I
A
E
S
I
n
t
e
r
n
at
io
n
al
Jou
r
n
al
of
A
r
t
if
ic
ia
l
I
n
t
e
ll
ig
e
n
c
e
(
I
J
-
AI
)
V
ol
. 15, No. 1, Febr
ua
r
y 2026
, pp.
672
~
680
I
S
S
N
:
2252
-
8938
,
D
O
I
:
10.11591/
ij
a
i.
v
15
.i
1
.pp
672
-
680
672
Jou
r
n
al
h
om
e
page
:
ht
tp
:
//
ij
ai
.
ia
e
s
c
or
e
.c
om
D
e
t
e
c
t
i
on
an
d
f
or
e
c
ast
i
n
g of
m
e
n
t
al
h
e
al
t
h
d
i
sor
d
e
r
s u
si
n
g
m
ac
h
i
n
e
l
e
ar
n
i
n
g
m
od
e
l
s o
n
soc
i
al
m
e
d
i
a d
at
a
C
h
ai
t
h
r
a
I
n
d
ava
r
a
V
e
n
k
at
e
s
h
ag
ow
d
a
1
,
R
oop
as
h
r
e
e
H
e
j
j
aj
j
i
R
an
gan
at
h
a
s
h
ar
m
a
2
,
Y
oge
e
s
h
A
m
b
al
ag
e
r
e
C
h
an
d
r
as
h
e
k
ar
ai
ah
3
,
N
ar
ve
L
ak
s
h
m
i
n
ar
ayan
T
a
r
an
at
h
4
1
D
e
pa
r
t
m
e
nt
of
C
om
put
e
r
S
c
i
e
nc
e
a
nd E
ngi
ne
e
r
i
ng, G
S
S
S
I
ns
t
i
t
ut
e
of
E
ngi
ne
e
r
i
ng a
nd T
e
c
hnol
ogy f
or
W
om
e
n,
V
i
s
ve
s
va
r
a
ya
T
e
c
hnol
ogi
c
a
l
U
ni
ve
r
s
i
t
y, B
e
l
a
ga
vi
, I
ndi
a
2
D
e
pa
r
t
m
e
nt
of
A
r
t
i
f
i
c
i
a
l
I
nt
e
l
l
i
ge
nc
e
a
nd D
a
t
a
S
c
i
e
nc
e
, G
S
S
S
I
ns
t
i
t
ut
e
of
E
ngi
ne
e
r
i
ng a
nd T
e
c
hnol
ogy f
or
W
om
e
n,
V
i
s
ve
s
va
r
a
ya
T
e
c
hnol
ogi
c
a
l
U
ni
ve
r
s
i
t
y, B
e
l
a
ga
vi
, I
ndi
a
3
D
e
pa
r
t
m
e
nt
of
C
om
put
e
r
S
c
i
e
nc
e
a
nd E
ngi
ne
e
r
i
ng, G
ove
r
nm
e
nt
E
ngi
ne
e
r
i
ng
C
ol
l
e
ge
, V
i
s
ve
s
va
r
a
ya
T
e
c
hnol
ogi
c
a
l
U
ni
ve
r
s
i
t
y,
B
e
l
a
ga
vi
, I
ndi
a
4
S
c
hool
of
C
om
put
e
r
S
c
i
e
nc
e
a
nd E
ngi
ne
e
r
i
ng, P
r
e
s
i
de
nc
y U
ni
ve
r
s
i
t
y, B
e
nga
l
u
r
u, I
ndi
a
A
r
t
ic
le
I
n
f
o
A
B
S
T
R
A
C
T
A
r
ti
c
le
h
is
to
r
y
:
R
e
c
e
iv
e
d
M
a
r
25, 2025
R
e
vi
s
e
d
D
e
c
26, 2025
A
c
c
e
pt
e
d
J
a
n 22, 2026
The
detection
and
classification
of
depression
and
other
mental
dis
orders
have
become
crucial
in
the
modern
era,
particularly
with
the
gr
owing
reliance
on
social
media
for
self
-
expressio
n.
Existi
ng
systems
often
face
challenges
like
limit
ed
predicti
on
accuracy,
difficul
ty
forecastin
g
future
mental
illnesses,
and
handling
both
clinical
and
non
-
clinical
data.
This
study
proposes
a
novel
analytical
model
that
not
only
screens
individuals'
current
mental
health
status
from
social
media
content
but
also
predic
ts
the
likelihood
of
future
mental
health
issues.
The
proposed
metho
dology
integrates
classical
machine
learning
(ML)
models,
ensemble
le
arning
approaches,
and
pretrained
models
for
enhanced
detection
and
fore
casting
accuracy.
The
outcome
shows
that
pre
-
trained
language
models
accomplis
hed
maximi
zed
F1
-
score
and
overall
performance
significantly
better
than
conventional
ML
and
ensemble
models.
The
system
outpe
rforms
existin
g
methods
with
a
signifi
cant
accuracy
improvem
ent,
achieving
90.9%
overall
accuracy,
a
7.2%
improvement
over
traditional
ML
classifiers
,
5.8%
over ensemble models, and 11.3% over la
nguage models.
K
e
y
w
o
r
d
s
:
C
la
s
s
if
ic
a
ti
on
D
e
pr
e
s
s
io
n
M
a
c
hi
ne
l
e
a
r
ni
ng
M
e
nt
a
l
he
a
lt
h pr
e
di
c
ti
on
S
oc
ia
l
m
e
di
a
a
na
ly
ti
c
s
This is an
open
acce
ss artic
le unde
r the
CC BY
-
SA
license.
C
or
r
e
s
pon
di
n
g A
u
th
or
:
C
ha
it
hr
a
I
nda
va
r
a
V
e
nka
te
s
ha
gow
da
D
e
p
a
r
tm
e
nt
o
f
C
om
pu
t
e
r
S
c
i
e
n
c
e
a
nd
E
ng
in
e
e
r
in
g,
G
S
S
S
I
n
s
ti
tu
te
of
E
ng
in
e
e
r
in
g
a
n
d
T
e
c
hn
ol
o
gy
f
or
W
o
m
e
n
V
is
ve
s
va
r
a
y
a
T
e
c
hnol
ogi
c
a
l
U
ni
ve
r
s
it
y
B
e
la
ga
vi
, K
R
S
R
d,
M
e
ta
ga
ll
i,
M
y
s
ur
u, K
a
r
na
ta
ka
570016, I
ndi
a
E
m
a
il
:
c
ha
it
hr
a
.i
v@
gm
a
il
.c
om
1.
I
N
T
R
O
D
U
C
T
I
O
N
E
a
r
ly
de
te
c
ti
on
a
nd
c
or
r
e
c
t
c
la
s
s
if
ic
a
ti
on
of
d
e
pr
e
s
s
io
n
a
r
e
c
r
it
ic
a
l
f
or
e
f
f
e
c
ti
ve
in
te
r
ve
nt
io
n
a
nd
tr
e
a
tm
e
nt
,
a
s
de
pr
e
s
s
io
n
is
th
e
le
a
di
ng
c
a
us
e
of
di
s
a
bi
li
ty
gl
oba
ll
y
[
1]
.
I
de
nt
if
yi
ng
s
ym
pt
om
s
e
a
r
ly
on
pr
ovi
de
s
f
or
ti
m
e
ly
m
e
nt
a
l
he
a
lt
h
c
a
r
e
,
lo
w
e
r
in
g
th
e
c
ha
n
c
e
of
th
e
di
s
e
a
s
e
w
or
s
e
ni
ng
a
nd
pe
r
ha
ps
l
e
a
di
ng
to
m
or
e
s
e
r
io
us
di
f
f
ic
ul
ti
e
s
s
uc
h
a
s
s
ui
c
id
a
l
id
e
a
ti
on
or
lo
ng
-
te
r
m
im
pa
ir
m
e
nt
in
e
ve
r
yda
y
f
unc
ti
oni
ng
[
2]
–
[
5]
.
R
e
li
a
bl
e
c
la
s
s
if
ic
a
ti
on
of
de
pr
e
s
s
io
n
e
ns
ur
e
s
th
a
t
pe
opl
e
r
e
c
e
i
ve
a
ppr
opr
ia
te
c
a
r
e
ba
s
e
d
on
th
e
de
gr
e
e
a
nd
ty
pe
of
th
e
ir
a
il
m
e
nt
,
w
hi
c
h
im
pr
ove
s
tr
e
a
tm
e
nt
out
c
om
e
s
[
6]
.
T
he
r
e
a
r
e
va
r
io
us
li
m
it
a
ti
ons
to
e
xi
s
ti
ng
de
pr
e
s
s
io
n
de
te
c
ti
on
a
nd
c
la
s
s
if
ic
a
ti
on
s
ys
te
m
s
.
O
ne
bi
g
is
s
ue
is
th
a
t
s
oc
ia
l
m
e
di
a
da
ta
is
noi
s
y
a
nd
uns
tr
uc
tu
r
e
d,
w
it
h
c
ol
lo
qui
a
l
la
ngua
ge
,
s
la
ng,
a
nd
a
c
r
ony
m
s
,
m
a
ki
ng
it
di
f
f
ic
ul
t
to
r
e
li
a
bl
y
di
a
gnos
e
de
pr
e
s
s
io
n
s
ym
pt
om
s
.
M
a
ny
m
ode
ls
a
ls
o
s
tr
uggl
e
to
m
a
na
ge
bot
h
c
li
ni
c
a
l
a
nd
non
-
c
li
ni
c
a
l
d
a
ta
,
f
r
e
que
nt
ly
m
is
s
in
g
ke
y
in
f
or
m
a
ti
on
f
r
om
uns
tr
uc
tu
r
e
d
s
our
c
e
s
s
uc
h
a
s
s
oc
i
a
l
m
e
di
a
[
7]
.
F
ur
th
e
r
m
or
e
,
s
e
pa
r
a
ti
ng
Evaluation Warning : The document was created with Spire.PDF for Python.
I
nt
J
A
r
ti
f
I
nt
e
ll
I
S
S
N
:
2252
-
8938
D
e
te
c
ti
on and for
e
c
a
s
ti
ng of me
nt
al
he
al
th
di
s
or
d
e
r
s
u
s
in
g
…
(
C
hai
th
r
a I
ndav
ar
a V
e
nk
at
e
s
hagow
da
)
673
de
pr
e
s
s
io
n
f
r
om
d
if
f
e
r
e
n
t
f
or
m
of
m
e
n
ta
l
i
ll
ne
s
s
e
s
(
v
iz
.
a
n
xi
e
t
y
a
nd
b
ip
ol
a
r
d
is
or
de
r
)
,
r
e
m
a
in
s
d
if
f
ic
ul
t,
a
n
d
r
e
s
ul
ti
ng
in
m
is
di
a
gnos
is
.
M
a
ny
a
ppr
oa
c
he
s
f
a
i
l
to
a
nt
ic
i
pa
t
e
f
ut
u
r
e
m
e
n
ta
l
he
a
lt
h
d
is
or
de
r
s
by
f
oc
us
in
g
s
ol
e
ly
on
c
ur
r
e
nt
s
y
m
pt
o
m
s
.
F
i
na
ll
y
,
a
la
c
k
of
di
ve
r
s
e
tr
a
in
in
g
da
ta
s
e
ts
m
i
ght
le
a
d
to
bi
a
s
e
d
o
r
ove
r
f
it
te
d
m
ode
ls
,
w
hi
c
h
r
e
d
uc
e
s
th
e
ir
r
e
li
a
bi
li
ty
a
c
r
os
s
pop
ul
a
ti
ons
.
A
r
ti
f
ic
ia
l
i
nt
e
l
li
ge
nc
e
(
A
I
)
o
f
f
e
r
s
e
no
r
m
o
us
pot
e
nt
ia
l
in
t
he
e
a
r
ly
d
ia
gnos
is
a
nd
c
la
s
s
i
f
ic
a
t
io
n
o
f
de
pr
e
s
s
io
n,
a
ll
ow
i
ng
f
o
r
p
r
om
p
t
th
e
r
a
pi
e
s
vi
a
a
na
ly
s
is
of
va
r
ie
d
da
ta
s
our
c
e
s
s
uc
h a
s
s
oc
ia
l
m
e
di
a
,
voi
c
e
, a
nd
c
li
ni
c
a
l
r
e
c
o
r
ds
[
8]
–
[
11
]
.
H
ow
e
ve
r
,
obs
ta
c
le
s
in
c
lu
de
de
a
li
ng
w
it
h
no
is
y,
uns
t
r
uc
t
ur
e
d
da
ta
,
c
om
bi
ni
ng
c
li
ni
c
a
l
a
n
d
non
-
c
li
ni
c
a
l
in
f
or
m
a
ti
on,
a
nd
g
ua
r
a
n
te
e
in
g
m
ode
l
ge
ne
r
a
li
z
a
b
il
i
ty
a
c
r
os
s
va
r
ie
d
popu
la
ti
o
ns
.
