code mo e easily
in di e en scena ios and deploymen s as well as main aining i
in he u u e. Cloju e smoo hly in e ope a es wi h Ja a (bo h a e
execu ed in he JVM) and acili a es he ask o p o iding a sel -
documen ed REST ul API as well as da a cleansing and p epa a ion
o he lea ning p ocess pe o med by he Ja a code.
Heal h Recommende Sys em design in he con ex o CAREGIVERSPRO-MMD P ojec PETRA ’18, June 26–29, 2018, Co u, G eece
As men ioned be o e, he HRS implemen s a con en -based ap-
p oach which has been e ined wi h p e- il e ing and pos - ea men
p ocesses. The o me emo es hose in e en ions ha a e i el-
e an o he use acco ding o he in e es s decla ed by he use ,
his/he ca egi e and doc o and his/he p o ile (e.g. language, co-
mo bidi ies, loca ion). A e wa ds, he sys em p edic s he es ima ed
in e es o he use owa ds he po en ial in e en ion candida es ac-
co ding o use ’s pe sonal da a (e.g., use ’s de ails), use -gene a ed
con en (i.e., a ings, likes, sha es o con en ha was ecommended).
The las s ep pos -p ocesses his lis o ank hem acco ding o use ’s
in e es s and use ’s ca egi e and doc o indica ions.
In his way, he use is ecei ing a lis o in e en ions which
a e g ouped acco ding o no only on use ’s in e es s bu also on
wha his/he doc o and ca egi e conside mo e use ul o sui able
and, wi hin each g oup, anked o he es ima ed p e e ence o he
use owa ds hose in e en ions.
The lea ning p ocess is pe o med daily and he esul is s o ed
o consump ion by he C-MMD pla o m which schedules he
appea ance o ecommended in e en ions in use ’s news eed
h oughou he day, hus cons an ly p o iding in e es ing in e en-
ions o keep use ’s a en ion.
5 CONCLUSIONS
In his pape , we ha e ou lined he design o an HRS o he C-
MMD pla o m ha is al eady being pilo ed by 600 dyads in ou
coun ies. The da a collec ed he nex mon hs will allow us o
ine- une he ecommending p ocess and o p o ide esul s on he
con en dis ibu ion and consump ion by he di e en s akeholde s.
Elde ly people wi h cogni i e impai men eel he e is a g ea
need o mo e public awa eness o he disease and mo e suppo o
ca egi e s. While ea men s o e e se o hal disease p og ession
a e no a ailable o mos o he demen ias, and o he o eseeable
u u e, ea men and medica ion o demen ia will emain cen ed
en i ely on disease managemen . Slowing down he a e o cogni-
i e decline and imp o ing ea ly diagnosis is essen ial in his as i
gi es PLWDs he bes chance o main ain hei cogni i e abili y. I
also allows ca egi e s and PLWDs o plan o he u u e. C-MMD
suppo s PLWDs and Ca egi e s, p o iding ele an in e en ions
o hem a he poin o ca e in he communi y, empowe ing hem
by building up pa ien s’ capaci y o a be e sel -managemen o
he disease and o become ac i e pa ne s in hei own ca e, and o
con ibu e o a wide pe spec i e in he heal h ca e sys em.
5.1 Fu u e Wo k
Nex de elopmen s eps in he ecommende componen a e headed
owa ds he implemen a ion o a second ecommende unc ionali y
o p o ide a lis o po en ially in e es ing acquain ances o a gi en
use . This will suppo he social ne wo k ha Ca egi e s pla o m
is aimed o build. This lis will be based on use s’ simila i ies based
on in e es s and in e ac ions wi h p e iously ecommended in e -
en ions and also based on use s’ p o iles (e.g., sc eening esul s,
como bidi ies, language).
ACKNOWLEDGMENTS
The wo k p esen ed in his pape has been pe o med unde he
Eu opean P ojec CAREGIVERSPRO-MMD[
6
], which has ecei ed
unding om he Eu opean Union Ho izon 2020 esea ch and inno-
a ion p og amme unde g an ag eemen No 690211.
P o . Ulises Co és is a membe o Sis ema Nacional de In es i-
gado es (SNI-III), México.
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