I
AE
S
I
nte
rna
t
io
na
l
J
o
urna
l
of
Art
if
icia
l
I
nte
llig
ence
(
I
J
-
AI
)
Vo
l.
15
,
No
.
4
,
A
u
g
u
s
t
20
26
,
pp.
3
0
5
3
~
3
0
6
7
I
SS
N:
2252
-
8
9
3
8
,
DOI
:
1
0
.
1
1
5
9
1
/ijai.v
15
.i
4
.
p
p
3
0
5
3
-
3
0
6
7
3053
J
o
ur
na
l
ho
m
ep
a
g
e
:
h
ttp
:
//ij
a
i
.
ia
esco
r
e.
co
m
Bridg
ing
g
a
ps
in
hea
lth
a
rti
ficial
in
telligence
:
cha
llen
g
es
in
M
DPI
resea
rch
a
rticles
I
rwa
n
B
a
s
t
ia
n
1
,
Aq
illa
Ra
h
m
a
n
M
us
y
a
f
f
a
2
,
L
uk
ma
n
Nulh
a
k
im
2
,
No
v
ia
P
utr
i
B
a
hira
h
2
,
Dewi
Ag
us
hi
nta
R
.
1
1
D
e
p
a
r
t
me
n
t
of
I
n
f
o
r
mat
i
o
n
S
y
s
t
e
ms
,
F
a
c
u
l
t
y
of
C
o
m
p
u
t
e
r
S
c
i
e
n
c
e
a
n
d
I
n
f
o
r
mat
i
o
n
T
e
c
h
n
o
l
o
g
y
,
U
n
i
v
e
r
s
i
t
a
s
G
u
n
a
d
a
r
m
a
,
D
e
p
o
k
,
I
n
d
o
n
e
si
a
2
D
e
p
a
r
t
me
n
t
of
H
e
a
l
t
h
A
r
t
i
f
i
c
i
a
l
I
n
t
e
l
l
i
g
e
n
c
e
,
G
r
a
d
u
a
t
e
P
r
o
g
r
a
m,
U
n
i
v
e
r
si
t
a
s
G
u
n
a
d
a
r
ma
,
D
e
p
o
k
,
I
n
d
o
n
e
si
a
Art
icle
I
nfo
AB
S
T
RAC
T
A
r
ticle
his
to
r
y:
R
ec
eiv
ed
Mar
23
,
2
0
2
5
R
ev
is
ed
May
21
,
2
0
2
6
Acc
ep
ted
J
u
n
19
,
2
0
2
6
Tec
h
n
o
l
o
g
ica
l
a
d
v
a
n
c
e
m
e
n
ts
in
a
rti
ficia
l
in
telli
g
e
n
c
e
(AI)
h
a
v
e
tra
n
sfo
rm
e
d
h
e
a
lt
h
c
a
re
by
imp
r
o
v
i
n
g
e
a
rly
d
ise
a
se
d
e
tec
ti
o
n
,
p
e
rso
n
a
li
z
e
d
trea
tme
n
t,
p
re
d
ictiv
e
a
n
a
ly
ti
c
s,
a
n
d
c
li
n
ica
l
d
e
c
isio
n
su
p
p
o
r
t
s
y
ste
m
s.
Ho
we
v
e
r,
AI
a
d
o
p
t
io
n
in
h
e
a
lt
h
c
a
re
fa
c
e
s
c
r
it
ica
l
c
h
a
ll
e
n
g
e
s,
in
c
l
u
d
i
n
g
d
a
t
a
p
riv
a
c
y
c
o
n
c
e
rn
s,
a
lg
o
r
it
h
m
ic
b
ias
,
re
g
u
l
a
to
ry
b
a
rriers
,
u
sa
b
il
it
y
issu
e
s,
a
n
d
sy
ste
m
in
tero
p
e
ra
b
il
it
y
.
Ad
d
re
ss
in
g
th
e
s
e
issu
e
s
re
q
u
ires
sta
n
d
a
rd
ize
d
re
g
u
lati
o
n
s,
e
th
ica
l
fra
m
e
wo
rk
s,
a
n
d
in
terd
isc
ip
li
n
a
r
y
c
o
ll
a
b
o
ra
ti
o
n
to
e
n
s
u
re
re
sp
o
n
sib
le
AI
in
teg
ra
ti
o
n
.
Th
is
stu
d
y
sy
ste
m
a
ti
c
a
ll
y
a
n
a
ly
z
e
s
re
se
a
rc
h
tren
d
s
in
h
e
a
lt
h
AI
o
v
e
r
th
e
p
a
st
six
y
e
a
rs
th
ro
u
g
h
a
sy
ste
m
a
ti
c
li
tera
tu
re
re
v
iew
(S
LR)
a
n
d
a
b
ib
li
o
m
e
tri
c
a
n
a
ly
sis
u
si
n
g
VO
S
v
iew
e
r.
Th
e
re
v
iew
fo
c
u
se
s
on
M
u
lt
i
d
isc
ip
li
n
a
ry
Dig
it
a
l
P
u
b
li
s
h
in
g
In
stit
u
te
(
M
DPI
)
jo
u
rn
a
l
a
rti
c
les
to
id
e
n
ti
f
y
k
e
y
c
o
n
tri
b
u
t
o
rs,
e
m
e
rg
i
n
g
tre
n
d
s,
a
n
d
re
se
a
rc
h
g
a
p
s
in
AI
-
d
riv
e
n
h
e
a
lt
h
c
a
re
.
F
i
n
d
i
n
g
s
h
i
g
h
li
g
h
t
d
o
m
in
a
n
t
re
se
a
rc
h
a
re
a
s,
in
c
lu
d
i
n
g
m
a
c
h
in
e
lea
rn
in
g
f
o
r
d
iag
n
o
sis,
AI
-
d
ri
v
e
n
h
o
sp
i
tal
m
a
n
a
g
e
m
e
n
t,
a
n
d
p
re
d
ictiv
e
a
n
a
ly
ti
c
s,
wh
il
e
e
x
p
o
si
n
g
p
e
rsiste
n
t
c
h
a
ll
e
n
g
e
s
su
c
h
as
a
lac
k
of
sta
n
d
a
rd
ize
d
AI
m
o
d
e
ls,
e
th
ica
l
c
o
n
c
e
rn
s,
a
n
d
a
c
c
e
ss
ib
il
it
y
d
isp
a
rit
ies
.
By
m
a
p
p
in
g
t
h
e
re
se
a
rc
h
lan
d
sc
a
p
e
,
th
is
stu
d
y
p
ro
v
id
e
s
e
v
i
d
e
n
c
e
-
b
a
se
d
i
n
sig
h
ts
a
n
d
re
c
o
m
m
e
n
d
a
ti
o
n
s
to
a
d
d
re
ss
AI
a
d
o
p
ti
o
n
b
a
rriers
,
imp
ro
v
e
tran
sp
a
re
n
c
y
,
a
n
d
g
u
i
d
e
fu
t
u
re
re
se
a
rc
h
in
h
e
a
lt
h
c
a
re
AI.
Th
e
re
su
lt
s
c
o
n
tri
b
u
te
to
d
e
v
e
lo
p
in
g
a
m
o
re
e
q
u
it
a
b
le,
e
fficie
n
t,
a
n
d
t
ru
s
two
rth
y
AI
-
d
ri
v
e
n
h
e
a
lt
h
c
a
re
sy
st
e
m
.
K
ey
w
o
r
d
s
:
Ar
tific
ial
in
tellig
en
ce
B
ib
lio
m
etr
ic
an
aly
s
is
Hea
lth
R
esear
ch
ch
allen
g
e
VOSv
iewe
r
T
h
is
is
an
o
p
e
n
a
c
c
e
ss
a
rticle
u
n
d
e
r
th
e
CC
BY
-
SA
li
c
e
n
se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
I
r
wan
B
asti
an
Dep
ar
tm
en
t
of
I
n
f
o
r
m
atio
n
Sy
s
tem
s
,
Facu
lty
of
C
o
m
p
u
ter
S
cien
ce
an
d
I
n
f
o
r
m
atio
n
T
ec
h
n
o
lo
g
y
Un
iv
er
s
itas
Gu
n
ad
ar
m
a
Ma
r
g
o
n
d
a
R
ay
a
s
tr
ee
t
,
no
.
1
0
0
,
Dep
o
k
1
6
4
2
4
,
W
est
J
av
a,
I
n
d
o
n
esia
E
m
ail:
b
asti
an
@
s
taf
f
.
g
u
n
ad
ar
m
a.
ac
.
id
1.
I
NT
RO
D
UCT
I
O
N
T
ec
h
n
o
lo
g
ical
ad
v
a
n
ce
m
en
ts
co
n
tin
u
e
to
r
ev
o
lu
tio
n
ize
v
ar
io
u
s
i
n
d
u
s
tr
ies,
with
h
ea
lth
ca
r
e
u
n
d
er
g
o
in
g
s
o
m
e
of
th
e
m
o
s
t
p
r
o
f
o
u
n
d
tr
an
s
f
o
r
m
atio
n
s
.
I
n
n
o
v
atio
n
s
lik
e
telem
ed
icin
e,
wea
r
ab
le
h
ea
lth
d
ev
ices,
an
d
elec
tr
o
n
ic
m
e
d
ical
r
ec
o
r
d
s
h
a
v
e
s
ig
n
if
ican
tly
im
p
r
o
v
e
d
p
atien
t
ca
r
e
an
d
s
tr
ea
m
lin
ed
o
p
e
r
atio
n
s
.
T
h
ese
ad
v
an
ce
m
e
n
ts
h
av
e
p
a
v
ed
th
e
way
f
o
r
th
e
in
teg
r
atio
n
of
ar
tific
ial
in
tellig
en
ce
(
AI
)
,
wh
ich
is
r
ed
ef
in
in
g
h
ea
lth
ca
r
e
d
eliv
er
y
an
d
m
an
a
g
em
en
t.
AI
h
as
p
r
o
v
en
b
e
n
ef
icial
in
en
h
an
cin
g
clin
ical
d
ec
is
io
n
-
m
ak
in
g
,
o
p
tim
izin
g
h
o
s
p
ital
wo
r
k
f
lo
ws,
r
ef
in
in
g
m
e
d
ical
im
ag
in
g
,
an
d
tr
an
s
f
o
r
m
i
n
g
p
atien
t
m
o
n
it
o
r
in
g
th
r
o
u
g
h
AI
-
d
r
iv
en
wea
r
ab
les
[
1
]
.
Ma
ch
i
n
e
lear
n
in
g
a
n
d
d
ata
-
d
r
iv
e
n
ap
p
r
o
ac
h
es
en
ab
le
AI
to
a
n
aly
ze
l
ar
g
e
m
ed
ical
d
atasets
,
lead
in
g
to
m
o
r
e
ac
c
u
r
ate
d
iag
n
o
s
tics
an
d
o
p
tim
ize
d
tr
e
atm
en
t
p
lan
s
[
2
]
.
AI
’
s
im
p
ac
t
is
p
ar
ticu
lar
ly
ev
id
en
t
in
ar
ea
s
lik
e
ea
r
ly
d
is
ea
s
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
9
3
8
I
n
t
J
Ar
tif
I
n
tell
,
Vo
l.
15
,
No
.
4
,
Au
g
u
s
t
20
26
:
3
0
5
3
-
3
0
6
7
3054
d
etec
tio
n
,
p
er
s
o
n
alize
d
tr
ea
tm
en
t,
an
d
clin
ical
d
ec
is
io
n
s
u
p
p
o
r
t,
wh
ich
im
p
r
o
v
e
p
r
ec
is
io
n
an
d
ef
f
icien
cy
in
h
ea
lth
ca
r
e
d
eliv
e
r
y
[
1
]
,
[
2
]
.
Ad
d
itio
n
ally
,
AI
co
n
t
r
ib
u
t
es
to
in
n
o
v
ati
v
e
h
ea
lth
ca
r
e
s
o
lu
tio
n
s
th
r
o
u
g
h
b
io
s
en
s
in
g
,
r
em
o
te
p
atien
t
m
o
n
ito
r
in
g
,
a
n
d
p
r
ed
ictiv
e
a
n
aly
t
ics,
d
em
o
n
s
tr
atin
g
its
b
r
o
a
d
ap
p
licab
ilit
y
[
3
]
,
[
4
]
.
Desp
ite
th
ese
ad
v
an
ce
m
en
ts
,
i
n
teg
r
atin
g
AI
i
n
to
h
ea
lth
ca
r
e
p
r
esen
ts
p
er
s
is
ten
t
ch
allen
g
es.