F
u
r
th
e
r
m
o
r
e
,
id
e
nt
i
f
yi
ng
de
pr
e
s
s
io
n
f
r
om
ot
he
r
m
e
n
ta
l
he
a
lt
h
di
s
e
a
s
e
s
r
e
m
a
in
s
c
ha
ll
e
n
gi
ng
,
a
nd
f
or
e
c
a
s
ti
ng
f
ut
u
r
e
m
e
nt
a
l
he
a
lt
h
c
onc
e
r
ns
is
a
c
o
ns
ta
nt
l
y
de
ve
lo
pi
ng
ta
s
k
[
12
]
–
[
16
]
.
O
ve
r
c
om
in
g
th
e
s
e
p
r
obl
e
m
s
w
il
l
in
c
r
e
a
s
e
th
e
a
c
c
u
r
a
c
y
a
nd
e
f
f
e
c
ti
ve
ne
s
s
o
f
AI
-
p
ow
e
r
e
d s
ys
te
m
s
.
T
he
r
e
la
te
d
w
or
k
c
a
r
r
ie
d
out
to
w
a
r
ds
th
i
s
di
r
e
c
ti
on
a
r
e
a
s
f
ol
lo
w
s
:
K
im
e
t
al
.
[
17]
de
ve
lo
pe
d
a
c
la
s
s
if
ic
a
ti
on
m
ode
l
ba
s
e
d
on
te
xt
ua
l
e
le
m
e
nt
s
f
r
om
s
oc
ia
l
m
e
di
a
pos
ts
a
nd
a
c
hi
e
ve
d
s
ig
ni
f
ic
a
nt
a
c
c
ur
a
c
y
in
di
a
gnos
in
g
de
pr
e
s
s
e
d
s
ym
pt
om
s
,
de
m
ons
tr
a
ti
ng
th
e
pr
om
i
s
e
of
uns
tr
uc
tu
r
e
d
da
ta
f
or
m
e
nt
a
l
he
a
lt
h
a
ppl
ic
a
ti
ons
.
M
ount
z
our
is
e
t
al
.
[
18]
in
ve
s
ti
ga
te
d
th
e
us
e
of
de
e
p
le
a
r
ni
ng
(
D
L
)
m
ode
ls
,
s
pe
c
if
ic
a
ll
y
c
onvolut
io
na
l
ne
ur
a
l
ne
twor
ks
(
C
N
N
)
, t
o
de
te
c
t
s
a
dne
s
s
f
r
om
s
pe
e
c
h a
nd t
e
xt
da
ta
. K
a
bi
r
e
t
al
.
[
19]
p
r
opos
e
d
us
in
g
na
tu
r
a
l
la
ngua
ge
pr
oc
e
s
s
in
g
(
N
L
P
)
a
nd
m
a
c
hi
ne
le
a
r
ni
ng
(
M
L
)
a
ppr
oa
c
he
s
to
de
te
c
t
de
pr
e
s
s
io
n
-
r
e
la
te
d
te
r
m
s
in
pos
ts
.
M
ye
e
e
t
al
.
[
20]
s
ugge
s
te
d
a
hybr
id
m
ode
l
t
ha
t
us
e
s
M
L
a
nd
D
L
a
ppr
oa
c
he
s
to
id
e
nt
if
y
de
pr
e
s
s
io
n
in
us
e
r
-
ge
ne
r
a
te
d
c
ont
e
nt
on
s
oc
ia
l
m
e
di
a
pl
a
tf
or
m
s
.
S
of
ia
e
t
al
.
[
21]
de
m
ons
tr
a
te
d
th
e
pot
e
nt
ia
l
of
D
L
f
or
m
ode
li
ng
c
om
pl
ic
a
te
d
r
e
la
ti
ons
hi
ps
in
da
ta
f
or
im
pr
ovi
ng
de
pr
e
s
s
io
n
id
e
nt
if
ic
a
ti
on
us
in
g
uns
upe
r
vi
s
e
d
D
L
m
ode
ls
,
w
it
h
f
e
a
tu
r
e
e
xt
r
a
c
ti
on
a
nd
r
e
pr
e
s
e
nt
a
ti
on
le
a
r
ni
ng
pl
a
yi
ng
c
r
it
ic
a
l
r
ol
e
s
.
X
u
e
t
al
.
[
22]
s
ugge
s
te
d
a
M
L
-
ba
s
e
d
a
ppr
oa
c
h
f
or
di
a
gnos
in
g
de
pr
e
s
s
io
n
in
c
li
ni
c
a
l
s
e
tt
in
gs
by
a
na
ly
z
in
g
s
tr
uc
tu
r
e
d da
ta
s
uc
h a
s
m
e
di
c
a
l
r
e
c
or
ds
. A
m
a
na
t
e
t
al
.
[
23]
us
e
d r
e
c
ur
r
e
nt
ne
ur
a
l
ne
twor
ks
(
R
N
N
s
)
t
o c
la
s
s
if
y
de
pr
e
s
s
io
n
f
r
om
te
xt
-
ba
s
e
d
da
ta
in
s
oc
ia
l
m
e
di
a
f
or
um
s
.
W
a
n
g
e
t
al
.
[
24]
in
ve
s
ti
ga
te
d
th
e
in
te
gr
a
ti
on
of
A
I
a
nd
w
e
a
r
a
bl
e
te
c
hnol
ogi
e
s
f
or
de
pr
e
s
s
io
n
di
a
gnos
is
.
T
he
y
c
r
e
a
te
d
a
s
ys
te
m
th
a
t
de
te
c
ts
de
pr
e
s
s
iv
e
s
t
a
te
s
in
r
e
a
l
ti
m
e
by me
r
g
in
g phys
io
lo
gi
c
a
l
in
put
s
w
it
h
M
L
a
lg
or
i
th
m
s
, r
e
ve
a
li
ng t
he
pr
om
is
e
o
f
w
e
a
r
a
bl
e
t
e
c
hnol
ogy
in
m
e
nt
a
l
he
a
lt
h
m
oni
to
r
in
g.
L
in
e
t
a
l.
[
25]
in
ve
s
ti
ga
te
d
s
pe
e
c
h
-
ba
s
e
d
de
pr
e
s
s
io
n
de
te
c
ti
on
w
it
h
de
e
p
ne
ur
a
l
ne
twor
ks
(
D
N
N
s
)
.
T
he
ir
r
e
s
e
a
r
c
h
s
how
n
th
a
t
D
N
N
s
m
a
y
e
f
f
ic
ie
nt
ly
c
a
pt
ur
e
s
m
a
ll
voc
a
l
c
ue
s
a
s
s
oc
ia
te
d
w
it
h
s
a
dne
s
s
,
pr
ovi
di
ng
a
non
-
in
va
s
iv
e
w
a
y
f
or
e
a
r
ly
di
a
gnos
is
in
c
l
in
ic
a
l
s
e
tt
in
gs
.
H
a
d
z
ic
e
t
al
.
[
26]
us
e
d
tr
a
n
s
f
e
r
le
a
r
ni
ng
to
de
te
c
t
de
pr
e
s
s
io
n
in
te
xt
da
ta
,
f
in
e
-
tu
ni
ng
a
p
r
e
-
tr
a
in
e
d
la
ngua
ge
m
ode
l
known
a
s
bi
di
r
e
c
ti
ona
l
e
nc
ode
r
r
e
pr
e
s
e
nt
a
ti
ons
f
r
om
tr
a
ns
f
or
m
e
r
s
(
B
E
R
T
)
.
T
hi
s
s
tr
a
te
gy
c
ons
id
e
r
a
bl
y
in
c
r
e
a
s
e
d
th
e
a
c
c
ur
a
c
y
of
te
xt
-
ba
s
e
d de
pr
e
s
s
io
n di
a
gnos
is
, d
e
m
ons
tr
a
ti
ng t
he
us
e
of
t
r
a
ns
f
or
m
e
r
m
ode
ls
i
n m
e
nt
a
l
he
a
lt
h a
ppl
ic
a
ti
ons
.
T
he
r
e
s
e
a
r
c
h
pr
obl
e
m
s
in
de
pr
e
s
s
io
n
id
e
nt
if
ic
a
ti
on
ut
il
iz
in
g
A
I
,
M
L
,
a
nd
D
L
in
c
lu
de
th
e
di
f
f
ic
ul
t
y
of
e
f
f
e
c
ti
ve
ly
r
e
a
di
ng
uns
tr
uc
tu
r
e
d,
noi
s
y
d
a
ta
f
r
om
s
oc
ia
l
m
e
di
a
a
nd
ot
he
r
in
f
or
m
a
l
s
our
c
e
s
,
w
hi
c
h
f
r
e
que
nt
ly
r
e
s
ul
ts
i
n m
is
c
la
s
s
if
ic
a
ti
on. Anothe
r
pr
obl
e
m
i
s
t
ha
t
e
xi
s
ti
ng mode
ls
c
a
nnot
pr
ope
r
ly
c
om
bi
ne
bot
h
c
li
ni
c
a
l
a
nd
non
-
c
li
ni
c
a
l
da
ta
,
li
m
it
in
g
th
e
s
c
op
e
a
nd
a
c
c
ur
a
c
y
of
pr
e
di
c
ti
ons
.
F
ur
th
e
r
m
or
e
,
th
e
la
c
k
of
va
s
t,
di
ve
r
s
e
,
a
nd
r
e
pr
e
s
e
nt
a
ti
ve
da
ta
s
e
ts
a
dopt
e
d
f
or
tr
a
in
in
g
m
ode
l
s
le
a
ds
to
bi
a
s
e
d
or
ove
r
f
it
te
d
s
ys
te
m
s
,
w
hi
c
h
r
e
duc
e
s
th
e
ir
ge
ne
r
a
li
z
a
bi
li
ty
a
c
r
os
s
popula
ti
ons
.
M
ode
ls
a
ls
o
ha
ve
di
f
f
ic
ul
ty
di
s
ti
ngui
s
hi
ng
b
e
twe
e
n
de
pr
e
s
s
io
n
a
nd
ot
he
r
m
e
nt
a
l
he
a
lt
h
pr
obl
e
m
s
,
w
hi
c
h
le
a
ds
to
m
is
di
a
gnos
e
s
.
F
ur
th
e
r
m
or
e
,
w
hi
le
e
xi
s
ti
ng
m
e
th
ods
f
oc
us
on
r
e
c
ogni
z
in
g
c
ur
r
e
nt
s
ym
pt
om
s
,
pr
e
di
c
ti
ng
f
ut
ur
e
m
e
nt
a
l
he
a
lt
h
di
f
f
ic
ul
ti
e
s
r
e
m
a
in
s
a
di
f
f
ic
ul
t
c
ha
ll
e
nge
t
ha
t
ne
c
e
s
s
it
a
te
s
m
or
e
s
ophi
s
ti
c
a
te
d f
or
e
c
a
s
ti
ng mode
ls
.
T
he
pr
opos
e
d
s
tu
dy
a
im
s
f
or
c
r
e
a
ti
ng
a
n
in
nova
ti
ve
a
na
ly
ti
c
a
l
f
r
a
m
e
w
or
k
th
a
t
not
onl
y
de
te
c
ts
de
pr
e
s
s
io
n
but
a
ls
o
pr
e
di
c
ts
th
e
li
ke
li
hood
of
f
ut
u
r
e
m
e
nt
a
l
h
e
a
lt
h
c
onc
e
r
ns
(
bi
pol
a
r
di
s
or
de
r
,
a
nxi
e
ty
,
a
nd
a
tt
e
nt
io
n
de
f
ic
it
hype
r
a
c
ti
vi
ty
d
is
or
de
r
(
ADHD
)
)
us
in
g
s
oc
ia
l
m
e
di
a
da
ta
.
T
hi
s
a
ppr
oa
c
h
s
e
e
k
s
to
pr
ovi
de
a
c
om
pr
e
he
ns
iv
e
a
nd
e
f
f
ic
ie
nt
to
ol
f
or
de
te
c
ti
on
a
nd
m
oni
to
r
in
g
of
m
e
nt
a
l
he
a
lt
h
di
s
or
de
r
s
in
e
a
r
ly
s
ta
ge
us
in
g
a
n
in
di
vi
dua
l'
s
s
oc
ia
l
m
e
di
a
in
f
or
m
a
ti
on.
T
he
s
tu
dy
a
ddr
e
s
s
e
s
t
he
di
f
f
ic
ul
ty
of
di
a
gnos
in
g
m
e
nt
a
l
di
s
e
a
s
e
s
in
bot
h
c
li
ni
c
a
l
a
nd
non
-
c
li
ni
c
a
l
s
it
ua
ti
ons
,
br
oa
de
ni
ng
th
e
s
c
ope
of
m
e
nt
a
l
he
a
lt
h
r
e
s
e
a
r
c
h
be
yond
ty
pi
c
a
l
c
li
ni
c
a
l
da
ta
.
T
he
not
a
bl
e
c
ont
r
ib
ut
io
n
of
pr
opos
e
d
s
tu
dy
is
a
s
f
ol
lo
w
s
:
i)
th
e
pr
e
s
e
nt
e
d
m
ode
l
in
tr
oduc
e
s
a
two
-
s
te
p
c
la
s
s
if
ic
a
ti
on
s
tr
a
te
gy
th
a
t
c
om
bi
ne
s
M
L
a
nd
la
ngua
ge
m
ode
l
te
c
hni
que
s
to
a
s
s
e
s
s
bot
h
th
e
c
ur
r
e
nt
a
nd
f
ut
ur
e
m
e
nt
a
l
he
a
lt
h
c
ondi
ti
on
of
in
di
vi
dua
ls
us
in
g
s
o
c
ia
l
m
e
di
a
pos
ts
;
ii
)
unl
ik
e
e
xi
s
ti
ng
m
e
th
odol
ogi
e
s
th
a
t
la
r
ge
ly
f
oc
us
on
c
li
ni
c
a
l
da
ta
,
th
e
pr
opos
e
d
s
tu
dy
e
xpl
or
e
s
bot
h
c
li
ni
c
a
l
a
nd
non
-
c
li
ni
c
a
l
s
o
c
ia
l
m
e
di
a
s
it
ua
ti
ons
.
T
hi
s
a
ll
ow
s
f
or
a
m
or
e
th
or
ough
knowle
dge
o
f
m
e
nt
a
l
he
a
lt
h
;
ii
i)
a
f
unda
m
e
nt
a
l
f
e
a
tu
r
e
o
f
th
e
pr
opos
e
d
s
ys
te
m
is
it
s
a
bi
li
ty
to
f
or
e
s
e
e
pot
e
nt
ia
l
m
e
nt
a
l
he
a
l
th
c
onc
e
r
ns
in
th
e
f
ut
ur
e
,
r
a
th
e
r
th
a
n
s
im
pl
y
r
e
c
ogni
z
in
g
e
xi
s
ti
ng
c
ondi
ti
ons
;
iv
)
th
e
s
tu
dy
pr
ovi
de
s
m
or
e
a
c
c
ur
a
c
y
th
r
ough
th
or
ough
da
ta
pr
e
pr
oc
e
s
s
in
g,
s
uc
h
a
s
r
e
m
ovi
ng
noi
s
e
,
s
ta
nda
r
di
z
in
g
la
ngua
ge
,
a
nd
tr
a
ns
la
ti
ng
da
ta
in
to
f
e
a
tu
r
e
r
e
pr
e
s
e
nt
a
ti
ons
;
a
nd
v)
th
e
s
ugge
s
te
d
s
ys
te
m
i
s
e
va
lu
a
te
d
u
s
in
g
a
c
c
ur
a
c
y,
F
1
-
s
c
or
e
,
a
n
d
pr
e
c
is
io
n.
I
t
be
a
ts
pr
e
vi
ous
a
lg
or
it
hm
s
in
pr
e
di
c
ti
ng
f
ut
ur
e
m
e
nt
a
l
he
a
lt
h
di
s
or
de
r
s
a
nd
di
a
gno
s
in
g
de
pr
e
s
s
io
n.
T
h
e
c
on
s
e
c
ut
iv
e
s
e
c
ti
on
di
s
c
us
s
e
s
a
bout
a
dopt
e
d r
e
s
e
a
r
c
h m
e
th
odol
ogy towa
r
ds
i
t
s
i
m
pl
e
m
e
nt
a
ti
on.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
2252
-
8938
I
nt
J
A
r
ti
f
I
nt
e
ll
,
V
ol
. 15, No. 1, Febr
ua
r
y 2026
:
672
-
680
674
2.
M
E
T
H
O
D
T
he
m
e
th
odol
ogy
us
e
d
in
th
is
w
or
k
a
tt
e
m
pt
s
to
c
r
e
a
te
a
n
e
f
f
e
c
ti
ve
s
ys
te
m
f
or
di
a
gnos
in
g
a
nd
f
or
e
c
a
s
ti
ng
m
e
nt
a
l
he
a
lt
h
di
s
or
de
r
s
,
s
uc
h
a
s
d
e
pr
e
s
s
io
n,
u
s
in
g
s
oc
ia
l
m
e
di
a
da
ta
a
s
s
how
n
in
F
ig
ur
e
1.
T
h
e
a
ppr
oa
c
h
be
gi
ns
w
it
h
th
e
a
c
qui
s
it
io
n
of
a
br
oa
d
da
ta
s
e
t
f
r
om
c
li
ni
c
a
l
a
nd
non
-
c
li
ni
c
a
l
s
oc
ia
l
m
e
di
a
c
ont
e
xt
s
,
w
hi
c
h i
s
t
he
n pr
e
pr
oc
e
s
s
e
d a
nd t
e
xt
nor
m
a
li
z
e
d t
o i
m
pr
ove
da
ta
qua
li
ty
. F
e
a
tu
r
e
e
xt
r
a
c
ti
on i
s
c
a
r
r
ie
d out wit
h
a
dva
nc
e
d
te
c
hni
que
s
s
uc
h
a
s
te
r
m
f
r
e
que
nc
y a
nd
in
ve
r
s
e
do
c
u
m
e
nt
f
r
e
que
nc
y
(
T
F
-
I
D
F
)
f
or
c
onve
nt
io
na
l
M
L
f
r
a
m
e
w
or
k
a
lo
ng
w
it
h
e
m
be
ddi
ng
ve
c
to
r
s
f
or
la
ngua
ge
m
ode
l
a
ppr
oa
c
he
s
.
F
in
a
ll
y,
a
two
-
s
te
p
c
la
s
s
if
ic
a
ti
on
te
c
hni
que
is
us
e
d,
c
om
bi
ni
ng
s
e
v
e
r
a
l
M
L
a
nd
la
ngua
g
e
m
ode
l
s
to
im
pr
ove
pr
e
di
c
ti
ve
a
c
c
ur
a
c
y
a
nd
pr
e
di
c
t
f
ut
ur
e
m
e
nt
a
l
he
a
lt
h di
f
f
ic
ul
ti
e
s
.
F
ig
ur
e
1
e
la
bor
a
te
s
a
de
t
a
il
e
d
w
or
kf
lo
w
th
a
t
be
gi
ns
w
it
h
te
xt
pr
e
pr
oc
e
s
s
in
g
f
ol
lo
w
e
d
by
e
xt
r
a
c
ti
on
of
s
ig
ni
f
ic
a
nt
f
e
a
tu
r
e
s
a
dopt
in
g
T
F
-
I
D
F
to
w
a
r
ds
c
onve
nt
io
na
l
M
L
m
ode
ls
w
hi
le
pr
e
-
tr
a
in
e
d
la
ngua
ge
m
ode
ls
is
us
e
d
f
or
c
ont
e
xt
ua
l
e
m
be
ddi
ng.
T
he
in
it
ia
l
s
ta
ge
de
t
e
c
ts
e
xi
s
ti
ng
di
s
or
de
r
w
hi
le
th
e
s
e
c
onda
r
y
s
ta
ge
pr
e
di
c
ts
th
e
upc
om
in
g
th
r
e
a
ts
d
e
pe
ndi
ng
upon
s
o
c
ia
l
a
nd
te
m
por
a
l
be
ha
vi
or
.
T
he
m
ode
l
a
l
s
o
c
om
bi
ne
s
m
ul
ti
pl
e
c
la
s
s
if
ie
r
s
e
.g.
B
E
R
T
,
r
obus
t
bi
di
r
e
c
ti
ona
l
e
nc
ode
r
r
e
pr
e
s
e
nt
a
ti
ons
f
r
om
tr
a
ns
f
or
m
e
r
s
(
R
oB
E
R
T
a
)
,
gr
a
di
e
nt
bo
os
ti
ng,
r
a
ndom
f
or
e
s
t,
a
nd
lo
gi
s
ti
c
r
e
gr
e
s
s
i
on
a
dopt
in
g
e
ns
e
m
bl
e
s
tr
a
te
gy
of
ha
r
d
vot
in
g.
T
hi
s
of
f
e
r
s
a
n a
s
s
ur
a
nc
e
t
ow
a
r
ds
s
tr
e
ngt
he
ni
ng of
e
a
c
h f
r
a
m
e
w
or
k t
ow
a
r
ds
opt
im
a
l
pe
r
f
or
m
a
nc
e
ga
in
.
F
ig
ur
e
1. C
om
pr
e
he
ns
iv
e
e
va
lu
a
ti
on pla
tf
or
m
f
or
di
a
gnos
is
de
p
r
e
s
s
io
n
2.1. Dat
a aggr
e
gat
io
n
w
it
h
p
r
e
p
r
oc
e
s
s
in
g
T
he
pr
e
li
m
in
a
r
y
im
pl
e
m
e
nt
a
ti
on
s
ta
ge
is
to
c
ol
le
c
t
publ
ic
ly
a
va
il
a
bl
e
da
ta
a
bout
m
e
nt
a
l
he
a
lt
h
de
r
iv
e
d
f
r
om
s
oc
ia
l
m
e
di
a
.
T
he
da
ta
s
e
t
w
il
l
in
c
lu
de
s
o
c
ia
l
m
e
di
a
pos
ts
f
r
om
pl
a
tf
or
m
s
li
ke
R
e
ddi
t,
T
w
it
te
r
,
a
nd
ot
he
r
onl
in
e
f
or
u
m
s
,
w
it
h
a
f
oc
us
on
in
f
o
r
m
a
ti
on
r
e
la
te
d
to
de
pr
e
s
s
io
n,
bi
pol
a
r
di
s
or
de
r
,
a
nxi
e
ty
,
a
nd
A
D
H
D
.
E
a
c
h
pos
t
is
a
te
xt
doc
um
e
nt
th
a
t
m
us
t
be
pr
oc
e
s
s
e
d
a
nd
pr
e
pa
r
e
d
be
f
or
e
f
ur
th
e
r
in
ve
s
ti
ga
ti
on.
L
e
t
=
{
1
,
2
,
.
.
.
,
}
r
e
pr
e
s
e
nt
th
e
d
a
ta
s
e
t
w
it
h
N
doc
um
e
nt
s
(
s
o
c
ia
l
m
e
di
a
po
s
t
s
)
.
E
a
c
h
doc
um
e
nt
di
i
s
pr
oc
e
s
s
e
d
to
e
xt
r
a
c
t
f
e
a
tu
r
e
s
.
=
{
1
,
2
,
.
.
.
,
}
,
w
he
r
e
m
is
th
e
num
be
r
o
f
pr
e
p
r
oc
e
s
s
e
d
w
or
ds
or
to
ke
ns
.
T
he
a
c
qui
r
e
d
da
ta
goe
s
th
r
ough
m
a
ny
pr
e
pr
oc
e
s
s
in
g
pr
oc
e
s
s
e
s
(
s
to
pw
or
d
r
e
m
ova
l,
to
ke
ni
z
a
ti
on,
te
xt
nor
m
a
li
z
a
ti
on,
a
nd
noi
s
e
e
li
m
in
a
ti
on)
to
a
s
s
ur
e
it
s
qua
li
ty
a
nd
e
li
m
in
a
te
noi
s
e
,
w
hi
c
h
c
a
n
ha
ve
a
de
tr
im
e
nt
a
l
in
f
lu
e
nc
e
d on the
pe
r
f
or
m
a
nc
e
of
t
he
f
r
a
m
e
w
or
k.
I
t
s
houl
d
be
not
e
d
th
a
t
c
li
ni
c
a
l
da
ta
r
e
f
e
r
s
to
th
e
pos
t
of
s
oc
ia
l
m
e
di
a
th
a
t
di
s
c
r
e
te
ly
m
e
nt
io
n
c
ondi
ti
on
of
m
e
nt
a
l
he
a
lt
h,
t
r
e
a
tm
e
nt
,
a
s
w
e
ll
a
s
s
ym
pt
om
s
.
F
or
a
n
e
xa
m
pl
e
:
“
I
w
a
s
di
a
gnos
e
d
w
it
h
de
pr
e
s
s
io
n
la
s
t
ye
a
r
”
or
“
M
y
doc
to
r
r
e
c
om
m
e
nde
d
m
e
di
c
a
ti
on
f
or
de
pr
e
s
s
io
n.”
O
n
th
e
ot
he
r
ha
nd,
th
e
non
-
c
li
ni
c
a
l
da
ta
in
vol
ve
s
e
ve
r
yda
y
s
oc
ia
l
po
s
t
th
a
t
m
a
y
s
ho
w
c
a
s
e
s
ta
te
s
of
m
e
nt
a
l
he
a
lt
h
in
di
r
e
c
tl
y
e
.g.,
“
I
f
e
e
l
non
-
e
ne
r
ge
ti
c
to
w
or
k”
or
“
I
c
oul
dn’
t
ge
t
be
tt
e
r
s
le
e
p
”
.
I
nc
lu
s
io
n
of
th
e
s
e
two
c
la
s
s
e
s
pe
r
m
it
s
th
e
s
ys
te
m
t
o l
e
a
r
n pa
tt
e
r
ns
of
s
ubt
le
b
e
ha
vi
or
a
nd l
e
a
r
n ove
r
t
a
s
s
o
c
ia
te
d w
it
h m
e
nt
a
l
w
e
ll
-
be
in
g.