AI
p
lay
s
a
cr
itical
r
o
le
in
in
f
ec
tio
u
s
d
is
ea
s
e
m
an
ag
em
en
t,
r
eso
u
r
ce
a
llo
ca
tio
n
,
an
d
p
u
b
lic
h
ea
lth
c
r
is
is
r
esp
o
n
s
es
[
5
]
.
I
ts
p
o
ten
tial
to
o
p
tim
ize
h
ea
lth
ca
r
e
m
an
ag
e
m
en
t
—
in
clu
d
in
g
p
r
o
ce
s
s
au
to
m
atio
n
,
r
eso
u
r
ce
d
is
tr
ib
u
tio
n
,
an
d
cr
is
is
p
r
ep
ar
ed
n
ess
—
illu
s
tr
ates
its
tr
an
s
f
o
r
m
ativ
e
ca
p
ab
i
lity
[
6
]
.
Ho
wev
er
,
r
ea
l
-
w
o
r
ld
im
p
lem
en
tatio
n
r
em
ain
s
co
m
p
lex
,
p
a
r
ticu
lar
ly
in
em
er
g
en
cy
p
r
ep
ar
e
d
n
ess
,
wh
er
e
f
u
r
th
e
r
r
esear
ch
is
r
eq
u
ir
ed
to
ev
alu
ate
AI
’
s
ef
f
ec
tiv
en
ess
[
6
]
.
Mo
r
eo
v
e
r
,
in
c
o
u
n
tr
ies
lik
e
I
n
d
ia,
AI
ad
o
p
tio
n
f
ac
es
h
u
r
d
les
d
u
e
to
i
n
f
r
astru
ctu
r
e
lim
itatio
n
s
,
r
eg
u
lato
r
y
g
a
p
s
,
an
d
a
lack
of
awa
r
e
n
ess
r
eg
ar
d
in
g
AI
eth
ics
[
7
]
.
R
ec
en
t
s
tu
d
ies
in
d
icate
th
at
AI
im
p
lem
en
tatio
n
in
h
ea
lth
ca
r
e
is
s
tr
o
n
g
ly
in
f
lu
en
ce
d
by
o
r
g
an
izatio
n
al
r
ea
d
in
ess
,
clin
ician
tr
u
s
t,
an
d
g
o
v
er
n
an
ce
s
tr
u
ctu
r
es.
Hea
lth
ca
r
e
p
r
o
v
i
d
er
s
o
f
ten
ex
p
r
ess
co
n
ce
r
n
s
r
eg
ar
d
in
g
wo
r
k
f
lo
w
in
teg
r
atio
n
,
tr
an
s
p
ar
en
cy
,
an
d
ac
c
o
u
n
ta
b
ilit
y
of
AI
-
d
r
iv
en
s
y
s
tem
s
,
wh
ich
s
ig
n
if
ican
tly
af
f
ec
t
ad
o
p
tio
n
r
ates
[
8
]
,
[
9
]
.
To
s
y
s
tem
atica
lly
an
aly
ze
r
esear
ch
tr
en
d
s
in
h
ea
lth
ca
r
e
A
I
,
b
ib
lio
m
etr
ic
m
eth
o
d
s
p
r
o
v
id
e
v
alu
ab
le
in
s
ig
h
ts
in
to
ex
is
tin
g
liter
atu
r
e
an
d
k
e
y
ar
ea
s
of
s
tu
d
y
.
V
OSv
iewe
r
is
a
s
o
f
twar
e
to
o
l
f
o
r
co
n
s
tr
u
ctin
g
an
d
v
is
u
alizin
g
b
ib
lio
m
etr
ic
n
et
wo
r
k
s
,
in
clu
d
in
g
jo
u
r
n
als,
r
esear
ch
er
s
,
an
d
p
u
b
licatio
n
s
,
b
ased
on
citatio
n
,
b
ib
lio
g
r
ap
h
ic
c
o
u
p
lin
g
,
co
-
ci
tatio
n
,
an
d
co
-
au
th
o
r
s
h
ip
r
el
atio
n
s
[
1
0
]
.
T
h
is
s
tu
d
y
em
p
lo
y
s
b
ib
li
o
m
etr
ic
an
aly
s
is
to
ass
ess
AI
d
ev
elo
p
m
en
ts
in
h
ea
lth
ca
r
e
u
s
in
g
Mu
l
tid
is
cip
lin
ar
y
Dig
ital
Pu
b
lis
h
in
g
I
n
s
titu
te
(
MD
PI
)
jo
u
r
n
al
ar
ticles,
m
ap
p
in
g
k
ey
t
r
en
d
s
,
r
esear
ch
g
ap
s
,
an
d
p
o
te
n
tial
f
u
tu
r
e
d
ir
ec
tio
n
s
.
Ho
wev
er
,
b
ey
o
n
d
tech
n
ical
ca
p
ab
ilit
ies,
AI
ad
o
p
tio
n
is
i
n
f
lu
en
ce
d
by
wo
r
k
f
o
r
ce
r
ea
d
in
ess
an
d
h
ea
lth
ca
r
e
p
r
o
f
ess
io
n
als’
co
m
p
eten
cy
in
in
teg
r
atin
g
AI
i
n
to
clin
ical
s
ettin
g
s
[
1
1
]
.
T
h
e
p
er
ce
p
tio
n
of
AI
am
o
n
g
m
ed
ical
p
er
s
o
n
n
el
also
p
lay
s
a
cr
u
cial
r
o
le
in
ad
o
p
tio
n
r
ates.
Fu
r
th
er
m
o
r
e,
d
is
p
ar
ities
in
ac
ce
s
s
to
AI
-
p
o
wer
ed
h
ea
lth
ca
r
e
s
o
lu
tio
n
s
r
em
ain
a
co
n
ce
r
n
,
esp
ec
ially
in
lo
w
-
r
eso
u
r
c
e
s
ettin
g
s
,
wh
ich
lim
it
eq
u
itab
le
h
ea
lth
ca
r
e
d
is
tr
ib
u
tio
n
[
1
2
]
.
E
n
s
u
r
in
g
co
m
p
lian
ce
with
eth
i
ca
l
an
d
r
eg
u
lato
r
y
s
tan
d
ar
d
s
,
p
ar
ticu
lar
ly
in
d
ata
s
ec
u
r
ity
an
d
m
o
d
el
tr
an
s
p
ar
en
cy
,
is
cr
u
cial
f
o
r
wid
esp
r
ea
d
AI
a
d
o
p
tio
n
in
h
ea
lth
ca
r
e
[
7
]
.
In
ad
d
itio
n
,
p
r
iv
ac
y
-
p
r
eser
v
in
g
tech
n
i
q
u
e
s
s
u
ch
as
f
ed
er
ated
lear
n
in
g
an
d
s
ec
u
r
e
m
u
ltip
ar
ty
co
m
p
u
tatio
n
h
a
v
e
b
ee
n
p
r
o
p
o
s
ed
to
m
itig
ate
d
ata
l
ea
k
ag
e
r
is
k
s
a
n
d
e
n
h
an
ce
c
o
m
p
lian
ce
with
d
ata
p
r
o
tect
io
n
r
e
g
u
latio
n
s
in
AI
-
en
ab
led
h
ea
lth
ca
r
e
s
y
s
tem
s
[
1
3
]
.
E
m
p
ir
ical
ev
i
d
en
ce
d
em
o
n
s
tr
ates
th
at
alg
o
r
ith
m
ic
b
ias
m
ay
lead
to
u
n
e
q
u
al
d
iag
n
o
s
tic
p
er
f
o
r
m
an
ce
ac
r
o
s
s
d
em
o
g
r
ap
h
ic
g
r
o
u
p
s
,
t
h
er
eb
y
r
ein
f
o
r
cin
g
e
x
is
tin
g
h
ea
lth
d
is
p
ar
ities
if
not
p
r
o
p
er
ly
ad
d
r
ess
ed
[
1
4
]
.
An
o
th
er
p
r
ess
in
g
is
s
u
e
is
AI
r
eliab
ilit
y
,
as
m
o
d
el
b
ias,
p
r
ed
ictio
n
er
r
o
r
s
,
a
n
d
lack
of
s
tan
d
ar
d
izatio
n
can
u
n
d
er
m
i
n
e
clin
ical
d
ec
is
io
n
-
m
ak
in
g
an
d
co
m
p
r
o
m
is
e
p
atien
t
s
af
ety
[
1
5
]
.
Ad
d
r
ess
in
g
a
lg
o
r
ith
m
ic
b
ias
an
d
f
o
s
ter
in
g
tr
u
s
t
in
AI
m
o
d
els
r
eq
u
ir
es
en
h
an
ce
d
tr
an
s
p
a
r
en
c
y
an
d
in
te
r
p
r
etab
ilit
y
.
Ad
d
itio
n
ally
,
th
e
s
u
cc
ess
f
u
l
in
teg
r
atio
n
of
AI
r
elies
on
s
y
s
tem
in
ter
o
p
e
r
ab
ilit
y
an
d
u
s
er
-
f
r
ien
d
ly
ap
p
licatio
n
s
,
as
te
ch
n
ical
co
m
p
lex
ity
o
f
ten
h
in
d
er
s
ad
o
p
tio
n
[
1
1
]
.
B
ey
o
n
d
tech
n
ical
a
n
d
o
p
er
atio
n
al
ch
allen
g
es,
AI
f
ac
e
s
g
o
v
er
n
a
n
ce
,
p
o
licy
,
an
d
r
eg
u
lato
r
y
co
n
s
tr
ain
ts
.
E
th
ical
co
n
s
id
er
atio
n
s
,
d
ata
p
r
iv
ac
y
co
n
ce
r
n
s
,
an
d
leg
al
r
eq
u
ir
em
en
ts
d
em
an
d
s
tr
in
g
en
t
co
m
p
lian
ce
with
h
ea
lth
ca
r
e
r
eg
u
latio
n
s
[
1
6
]
,
[
1
7
]
.
T
h
e
in
cr
ea
s
in
g
s
o
p
h
is
ticatio
n
of
AI
n
ec
ess
itates
ex
p
lain
ab
le
an
d
in
ter
p
r
etab
le
m
o
d
els
to
b
u
ild
tr
u
s
t
am
o
n
g
clin
ician
s
a
n
d
p
atien
ts
.
E
x
p
lain
ab
le
ar
tific
ial
in
tellig
en
ce
(
XAI
)
f
r
am
ewo
r
k
s
h
av
e
b
ee
n
d
ev
elo
p
e
d
to
en
h
an
ce
in
ter
p
r
etab
ilit
y
an
d
s
tr
en
g
th
en
clin
ician
tr
u
s
t
in
AI
-
ass
is
ted
d
ec
is
io
n
-
m
ak
i
n
g
p
r
o
ce
s
s
es
[
1
8
]
,
[
1
9
]
.
W
it
h
o
u
t
p
r
o
p
e
r
r
is
k
m
an
ag
e
m
e
n
t
f
r
am
ewo
r
k
s
,
AI
in
teg
r
atio
n
m
a
y
in
tr
o
d
u
ce
in
e
f
f
icien
cies
r
ath
er
th
an
e
n
h
an
ce
h
ea
lth
ca
r
e
d
eliv
er
y
[
2
0
]
.
Desp
ite
th
ese
o
b
s
tacle
s
,
AI
r
em
ain
s
a
tr
an
s
f
o
r
m
ativ
e
f
o
r
ce
in
h
ea
lth
ca
r
e,
im
p
r
o
v
in
g
p
atien
t
o
u
tco
m
es,
ad
v
an
cin
g
p
h
a
r
m
a
ce
u
tical
r
esear
ch
,
an
d
o
p
tim
i
zin
g
r
eso
u
r
ce
m
an
a
g
em
en
t
[
1
9
]
.
Ad
d
r
ess
in
g
k
ey
ch
allen
g
es
s
u
ch
as
ac
ce
s
s
ib
ili
ty
,
u
s
ab
ilit
y
,
cr
o
s
s
-
s
ec
to
r
co
ll
ab
o
r
atio
n
,
an
d
r
eg
u
lato
r
y
co
n
ce
r
n
s
is
ess
en
tial
to
m
ax
im
izin
g
AI
’
s
im
p
ac
t.
T
h
is
s
tu
d
y
aim
s
to
b
r
id
g
e
ex
is
tin
g
g
ap
s
by
id
en
tify
i
n
g
u
n
r
eso
lv
ed
is
s
u
es
an
d
ex
p
lo
r
in
g
in
s
ig
h
ts
f
r
o
m
MD
PI
jo
u
r
n
al
ar
ticles
to
g
u
id
e
f
u
t
u
r
e
ad
v
a
n
ce
m
en
ts
in
h
ea
lth
ca
r
e
AI
.