2.2. F
e
at
u
r
e
e
xt
r
ac
t
io
n
T
he
pr
e
pr
oc
e
s
s
e
d
da
ta
is
r
e
qui
r
e
d
to
be
s
ui
ta
bl
e
f
or
M
L
-
f
r
ie
n
dl
y
f
or
m
a
t.
W
e
us
e
two
m
a
in
f
e
a
tu
r
e
e
xt
r
a
c
ti
on
m
e
th
ods
:
TF
-
I
D
F
f
or
M
L
a
nd
e
m
be
ddi
ng
ve
c
to
r
s
f
or
la
ngua
ge
m
ode
li
ng.
T
hi
s
m
e
th
od
is
a
Evaluation Warning : The document was created with Spire.PDF for Python.
I
nt
J
A
r
ti
f
I
nt
e
ll
I
S
S
N
:
2252
-
8938
D
e
te
c
ti
on and for
e
c
a
s
ti
ng of me
nt
al
he
al
th
di
s
or
d
e
r
s
u
s
in
g
…
(
C
hai
th
r
a I
ndav
ar
a V
e
nk
at
e
s
hagow
da
)
675
s
ta
ti
s
ti
c
a
l
m
e
a
s
ur
e
th
a
t
e
va
lu
a
te
s
th
e
s
ig
ni
f
ic
a
nc
e
of
a
te
r
m
s
pr
e
s
e
nt
in
a
doc
um
e
nt
in
r
e
la
ti
on
to
a
la
r
ge
r
c
or
pus
of
i
nf
or
m
a
ti
on. T
he
m
a
th
e
m
a
ti
c
a
l
f
or
m
ul
a
ti
on f
or
T
F
-
I
D
F
i
s
pr
ovi
de
d by
(
1)
.
−
(
,
)
=
(
,
)
×
(
)
(
1)
A
s
in
(
1)
,
th
e
c
om
put
a
ti
on
o
f
f
ir
s
t
a
nd
s
e
c
ond
c
om
pone
nt
is
c
a
r
r
ie
d
out
a
s
f
ol
lo
w
:
(
,
)
=
(
,
)
∑
(
,
)
−
1
a
nd
(
)
=
(
(
)
)
r
e
s
pe
c
ti
ve
ly
. T
h
e
f
ir
s
t
c
om
pone
nt
T
F
(
w
,
d
)
r
e
pr
e
s
e
nt
s
t
e
r
m
w
f
r
e
que
nc
e
pr
e
s
e
nt
i
n
d
doc
um
e
nt
w
hi
le
I
D
F
(
w
,
d
)
r
e
pr
e
s
e
nt
s
in
ve
r
s
e
doc
um
e
nt
f
r
e
que
nc
y
f
or
w
w
or
ds
w
hi
le
D
F
(
w
)
r
e
pr
e
s
e
nt
s
qua
nt
it
y
of
doc
um
e
nt
w
hi
le
N
i
s
to
ta
l
do
c
um
e
nt
s
.
D
L
-
ba
s
e
d
a
lg
or
it
hm
s
us
e
pr
e
-
tr
a
in
e
d
la
ngu
a
ge
m
ode
l
s
to
c
onve
r
t
e
a
c
h
pos
t
in
to
a
w
or
d
e
m
be
ddi
ng
ve
c
to
r
.
T
he
s
e
m
o
de
ls
tr
a
ns
f
or
m
e
a
c
h
w
or
d
or
s
e
nt
e
nc
e
in
to
a
c
ont
in
uous
ve
c
to
r
c
ont
a
in
in
g
s
e
m
a
nt
ic
a
s
s
o
c
ia
ti
ons
.
P
a
s
s
in
g
a
doc
um
e
nt
d
i
th
r
ough
a
pr
e
-
tr
a
in
e
d
la
ngua
ge
m
ode
l
yi
e
ld
s
th
e
e
m
be
ddi
ng
ve
c
to
r
E
i
.
T
he
e
m
be
ddi
ng
ve
c
to
r
c
a
pt
ur
e
s
m
or
e
s
e
m
a
nt
ic
in
f
or
m
a
ti
on
th
a
n
ba
s
ic
f
r
e
que
nc
y
-
ba
s
e
d a
ppr
oa
c
he
s
s
uc
h
a
s
T
F
-
I
D
F
.
2.3. M
od
e
l
d
e
s
ig
n
an
d
c
la
s
s
if
ic
at
io
n
D
ur
in
g
th
e
c
la
s
s
if
ic
a
ti
on
pha
s
e
of
th
e
pr
oc
e
s
s
,
va
r
io
us
M
L
a
nd
la
ngua
ge
m
ode
l
s
a
r
e
tr
a
in
e
d
to
pr
e
di
c
t
th
e
s
e
ve
r
it
y
of
de
pr
e
s
s
io
n
a
nd
ot
he
r
m
e
nt
a
l
he
a
lt
h
c
ondi
ti
ons
.
T
w
o
s
or
ts
of
c
la
s
s
if
ic
a
ti
on
a
r
e
us
e
d:
c
la
s
s
if
ic
a
ti
on
of
c
ur
r
e
nt
di
s
or
de
r
s
t
a
tu
s
a
nd
pr
e
di
c
ti
ng
of
f
ut
ur
e
m
e
nt
a
l
he
a
lt
h
s
ta
tu
s
.
T
he
s
ugge
s
te
d
s
y
s
te
m
ut
il
iz
e
s
va
r
io
us
M
L
m
ode
ls
lo
gi
s
ti
c
r
e
gr
e
s
s
io
n
,
s
uppor
t
ve
c
to
r
m
a
c
hi
ne
s
,
r
a
ndom f
or
e
s
ts
,
k
-
ne
a
r
e
s
t
ne
ig
hbor
s
,
a
nd
gr
a
di
e
nt
boos
ti
ng.
W
e
us
e
la
ngua
ge
m
ode
ls
to
ga
in
a
gr
e
a
te
r
knowle
dge
of
s
e
m
a
nt
ic
s
.
T
he
out
put
of
th
e
s
e
m
ode
ls
, a
n
e
m
be
ddi
ng ve
c
to
r
E
i
, i
s
f
e
d i
nt
o a
c
la
s
s
if
ic
a
ti
o
n l
a
ye
r
t
o f
or
e
c
a
s
t
th
e
l
ik
e
li
hood of
e
a
c
h c
la
s
s
.
T
he
m
ode
l
pr
e
di
c
ts
P
(
c
∣
d
i
)
ba
s
e
d on the
po
s
t
di
, w
it
h
c
r
e
pr
e
s
e
n
ti
ng t
he
c
la
s
s
(
e
.g., de
pr
e
s
s
io
n
a
nd
a
nxi
e
ty
)
. A
S
of
tM
a
x
f
unc
ti
on de
te
r
m
in
e
s
t
he
l
a
ngua
ge
m
ode
l'
s
out
put
f
or
e
a
c
h c
la
s
s
.
(
|
)
=
(
,
)
∑
(
′
,
)
′
(
2)
A
c
c
or
di
ng
to
(
2)
,
th
e
s
c
or
e
of
c
la
s
s
c
f
or
doc
um
e
nt
di
i
s
r
e
pr
e
s
e
nt
e
d
by
f(
c
,d
i
)
,
w
he
r
e
a
s
P
(
c
∣
d
i
)
pr
ovi
de
s
th
e
pr
oba
bi
li
ty
di
s
tr
ib
ut
io
n
a
c
r
os
s
a
ll
pos
s
ib
le
c
la
s
s
e
s
.
A
f
te
r
tr
a
in
in
g
m
ul
ti
pl
e
m
ode
ls
,
w
e
us
e
dua
l
-
c
la
s
s
c
l
a
s
s
if
ic
a
ti
on
vi
z
.
i)
c
ur
r
e
nt
di
s
or
de
r
pr
e
di
c
ti
on
f
or
id
e
nt
if
yi
ng
m
e
nt
a
l
he
a
lt
h
c
ondi
ti
on
(
e
.g.,
de
pr
e
s
s
io
n
a
nd
a
nxi
e
ty
)
a
nd
ii
)
f
or
e
c
a
s
ts
f
ut
ur
e
m
e
nt
a
l
di
s
e
a
s
e
s
us
in
g
hi
s
to
r
y
pos
ts
a
nd
id
e
nt
if
ie
d
pa
tt
e
r
ns
.
T
o
im
pr
ove
pr
e
di
c
ti
on
a
c
c
ur
a
c
y,
w
e
e
m
pl
oy
te
c
hni
que
s
of
e
ns
e
m
bl
e
le
a
r
ni
ng
(
ha
r
d
vot
in
g
a
nd
s
ta
c
ki
ng)
. T
he
ha
r
d voti
ng a
ppr
oa
c
h i
nvol
ve
s
nume
r
ous
c
la
s
s
if
ie
r
s
vot
in
g on the
pr
e
di
c
te
d c
la
s
s
, a
nd t
he
c
la
s
s
w
it
h t
he
hi
ghe
s
t
num
be
r
of
c
ons
e
ns
u
s
e
s
i
s
c
hos
e
n a
s
t
he
e
nd
-
le
ve
l
pr
e
di
c
ti
on.
3.
A
C
C
O
M
P
L
I
S
H
E
D
R
E
S
U
L
T
S
T
he
s
im
ul
a
ti
ons
a
r
e
c
a
r
r
ie
d
out
on
a
hi
gh
-
pe
r
f
or
m
a
nc
e
s
e
r
ve
r
w
it
h
s
tr
ong
ha
r
dw
a
r
e
s
pe
c
s
.
T
h
e
s
e
r
ve
r
ha
d
a
n
I
nt
e
l
X
e
on
S
il
ve
r
4210R
C
P
U
w
it
h
10
c
or
e
s
ho
s
te
d
a
t
a
ba
s
e
f
r
e
que
nc
y
of
2.40
G
H
z
a
nd
a
n
N
V
I
D
I
A
T
e
s
la
T
4
G
P
U
w
it
h
16
G
B
of
V
R
A
M
,
a
ll
ow
in
g
f
o
r
f
a
s
te
r
tr
a
in
in
g
o
f
la
ngua
ge
m
ode
ls
in
c
lu
di
ng
B
E
R
T
,
R
oB
E
R
T
a
.
I
n
a
ddi
ti
on,
th
e
s
e
r
ve
r
c
ont
a
in
e
d
64
G
B
of
D
D
R
4
R
A
M
a
nd
a
2
T
B
S
S
D
f
or
qui
c
k
da
ta
r
e
tr
ie
va
l
a
nd
m
ode
l
c
h
e
c
kpoi
nt
s
to
r
a
ge
.
T
h
e
s
e
c
r
it
e
r
ia
e
na
bl
e
d
th
e
e
f
f
ic
ie
nt
pr
oc
e
s
s
in
g
of
e
nor
m
ous
d
a
ta
s
e
t
s
,
gua
r
a
nt
e
e
in
g
th
a
t
m
ode
l
tr
a
in
in
g
a
nd
te
s
ti
ng
w
e
r
e
c
om
pl
e
te
d
w
it
hi
n
to
le
r
a
bl
e
ti
m
e
c
ons
tr
a
in
ts
,
e
s
pe
c
ia
ll
y
w
he
n
de
a
li
ng
w
it
h
s
ophi
s
ti
c
a
te
d
m
ode
ls
s
uc
h
a
s
l
a
ngua
ge
m
ode
ls
a
nd
e
ns
e
m
bl
e
le
a
r
ni
ng
te
c
hni
que
s
.
T
he
s
of
twa
r
e
s
ta
c
k
ope
r
a
te
d
on
U
bunt
u
20.04
L
T
S
w
h
e
r
e
P
yt
hon
3.8
is
us
e
d
in
f
or
m
of
pr
ogr
a
m
m
in
g
la
ngua
ge
.
S
e
ve
r
a
l
li
br
a
r
ie
s
a
nd
f
r
a
m
e
w
or
ks
w
e
r
e
us
e
d,
in
c
lu
di
ng
s
c
ik
it
-
le
a
r
n
f
or
im
pl
e
m
e
nt
in
g
c
la
s
s
ic
a
l
M
L
m
ode
ls
li
ke
lo
gi
s
ti
c
r
e
gr
e
s
s
io
n
m
e
th
od,
s
uppor
t
ve
c
to
r
m
a
c
hi
ne
,
a
nd
k
-
ne
a
r
e
s
t
ne
ig
hbor
s
;
T
e
ns
or
F
lo
w
a
nd
P
yT
or
c
h
f
or
tr
a
in
in
g
a
nd
f
in
e
-
tu
ni
ng
la
ngua
ge
m
ode
ls
li
ke
B
E
R
T
,
R
oB
E
R
T
a
;
a
nd
X
G
B
oos
t
a
nd
L
ig
ht
G
B
M
f
o
r
e
ns
e
m
bl
e
le
a
r
ni
ng.
N
a
tu
r
a
l
la
ngua
g
e
to
ol
ki
t
(
N
L
T
K
)
a
nd
s
pa
C
y
ha
ndl
e
d
te
xt
pr
e
pr
oc
e
s
s
in
g
a
nd
f
e
a
tu
r
e
e
xt
r
a
c
ti
on.
F
ur
th
e
r
,
th
e
m
ode
l
us
e
s
H
uggi
ng
F
a
c
e
'
s
T
r
a
ns
f
or
m
e
r
s
L
ib
r
a
r
y
to
gi
ve
e
a
s
y
a
c
c
e
s
s
to
s
ta
nda
r
d
pr
e
tr
a
in
e
d
m
ode
ls
.
F
in
a
ll
y,
M
a
tp
lo
tl
ib
a
nd
S
e
a
bor
n
w
e
r
e
ut
i
li
z
e
d
to
vi
s
ua
ll
y
r
e
pr
e
s
e
nt
m
ode
l
pe
r
f
o
r
m
a
nc
e
m
e
tr
ic
s
a
nd F
1
-
s
c
or
e
s
.
T
o
de
te
c
t
c
ont
e
m
por
a
r
y
m
e
nt
a
l
di
s
e
a
s
e
s
(
de
pr
e
s
s
io
n,
a
nxi
e
ty
,
bi
pol
a
r
di
s
or
de
r
,
a
nd
A
D
H
D
)
,
th
e
m
ode
ls
w
e
r
e
ta
ught
to
c
a
te
gor
iz
e
s
oc
ia
l
m
e
di
a
pos
ts
.
U
s
in
g
s
ta
nda
r
d
da
ta
s
e
t
[
27]
,
th
e
f
in
di
ngs
a
r
e
de
s
c
r
ib
e
d
in
T
a
bl
e
1,
w
hi
c
h
s
how
s
th
e
a
ve
r
a
ge
F
1
-
s
c
or
e
f
or
e
a
c
h
di
s
e
a
s
e
a
c
r
os
s
a
ll
m
ode
ls
.
T
h
e
f
ir
s
t
obj
e
c
ti
ve
of
th
e
s
tu
dy
is
to
de
te
r
m
in
e
th
e
p
r
e
s
e
nt
s
ta
te
of
m
e
nt
a
l
di
s
or
de
r
o
r
i
gi
na
ti
ng
f
r
om
th
e
s
oc
ia
l
m
e
di
a
pos
t
w
hi
le
th
e
s
e
c
ond
obj
e
c
ti
ve
e
m
ph
a
s
iz
e
s
on
pr
e
di
c
ti
ng
th
e
pr
oba
bi
li
ty
of
i
s
s
ue
s
r
e
la
te
d
to
m
e
nt
a
l
he
a
lt
h
in
f
ut
ur
e
ba
s
e
d
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
2252
-
8938
I
nt
J
A
r
ti
f
I
nt
e
ll
,
V
ol
. 15, No. 1, Febr
ua
r
y 2026
:
672
-
680
676
on
be
ha
vi
or
of
p
r
e
vi
ous
s
oc
ia
l
m
e
di
a
pos
t.
T
he
num
e
r
ic
a
l
out
c
om
e
s
of
bot
h
th
e
s
e
obj
e
c
ti
ve
s
a
r
e
s
how
n
in
T
a
bl
e
1 a
s
w
e
ll
a
s
T
a
bl
e
2.
T
he
a
c
c
om
pl
is
h
e
d outc
om
e
a
c
qui
r
e
d T
a
bl
e
1 a
s
w
e
ll
a
s
i
t
is
r
e
p
r
e
s
e
nt
e
d i
n F
ig
ur
e
2 i
nf
e
r
s
f
ol
lo
w
in
g
:
B
E
R
T
a
nd
R
o
B
E
R
T
a
c
ons
i
s
te
nt
ly
pe
r
f
or
m
e
d
th
e
b
e
s
t
a
c
r
os
s
a
ll
m
e
nt
a
l
di
s
e
a
s
e
s
,
w
it
h
F
1
-
s
c
or
e
s
w
hos
e
num
e
r
ic
a
l
va
lu
e
s
r
e
s
id
e
s
be
twe
e
n
0.79
to
0.81.
T
he
s
e
m
ode
ls
r
e
ve
a
le
d
a
hi
ghe
r
a
bi
li
ty
to
c
om
pr
e
he
nd
a
nd
c
a
te
gor
iz
e
th
e
in
tr
ic
a
te
m
a
te
r
ia
l
a
s
s
oc
ia
te
d
w
it
h
m
e
nt
a
l
he
a
lt
h
is
s
ue
s
.
B
ot
h
lo
gi
s
ti
c
r
e
gr
e
s
s
io
n
a
nd
r
a
ndom
f
or
e
s
t
f
a
r
e
d
w
e
ll
,
w
it
h
a
ve
r
a
ge
F
1
-
s
c
or
e
s
of
0.69
a
nd
0.72,
r
e
s
pe
c
ti
ve
ly
.
T
he
s
e
m
ode
ls
w
e
r
e
e
s
pe
c
i
a
ll
y
good
a
t
r
e
c
ogni
z
in
g
de
pr
e
s
s
io
n
a
nd
bi
pol
a
r
di
s
or
de
r
,
w
hi
c
h
a
r
e
m
or
e
ope
nl
y
e
xpr
e
s
s
e
d
in
s
oc
ia
l
m
e
di
a
po
s
ts
.
K
-
ne
a
r
e
s
t
ne
ig
hbor
s
obt
a
in
e
d
th
e
lo
w
e
s
t
F
1
-
s
c
or
e
f
or
de
te
c
ti
ng
A
D
H
D
a
nd
a
nxi
e
ty
,
in
di
c
a
ti
ng
it
s
li
m
it
s
in
a
na
ly
z
in
g c
om
pl
e
x m
e
nt
a
l
he
a
lt
h
-
r
e
la
te
d t
e
xt
s
.
T
a
bl
e
1. P
e
r
f
or
m
a
nc
e
of
di
f
f
e
r
e
nt
m
ode
ls
f
or
c
ur
r
e
nt
di
s
or
de
r
d
e
te
c
ti
on
M
ode
l
D
e
pr
e
s
s
i
on (
F
1)
A
nxi
e
t
y (
F
1)
B
i
pol
a
r
(
F
1)
A
D
H
D
(
F
1)
A
ve
r
a
ge
F
1
L
ogi
s
t
i
c
r
e
gr
e
s
s
i
on
0.72
0.68
0.70
0.66
0.69
S
uppor
t
ve
c
t
or
m
a
c
hi
ne
0.68
0.64
0.69
0.63
0.66
K
-
ne
a
r
e
s
t
ne
i
ghbor
s
0.45
0.41
0.48
0.37
0.43
R
a
ndom
f
or
e
s
t
0.72
0.70
0.74
0.71
0.72
G
r
a
di
e
nt
boos
t
i
ng
0.71
0.69
0.73
0.70
0.71
B
E
R
T
0.80
0.77
0.79
0.75
0.78
R
oB
E
R
T
a
0.81
0.78
0.80
0.76
0.79
T
a
bl
e
2. P
e
r
f
or
m
a
nc
e
of
di
f
f
e
r
e
nt
m
ode
ls
f
or
f
ut
ur
e
di
s
or
de
r
pr
e
di
c
ti
on
M
ode
l
D
e
pr
e
s
s
i
on (
F
1)
A
nxi
e
t
y (
F
1)
B
i
pol
a
r
(
F
1)
A
D
H
D
(
F
1)
A
ve
r
a
ge
F
1
L
ogi
s
t
i
c
r
e
gr
e
s
s
i
on
0.66
0.61
0.64
0.58
0.62
S
uppor
t
ve
c
t
or
m
a
c
hi
ne
0.63
0.58
0.62
0.57
0.60
K
-
ne
a
r
e
s
t
ne
i
ghbor
s
0.41
0.36
0.43
0.31
0.38
R
a
ndom
f
or
e
s
t
0.68
0.65
0.69
0.63
0.66
G
r
a
di
e
nt
boos
t
i
ng
0.67
0.63
0.68
0.62
0.65
B
E
R
T
0.74
0.70
0.73
0.68
0.71
R
oB
E
R
T
a
0.76
0.72
0.75
0.71
0.73
F
ig
ur
e
2. V
is
ua
l
out
c
om
e
s
f
or
of
di
f
f
e
r
e
nt
m
ode
ls
f
or
c
ur
r
e
nt
di
s
or
de
r
de
te
c
ti
on
T
he
out
c
om
e
obt
a
in
e
d
in
T
a
bl
e
2
a
nd
F
ig
u
r
e
3
in
f
e
r
s
f
ol
lo
w
in
g:
R
oB
E
R
T
a
out
pe
r
f
or
m
e
d
ot
he
r
m
ode
ls
in
f
or
e
c
a
s
ti
ng
f
ut
ur
e
di
s
e
a
s
e
s
,
w
it
h
a
n
a
ve
r
a
ge
F
1
-
s
c
or
e
of
0.73.
I
t
is
s
e
e
n
th
a
t
r
a
ndom
f
or
e
s
t
a
nd
gr
a
di
e
nt
boos
ti
ng
pe
r
f
or
m
e
d
w
e
ll
a
t
pr
e
di
c
ti
ng
f
ut
ur
e
di
s
or
de
r
s
,
w
it
h
a
ve
r
a
g
e
F
1
-
s
c
or
e
s
of
0.66
a
nd
0.65,
r
e
s
pe
c
ti
ve
ly
.
K
-
ne
a
r
e
s
t
n
e
ig
hbor
s
s
c
or
e
d ba
dl
y, p
a
r
ti
c
ul
a
r
ly
i
n pr
e
di
c
ti
ng f
ut
ur
e
m
e
nt
a
l
he
a
lt
h di
s
or
de
r
s
.
F
ig
ur
e
4
s
how
c
a
s
e
th
e
f
in
a
ll
y
a
c
c
om
pl
is
he
d
s
tu
dy
out
c
om
e
s
.
F
ig
ur
e
4(
a
)
s
how
s
out
c
om
e
w
it
h
r
e
s
pe
c
t
to
de
pr
e
s
s
io
n
e
xhi
bi
ti
ng
R
oB
E
R
T
a
to
a
c
c
om
pl
is
h
m
a
xi
m
um
F
1
-
s
c
or
e
of
0.81
s
how
in
g
in
c
r
e
a
s
e
d
pr
e
di
c
ti
ve
c
a
pa
bi
li
ty
a
nd
s
tr
ong
de
te
c
ti
on.
F
ig
ur
e
4(
b)
s
how
s
o
ut
c
om
e
w
it
h
r
e
s
pe
c
t
to
a
nxi
e
ty
s
ta
ti
ng
s
im
il
a
r
pe
r
f
or
m
a
nc
e
f
or
B
E
R
T
a
nd
R
oB
E
R
T
a
;
how
e
ve
r
,
R
oB
E
R
T
a
pe
r
f
or
m
s
s
li
ght
ly
be
tt
e
r
to
w
a
r
ds
f
u
tu
r
e
pr
e
di
c
ti
on.
F
ig
ur
e
4(
c
)
e
xhi
bi
ts
out
c
om
e
f
o
r
bi
pol
a
r
e
xhi
bi
ti
ng
pot
e
nt
ia
l
pe
r
f
or
m
a
nc
e
f
or
r
a
ndom
f
or
e
s
t
a
nd
B
E
R
T
;
how
e
v
e
r
c
onve
nt
io
na
l
M
L
m
ode
ls
out
pe
r
f
or
m
e
d
la
n
gua
ge
m
ode
ls
.
F
in
a
ll
y,
F
ig
ur
e
4(
d)
e
xhi
bi
ts
out
c
om
e
f
or
A
D
H
D
t
o e
xhi
bi
t
a
c
c
ur
a
te
de
te
c
ti
on w
it
h R
o
B
E
R
T
a
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
nt
J
A
r
ti
f
I
nt
e
ll
I
S
S
N
:
2252
-
8938
D
e
te
c
ti
on and for
e
c
a
s
ti
ng of me
nt
al
he
al
th
di
s
or
d
e
r
s
u
s
in
g
…
(
C
hai
th
r
a I
ndav
ar
a V
e
nk
at
e
s
hagow
da
)
677
F
ig
ur
e
3. V
is
ua
l
out
c
om
e
s
f
or
of
di
f
f
e
r
e
nt
m
ode
ls
f
or
f
ut
u
r
e
di
s
or
de
r
de
te
c
ti
on
(
a
)
(
b)
(
c
)
(
d)
F
ig
ur
e
4. A
c
c
om
pl
is
he
d a
c
c
ur
a
c
y r
e
s
ul
ts
of
(
a
)
de
pr
e
s
s
io
n, (
b)
a
nxi
e
ty
, (
c
)
bi
pol
a
r
, a
nd (
d)
A
D
H
D
T
he
r
e
s
ul
ts
e
xhi
bi
t
s
ugge
s
te
d
a
ppr
oa
c
h
is
s
upe
r
io
r
a
t
de
te
c
ti
ng
a
nd
f
or
e
c
a
s
ti
ng
m
e
nt
a
l
di
s
e
a
s
e
s
f
r
om
s
oc
ia
l
m
e
di
a
da
ta
.
T
he
s
ugge
s
te
d
te
c
hni
que
ha
s
a
n
a
ve
r
a
g
e
a
c
c
ur
a
c
y
of
90.9%
,
e
xc
e
e
di
ng
a
ll
known
a
ppr
oa
c
he
s
f
or
a
ll
il
ln
e
s
s
e
s
.