B
ib
lio
m
etr
ic
an
aly
s
is
h
as
b
ee
n
wid
ely
u
tili
ze
d
to
m
ap
r
esear
ch
t
r
en
d
s
,
i
d
en
tify
t
h
em
atic
s
tr
u
ctu
r
es,
a
n
d
d
etec
t
em
er
g
i
n
g
to
p
ics
in
h
ea
lth
ca
r
e
AI
s
tu
d
ies
[
2
1
]
,
[
2
2
]
.
I
n
teg
r
atin
g
b
ib
lio
m
etr
ic
m
ap
p
i
n
g
with
v
is
u
aliza
tio
n
to
o
ls
e
n
ab
les
a
s
y
s
tem
atic
an
d
ev
id
e
n
ce
-
b
ase
d
ex
p
lo
r
atio
n
of
ev
o
lv
in
g
r
es
ea
r
ch
lan
d
s
ca
p
es.
Pre
v
io
u
s
b
i
b
lio
m
etr
ic
an
al
y
s
es
of
AI
in
h
ea
lth
ca
r
e
h
av
e
ty
p
ically
d
r
awn
on
lar
g
e
d
ata
b
ases
s
u
ch
as
Scien
ce
Dir
ec
t
an
d
Sco
p
u
s
[
2
3
]
.
In
co
n
tr
ast,
th
is
s
tu
d
y
f
o
c
u
s
es
ex
clu
s
iv
ely
on
MD
PI
jo
u
r
n
al
ar
ticles.
T
h
is
f
o
cu
s
is
s
cie
n
tific
ally
v
alu
ab
le
b
ec
au
s
e
MD
PI'
s
f
u
lly
o
p
en
-
ac
ce
s
s
m
o
d
el
en
s
u
r
es
th
at
all
f
u
ll
tex
ts
ar
e
f
r
ee
l
y
av
ailab
le
[
2
4
]
,
en
ab
lin
g
m
o
r
e
in
-
d
ep
th
tex
t
m
in
in
g
b
e
y
o
n
d
ab
s
tr
ac
t
-
o
n
l
y
an
aly
s
es.
Mo
r
eo
v
er
,
MD
PI'
s
ex
p
an
s
iv
e
p
o
r
tf
o
lio
(
n
o
w
o
v
e
r
5
0
0
jo
u
r
n
als
[
2
5
]
)
an
d
ex
p
e
d
ited
ed
ito
r
ial
p
r
o
c
ess
(
tar
g
etin
g
p
u
b
licatio
n
wit
h
in
≈
5
–
7
wee
k
s
of
s
u
b
m
is
s
io
n
[
2
6
]
)
cr
ea
te
a
lar
g
e,
up
-
to
-
d
ate
c
o
r
p
u
s
,
m
ak
in
g
it
a
s
en
s
itiv
e
b
ar
o
m
eter
of
em
e
r
g
in
g
t
r
en
d
s
in
th
e
r
ap
id
ly
e
v
o
lv
in
g
f
ield
of
h
ea
lth
AI
.
I
m
p
o
r
tan
tly
,
th
is
MD
PI
-
ce
n
tr
ic
ap
p
r
o
ac
h
is
in
ten
d
ed
to
co
m
p
lem
en
t
r
ath
er
th
an
r
e
p
lace
b
r
o
ad
er
b
ib
lio
m
etr
ic
s
u
r
v
ey
s
by
p
r
o
v
id
in
g
a
d
etailed
p
er
s
p
ec
tiv
e
on
one
in
f
lu
en
tial
o
p
en
-
ac
ce
s
s
ec
o
s
y
s
tem
.
T
h
e
p
r
esen
t
s
tu
d
y
an
aly
ze
s
MD
PI
h
ea
lth
AI
p
u
b
licatio
n
s
f
r
o
m
2
0
1
9
to
2
0
2
4
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t
J
Ar
tif
I
n
tell
I
SS
N:
2252
-
8
9
3
8
B
r
id
g
in
g
g
a
p
s
in
h
e
a
lth
a
r
tifi
cia
l
in
tellig
en
ce
:
ch
a
llen
g
es
in
MDPI
r
esea
r
ch
a
r
ticle
s
(
I
r
w
a
n
B
a
s
tia
n
)
3055
2.
M
E
T
H
O
D
T
h
is
s
ec
tio
n
d
escr
ib
es
th
e
m
eth
o
d
o
lo
g
ical
f
r
a
m
ewo
r
k
ap
p
lied
in
th
is
s
tu
d
y
to
an
aly
z
e
r
esear
ch
tr
en
d
s
an
d
c
h
allen
g
es
in
h
ea
lt
h
AI
.
T
h
e
r
esear
ch
d
esig
n
in
t
eg
r
ates
a
s
y
s
tem
atic
liter
atu
r
e
r
ev
iew
(
SLR)
with
b
ib
lio
m
etr
ic
an
aly
s
is
to
en
s
u
r
e
a
s
tr
u
ctu
r
ed
,
tr
an
s
p
ar
e
n
t
,
an
d
r
ep
r
o
d
u
cib
le
in
v
esti
g
a
tio
n
p
r
o
ce
s
s
.
T
h
is
co
m
b
in
ed
ap
p
r
o
ac
h
en
a
b
les
s
y
s
tem
atic
ar
ticle
s
elec
tio
n
,
k
e
y
wo
r
d
m
ap
p
i
n
g
,
an
d
tr
en
d
v
is
u
aliza
tio
n
b
ased
on
clea
r
ly
d
ef
in
e
d
in
clu
s
io
n
c
r
iter
ia
an
d
an
aly
tical
p
r
o
ce
d
u
r
es.
2
.
1
.
M
et
ho
do
lo
g
y
dia
g
ra
m
T
h
e
m
e
t
h
o
d
o
l
o
g
y
u
s
e
d
f
o
r
t
h
is
p
a
p
e
r
can
be
s
e
e
n
in
F
i
g
u
r
e
1
as
a
S
L
R
b
a
s
e
d
on
[
2
7
]
.
Ac
co
r
d
in
g
to
Far
o
o
q
et
a
l.
[
2
7
]
,
th
e
r
ea
s
o
n
f
o
r
f
o
llo
win
g
th
e
m
et
h
o
d
o
lo
g
y
p
r
o
p
o
s
ed
by
Kitch
en
h
am
a
n
d
C
h
ar
ter
s
[
2
8
]
is
so
th
at
th
e
s
elec
tio
n
of
in
f
o
r
m
ati
o
n
an
d
r
e
p
r
esen
tatio
n
r
esu
lts
to
s
u
p
p
o
r
t
th
e
m
a
k
in
g
of
r
esear
ch
is
ca
r
r
ied
out
im
p
ar
tially
.
Fo
r
th
e
s
tep
s
ca
r
r
i
ed
out
in
t
h
is
p
ap
er
,
we
tak
e
4
p
ar
ts
of
th
e
p
r
o
ce
s
s
f
r
o
m
[
2
7
]
,
n
am
el
y
,
s
p
ec
if
y
th
e
r
esear
ch
o
b
jectiv
e;
d
ef
i
n
e
th
e
r
esear
ch
q
u
esti
o
n
(
R
Q)
;
estab
lis
h
th
e
s
ea
r
ch
s
tr
in
g
;
an
d
s
elec
t
th
e
k
ey
wo
r
d
,
as
s
h
o
wn
in
Fig
u
r
e
1.
Dete
r
m
i
n
e
th
e
r
esear
ch
o
b
jectiv
e
to
be
ac
h
iev
ed
.
Ne
x
t,
d
eter
m
i
n
e
th
e
RQ
an
d
th
e
m
ain
m
o
tiv
atio
n
f
o
r
wr
itin
g
th
is
ar
ticle.
C
o
llect
ar
ticles
r
elate
d
to
th
e
r
esear
ch
t
o
p
ic
on
o
n
e
of
th
e
p
u
b
lis
h
er
s
,
n
am
ely
MD
PI,
by
en
ter
in
g
r
e
lev
an
t
s
ea
r
ch
s
tr
in
g
s
.
T
h
e
n
,
s
elec
t
th
e
k
ey
wo
r
d
s
th
at
s
u
it
th
e
an
aly
s
is
n
ee
d
s
in
th
is
ar
ticle.
AI
tech
n
o
l
o
g
y
,
s
u
ch
as
C
h
atGPT
,
Dee
p
Seek
,
a
n
d
C
lau
d
e
AI
,
is
also
u
s
ed
as
a
to
o
l
th
at
h
elp
s
in
th
e
wo
r
k
of
th
is
ar
ticle.
Fig
u
r
e
1.
Me
th
o
d
i
m
p
le
m
en
tat
io
n
2
.
2
.
Resea
rc
h
o
bje
ct
i
v
es
T
h
e
p
u
r
p
o
s
e
o
f
th
is
ar
ticle
is
p
r
esen
ted
th
r
o
u
g
h
two
r
esear
ch
o
b
jectiv
es
:
R
O1
:
f
o
cu
s
ed
s
tate
-
of
-
th
e
-
a
r
t
r
esear
ch
wo
r
k
h
as
b
ee
n
i
d
en
tif
ied
in
th
e
f
ield
of
h
ea
lth
AI
.
R
O2
:
id
en
tify
th
e
r
esear
ch
g
ap
s
in
ter
m
s
of
ch
allen
g
es
.
2
.
3
.
Resea
rc
h
qu
estio
ns
a
nd
m
a
in
mo
t
iv
a
t
io
n
T
ab
le
1
d
is
p
lay
s
th
e
RQ
an
d
m
ain
m
o
tiv
atio
n
of
th
is
p
ap
er
.
To
f
o
r
m
u
late
th
e
RQ
an
d
d
eter
m
in
e
th
e
m
ain
m
o
tiv
atio
n
of
th
is
p
a
p
er
,
th
e
h
elp
o
f
o
n
e
o
f
th
e
AI
t
o
o
ls
,
C
h
atGPT
,
wa
s
u
tili
ze
d
.
T
h
ese
ar
e
p
r
esen
ted
in
T
ab
le
1
.
T
ab
le
1.
RQ
an
d
m
ain
m
o
tiv
at
io
n
ID
RQ
M
a
i
n
m
o
t
i
v
a
t
i
o
n
R
Q
1
H
o
w
do
r
e
l
a
t
i
o
n
sh
i
p
s
b
e
t
w
e
e
n
k
e
y
w
o
r
d
s
in
t
h
e
h
e
a
l
t
h
A
I
st
u
d
y
r
e
l
a
t
e
to
c
h
a
l
l
e
n
g
e
s
in
t
h
i
s
f
i
e
l
d
?
I
d
e
n
t
i
f
y
i
n
g
t
h
e
r
e
l
a
t
i
o
n
sh
i
p
b
e
t
w
e
e
n
k
e
y
w
o
r
d
s
w
i
t
h
i
n
k
e
y
w
o
r
d
s
in
t
h
e
s
t
u
d
y
o
f
A
I
i
n
h
e
a
l
t
h
a
n
d
c
h
a
l
l
e
n
g
e
s
in
t
h
i
s
f
i
e
l
d
u
si
n
g
V
O
S
v
i
e
w
e
r
w
i
t
h
n
e
t
w
o
r
k
v
i
s
u
a
l
i
z
a
t
i
o
n
h
e
l
p
s
to
u
n
d
e
r
st
a
n
d
t
h
e
i
n
t
e
r
c
o
n
n
e
c
t
e
d
n
e
ss
of
c
o
n
c
e
p
t
s
a
n
d
i
n
t
e
r
c
o
n
n
e
c
t
e
d
r
e
se
a
r
c
h
f
i
e
l
d
s.
R
Q
2
H
o
w
h
a
s
t
h
e
d
e
v
e
l
o
p
m
e
n
t
of
r
e
se
a
r
c
h
t
r
e
n
d
s
r
e
l
a
t
e
d
to
h
e
a
l
t
h
A
I
o
v
e
r
t
i
me
?
F
i
n
d
o
u
t
t
h
e
d
e
v
e
l
o
p
me
n
t
of
H
e
a
l
t
h
AI
c
h
a
l
l
e
n
g
e
s
r
e
s
e
a
r
c
h
t
r
e
n
d
s
o
v
e
r
t
i
m
e
u
si
n
g
V
O
S
v
i
e
w
e
r
w
i
t
h
o
v
e
r
l
a
y
v
i
s
u
a
l
i
z
a
t
i
o
n
,
p
r
o
v
i
d
i
n
g
i
n
s
i
g
h
t
i
n
t
o
h
o
w
t
h
e
r
e
se
a
r
c
h
f
o
c
u
s
is
c
h
a
n
g
i
n
g
a
n
d
e
v
o
l
v
i
n
g
.