I
t
out
pe
r
f
or
m
s
e
xi
s
ti
ng
a
ppr
oa
c
h
e
s
1
(
M
L
c
la
s
s
if
ie
r
s
)
by
7.2%
,
2
(
e
n
s
e
m
bl
e
m
ode
ls
)
by
5.8%
,
a
nd
3
(
L
L
M
s
)
by
11.3%
.
T
he
hybr
id
m
ode
l
s
li
ke
w
is
e
in
di
c
a
te
a
4.9%
im
pr
ove
m
e
nt
ove
r
th
e
in
te
nde
d
s
y
s
te
m
.
T
he
s
e
f
in
di
ngs
s
uppor
t
th
e
us
e
f
ul
ne
s
s
of
a
n
in
te
gr
a
te
d
m
e
th
od
th
a
t
in
te
gr
a
te
s
num
e
r
ous
M
L
m
ode
ls
, e
ns
e
m
bl
e
a
ppr
oa
c
he
s
,
a
nd l
a
ngua
ge
m
ode
l
s
, r
e
s
ul
t
in
g i
n m
uc
h hi
ghe
r
pr
e
di
c
ti
on a
c
c
ur
a
c
y.
T
he
ove
r
a
ll
di
s
c
us
s
io
n of
t
he
a
c
c
om
pl
is
he
d outc
om
e
i
s
a
s
f
ol
lo
w
s
:
unl
ik
e
e
xi
s
ti
ng s
ys
te
m
s
t
ha
t
f
oc
us
onl
y
on
de
te
c
ti
ng
m
e
nt
a
l
he
a
lt
h
di
s
or
de
r
s
or
ig
in
a
ti
ng
f
r
om
c
on
te
nt
s
in
s
o
c
ia
l
m
e
di
a
,
our
m
ode
l
in
c
or
por
a
te
s
bot
h
de
te
c
ti
on
a
nd
pr
e
di
c
ti
on
a
s
pe
c
ts
.
I
t
not
onl
y
id
e
nt
i
f
ie
s
w
he
th
e
r
a
n
in
di
vi
dua
l
is
c
ur
r
e
nt
ly
a
f
f
e
c
te
d
by
a
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
2252
-
8938
I
nt
J
A
r
ti
f
I
nt
e
ll
,
V
ol
. 15, No. 1, Febr
ua
r
y 2026
:
672
-
680
678
di
s
or
de
r
,
but
a
ls
o
f
or
e
c
a
s
t
s
th
e
pos
s
ib
il
it
y
of
f
ut
ur
e
m
e
nt
a
l
h
e
a
lt
h
c
ondi
ti
ons
ba
s
e
d
on
p
a
s
t
b
e
ha
vi
or
a
nd
tr
e
nds
in
s
oc
ia
l
m
e
di
a
in
te
r
a
c
ti
ons
.
T
he
c
ont
r
ib
ut
io
n
of
th
e
p
r
e
s
e
nt
e
d
m
ode
l
is
th
e
a
bi
li
ty
to
a
na
ly
z
e
bot
h
c
li
ni
c
a
l
(
da
ta
in
vol
vi
ng
di
s
c
us
s
io
ns
of
m
e
nt
a
l
he
a
lt
h)
a
nd
non
-
c
li
ni
c
a
l
da
ta
(
e
ve
r
yda
y
s
oc
ia
l
m
e
di
a
c
onve
r
s
a
ti
ons
)
.
B
y
e
xt
e
ndi
ng
th
e
s
c
op
e
to
in
c
lu
de
non
-
c
li
ni
c
a
l
da
ta
,
th
e
m
ode
l
ga
in
s
e
xt
e
ns
iv
e
,
a
nd
c
om
pr
e
he
ns
iv
e
unde
r
s
ta
ndi
ng
of
m
e
nt
a
l
c
ondi
ti
on
of
a
n
in
di
vi
dua
l,
im
pr
ovi
ng
p
r
e
di
c
ti
on
a
c
c
ur
a
c
y
a
nd
br
oa
de
ni
ng
th
e
r
a
nge
of
us
e
r
s
it
c
a
n
a
s
s
is
t.
T
he
s
ugge
s
te
d
s
y
s
te
m
ut
il
iz
e
s
a
nove
l
two
-
s
t
e
p
c
a
te
gor
iz
a
ti
on
m
e
th
od.
T
he
in
it
ia
l
s
ta
g
e
is
to
c
onv
e
r
t
te
xt
in
put
in
to
T
F
-
I
D
F
f
e
a
tu
r
e
s
f
or
tr
a
di
ti
ona
l
M
L
m
ode
ls
,
a
nd
e
m
be
ddi
ng
ve
c
to
r
s
f
or
D
L
-
ba
s
e
d
m
ode
ls
(
la
ngua
ge
m
ode
l
s
)
.
T
he
s
e
c
ond
s
ta
g
e
us
e
s
a
ha
r
d
vot
in
g
te
c
hni
que
w
it
h
m
a
ny
c
la
s
s
if
ie
r
s
to
im
pr
ove
f
or
e
c
a
s
t
a
c
c
ur
a
c
y.
T
h
e
j
oi
nt
us
a
ge
of
M
L
a
nd
D
L
m
ode
l
s
e
ns
ur
e
s
r
obus
tn
e
s
s
by
u
s
in
g
e
a
c
h
m
ode
l'
s
s
tr
e
ngt
hs
.
T
he
s
ys
te
m
out
p
e
r
f
or
m
s
ty
pi
c
a
l
M
L
m
ode
ls
by
us
in
g
e
n
s
e
m
bl
e
le
a
r
ni
ng
a
ppr
oa
c
he
s
(
e
.g.,
b
a
ggi
ng
,
X
G
B
oos
t,
a
nd
L
ig
ht
G
B
M
)
,
a
s
w
e
ll
a
s
pr
e
-
tr
a
in
e
d
la
ngua
ge
m
ode
ls
s
uc
h
a
s
B
E
R
T
a
nd
R
oB
E
R
T
a
.
T
he
vot
in
g
pr
oc
e
s
s
e
nha
n
c
e
s
pr
e
di
c
ti
on
a
c
c
ur
a
c
y
by
r
e
duc
in
g
th
e
po
s
s
ib
il
it
y
of
f
a
ls
e
ne
ga
ti
ve
s
a
nd e
ns
ur
in
g t
ha
t
pe
opl
e
w
ho
a
r
e
a
t
r
is
k of
m
e
nt
a
l
il
ln
e
s
s
r
e
c
e
iv
e
pr
om
pt
a
s
s
is
ta
n
c
e
.
4.
C
O
N
C
L
U
S
I
O
N
T
hi
s
s
tu
d
y
de
s
c
r
i
be
s
a
c
o
m
p
r
e
he
ns
iv
e
a
n
d
n
e
w
s
tr
a
te
gy
t
o
de
te
c
ti
ng
a
nd
f
o
r
e
c
a
s
ti
ng
m
e
nt
a
l
di
s
e
a
s
e
s
,
s
pe
c
if
ic
a
ll
y
de
p
r
e
s
s
io
n
,
a
nx
ie
ty
,
bi
p
ol
a
r
d
is
o
r
de
r
,
a
nd
A
D
H
D
,
us
in
g
s
oc
i
a
l
m
e
di
a
da
ta
.
T
hi
s
w
or
k'
s
s
ig
ni
f
ic
a
n
t
a
c
c
om
pl
is
h
m
e
n
ts
i
nc
l
ude
c
r
e
a
t
io
n
o
f
a
t
w
o
-
s
te
p
c
la
s
s
i
f
ic
a
ti
on
m
o
de
l
th
a
t
c
om
bi
ne
s
c
onv
e
nt
io
na
l
M
L
a
pp
r
oa
c
he
s
w
it
h
c
ut
ti
ng
-
e
d
ge
l
a
ng
ua
ge
m
ode
ls
t
o
bo
os
t
d
e
te
c
ti
on
a
nd
p
r
e
di
c
ti
on
a
c
c
u
r
a
c
y
.
T
he
p
r
o
pos
e
d
s
ys
te
m
n
ot
on
ly
e
f
f
e
c
t
iv
e
ly
id
e
nt
i
f
i
e
s
in
di
vi
dua
ls
c
ur
r
e
nt
ly
e
xp
e
r
ie
nc
in
g
m
e
n
ta
l
he
a
l
th
di
s
o
r
de
r
s
us
i
ng
bot
h
c
li
n
ic
a
l
a
n
d
n
on
-
c
l
in
ic
a
l
s
oc
ia
l
m
e
di
a
da
ta
,
b
ut
it
a
ls
o
pr
e
d
ic
ts
th
e
oc
c
u
r
r
e
n
c
e
of
th
e
s
e
di
s
o
r
de
r
s
i
n
t
he
f
u
tu
r
e
,
r
e
p
r
e
s
e
n
ti
ng
a
s
ig
ni
f
ic
a
n
t
a
d
v
a
nc
e
m
e
nt
in
e
a
r
l
y
m
e
nt
a
l
h
e
a
l
th
in
te
r
ve
nt
io
n
.
T
he
h
yb
r
i
d
te
c
hn
iq
ue
th
a
t
b
le
nds
M
L
a
n
d
la
n
gua
ge
m
o
de
ls
,
a
s
w
e
l
l
a
s
th
e
ha
r
d
vo
ti
ng
m
e
c
ha
ni
s
m
th
a
t
a
gg
r
e
ga
te
s
th
e
s
t
r
e
ng
th
s
of
n
um
e
r
o
us
m
o
de
ls
t
o
p
r
o
duc
e
im
pr
ove
d
p
r
e
di
c
ti
ve
a
c
c
u
r
a
c
y,
m
a
k
e
t
hi
s
w
o
r
k
uni
que
.
B
y
ta
ki
n
g
in
to
a
c
c
ou
nt
b
ot
h
c
li
ni
c
a
l
a
n
d
non
-
c
li
ni
c
a
l
s
it
ua
t
io
ns
,
th
is
s
tu
dy
e
x
pa
n
ds
th
e
s
c
ope
o
f
m
e
n
ta
l
he
a
l
th
a
na
l
ys
is
b
e
yo
nd
s
ta
n
da
r
d
c
l
in
ic
a
l
s
e
t
ti
n
gs
,
s
ha
p
in
g
it
m
o
r
e
s
u
it
a
bl
e
to
r
e
a
l
-
w
o
r
l
d
c
ir
c
um
s
ta
n
c
e
s
.
F
u
r
t
he
r
m
or
e
,
t
he
c
a
p
a
b
il
it
y
o
f
s
ys
te
m
t
o
i
de
n
ti
f
y
p
r
oba
bl
e
f
u
tu
r
e
m
e
n
ta
l
he
a
l
th
di
s
o
r
de
r
s
pr
ovi
de
s
a
p
r
e
ve
nt
a
ti
ve
d
im
e
ns
i
on
t
o
m
e
n
ta
l
he
a
lt
h
di
a
gnos
i
s
,
w
it
h
th
e
po
te
n
ti
a
l
to
dr
a
m
a
ti
c
a
ll
y
i
m
p
r
o
ve
th
e
r
a
pe
ut
ic
ta
c
t
ic
s
.
F
ut
ur
e
s
tu
dy
w
i
ll
c
onc
e
nt
r
a
te
o
n
e
n
ha
nc
i
ng
t
he
m
ode
l
b
y
i
nc
l
ud
in
g
ne
w
da
ta
s
o
ur
c
e
s
s
uc
h
a
s
p
hot
os
a
n
d
vi
de
os
,
he
n
c
e
i
nc
r
e
a
s
i
ng
a
c
c
ur
a
c
y
.
F
u
r
th
e
r
m
or
e
,
e
t
hi
c
a
l
c
onc
e
r
ns
a
bo
ut
t
he
u
ti
li
z
a
t
io
n
of
da
ta
de
r
i
ve
d
f
r
o
m
s
oc
ia
l
m
e
d
ia
f
o
r
m
e
nt
a
l
he
a
l
th
pr
e
d
i
c
ti
ons
w
il
l
be
s
ub
je
c
te
d
to
s
tr
ong
e
r
pr
iv
a
c
y
s
a
f
e
g
ua
r
ds
a
nd
a
d
he
r
e
nc
e
t
o
da
ta
p
r
i
va
c
y l
e
g
is
la
ti
on.
F
U
N
D
I
N
G
I
N
F
O
R
M
A
T
I
O
N
T
he
a
ut
hor
s
de
c
la
r
e
s
th
a
t
th
e
r
e
i
s
i
nvol
ve
m
e
nt
of
f
undi
ng f
or
t
h
is
w
or
k.
A
U
T
H
O
R
C
O
N
T
R
I
B
U
T
I
O
N
S
S
T
A
T
E
M
E
N
T
T
hi
s
jo
ur
na
l
us
e
s
th
e
C
ont
r
ib
ut
or
R
ol
e
s
T
a
xonomy
(
C
R
e
di
T
)
to
r
e
c
ogni
z
e
in
di
vi
dua
l
a
ut
hor
c
ont
r
ib
ut
io
ns
, r
e
duc
e
a
ut
hor
s
hi
p di
s
put
e
s
,
a
nd f
a
c
il
it
a
te
c
ol
la
bo
r
a
ti
on.