R
Q
3
W
h
a
t
a
r
e
t
h
e
mo
s
t
r
e
s
e
a
r
c
h
e
d
a
n
d
s
t
i
l
l
r
a
r
e
l
y
e
x
p
l
o
r
e
d
r
e
se
a
r
c
h
a
r
e
a
s
in
k
e
y
w
o
r
d
s
i
n
t
h
e
h
e
a
l
t
h
A
I
st
u
dy
a
n
d
C
h
a
l
l
e
n
g
e
s
in
t
h
i
s
f
i
e
l
d
?
I
d
e
n
t
i
f
y
t
h
e
m
o
s
t
r
e
s
e
a
r
c
h
e
d
t
o
p
i
c
s
as
w
e
l
l
as
a
r
e
a
s
t
h
a
t
a
r
e
s
t
i
l
l
r
a
r
e
l
y
e
x
p
l
o
r
e
d
u
s
i
n
g
V
O
S
v
i
e
w
e
r
w
i
t
h
d
e
n
s
i
t
y
v
i
s
u
a
l
i
z
a
t
i
o
n
in
k
e
y
w
o
r
d
s
in
t
h
e
h
e
a
l
t
h
A
I
st
u
d
y
a
n
d
c
h
a
l
l
e
n
g
e
s
in
t
h
i
s
f
i
e
l
d
,
t
h
u
s
sh
o
w
i
n
g
p
o
t
e
n
t
i
a
l
r
e
sea
r
c
h
g
a
p
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
9
3
8
I
n
t
J
Ar
tif
I
n
tell
,
Vo
l.
15
,
No
.
4
,
Au
g
u
s
t
20
26
:
3
0
5
3
-
3
0
6
7
3056
2
.
4
.
M
DP
I
pu
bli
s
her
MD
PI
is
a
p
u
b
lis
h
er
of
p
ee
r
-
r
ev
iewe
d
,
o
p
en
-
ac
ce
s
s
jo
u
r
n
als
k
n
o
wn
f
o
r
its
r
ig
o
r
o
u
s
a
n
d
f
a
s
t
ed
ito
r
ial
wo
r
k
f
lo
w
[
2
9
]
.
E
s
tab
lis
h
ed
in
J
u
n
e
1
9
9
6
by
Dr
.
Sh
u
-
Ku
n
L
i
n
an
d
Dr
.
B
en
o
it
R.
T
u
r
in
as
a
non
-
p
r
o
f
it
in
s
titu
te
in
B
asel,
MD
PI
later
ev
o
lv
e
d
in
to
th
e
p
r
esen
t
-
d
ay
p
u
b
li
s
h
in
g
h
o
u
s
e
k
n
o
w
n
as
MD
PI
AG
[
3
0
]
.
MD
PI
lau
n
ch
ed
its
f
ir
s
t
o
n
lin
e
j
o
u
r
n
a
l,
Mo
lecu
les,
in
1
9
9
6
an
d
h
as
s
in
ce
p
o
s
itio
n
ed
its
elf
at
th
e
f
o
r
ef
r
o
n
t
of
th
e
o
p
en
ac
ce
s
s
m
o
v
em
en
t,
en
s
u
r
in
g
th
at
h
ig
h
-
q
u
ality
r
esear
c
h
is
q
u
ick
ly
v
er
if
ied
an
d
m
ad
e
av
ailab
le
to
th
e
co
m
m
u
n
ity
u
n
d
er
a
C
r
ea
tiv
e
C
o
m
m
o
n
s
Attr
ib
u
tio
n
L
icen
s
e.
MD
PI'
s
p
u
b
licatio
n
s
co
p
e
co
v
er
s
a
wid
e
r
an
g
e
of
s
cien
tific
d
is
cip
lin
es,
in
clu
d
in
g
b
i
o
lo
g
y
,
ch
em
is
tr
y
,
en
g
i
n
ee
r
in
g
,
m
e
d
icin
e,
s
o
cial
s
cien
c
es,
ec
o
n
o
m
ics,
an
d
m
an
y
o
t
h
er
f
ield
s
[
2
9
]
.
2
.
5
.
VO
SViewer
VOSv
iewe
r
is
a
s
o
f
twar
e
to
o
l
f
o
r
c
o
n
s
tr
u
ctin
g
an
d
v
is
u
a
lizin
g
b
ib
lio
m
etr
ic
n
etwo
r
k
s
,
in
clu
d
in
g
jo
u
r
n
als,
r
esear
ch
er
s
,
an
d
p
u
b
licatio
n
s
,
b
ased
on
citatio
n
,
b
ib
lio
g
r
ap
h
ic
co
u
p
lin
g
,
co
-
citatio
n
,
an
d
co
-
au
th
o
r
s
h
ip
r
elatio
n
s
[
1
0
]
.
It
also
p
r
o
v
id
es
tex
t
m
in
in
g
f
u
n
ctio
n
ality
to
co
n
s
tr
u
ct
an
d
v
is
u
alize
co
-
o
cc
u
r
r
en
ce
n
etwo
r
k
s
of
i
m
p
o
r
tan
t
ter
m
s
ex
t
r
ac
ted
f
r
o
m
s
cien
tific
liter
atu
r
e.
Fig
u
r
e
2
s
h
o
ws
th
e
i
n
itial
v
iew
of
t
h
e
VOS
v
iewe
r
in
te
r
f
ac
e.
T
h
e
g
u
id
e
to
u
s
in
g
VOS
v
iewe
r
can
be
ac
ce
s
s
ed
by
cli
ck
in
g
th
e
“
Ma
n
u
al
”
b
u
tto
n
on
th
e
lef
t
p
an
el
in
t
h
e
“
in
f
o
”
s
ec
tio
n
,
wh
ic
h
can
be
s
ee
n
in
Fig
u
r
e
2.
Fig
u
r
e
2.
I
n
itial
of
th
e
VOSv
ie
wer
in
ter
f
ac
e
2.
6
.
K
ey
wo
rd
T
h
is
s
u
b
s
ec
tio
n
d
escr
ib
es
th
e
k
ey
wo
r
d
s
elec
tio
n
an
d
s
ea
r
c
h
s
tr
ateg
y
ap
p
lied
to
r
etr
iev
e
r
elev
an
t
p
u
b
licatio
n
s
f
r
o
m
th
e
MD
PI
d
atab
ase.
T
h
e
k
ey
wo
r
d
s
wer
e
d
ef
in
ed
to
r
ef
lect
th
e
co
r
e
f
o
cu
s
of
th
e
s
tu
d
y
,
wh
ich
ex
am
in
es
r
esear
c
h
tr
en
d
s
an
d
im
p
lem
en
tatio
n
c
h
allen
g
es
in
h
ea
lth
AI
.
By
s
tr
u
ctu
r
i
n
g
th
e
s
ea
r
c
h
ter
m
s
ar
o
u
n
d
th
e
co
n
ce
p
ts
of
h
ea
lth
,
AI
,
an
d
c
h
allen
g
es.
2.
6
.1
.
Sea
rc
h
s
t
ring
Dete
r
m
in
in
g
th
e
s
ea
r
ch
s
tr
in
g
is
u
s
ed
to
co
llect
p
u
b
lis
h
ed
ar
ticles
r
elate
d
to
th
e
r
esear
ch
to
p
ic
[
6
]
.
A
s
ea
r
ch
s
tr
in
g
is
a
co
m
b
in
ati
o
n
of
k
ey
wo
r
d
s
,
tr
u
n
ca
tio
n
s
y
m
b
o
ls
,
an
d
B
o
o
lean
o
p
er
ato
r
s
you
en
ter
in
to
th
e
s
ea
r
ch
box
of
a
lib
r
a
r
y
d
atab
ase
or
s
ea
r
ch
en
g
i
n
e
[
3
1
]
.
B
o
o
lean
o
p
e
r
ato
r
s
ar
e
co
n
n
ec
to
r
wo
r
d
s
th
at
aim
to
co
m
b
in
e
or
ex
clu
d
e
wo
r
d
s
in
a
s
ea
r
ch
s
tr
in
g
so
th
at
th
e
r
esu
ltin
g
o
u
t
p
u
t
b
ec
o
m
es
m
o
r
e
f
o
c
u
s
ed
,
s
u
ch
as
AND
(
d
is
p
lay
in
g
r
esu
lts
th
at
co
n
tai
n
all
en
ter
ed
ter
m
s
)
,
OR
(
d
is
p
lay
in
g
r
esu
lts
th
at
co
n
tain
at
least
one
en
ter
ed
ter
m
)
,
NOT
(
d
is
p
lay
in
g
r
esu
lts
th
at
co
n
tain
th
e
f
ir
s
t
s
ea
r
ch
ter
m
but
ex
clu
d
in
g
or
not
d
is
p
lay
in
g
s
ea
r
ch
r
esu
lts
th
at
co
n
tain
th
e
s
ec
o
n
d
s
ea
r
ch
ter
m
)
[
3
1
]
,
[
3
2
]
.
T
ab
le
2
s
h
o
ws
th
e
s
ea
r
ch
s
tr
i
n
g
u
s
ed
in
th
is
ar
ticle.
T
h
is
ar
ticle
u
s
es
3
s
tr
in
g
s
to
s
ea
r
ch
f
o
r
r
elev
a
n
t
ar
ticles
in
MD
PI,
in
clu
d
in
g
“
Hea
lth
”,
“Ar
tific
ial
I
n
tellig
en
ce
”,
an
d
“Ch
allen
g
e”
,
a
n
d
th
e
B
o
o
lean
o
p
er
ato
r
u
s
ed
is
AND.
Fo
r
th
e
s
ea
r
ch
ty
p
e
s
ec
tio
n
,
th
e
o
n
e
s
elec
ted
is
“All
f
ield
s
”.
Fig
u
r
e
3
s
h
o
ws
th
e
im
p
lem
en
tatio
n
o
f
th
e
s
ea
r
ch
s
tr
in
g
to
MD
PI
u
s
in
g
th
e
“Ad
v
an
ce
d
”
f
ea
tu
r
e.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t
J
Ar
tif
I
n
tell
I
SS
N:
2252
-
8
9
3
8
B
r
id
g
in
g
g
a
p
s
in
h
e
a
lth
a
r
tifi
cia
l
in
tellig
en
ce
:
ch
a
llen
g
es
in
MDPI
r
esea
r
ch
a
r
ticle
s
(
I
r
w
a
n
B
a
s
tia
n
)
3057
T
ab
le
2.
Sear
ch
s
tr
in
g
S
o
u
r
c
e
S
e
a
r
c
h
s
t
r
i
n
g
C
o
n
t
e
x
t
M
D
P
I
(
“
H
e
a
l
t
h
”
)
AND
(
“
A
r
t
i
f
i
c
i
a
l
I
n
t
e
l
l
i
g
e
n
c
e
”
)
AND
(
“
C
h
a
l
l
e
n
g
e
”
)
H
e
a
l
t
h
A
r
t
i
f
i
c
i
a
l
I
n
t
e
l
l
i
g
e
n
c
e
Fig
u
r
e
3.
Ap
p
lied
th
e
s
ea
r
ch
s
tr
in
g
in
MD
PI
2.
6
.2
.
I
nclus
io
n
a
nd
ex
clu
s
io
n
cr
it
er
ia
T
h
e
s
tu
d
y
s
elec
tio
n
p
r
o
ce
s
s
f
o
llo
wed
a
s
tr
u
ct
u
r
ed
s
cr
ee
n
in
g
ap
p
r
o
ac
h
.
Firstl
y
,
ar
ticles
wer
e
r
etr
iev
e
d
u
s
in
g
p
r
e
d
ef
in
ed
k
ey
wo
r
d
s
f
r
o
m
th
e
MD
PI
d
atab
ase.
S
u
b
s
eq
u
en
tly
,
title
an
d
a
b
s
tr
ac
t
s
cr
ee
n
in
g
wer
e
co
n
d
u
cte
d
to
ex
clu
d
e
i
r
r
elev
a
n
t
s
tu
d
ies,
f
o
llo
wed
by
elig
ib
il
ity
ass
es
s
m
en
t.
On
ly
s
tu
d
ies
m
ee
tin
g
all
in
clu
s
io
n
cr
iter
ia
wer
e
r
etain
ed
f
o
r
b
ib
lio
m
etr
ic
an
d
SLR
an
aly
s
is
,
p
r
e
s
en
ted
in
T
ab
le
3.
T
ab
le
3.
C
r
iter
io
n
e
x
p
lan
atio
n
Ty
p
e
C
r
i
t
e
r
i
o
n
R
a
t
i
o
n
a
l
e
I
n
c
l
u
s
i
o
n
P
u
b
l
i
s
h
e
d
b
e
t
w
e
e
n
2
0
1
9
a
n
d
2
0
2
4
To
c
a
p
t
u
r
e
r
e
c
e
n
t
d
e
v
e
l
o
p
me
n
t
s i
n
h
e
a
l
t
h
A
I
r
e
sea
r
c
h
I
n
c
l
u
s
i
o
n
En
g
l
i
sh
-
l
a
n
g
u
a
g
e
a
r
t
i
c
l
e
s
To
e
n
s
u
r
e
c
o
n
s
i
st
e
n
c
y
i
n
d
a
t
a
e
x
t
r
a
c
t
i
o
n
a
n
d
a
n
a
l
y
s
i
s
I
n
c
l
u
s
i
o
n
I
n
d
e
x
e
d
i
n
M
D
P
I
a
r
t
i
c
l
e
s
To
m
a
i
n
t
a
i
n
d
a
t
a
s
e
t
c
o
n
s
i
st
e
n
c
y
I
n
c
l
u
s
i
o
n
R
e
l
e
v
a
n
t
t
o
h
e
a
l
t
h
A
I
a
n
d
c
h
a
l
l
e
n
g
e
s
To
a
l
i
g
n
w
i
t
h
r
e
s
e
a
r
c
h
o
b
j
e
c
t
i
v
e
s
I
n
c
l
u
s
i
o
n
R
e
se
a
r
c
h
a
n
d
r
e
v
i
e
w
a
r
t
i
c
l
e
s
To
i
n
c
l
u
d
e
b
o
t
h
e
mp
i
r
i
c
a
l
a
n
d
s
y
n
t
h
e
s
i
s st
u
d
i
e
s
Ex
c
l
u
si
o
n
C
o
n
f
e
r
e
n
c
e
a
b
s
t
r
a
c
t
s
O
f
t
e
n
l
a
c
k
me
t
h
o
d
o
l
o
g
i
c
a
l
c
o
mp
l
e
t
e
n
e
ss
Ex
c
l
u
si
o
n
Ed
i
t
o
r
i
a
l
,
n
o
t
e
s,
b
o
o
k
r
e
v
i
e
w
s
N
o
n
-
e
mp
i
r
i
c
a
l
a
n
d
l
i
mi
t
e
d
a
n
a
l
y
t
i
c
a
l
v
a
l
u
e
Ex
c
l
u
si
o
n
I
r
r
e
l
e
v
a
n
t
a
f
t
e
r
t
i
t
l
e
/
a
b
s
t
r
a
c
t
s
c
r
e
e
n
i
n
g
To
r
e
mo
v
e
o
f
f
-
t
o
p
i
c
st
u
d
i
e
s
2.
6
.
3.
Art
icle
s
elec
t
io
n
a
nd
da
t
a
ex
po
r
t
Ar
ticle
s
elec
tio
n
was
co
n
d
u
cted
th
r
o
u
g
h
a
s
y
s
tem
atic
f
ilter
in
g
p
r
o
ce
s
s
u
s
in
g
th
e
MD
PI
s
ea
r
ch
en
g
in
e
to
en
s
u
r
e
r
elev
a
n
ce
to
h
ea
lth
AI
an
d
c
h
allen
g
es
r
esear
ch
.
To
o
p
er
atio
n
alize
th
is
s
elec
tio
n
p
r
o
ce
s
s
,
a
s
et
of
s
p
ec
if
ic
s
ea
r
ch
p
ar
am
eter
s
was
d
ef
in
ed
to
n
ar
r
o
w
th
e
s
co
p
e
an
d
r
etr
iev
e
th
e
m
o
s
t
r
e
lev
an
t
p
u
b
licatio
n
s
.
T
ab
le
4
s
h
o
ws
th
e
p
ar
am
ete
r
s
ap
p
lied
to
th
e
MD
PI
s
ea
r
ch
e
n
g
in
e
to
f
o
c
u
s
th
e
liter
atu
r
e
r
e
v
iew
p
r
o
ce
s
s
in
th
e
f
ield
of
h
ea
lth
AI
at
MD
PI
p
u
b
lis
h
er
s
.
T
ab
le
4.
Ap
p
lied
p
ar
a
m
eter
s
f
o
r
th
e
MD
PI
s
ea
r
ch
e
n
g
in
e
P
a
r
a
me
t
e
r
s/
f
i
l
t
e
r
s
V
a
l
u
e
Y
e
a
r
s
2
0
1
9
-
2
0
2
4
S
u
b
j
e
c
t
s
P
u
b
l
i
c
h
e
a
l
t
h
a
n
d
h
e
a
l
t
h
c
a
r
e
Jo
u
r
n
a
l
s
H
e
a
l
t
h
c
a
r
e
A
r
t
i
c
l
e
s
t
y
p
e
A
r
t
i
c
l
e
T
ab
le
4
p
r
esen
ts
th
e
s
ea
r
ch
p
ar
am
eter
s
an
d
f
ilter
s
ap
p
lied
in
th
e
MD
PI
d
atab
ase
f
o
r
co
n
d
u
ctin
g
a
liter
atu
r
e
r
ev
iew
on
AI
in
th
e
h
ea
lth
f
ield
.
T
h
e
tim
e
r
an
g
e
f
o
r
th
e
s
ea
r
ch
is
d
ef
in
ed
f
r
o
m
2
0
1
9
to
2
0
2
4
,
en
s
u
r
in
g
th
e
r
etr
iev
al
of
th
e
m
o
s
t
r
ec
en
t
an
d
r
elev
an
t
s
tu
d
i
es
th
at
r
ef
lect
ad
v
an
ce
m
en
ts
in
h
ea
lth
AI
o
v
er
th
e
last
s
ix
y
ea
r
s
.
T
h
e
s
u
b
ject
s
ele
cted
is
“
Pu
b
lic
h
ea
lth
an
d
h
ea
lth
ca
r
e”
,
em
p
h
asizin
g
th
e
in
ter
d
is
cip
lin
ar
y
n
atu
r
e
of
h
ea
lth
A
I
,
wh
ich
ex
p
lain
s
AI
im
p
lem
en
tatio
n
in
h
ea
lth
ca
r
e.
T
h
e
j
o
u
r
n
als
s
p
ec
if
ically
tar
g
eted
ar
e
h
ea
lth
ca
r
e,
wh
ich
is
a
p
r
o
m
in
en
t
MD
PI
p
u
b
licatio
n
k
n
o
wn
f
o
r
h
o
s
tin
g
p
ee
r
-
r
e
v
iewe
d
ar
t
icles
on
h
ea
lth
ca
r
e
tech
n
o
lo
g
y
.
T
h
e
a
r
ticle
ty
p
e
f
ilter
is
s
et
to
“
Ar
ticle
”,
f
o
cu
s
i
n
g
on
o
r
ig
in
al
r
esear
ch
co
n
tr
ib
u
tio
n
s
r
ath
er
t
h
a
n
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
9
3
8
I
n
t
J
Ar
tif
I
n
tell
,
Vo
l.
15
,
No
.
4
,
Au
g
u
s
t
20
26
:
3
0
5
3
-
3
0
6
7
3058
r
ev
iews,
ed
ito
r
ials
,
or
o
th
er
c
o
n
ten
t
ty
p
es.
T
h
ese
f
ilter
s
co
llectiv
ely
aim
to
s
tr
ea
m
lin
e
t
h
e
s
ea
r
ch
p
r
o
ce
s
s
,
en
s
u
r
in
g
a
f
o
cu
s
ed
,
h
ig
h
-
q
u
ality
d
ataset
of
r
elev
an
t
p
u
b
licatio
n
s
f
o
r
a
co
m
p
r
eh
e
n
s
iv
e
r
e
v
ie
w
in
th
e
h
ea
lth
AI
f
ield
.
Fig
u
r
e
4
s
h
o
ws
th
e
im
p
l
em
en
tatio
n
of
th
e
s
ea
r
ch
f
ilter
to
MD
PI.
Fig
u
r
e
4.
Ap
p
lied
s
ea
r
ch
f
ilter
in
MD
PI
Data
u
s
ed
in
th
is
s
tu
d
y
u
s
es
ex
p
o
r
ted
R
esear
ch
I
n
f
o
r
m
atio
n
Sy
s
tem
s
(
R
I
S
)
f
o
r
m
atted
d
ata
f
r
o
m
ar
ticles
p
u
b
lis
h
ed
on
MD
PI.
Fig
u
r
e
5
s
h
o
ws
h
o
w
to
ex
p
o
r
t
d
ata
u
s
in
g
t
h
e
R
I
S
f
o
r
m
at.
Af
ter
s
u
cc
ess
f
u
lly
ap
p
ly
in
g
th
e
s
ea
r
ch
s
tr
in
g
a
n
d
p
er
f
o
r
m
in
g
a
s
ea
r
ch
f
ilter
ac
c
o
r
d
in
g
to
t
h
e
an
aly
s
is
n
ee
d
s
in
th
is
ar
ticle,
s
elec
t
“
T
im
e
c
ited
”
in
th
e
o
r
d
er
r
esu
lts
,
“
No
r
m
al
”
in
th
e
r
esu
lt
d
etails,
an
d
s
elec
t
“
2
0
0
”
ar
ticles
d
is
p
lay
ed
p
e
r
p
a
g
e
of
th
e
web
s
ite.
Nex
t,
click
“
Sh
o
w
ex
p
o
r
t
o
p
tio
n
s
”
,
th
en
s
elec
t
“
R
I
S
”
,
th
en
c
h
ec
k
th
e
box
on
“
Select
all
”
to
s
elec
t
all
th
e
jo
u
r
n
als
to
ex
p
o
r
t
d
ata,
a
n
d
th
e
n
click
th
e
“
e
x
p
o
r
t
”
b
u
tto
n
.
T
h
is
is
d
o
n
e
f
o
r
each
web
s
ite
p
a
g
e
th
at
d
is
p
lay
s
a
m
ax
im
u
m
of
2
0
0
ar
ticles
u
n
til
th
e
last
p
ag
e.
Fig
u
r
e
5.
E
x
p
o
r
t
d
ata
u
s
in
g
th
e
R
I
S
f
o
r
m
at
in
MD
PI
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t
J
Ar
tif
I
n
tell
I
SS
N:
2252
-
8
9
3
8
B
r
id
g
in
g
g
a
p
s
in
h
e
a
lth
a
r
tifi
cia
l
in
tellig
en
ce
:
ch
a
llen
g
es
in
MDPI
r
esea
r
ch
a
r
ticle
s
(
I
r
w
a
n
B
a
s
tia
n
)
3059
3.
RE
SU
L
T
S
AND
D
I
SCU
SS
I
O
N
B
ased
on
th
e
s
ea
r
ch
r
esu
lts
of
th
e
ar
ticles
with
k
e
y
wo
r
d
s
h
ea
lth
,
AI
,
a
n
d
c
h
allen
g
e
in
2019
-
2
0
2
4
(6
y
ea
r
s
)
,
th
er
e
ar
e
245
ar
ticles
o
b
tain
ed
.
T
h
e
e
x
p
o
r
te
d
d
ata
was
th
en
v
is
u
alize
d
with
VOS
v
iewe
r
to
v
iew
t
h
e
r
elatio
n
o
f
a
u
th
o
r
s
h
i
p
,
t
h
e
r
el
atio
n
o
f
k
ey
wo
r
d
s
,
as
well
as
th
e
r
esear
ch
tr
en
d
o
f
r
elate
d
to
p
ics
in
h
ea
lth
A
I
b
etwe
en
2
0
1
9
an
d
2
0
2
4
.
T
ab
l
e
5
ex
p
lain
s
th
e
tr
en
d
in
th
e
n
u
m
b
er
o
f
ar
ticles
p
u
b
lis
h
ed
in
th
e
f
ield
o
f
h
ea
lth
AI
f
r
o
m
2
0
1
9
to
2
0
2
4
,
s
p
ec
i
f
ically
,
th
o
s
e
t
h
at
also
h
av
e
th
e
wo
r
d
“c
h
allen
g
e”
b
ased
o
n
th
e
s
ea
r
ch
s
tr
in
g
r
esu
lts
ap
p
lied
,
to
talin
g
2
4
5
p
u
b
licatio
n
s
.
I
n
2
0
1
9
,
th
er
e
we
r
e
2
p
u
b
licatio
n
ar
ticles,
th
en
in
cr
ea
s
ed
in
2
0
2
0
with
1
2
p
u
b
licatio
n
ar
ticles,
th
en
ex
p
e
r
ien
ce
d
a
s
ig
n
if
ica
n
t
en
o
u
g
h
in
cr
ea
s
e
in
2
0
2
1
w
ith
4
4
p
u
b
licatio
n
ar
ticles,
th
en
also
i
n
cr
ea
s
ed
i
n
2
0
2
2
with
6
8
p
u
b
licatio
n
ar
ticles,
b
u
t
th
e
r
e
was
a
d
ec
r
ea
s
e
in
2
0
2
3
with
6
6
p
u
b
licatio
n
ar
ticles an
d
d
ec
r
ea
s
ed
ag
ain
in
2
0
2
4
with
5
3
p
u
b
licatio
n
ar
ticles.