N
am
e
o
f
A
u
t
h
or
C
M
So
Va
Fo
I
R
D
O
E
Vi
Su
P
Fu
C
ha
it
hr
a
I
nda
va
r
a
V
e
nka
te
s
ha
go
w
da
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
R
oopa
s
hr
e
e
H
e
jj
a
jj
i
R
a
nga
na
th
a
s
ha
r
m
a
✓
✓
✓
✓
✓
✓
✓
Y
oge
e
s
h A
m
ba
la
g
e
r
e
C
ha
ndr
a
s
he
ka
r
a
ia
h
✓
✓
✓
✓
✓
✓
✓
✓
N
a
r
ve
L
a
ks
hm
in
a
r
a
ya
n
T
a
r
a
na
th
✓
✓
✓
✓
✓
✓
✓
✓
C
:
C
onc
e
pt
ua
l
i
z
a
t
i
on
M
:
M
e
t
hodol
ogy
So
:
So
f
t
w
a
r
e
Va
:
Va
l
i
da
t
i
on
Fo
:
Fo
r
m
a
l
a
na
l
ys
i
s
I
:
I
nve
s
t
i
ga
t
i
on
R
:
R
e
s
our
c
e
s
D
:
D
a
t
a
C
ur
a
t
i
on
O
:
W
r
i
t
i
ng
-
O
r
i
gi
na
l
D
r
a
f
t
E
:
W
r
i
t
i
ng
-
R
e
vi
e
w
&
E
di
t
i
ng
Vi
:
Vi
s
ua
l
i
z
a
t
i
on
Su
:
Su
pe
r
vi
s
i
on
P
:
P
r
oj
e
c
t
a
dm
i
ni
s
t
r
a
t
i
on
Fu
:
Fu
ndi
ng a
c
qui
s
i
t
i
on
Evaluation Warning : The document was created with Spire.PDF for Python.
I
nt
J
A
r
ti
f
I
nt
e
ll
I
S
S
N
:
2252
-
8938
D
e
te
c
ti
on and for
e
c
a
s
ti
ng of me
nt
al
he
al
th
di
s
or
d
e
r
s
u
s
in
g
…
(
C
hai
th
r
a I
ndav
ar
a V
e
nk
at
e
s
hagow
da
)
679
C
O
N
F
L
I
C
T
O
F
I
N
T
E
R
E
S
T
S
T
A
T
E
M
E
N
T
T
he
a
ut
hor
s
de
c
la
r
e
s
th
a
t
c
ur
r
e
nt
w
or
k ha
s
no
c
onf
li
c
t
of
i
nt
e
r
e
s
t
w
it
h a
ny othe
r
e
xi
s
ti
ng w
or
ks
.
D
A
T
A
A
V
A
I
L
A
B
I
L
I
T
Y
T
he
d
a
ta
le
ve
r
a
gi
ng
th
e
out
c
om
e
of
th
is
pr
e
s
e
nt
w
or
k
c
a
n
be
m
a
de
a
va
il
a
bl
e
by
c
ont
a
c
ti
ng
th
e
c
or
r
e
s
ponding a
ut
hor
s
, [
C
I
V
]
,
s
ta
ti
ng j
us
ti
f
ie
d r
e
a
s
on of
i
ts
us
a
ge
.
R
E
F
E
R
E
N
C
E
S
[
1]
L
.
C
ui
e
t
al
.
,
“
M
a
j
or
de
pr
e
s
s
i
ve
di
s
or
de
r
:
hypot
he
s
i
s
,
m
e
c
ha
ni
s
m
,
pr
e
ve
nt
i
on
a
nd
t
r
e
a
t
m
e
nt
,”
Si
gnal
T
r
ans
duc
t
i
on
and
T
ar
ge
t
e
d
T
he
r
apy
, vol
. 9, no. 1, 2024, doi
:
10.1038/
s
41392
-
024
-
01738
-
y.
[
2]
M
.
A
.
L
a
ouf
i
,
B
.
W
a
c
qui
e
r
,
T
.
L
a
r
t
i
gol
l
e
,
G
.
L
oa
s
,
a
nd
M
.
H
e
i
n,
“
S
ui
c
i
da
l
i
de
a
t
i
on
i
n
m
a
j
o
r
de
pr
e
s
s
e
d
i
ndi
vi
dua
l
s
:
r
ol
e
of
t
y
pe
D
pe
r
s
ona
l
i
t
y,”
J
our
nal
of
C
l
i
ni
c
al
M
e
di
c
i
ne
, vol
. 11, no. 22, 2022, doi
:
10.3390/
j
c
m
11226611.
[
3]
M
.
O
r
l
a
ndi
e
t
al
.
,
“
S
ui
c
i
da
l
i
t
y
i
n
a
dol
e
s
c
e
nc
e
:
i
ns
i
ght
s
f
r
om
s
e
l
f
-
r
e
por
t
s
on
de
pr
e
s
s
i
on
a
nd
s
ui
c
i
da
l
t
e
nde
nc
i
e
s
,”
J
our
nal
o
f
C
l
i
ni
c
al
M
e
di
c
i
ne
, vol
. 14, no. 4, 2025, doi
:
10.3390/
j
c
m
14041106.
[
4]
S
.
K
i
m
a
nd
K
.
L
e
e
,
“
T
he
e
f
f
e
c
t
i
ve
ne
s
s
of
pr
e
di
c
t
i
ng
s
ui
c
i
da
l
i
de
a
t
i
on
t
hr
oug
h
de
pr
e
s
s
i
ve
s
ym
pt
om
s
a
nd
s
o
c
i
a
l
i
s
ol
a
t
i
on
us
i
ng
m
a
c
hi
ne
l
e
a
r
ni
ng t
e
c
hni
que
s
,”
J
ou
r
nal
of
P
e
r
s
onal
i
z
e
d M
e
di
c
i
ne
, vol
. 12, no. 4
, 2022, doi
:
10.3390/
j
pm
12040516.
[
5]
E
.
R
.
I
s
a
e
va
,
D
.
M
.
R
yz
hova
,
A
.
V
.
S
t
e
pa
nova
,
a
nd
I
.
N
.
M
i
t
r
e
v,
“
A
s
s
e
s
s
m
e
nt
of
s
ui
c
i
de
r
i
s
k
i
n
pa
t
i
e
nt
s
w
i
t
h
de
pr
e
s
s
i
ve
e
pi
s
o
de
s
due
t
o
a
f
f
e
c
t
i
ve
di
s
or
de
r
s
a
nd
bor
de
r
l
i
ne
pe
r
s
ona
l
i
t
y
di
s
or
de
r
:
a
pi
l
ot
c
om
pa
r
a
t
i
ve
s
t
udy,”
B
r
ai
n
Sc
i
e
nc
e
s
,
vol
.
14,
no.
5,
2024,
doi
:
10.3390/
br
a
i
ns
c
i
14050463.
[
6]
C
.
F
r
a
n
c
i
s
a
nd
A
.
Y
.
S
.
A
l
-
H
a
ba
b
i
,
“
N
e
w
m
e
t
h
od
f
o
r
a
s
s
e
s
s
i
n
g
s
ui
c
i
de
i
de
a
t
i
o
n
b
a
s
e
d
on
a
n
a
t
t
e
nt
i
on
m
e
c
h
a
n
i
s
m
a
nd
s
p
i
k
i
ng
ne
u
r
a
l
n
e
t
w
o
r
k
,”
I
A
E
S
I
nt
e
r
na
t
i
o
na
l
J
our
n
al
of
A
r
t
i
f
i
c
i
al
I
n
t
e
l
l
i
g
e
n
c
e
,
vo
l
.
1
4,
n
o
.
1,
pp
.
35
0
–
3
5
7,
2
0
25
,
d
oi
:
1
0.
1
15
91
/
i
j
a
i
.
v1
4
.i
1
.p
p3
5
0
-
3
57
.
[
7]
A
.
S
ha
r
m
a
,
R
.
B
a
l
i
,
P
.
K
um
a
r
,
G
.
M
.
N
a
nda
n
a
,
a
nd
S
.
M
a
l
a
,
“
S
e
nt
i
m
e
nt
a
na
l
ys
i
s
of
us
e
r
r
e
a
c
t
i
ons
t
o
m
e
t
a
’
s
t
hr
e
a
ds
l
a
unc
h
a
nd
T
w
i
t
t
e
r
’
s
X
r
e
na
m
i
ng:
a
c
om
pa
r
a
t
i
ve
s
t
udy
us
i
ng
D
i
s
t
i
l
B
E
R
T
a
nd
m
a
c
hi
ne
l
e
a
r
ni
ng,”
i
n
D
at
a
-
D
r
i
v
e
n
B
us
i
ne
s
s
I
nt
e
l
l
i
ge
nc
e
Sy
s
t
e
m
s
f
o
r
Soc
i
o
-
T
e
c
hni
c
al
O
r
gani
z
at
i
ons
, 2024, pp. 385
–
405
,
doi
:
10.4018/
979
-
8
-
3693
-
1210
-
0.c
h015.
[
8]
T
.
R
i
c
ht
e
r
,
B
.
F
i
s
hba
i
n,
G
.
R
.
-
L
e
vi
n,
a
nd
H
.
O
.
-
S
i
nge
r
,
“
M
a
c
hi
ne
l
e
a
r
ni
ng
-
ba
s
e
d
be
ha
vi
or
a
l
di
a
gnos
t
i
c
t
ool
s
f
or
de
pr
e
s
s
i
on:
a
dva
nc
e
s
, c
h
a
l
l
e
nge
s
,
a
nd f
ut
ur
e
di
r
e
c
t
i
ons
,”
J
our
nal
of
P
e
r
s
onal
i
z
e
d M
e
di
c
i
ne
,
vol
. 11, no. 10, 2021, doi
:
10.3390/
j
pm
11100957.
[
9]
D
.
B
.
O
l
a
w
a
de
,
O
.
Z
.
W
a
da
,
A
.
O
de
t
a
yo,
A
.
C
.
D
.
-
O
l
a
w
a
de
,
F
.
A
s
a
ol
u,
a
nd J
. E
be
r
ha
r
dt
,
“
E
nha
nc
i
ng
m
e
nt
a
l
he
a
l
t
h
w
i
t
h
a
r
t
i
f
i
c
i
a
l
i
nt
e
l
l
i
ge
nc
e
:
c
ur
r
e
nt
t
r
e
nds
a
nd
f
ut
ur
e
pr
os
pe
c
t
s
,”
J
our
nal
of
M
e
di
c
i
ne
,
Sur
ge
r
y
,
and
P
ubl
i
c
H
e
al
t
h
,
vol
.
3,
2024
,
doi
:
10.1016/
j
.gl
m
e
di
.2024.100099.
[
10]
H
. L
i
u
e
t
al
.
, “
A
n hi
s
t
or
i
c
a
l
ove
r
vi
e
w
of
a
r
t
i
f
i
c
i
a
l
i
nt
e
l
l
i
ge
nc
e
f
or
di
a
gnos
i
s
of
m
a
j
or
de
pr
e
s
s
i
ve
di
s
or
de
r
,”
F
r
ont
i
e
r
s
i
n P
s
y
c
hi
at
r
y
,
vol
. 15, 2024, doi
:
10.3389/
f
ps
yt
.2024.1417253.
[
11]
P
.
C
.
-
G
onz
a
l
e
z
e
t
al
.
,
“
A
r
t
i
f
i
c
i
a
l
i
nt
e
l
l
i
ge
nc
e
i
n
m
e
nt
a
l
he
a
l
t
h
c
a
r
e
:
a
s
ys
t
e
m
a
t
i
c
r
e
vi
e
w
of
di
a
gnos
i
s
,
m
oni
t
o
r
i
ng,
a
nd
i
n
t
e
r
ve
nt
i
on
a
ppl
i
c
a
t
i
ons
,”
P
s
y
c
hol
ogi
c
al
M
e
di
c
i
ne
, vol
. 55, 2025, doi
:
10.1017/
S
00332917
24003295.
[
12]
M
.
M
.
I
s
l
a
m
,
S
.
H
a
s
s
a
n,
S
.
A
kt
e
r
,
F
.
A
.
J
i
bon,
a
nd
M
.
S
a
hi
dul
l
a
h,
“
A
c
om
pr
e
he
ns
i
ve
r
e
vi
e
w
of
pr
e
di
c
t
i
ve
a
na
l
yt
i
c
s
m
ode
l
s
f
or
m
e
nt
a
l
i
l
l
ne
s
s
us
i
ng m
a
c
hi
ne
l
e
a
r
ni
ng a
l
gor
i
t
hm
s
,”
H
e
al
t
hc
ar
e
A
nal
y
t
i
c
s
, vol
. 6, 2024, doi
:
10.1016/
j
.he
a
l
t
h.2024.100350.
[
13]
B
.
G
.
T
e
f
e
r
r
a
e
t
al
.