T
ab
le
5.
Nu
m
b
er
of
p
u
b
lis
h
ed
ar
ticles
f
r
o
m
2
0
1
9
to
2
0
2
4
Y
e
a
r
N
u
mb
e
r
of
a
r
t
i
c
l
e
s
2
0
1
9
2
2
0
2
0
12
2
0
2
1
44
2
0
2
2
68
2
0
2
3
66
2
0
2
4
53
To
t
a
l
p
u
b
l
i
sh
e
d
a
r
t
i
c
l
e
s
2
4
5
T
ab
le
6
ex
p
lain
s
a
r
ticle
citatio
n
d
ata
f
r
o
m
2
0
1
9
to
2
0
2
4
with
th
e
to
p
ic
o
f
h
ea
lth
AI
ar
ticle
s
b
ased
o
n
th
e
s
ea
r
ch
s
tr
in
g
r
esu
lts
a
p
p
li
ed
,
wh
ich
h
a
v
e
r
ec
ei
v
ed
s
ig
n
if
ican
t
atten
tio
n
.
T
h
is
r
esu
lt
is
o
b
tain
e
d
b
y
u
s
in
g
“T
im
e
cited
”
f
o
r
th
e
o
r
d
er
r
es
u
lts
s
ec
tio
n
an
d
1
0
f
o
r
r
esu
lts
p
er
p
a
g
e
o
n
th
e
f
ir
s
t
p
ag
e
in
MD
PI.
An
ar
ticle
b
y
W
an
g
et
a
l
.
[
3
3
]
lead
s
with
2
1
2
citatio
n
s
,
f
o
llo
wed
th
e
ar
ti
cle
b
y
R
esh
an
et
a
l
.
[
3
4
]
with
1
3
8
citatio
n
s
,
an
d
th
e
ar
ticle
b
y
B
attin
en
i
et
a
l
.
[
3
5
]
with
1
0
5
citatio
n
s
.
Oth
er
a
r
ticles,
s
u
ch
as
[
3
6
]
–
[
4
2
]
also
in
clu
d
ed
in
th
e
to
p
1
0
ar
ticles
with
th
e
h
ig
h
est
n
u
m
b
er
o
f
citatio
n
s
.
T
h
is
d
is
tr
ib
u
tio
n
o
f
h
i
g
h
ly
-
cited
ar
ticles
d
em
o
n
s
tr
ates
th
at
h
ea
lth
AI
r
esear
ch
d
u
r
i
n
g
th
i
s
p
er
io
d
was
lar
g
ely
d
r
iv
en
b
y
p
an
d
em
ic
r
esp
o
n
s
e
n
ee
d
s
an
d
tech
n
o
lo
g
ical
ad
v
an
ce
s
in
d
ee
p
lear
n
i
n
g
f
o
r
d
iag
n
o
s
tic
ap
p
licatio
n
s
.
T
ab
le
6.
Au
th
o
r
s
with
th
e
m
o
s
t
citatio
n
s
A
u
t
h
o
r
s
N
u
mb
e
r
of
c
i
t
a
t
i
o
n
s
W
a
n
g
et
al
.
[
3
3
]
2
1
2
R
e
s
h
a
n
et
al
.
[
3
4
]
1
3
8
B
a
t
t
i
n
e
n
i
et
al
.
[
3
5
]
1
0
5
K
h
a
n
n
a
et
al
.
[
3
6
]
92
Te
msa
h
et
al
.
[
3
7
]
76
A
h
sa
n
et
al
.
[
3
8
]
73
A
l
mal
k
i
et
al
.
[
3
9
]
72
A
mo
u
et
al
.
[
4
0
]
68
Tr
a
n
et
al
.
[
4
1
]
65
A
l
w
a
k
i
d
et
al
.
[
4
2
]
63
3
.
1
.
Net
w
o
rk
v
is
ua
liza
t
io
n
T
h
e
co
l
la
b
o
r
a
tiv
e
s
tr
u
c
tu
r
e
a
m
o
n
g
a
u
th
o
r
s
in
th
e
h
e
al
th
A
I
f
i
el
d
,
w
ith
d
i
s
ti
n
c
t
c
o
lo
r
-
co
d
ed
clu
s
ter
s
r
ep
r
e
s
e
n
t
in
g
g
r
o
u
p
s
of
r
e
s
ea
r
ch
er
s
f
r
eq
u
en
tly
co
-
au
th
o
r
i
n
g
p
u
b
l
ic
at
io
n
s
to
g
eth
er
i
llu
s
t
r
at
ed
in
F
ig
u
r
e
6.
T
h
er
e
ar
e
218
c
lu
s
ter
s
b
a
s
ed
on
th
e
r
e
s
u
lt
s
f
r
o
m
V
OS
v
i
ewe
r
,
am
o
n
g
wh
ich
th
e
d
en
s
e
ly
co
n
n
ec
ted
r
ed
clu
s
t
er
r
ep
r
e
s
en
t
s
a
l
ar
g
e
e
s
t
ab
l
is
h
ed
co
m
m
u
n
i
ty
,
wh
il
e
t
h
e
g
r
ee
n
,
b
lu
e,
y
e
llo
w,
p
u
r
p
le,
tu
r
q
u
o
i
s
e,
an
d
o
r
an
g
e
cl
u
s
te
r
s
co
n
s
ti
tu
t
e
s
u
b
s
t
an
t
ia
l
s
ec
o
n
d
ar
y
r
e
s
ea
r
ch
g
r
o
u
p
s
,
an
d
th
e
g
r
ey
c
lu
s
t
er
s
r
e
p
r
e
s
en
t
n
u
m
er
o
u
s
s
m
a
l
ler
r
e
s
e
ar
ch
co
m
m
u
n
i
ti
e
s
,
ea
ch
co
m
p
r
i
s
in
g
f
ew
er
th
a
n
20
au
th
o
r
s
.
T
h
e
v
i
s
u
al
iz
at
i
o
n
em
p
h
as
iz
e
s
th
e
f
o
r
m
a
tio
n
of
clu
s
ter
s
b
a
s
ed
on
co
-
au
th
o
r
s
h
i
p
f
r
eq
u
en
cy
,
wi
th
m
o
s
t
c
o
l
lab
o
r
at
io
n
s
o
c
c
u
r
r
in
g
wi
th
in
lar
g
e
clu
s
t
er
s
r
ep
r
e
s
en
tin
g
b
r
o
ad
er
co
l
lab
o
r
at
iv
e
e
f
f
o
r
ts
.
T
h
e
r
ed
clu
s
t
er
,
p
o
s
it
io
n
ed
s
ep
ar
a
te
ly
on
th
e
r
ig
h
t
s
id
e
of
th
e
n
et
wo
r
k
,
a
p
p
ea
r
s
as
o
n
e
of
th
e
l
ar
g
e
s
t
an
d
m
o
s
t
d
en
s
ely
co
n
n
ec
ted
g
r
o
u
p
s
,
s
u
g
g
e
s
t
in
g
a
h
ig
h
ly
p
r
o
d
u
ct
iv
e
b
u
t
r
e
la
tiv
el
y
s
e
lf
-
co
n
ta
in
ed
co
l
lab
o
r
a
tio
n
c
o
m
m
u
n
i
ty
w
it
h
li
m
i
ted
ex
t
er
n
a
l
p
ar
tn
er
s
h
ip
s
.
Ad
d
i
ti
o
n
a
l
m
a
jo
r
c
lu
s
t
er
s
,
in
clu
d
in
g
th
e
g
r
e
en
,
b
lu
e
,
an
d
y
el
lo
w
g
r
o
u
p
s
,
a
l
s
o
ex
h
ib
i
t
d
en
s
e
co
-
au
th
o
r
s
h
ip
ti
es
,
l
ik
ely
r
ef
le
ct
in
g
s
u
s
ta
in
e
d
co
l
lab
o
r
a
tio
n
p
a
tt
er
n
s
s
h
ap
e
d
by
s
h
ar
ed
in
s
t
itu
t
io
n
a
l
a
f
f
i
li
at
io
n
s
,
g
eo
g
r
ap
h
ic
p
r
o
x
im
ity
,
or
co
m
m
o
n
r
e
s
e
ar
ch
i
n
t
er
e
s
ts
.
In
co
n
tr
a
s
t,
th
e
l
ar
g
e
co
n
c
en
tr
a
tio
n
of
g
r
ey
n
o
d
es
in
t
h
e
ce
n
tr
a
l
r
eg
io
n
r
ep
r
es
en
ts
a
b
r
o
ad
er
b
ac
k
g
r
o
u
n
d
n
e
two
r
k
of
au
th
o
r
s
wi
th
w
ea
k
er
co
ll
ab
o
r
at
io
n
in
t
en
s
i
ty
,
s
m
al
ler
p
u
b
li
ca
tio
n
f
o
o
tp
r
in
ts
,
an
d
le
s
s
co
n
s
o
lid
at
ed
clu
s
ter
m
e
m
b
e
r
s
h
ip
.
Ov
er
a
ll
,
th
e
r
e
la
t
iv
e
ly
s
p
ar
s
e
in
ter
-
cl
u
s
te
r
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
9
3
8
I
n
t
J
Ar
tif
I
n
tell
,
Vo
l.
15
,
No
.
4
,
Au
g
u
s
t
20
26
:
3
0
5
3
-
3
0
6
7
3060
co
n
n
ec
t
io
n
s
an
d
l
im
it
ed
n
u
m
b
er
of
b
r
id
g
in
g
au
t
h
o
r
s
s
u
g
g
es
t
th
a
t
wh
i
le
s
tr
o
n
g
lo
ca
l
c
o
ll
ab
o
r
a
t
io
n
ex
i
s
t
s
wi
th
in
in
d
iv
id
u
a
l
r
es
ea
r
ch
c
o
m
m
u
n
i
ti
e
s
,
b
r
o
ad
er
cr
o
s
s
-
clu
s
t
er
an
d
cr
o
s
s
-
in
s
ti
tu
t
io
n
a
l
co
ll
ab
o
r
a
ti
o
n
r
e
m
a
in
s
wea
k
.
T
h
i
s
f
r
a
g
m
en
ted
co
l
la
b
o
r
at
io
n
lan
d
s
ca
p
e
m
ay
r
e
s
t
r
ic
t
in
te
r
d
i
s
cip
lin
ar
y
k
n
o
w
le
d
g
e
ex
ch
an
g
e
an
d
in
t
eg
r
a
ti
o
n
,
h
ig
h
lig
h
t
in
g
th
e
n
ee
d
f
o
r
s
tr
o
n
g
er
in
t
er
n
a
t
io
n
al
an
d
cr
o
s
s
-
d
is
ci
p
l
in
ar
y
p
ar
t
n
er
s
h
ip
s
to
ad
v
an
ce
in
n
o
v
a
ti
o
n
in
h
e
al
th
AI
r
e
s
ea
r
ch
.
Fig
u
r
e
6.
Co
-
a
u
th
o
r
s
h
ip
n
etwo
r
k
v
is
u
aliza
tio
n
T
ab
le
7
p
r
o
v
id
es
a
q
u
an
titativ
e
b
r
ea
k
d
o
wn
of
th
e
lar
g
est
clu
s
ter
s
id
en
tifie
d
in
th
e
co
-
a
u
th
o
r
s
h
i
p
n
etwo
r
k
v
is
u
aliza
tio
n
in
Fig
u
r
e
6,
illu
s
tr
atin
g
t
h
e
d
is
tr
ib
u
tio
n
of
au
th
o
r
co
llab
o
r
atio
n
s
in
t
h
e
h
ea
lth
AI
f
ield
.
T
h
e
lar
g
est
clu
s
ter
is
th
e
r
ed
clu
s
ter
,
co
n
s
is
tin
g
of
51
a
u
th
o
r
s
,
f
o
llo
wed
by
th
e
g
r
ee
n
clu
s
ter
with
35
au
t
h
o
r
s
an
d
th
e
b
lu
e
clu
s
ter
with
30
a
u
th
o
r
s
,
c
o
n
f
ir
m
i
n
g
t
h
at
th
e
m
o
s
t
in
ten
s
iv
e
r
esear
ch
co
llab
o
r
atio
n
s
o
cc
u
r
with
in
th
ese
lar
g
e
g
r
o
u
p
s
.
Me
an
wh
il
e,
th
e
r
em
ain
in
g
2
1
1
clu
s
ter
s
(
clu
s
ter
s
8
th
r
o
u
g
h
2
1
8
)
ar
e
co
lo
r
ed
g
r
ey
,
an
d
each
co
n
s
is
ts
of
f
ewe
r
t
h
an
20
au
th
o
r
s
,
in
d
icatin
g
th
e
p
r
esen
ce
of
n
u
m
e
r
o
u
s
s
m
all
r
esea
r
ch
team
s
or
more
lim
ited
r
esear
ch
er
co
m
m
u
n
itie
s
with
in
th
e
o
v
er
all
co
llab
o
r
ati
o
n
n
etwo
r
k
.
T
ab
le
7.
C
lu
s
ter
s
with
th
e
m
o
s
t
au
th
o
r
s
C
l
u
st
e
r
N
u
mb
e
r
of
a
u
t
h
o
r
s
C
l
u
st
e
r
c
o
l
o
r
1
51
R
e
d
2
35
G
r
e
e
n
3
30
B
l
u
e
4
30
Y
e
l
l
o
w
5
29
P
u
r
p
l
e
6
22
Tu
r
q
u
o
i
s
e
7
22
O
r
a
n
g
e
8
-
2
1
8
Le
ss
t
h
a
n
20
(1
to
1
9
)
G
r
e
y
Fig
u
r
e
7
illu
s
tr
ates
th
e
r
elatio
n
s
h
ip
s
an
d
c
o
-
o
cc
u
r
r
e
n
ce
s
am
o
n
g
k
ey
te
r
m
s
in
t
h
e
h
ea
lth
A
I
r
esear
ch
f
ield
,
with
n
o
d
es
r
ep
r
esen
tin
g
k
ey
wo
r
d
s
an
d
e
d
g
es
s
h
o
win
g
th
e
s
tr
en
g
th
o
f
th
eir
co
n
n
ec
tio
n
s
.
L
ar
g
er
n
o
d
es
in
d
icate
m
o
r
e
f
r
e
q
u
en
tly
u
s
ed
k
ey
wo
r
d
s
,
wh
ile
th
e
p
r
o
x
im
it
y
an
d
th
ick
n
ess
o
f
ed
g
es
r
ef
lect
th
e
clo
s
en
ess
an
d
f
r
eq
u
e
n
cy
o
f
th
eir
co
-
o
cc
u
r
r
e
n
ce
.
Alth
o
u
g
h
“
C
OVI
D
-
19
”
a
p
p
ea
r
s
as
o
n
e
o
f
th
e
lar
g
est
an
d
m
o
s
t
p
r
o
m
in
e
n
t
n
o
d
es,
in
d
icatin
g
its
h
ig
h
f
r
e
q
u
en
cy
an
d
im
p
o
r
ta
n
ce
in
th
e
d
ataset,
th
e
n
etwo
r
k
is
co
n
ce
p
tu
ally
ce
n
ter
e
d
ar
o
u
n
d
“
ar
tific
ial
in
tellig
en
ce
”
an
d
“
m
ac
h
in
e
lear
n
in
g
”
,
wh
ich
o
cc
u
p
y
m
o
r
e
ce
n
tr
al
p
o
s
itio
n
s
an
d
d
is
p
lay
ex
ten
s
iv
e
co
n
n
ec
tio
n
s
with
m
u
ltip
le
clu
s
ter
s
.
T
h
is
s
u
g
g
ests
th
at
wh
ile
C
OVI
D
-
1
9
s
er
v
ed
as
a
m
ajo
r
ap
p
licatio
n
d
o
m
ain
b
etwe
en
2
0
1
9
a
n
d
2
0
2
4
,
A
I
an
d
m
ac
h
in
e
lear
n
in
g
f
u
n
ctio
n
e
d
as
th
e
p
r
im
ar
y
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t
J
Ar
tif
I
n
tell
I
SS
N:
2252
-
8
9
3
8
B
r
id
g
in
g
g
a
p
s
in
h
e
a
lth
a
r
tifi
cia
l
in
tellig
en
ce
:
ch
a
llen
g
es
in
MDPI
r
esea
r
ch
a
r
ticle
s
(
I
r
w
a
n
B
a
s
tia
n
)
3061
m
eth
o
d
o
l
o
g
ical
b
ac
k
b
o
n
e
o
f
t
h
e
liter
atu
r
e.
T
h
e
r
ed
clu
s
ter
r
ep
r
esen
ts
th
e
co
r
e
m
eth
o
d
o
lo
g
ical
f
o
u
n
d
atio
n
o
f
AI
in
h
ea
lth
ca
r
e,
ce
n
ter
ed
o
n
“
ar
tific
ial
in
tellig
en
ce
”
an
d
“
m
ac
h
in
e
lear
n
in
g
”
,
with
ass
o
ciate
d
ter
m
s
s
u
ch
as
“
elec
tr
o
n
ic
h
ea
lth
r
ec
o
r
d
s
”
,
“
p
r
ed
ictio
n
”
,
an
d
“
d
iag
n
o
s
is
”
.
T
h
e
d
en
s
e
in
ter
n
al
co
n
n
ec
ti
v
ity
o
f
th
e
clu
s
ter
in
d
icate
s
th
e
s
tr
o
n
g
in
teg
r
atio
n
o
f
p
r
ed
ictiv
e
a
n
d
d
iag
n
o
s
tic
clin
ical
d
ata
p
r
o
ce
s
s
in
g
,
h
ig
h
lig
h
tin
g
th
e
wid
esp
r
ea
d
u
s
e
o
f
m
ac
h
in
e
le
ar
n
in
g
tec
h
n
iq
u
es
f
o
r
h
ea
lth
c
ar
e
d
ec
is
io
n
s
u
p
p
o
r
t
a
n
d
d
is
ea
s
e
p
r
ed
ictio
n
.
T
h
e
g
r
ee
n
clu
s
ter
f
o
c
u
s
es
o
n
d
e
ep
lear
n
in
g
a
p
p
licatio
n
s
in
m
ed
ical
im
ag
in
g
a
n
d
d
iag
n
o
s
tic
class
if
ica
tio
n
,
an
ch
o
r
e
d
b
y
th
e
n
o
d
e
“
d
ee
p
lear
n
in
g
”
an
d
co
n
n
ec
ted
to
ter
m
s
s
u
ch
as
“
x
-
r
ay
”
,
“
p
n
eu
m
o
n
ia
”
,
an
d
“
ex
p
lain
ab
le
AI
class
if
icatio
n
”
.
T
h
e
p
r
o
x
im
ity
am
o
n
g
t
h
ese
n
o
d
es
s
u
g
g
ests
a
h
i
g
h
ly
s
p
ec
ialized
an
d
tech
n
ically
co
h
esiv
e
r
esear
ch
ar
ea
ce
n
ter
ed
o
n
au
to
m
ate
d
im
ag
e
an
aly
s
is
an
d
r
ad
io
lo
g
ical
d
iag
n
o
s
is
.
T
h
e
y
ello
w
clu
s
ter
is
o
r
g
an
ized
ar
o
u
n
d
“
C
OVI
D
-
19
”
an
d
ca
p
tu
r
es
p
an
d
e
m
ic
-
d
r
iv
e
n
h
ea
lt
h
ca
r
e
ap
p
licatio
n
s
,
in
clu
d
in
g
“
telem
ed
icin
e
”
,
“
m
en
tal
h
ea
lth
”
,
“
r
ad
i
o
lo
g
y
”
,
a
n
d
“
f
o
r
ec
asti
n
g
”
.
T
h
e
p
r
o
m
in
e
n
ce
o
f
th
is
cl
u
s
ter
r
ef
lects
th
e
ac
ce
ler
atio
n
o
f
A
I
ad
o
p
tio
n
d
u
r
in
g
th
e
p
an
d
e
m
ic,
p
ar
ticu
lar
ly
f
o
r
r
em
o
te
h
ea
lth
ca
r
e
d
eliv
e
r
y
,
o
u
tb
r
ea
k
p
r
e
d
ictio
n
,
an
d
p
a
n
d
em
ic
-
r
elate
d
clin
ical
m
an
a
g
e
m
en
t.
Ov
er
all,
th
e
v
is
u
aliza
tio
n
d
em
o
n
s
tr
ates
th
at
cu
r
r
en
t
h
ea
lth
ca
r
e
AI
r
esear
ch
is
d
o
m
in
ate
d
b
y
m
eth
o
d
o
lo
g
ical
AI
d
ev
elo
p
m
en
t
an
d
p
an
d
em
ic
-
r
elate
d
ap
p
licatio
n
s
,
wh
ile
em
er
g
in
g
t
h
em
es
s
u
ch
as
X
AI
a
n
d
eth
ica
l
d
ig
ital
h
ea
lth
im
p
lem
en
tatio
n
r
em
ai
n
r
elativ
ely
p
er
ip
h
er
al.
T
h
e
s
e
p
e
r
i
p
h
e
r
a
l
y
e
t
g
r
o
w
i
n
g
c
l
u
s
t
e
r
s
s
u
g
g
e
s
t
i
m
p
o
r
t
a
n
t
f
u
t
u
r
e
r
e
s
e
a
r
c
h
o
p
p
o
r
t
u
n
i
t
i
es
,
p
a
r
t
i
c
u
l
a
r
l
y
i
n
i
n
t
e
g
r
a
t
i
n
g
A
I
i
n
n
o
v
a
ti
o
n
s
wi
t
h
h
u
m
a
n
-
c
e
n
t
e
r
e
d
h
e
a
l
t
h
c
a
r
e
a
p
p
l
i
c
a
t
i
o
n
s
,
a
s
c
o
n
c
l
u
d
e
d
i
n
T
a
b
l
e
8
.
Fig
u
r
e
7.
Key
w
o
r
d
r
elatio
n
s
h
ip
an
d
co
-
o
cc
u
r
r
en
ce
v
is
u
aliza
tio
n
T
ab
le
8.
Key
wo
r
d
clu
s
ter
s
with
th
e
m
o
s
t
o
cc
u
r
r
en
ce
s
C
l
u
st
e
r
C
o
l
o
r
K
e
y
w
o
r
d
O
c
c
u
r
r
e
n
c
e
s
Li
n
k
s
To
t
a
l
l
i
n
k
s
t
r
e
n
g
t
h
s
(
TLS)
1
Y
e
l
l
o
w
C
O
V
I
D
-
19
37
20
2
7
.
0
0
2
R
e
d
M
a
c
h
i
n
e
l
e
a
r
n
i
n
g
36
20
2
9
.
0
0
3
R
e
d
AI
36
21
2
1
.
0
0
4
G
r
e
e
n
D
e
e
p
l
e
a
r
n
i
n
g
24
14
1
9
.
0
0
T
h
e
p
o
ten
tial
of
AI
can
r
ev
o
lu
tio
n
ize
clin
ical
p
r
ac
tice
[
6
]
,
but
to
r
ea
lize
th
is
p
o
ten
t
ial
s
ev
er
al
ch
allen
g
es
m
u
s
t
be
ad
d
r
ess
ed
,
in
clu
d
in
g
th
e
lack
of
q
u
ality
of
m
ed
ical
d
ata
th
at
can
ca
u
s
e
in
ac
cu
r
ac
y
in
th
e
r
esu
lts
o
b
tain
ed
.
B
y
a
p
p
ly
in
g
AI
in
cli
n
i
ca
l
p
r
ac
tic
e
als
o
h
as
t
h
e
p
o
t
e
n
ti
al
to
be
li
m
it
e
d
in
te
r
m
s
of
d
a
ta
p
r
iv
ac
y
,
a
v
ail
ab
ilit
y
,
a
n
d
s
ec
u
r
ity
;
in
ad
d
iti
o
n
,
to
a
ch
ie
v
e
t
h
e
d
esi
r
ed
r
esu
lts
,
it
is
v
er
y
im
p
o
r
tan
t
to
d
ete
r
m
i
n
i
n
g
r
el
e
v
a
n
t
c
li
n
ic
al
m
et
r
i
cs
a
n
d
s
ele
cti
n
g
an
a
p
p
r
o
p
r
ia
te
m
et
h
o
d
o
l
o
g
y
.
T
h
e
n
,
in
t
h
e
co
n
cl
u
s
i
o
n
of
[
6
]
,
it
is
s
tated
th
at
o
v
er
co
m
i
n
g
ch
allen
g
es
lik
e
d
ata
q
u
ality
,
p
r
iv
ac
y
,
b
ias,
an
d
th
e
n
ee
d
f
o
r
h
u
m
an
ex
p
er
tis
e
is
e
s
s
en
tial
f
o
r
r
esp
o
n
s
ib
le
an
d
e
f
f
ec
tiv
e
AI
i
n
teg
r
atio
n
.
Fig
u
r
e
8
s
h
o
ws
th
e
r
esu
lts
o
f
n
etwo
r
k
v
is
u
aliza
tio
n
f
o
r
s
e
v
er
al
k
ey
w
o
r
d
s
r
elate
d
to
th
e
ch
allen
g
e.
T
h
is
r
esu
lt
is
o
b
tain
e
d
b
y
s
ele
ctin
g
“
C
r
ea
te
a
m
ap
b
ased
o
n
tex
t
d
ata
”
,
with
f
iel
d
s
f
r
o
m
wh
ich
ter
m
s
will
b
e
ex
tr
ac
ted
in
VOSv
iewe
r
f
r
o
m
th
e
titl
e
an
d
ab
s
tr
ac
t
f
iel
d
s
.
T
h
en
,
th
e
co
u
n
tin
g
m
eth
o
d
ch
o
s
en
is
“
Fu
ll
co
u
n
tin
g
”
.
T
h
e
k
e
y
wo
r
d
s
ch
o
s
en
ar
e
r
elate
d
to
c
h
allen
g
es
an
d
en
s
u
r
e
th
at
th
ese
k
e
y
wo
r
d
s
h
av
e
a
r
elatio
n
s
h
ip
with
AI
,
h
ea
lth
ca
r
e,
a
n
d
ch
all
en
g
es.
I
n
d
is
p
lay
in
g
th
is
n
etwo
r
k
v
is
u
aliza
tio
n
,
th
e
weig
h
t
u
s
ed
is
“
T
o
tal
lin
k
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
9
3
8
I
n
t
J
Ar
tif
I
n
tell
,
Vo
l.
15
,
No
.
4
,
Au
g
u
s
t
20
26
:
3
0
5
3
-
3
0
6
7
3062
s
tr
en
g
th
”
.
T
h
e
r
esu
ltin
g
v
is
u
aliza
tio
n
r
ev
ea
ls
a
n
etwo
r
k
s
tr
u
ctu
r
e
in
wh
ich
th
e
ter
m
“
d
ata
”
o
cc
u
p
ies
th
e
ce
n
tr
al
p
o
s
itio
n
,
with
th
e
lar
g
est
n
o
d
e
s
ize,
s
er
v
in
g
as
th
e
p
r
im
ar
y
h
u
b
th
r
o
u
g
h
wh
ich
a
ll
o
th
er
ch
allen
g
e
-
r
elate
d
k
ey
wo
r
d
s
co
n
n
ec
t.
Al
o
n
g
s
id
e
“
d
ata
”
,
th
e
te
r
m
“
ar
ti
f
icial
in
tellig
en
ce
”
ap
p
ea
r
s
as
th
e
s
ec
o
n
d
lar
g
est
n
o
d
e
p
o
s
itio
n
ed
in
th
e
lo
wer
-
ce
n
ter
o
f
th
e
n
etwo
r
k
,
c
o
n
f
ir
m
in
g
th
at
th
ese
two
co
n
ce
p
ts
to
g
eth
er
f
o
r
m
th
e
twin
ax
es
ar
o
u
n
d
wh
ich
h
ea
lt
h
AI
c
h
allen
g
e
r
esear
ch
r
ev
o
lv
es.
T
h
e
n
etwo
r
k
is
o
r
g
an
ize
d
in
to
th
r
ee
co
lo
r
-
co
d
ed
clu
s
ter
s
th
at
ar
e
in
ter
co
n
n
ec
ted
th
r
o
u
g
h
th
e
ce
n
tr
al
“
d
ata
”
n
o
d
e.
T
h
e
r
ed
cl
u
s
ter
,
o
cc
u
p
y
in
g
th
e
u
p
p
er
p
o
r
tio
n
o
f
th
e
n
etwo
r
k
,
g
r
o
u
p
s
“
ch
allen
g
e
”
with
“
q
u
ality
”
an
d
“
av
ailab
ilit
y
”
.
T
h
ese
th
r
e
e
ter
m
s
ar
e
tig
h
tly
lin
k
ed
to
o
n
e
an
o
t
h
er
an
d
ar
e
all
co
n
n
ec
ted
d
ir
ec
tly
to
th
e
ce
n
tr
al
“
d
ata
”
n
o
d
e,
in
d
icatin
g
th
at
d
ata
q
u
ality
an
d
d
ata
av
ailab
ilit
y
ar
e
co
n
s
is
te
n
tly
f
r
am
ed
as
co
r
e
d
im
en
s
i
o
n
s
o
f
th
e
b
r
o
ad
e
r
im
p
lem
e
n
tatio
n
ch
allen
g
es
d
is
cu
s
s
ed
in
th
e
liter
atu
r
e.
T
h
e
g
r
ee
n
cl
u
s
ter
,
o
cc
u
p
y
in
g
t
h
e
lo
wer
an
d
r
ig
h
t
p
o
r
tio
n
s
,
co
n
tain
s
“
ar
tific
ial
in
tellig
en
ce
”
,
“
h
ea
lth
ca
r
e
”
,
an
d
“
b
ias
”
—
th
e
latter
p
o
s
itio
n
e
d
at
t
h
e
f
ar
-
r
ig
h
t
p
er
ip
h
e
r
y
with
a
v
is
ib
ly
s
m
all
n
o
d
e
s
ize.
T
h
e
p
lace
m
e
n
t
o
f
“
b
ias
”
with
in
th
e
s
am
e
clu
s
ter
as
“
ar
tific
ial
in
tellig
en
ce
”
an
d
“
h
ea
lth
ca
r
e
”
y
et
at
its
ex
tr
em
e
e
d
g
e
s
u
g
g
ests
th
at
wh
ile
alg
o
r
ith
m
ic
b
ias
is
r
ec
o
g
n
ized
as
r
elev
a
n
t
to
AI
-
d
r
iv
e
n
h
ea
lth
ca
r
e,
it
h
as
n
o
t
y
et
d
ev
elo
p
ed
in
to
a
d
e
n
s
ely
co
n
n
ec
ted
,
ce
n
tr
al
r
esear
ch
th
em
e
in
its
o
wn
r
ig
h
t.
T
h
e
b
lu
e
clu
s
ter
,
p
o
s
itio
n
ed
o
n
th
e
lef
t
s
id
e
o
f
t
h
e
n
etwo
r
k
,
co
n
tain
s
“
s
ec
u
r
ity
”
an
d
“
p
r
iv
ac
y
”
in
p
r
o
x
im
it
y
to
ea
ch
o
th
er
.
B
o
th
ter
m
s
ar
e
co
n
n
ec
ted
to
th
e
ce
n
tr
al
“
d
ata
”
n
o
d
e,
a
n
d
also
ex
h
i
b
it
cr
o
s
s
-
clu
s
ter
co
n
n
ec
tio
n
s
—
“
p
r
iv
ac
y
”
lin
k
in
g
to
“
ar
tific
ial
in
tellig
en
ce
”
a
n
d
“
s
ec
u
r
ity
”
lin
k
i
n
g
to
“
ch
alle
n
g
e
”
—
in
d
icatin
g
t
h
at
th
ese
et
h
ical
co
n
ce
r
n
s
ar
e
n
o
t
en
tire
l
y
is
o
lated
b
u
t
h
av
e
b
eg
u
n
to
in
te
r
s
ec
t
with
co
r
e
r
esear
ch
to
p
ics.
Ho
wev
er
,
th
e
ir
s
m
all
n
o
d
e
s
izes
an
d
p
er
ip
h
er
al
p
o
s
itio
n
in
g
v
i
s
u
ally
co
n
v
ey
a
p
atter
n
o
f
ac
k
n
o
wled
g
e
d
im
p
o
r
tan
ce
y
et
l
im
ited
in
teg
r
atio
n
:
s
ec
u
r
ity
an
d
p
r
i
v
ac
y
a
r
e
r
ec
o
g
n
ized
as
co
n
ce
r
n
s
r
elev
an
t
t
o
d
ata
an
d
AI
c
h
allen
g
es,
b
u
t
th
eir
lo
w
o
cc
u
r
r
e
n
ce
f
r
eq
u
e
n
cy
in
d
icate
s
th
at
th
ey
r
em
ain
p
er
ip
h
er
al
th
em
es
r
ath
er
th
an
d
ee
p
ly
em
b
ed
d
e
d
r
esear
ch
p
r
io
r
ities
.
T
h
e
o
v
er
all
co
n
f
ig
u
r
atio
n
p
r
esen
ts
a
clea
r
v
is
u
al
h
ier
ar
c
h
y
o
f
r
ese
ar
ch
atten
tio
n
.
Fig
u
r
e
8.
Key
w
o
r
d
s
n
etwo
r
k
v
is
u
aliza
tio
n
with
th
e
s
tr
en
g
th
weig
h
t
3
.
2
.
O
v
er
la
y
v
is
ua
liza
t
io
n
Key
wo
r
d
o
cc
u
r
r
en
ce
s
in
th
e
p
u
b
lis
h
ed
ar
ticles
b
etwe
en
2
0
1
9
an
d
2
0
2
4
d
ep
icted
in
Fig
u
r
e
9.
T
h
e
co
lo
r
g
r
a
d
ien
t,
r
a
n
g
in
g
f
r
o
m
b
lu
e
to
y
ello
w,
r
e
p
r
esen
ts
th
e
tim
elin
e
of
r
esear
ch
f
o
c
u
s
f
r
o
m
2019
to
2
0
2
4
,
wit
h
n
ewe
r
k
e
y
wo
r
d
s
h
ig
h
lig
h
te
d
in
y
ello
w
an
d
o
ld
e
r
o
n
es
in
b
lu
e.
T
h
e
v
is
u
aliza
tio
n
d
e
m
o
n
s
tr
ates
a
clea
r
th
em
atic
tr
an
s
itio
n
f
r
o
m
p
a
n
d
em
ic
-
d
r
iv
en
a
p
p
licatio
n
s
to
war
d
b
r
o
ad
er
an
d
m
o
r
e
h
u
m
an
-
ce
n
te
r
ed
AI
im
p
lem
en
tatio
n
in
h
ea
lth
ca
r
e
.
Key
wo
r
d
s
s
u
ch
as
“
C
OVI
D
-
19
”
,
“
co
r
o
n
av
ir
u
s
”
,
an
d
r
e
lated
clin
ical
ter
m
s
ap
p
ea
r
in
b
lu
e
-
g
r
ee
n
to
n
es,
in
d
icatin
g
th
at
th
ese
to
p
ics
d
o
m
in
ated
th
e
ea
r
lier
p
h
ase
of
th
e
d
ataset
an
d
r
ef
lect
th
e
in
itial
r
esear
ch
em
p
h
asis
on
p
an
d
e
m
ic
-
r
elate
d
ch
allen
g
es,
in
clu
d
in
g
d
is
ea
s
e
p
r
ed
ictio
n
,
r
ap
id
d
iag
n
o
s
is
,
telem
ed
icin
e,
an
d
h
ea
lth
ca
r
e
m
an
ag
em
en
t
d
u
r
in
g
th
e
C
OVI
D
-
19
cr
is
is
.
C
o
r
e
m
eth
o
d
o
lo
g
ical
ter
m
s
,
in
clu
d
in
g
“
m
ac
h
in
e
lear
n
in
g
”
,
“
ar
tific
ial
in
tellig
en
ce
”
,
an
d
“
d
ee
p
lear
n
in
g
”
,
o
cc
u
p
y
ce
n
tr
al
p
o
s
itio
n
s
an
d
ar
e
r
ep
r
esen
ted
by
g
r
ee
n
t
o
n
es,
s
u
g
g
esti
n
g
s
u
s
tain
ed
r
esear
ch
atten
tio
n
d
u
r
in
g
th
e
in
ter
m
ed
iate
p
er
io
d
of
t
h
e
d
ataset.
T
h
eir
ce
n
tr
al
l
o
ca
tio
n
a
n
d
e
x
t
en
s
iv
e
co
n
n
ec
tio
n
s
in
d
icate
th
at
th
ese
m
eth
o
d
o
lo
g
ies
ev
o
lv
ed
b
ey
o
n
d
p
a
n
d
em
i
c
-
s
p
ec
if
ic
ap
p
licatio
n
s
an
d
b
e
ca
m
e
f
o
u
n
d
atio
n
al
tech
n
o
lo
g
ies
f
o
r
b
r
o
ad
er
h
ea
lth
ca
r
e
u
s
e
ca
s
es,
in
clu
d
in
g
elec
tr
o
n
ic
h
ea
lth
r
ec
o
r
d
s
,
p
r
ed
ictio
n
s
m
o
d
els,
Evaluation Warning : The document was created with Spire.PDF for Python.