,
“
S
c
r
e
e
ni
ng
f
or
de
pr
e
s
s
i
on
us
i
ng
na
t
ur
a
l
l
a
ngua
g
e
pr
o
c
e
s
s
i
ng:
l
i
t
e
r
a
t
ur
e
r
e
vi
e
w
,
”
I
nt
e
r
ac
t
i
v
e
J
ou
r
nal
of
M
e
di
c
al
R
e
s
e
ar
c
h
, vol
. 13, 2024, doi
:
10.2196/
55067.
[
14]
N
.
K
a
r
,
“
C
ha
l
l
e
nge
s
i
n
m
a
n
a
gi
ng
de
pr
e
s
s
i
on
i
n
c
l
i
ni
c
a
l
pr
a
c
t
i
c
e
:
r
e
s
ul
t
of
a
gl
oba
l
s
ur
ve
y,”
P
har
m
ac
oe
pi
de
m
i
ol
ogy
,
vol
.
4,
no.
1
,
2025, doi
:
10.3390/
pha
r
m
a
4010005.
[
15]
D
.
M
ul
c
,
J
.
V
ukoj
e
vi
c
,
E
.
K
a
l
a
f
a
t
i
c
,
M
.
C
i
f
r
e
k,
D
.
V
i
dovi
c
,
a
nd
A
.
J
ovi
c
,
“
O
ppor
t
uni
t
i
e
s
a
nd
c
ha
l
l
e
nge
s
f
or
c
l
i
ni
c
a
l
pr
a
c
t
i
c
e
i
n
de
t
e
c
t
i
ng de
pr
e
s
s
i
on u
s
i
ng E
E
G
a
nd m
a
c
hi
ne
l
e
a
r
ni
ng,”
Se
ns
or
s
, vol
. 25, no. 2,
2025, doi
:
10.3390/
s
25020409.
[
16]
B
.
A
dr
i
a
ni
e
t
al
.
,
“
C
ur
r
e
nt
di
a
gnos
t
i
c
c
ha
l
l
e
nge
s
i
n
l
a
t
e
-
l
i
f
e
de
p
r
e
s
s
i
on
a
nd
ne
ur
oc
ogni
t
i
ve
di
s
or
de
r
s
,”
P
s
y
c
hi
at
r
y
I
nt
e
r
nat
i
onal
,
vol
. 5, no. 4, pp. 904
–
916, 2024, doi
:
10.3390/
ps
yc
hi
a
t
r
yi
nt
5040061.
[
17]
N
.
H
.
K
i
m
,
J
.
M
.
K
i
m
,
D
.
M
.
P
a
r
k,
S
.
R
.
J
i
,
a
nd
J
.
W
.
K
i
m
,
“
A
na
l
ys
i
s
of
de
pr
e
s
s
i
on
i
n
s
oc
i
a
l
m
e
di
a
t
e
xt
s
t
hr
ough
t
he
pa
t
i
e
n
t
he
a
l
t
h que
s
t
i
onna
i
r
e
-
9 a
nd na
t
ur
a
l
l
a
ngua
ge
pr
oc
e
s
s
i
ng,”
D
i
gi
t
al
H
e
al
t
h
, vol
. 8,
2022, doi
:
10.1177/
20552076221114204.
[
18]
K
.
M
ount
z
our
i
s
,
I
.
P
e
r
i
kos
,
a
nd
I
.
H
a
t
z
i
l
yge
r
oudi
s
,
“
S
pe
e
c
h
e
m
ot
i
on
r
e
c
og
ni
t
i
on
us
i
ng
c
onvol
ut
i
ona
l
ne
ur
a
l
ne
t
w
or
ks
w
i
t
h
a
t
t
e
nt
i
on m
e
c
ha
ni
s
m
,”
E
l
e
c
t
r
oni
c
s
, vol
. 12, no. 20, 2023, doi
:
10.3390/
e
l
e
c
t
r
oni
c
s
12204376.
[
19]
M
.
K
.
K
a
bi
r
,
M
.
I
s
l
a
m
,
A
.
N
.
B
.
K
a
bi
r
,
A
.
H
a
que
,
a
nd
M
.
K
.
R
ha
m
a
n,
“
D
e
t
e
c
t
i
on
of
de
pr
e
s
s
i
on
s
e
v
e
r
i
t
y
us
i
ng
B
e
nga
l
i
s
o
c
i
a
l
m
e
di
a
pos
t
s
on
m
e
nt
a
l
he
a
l
t
h:
s
t
udy
us
i
ng
na
t
ur
a
l
l
a
ngua
ge
pr
oc
e
s
s
i
ng
t
e
c
hni
que
s
,”
J
M
I
R
F
or
m
at
i
v
e
R
e
s
e
ar
c
h
,
vol
.
6,
no.
9,
2022, doi
:
10.2196/
36118.
[
20]
M
.
K
.
M
ye
e
,
R
.
D
.
C
.
R
e
be
ka
h,
T
.
D
e
e
pa
,
G
.
D
.
Z
i
on,
a
nd
K
.
L
oke
s
h,
“
D
e
t
e
c
t
i
on
of
de
pr
e
s
s
i
on
i
n
s
o
c
i
a
l
m
e
di
a
po
s
t
s
us
i
n
g
e
m
ot
i
ona
l
i
nt
e
ns
i
t
y
a
na
l
ys
i
s
,
”
E
ngi
ne
e
r
i
ng,
T
e
c
hnol
ogy
&
A
ppl
i
e
d
Sc
i
e
nc
e
R
e
s
e
ar
c
h
,
vol
.
14,
no.
5,
pp.
16207
–
16211,
2024
,
doi
:
10.48084/
e
t
a
s
r
.7461.
[
21]
S
of
i
a
,
A
.
M
a
l
i
k,
M
.
S
ha
ba
z
,
a
nd
E
.
A
s
e
ns
o,
“
M
a
c
hi
ne
l
e
a
r
ni
ng
ba
s
e
d
m
ode
l
f
or
de
t
e
c
t
i
ng
de
pr
e
s
s
i
on
dur
i
ng
C
O
V
I
D
-
19
c
r
i
s
i
s
,”
Sc
i
e
nt
i
f
i
c
A
f
r
i
c
an
, vol
. 20, 2023, doi
:
10.1016/
j
.s
c
i
a
f
.2023.e
01716.
[
22]
Z
.
X
u
e
t
al
.
,
“
U
s
i
ng
m
a
c
hi
ne
l
e
a
r
ni
ng
t
o
pr
e
di
c
t
a
nt
i
de
pr
e
s
s
a
nt
t
r
e
a
t
m
e
nt
out
c
om
e
f
r
om
e
l
e
c
t
r
oni
c
he
a
l
t
h
r
e
c
or
ds
,
”
P
s
y
c
hi
at
r
i
c
R
e
s
e
ar
c
h and C
l
i
ni
c
al
P
r
ac
t
i
c
e
, vol
. 5, no. 4, pp. 118
–
125, 2023, doi
:
10.1176/
a
ppi
.pr
c
p.20220015.
[
23]
A
.
A
m
a
na
t
e
t
al
.
,
“
D
e
e
p
l
e
a
r
ni
ng
f
or
de
pr
e
s
s
i
on
de
t
e
c
t
i
on
f
r
om
t
e
xt
ua
l
da
t
a
,”
E
l
e
c
t
r
oni
c
s
,
vol
.
11,
no.
5,
2022,
doi
:
10.3390/
e
l
e
c
t
r
oni
c
s
11050676.
[
24]
W
.
W
a
ng
e
t
al
.
,
“
I
nt
e
g
r
a
t
i
on
of
a
r
t
i
f
i
c
i
a
l
i
nt
e
l
l
i
ge
nc
e
a
nd
w
e
a
r
a
bl
e
i
nt
e
r
ne
t
of
t
hi
ngs
f
or
m
e
nt
a
l
he
a
l
t
h
de
t
e
c
t
i
on,”
I
nt
e
r
nat
i
onal
J
our
nal
of
C
ogni
t
i
v
e
C
om
put
i
ng i
n E
ngi
ne
e
r
i
ng
, vol
. 5, pp. 307
–
315, 2024, doi
:
10.1016/
j
.i
j
c
c
e
.2024.07.002.
[
25]
Y
.
L
i
n
e
t
al
.
,
“
A
de
e
p
l
e
a
r
ni
ng
-
ba
s
e
d
m
ode
l
f
or
de
t
e
c
t
i
ng
de
pr
e
s
s
i
on
i
n
s
e
ni
or
popul
a
t
i
on,”
F
r
ont
i
e
r
s
i
n
P
s
y
c
hi
at
r
y
,
vol
.
13,
2022,
doi
:
10.3389/
f
ps
yt
.2022.1016676.
[
26]
B
.
H
a
dz
i
c
e
t
al
.
,
“
E
nha
nc
i
ng
e
a
r
l
y
de
pr
e
s
s
i
on
de
t
e
c
t
i
on
w
i
t
h
A
I
:
a
c
om
pa
r
a
t
i
ve
us
e
of
N
L
P
m
ode
l
s
,”
SI
C
E
J
our
nal
of
C
ont
r
ol
,
M
e
as
ur
e
m
e
nt
, and Sy
s
t
e
m
I
nt
e
gr
at
i
on
, vol
. 17, no. 1, pp. 135
–
143, 2024, doi
:
1
0.1080/
18824889.2024.2342624.
[
27]
P
.
K
um
a
r
,
P
.
S
a
m
a
nt
a
,
S
.
D
ut
t
a
,
M
.
C
ha
t
t
e
r
j
e
e
,
a
nd
D
.
S
a
r
ka
r
,
“
F
e
a
t
ur
e
b
a
s
e
d
de
pr
e
s
s
i
on
de
t
e
c
t
i
on
f
r
om
T
w
i
t
t
e
r
da
t
a
us
i
ng
m
a
c
hi
ne
l
e
a
r
ni
ng t
e
c
hni
que
s
,”
J
ou
r
nal
of
Sc
i
e
nt
i
f
i
c
R
e
s
e
a
r
c
h
, vol
. 66, no. 2, pp
. 220
–
228, 2022, doi
:
10.37398/
J
S
R
.2022.660229.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
2252
-
8938
I
nt
J
A
r
ti
f
I
nt
e
ll
,
V
ol
. 15, No. 1, Febr
ua
r
y 2026
:
672
-
680
680
B
I
O
G
R
A
P
H
I
E
S
O
F
A
U
T
H
O
R
S
Chaithra
Indavara
Venkateshagowd
a
received
the
maste
r’s
degree
in
Computer
Science
and
Enginee
ring
from
Visvesva
raya
Technol
ogic
al
Universi
ty,
Belagav
i.
Karnataka,
India
in
2013
and
is
currently
working
towards
Ph.D.
degree.
She
joined
the
Department
of
Computer
Science
and
Enginee
ring,
Adichunc
hanagir
i Institute
of
Technol
ogy,
Chikkamag
aluru
as
an
assistan
t
professor
,
in
2015.
Her
research
in
terests
include
machine
learning,
deep
learning,
artificia
l
intelligence
,
sentiment
analysis,
and
large
language
model
s.
She ca
n be c
ontact
ed at
email:
chaith
ra.iv@gmail.com.
Roopashree
Hejjajji
Ranganathas
harma
has
completed
B.E.
(
E
lectronics
and
Communicat
ion
Enginee
ring
)
and
M.Tech.
(
Computer
Science
and
Enginee
ring
)
from
Visvesvaraya
Technological
University
,
Belagavi,
Karnataka,
India
a
nd
Ph.D.
from
CHRIST
(Deemed
to
be
University
)
Bengaluru,
Karnataka,
India.
She
has
around
13
years
of
industrial
experience
and
5
years
of
teaching
experience.
She
is
presently
wor
king
as
a
p
rofessor
an
d
head
of
the
Department
of
Artificial
Intelligence
and
Data
Scienc
e
at
GSSS
Institute
of
Engineering
and
Technology
for
Women
,
Mysuru
,
India
.
She
can
be
contacted
at
email:
roopashreehr@
gsss.edu.i
n.
Yogeesh
Ambalagere
Chandrash
ekaraiah
has
completed
B.E
.
,
M.Tech
.
,
and
Ph.D.
from
Visvesvaraya
Technological
University
Belagavi,
K
ar
nataka,
India.
Currently
working
as
an
associate
profes
sor
in
Computer
Science
and
En
gineer
ing
,
Government
Engineering
College,
Chamarajanagar,
Karnataka,
India.
His
ar
ea
of
i
nterest
is
wireless
sensor
network,
internet
of
things,
and
m
achine
learning.
He
can
b
e
contacted
at
email:
yogeesh13@
gmail.com.
Narve Lakshmin
arayan Tarana
th
is curren
tly
working a
s
associa
te professor
in
the
School
of
Computer
Science
and
Enginee
ring
,
Presidency
University,
Bengaluru.
He
earned
his
d
octoral
degree
in
Computer
Science
from
Visvesvaraya
T
echnological
University
,
Belagav
i.
He
has
more
than
17
years
of
experie
nce
in
the
field
of
a
cad
emics
and
research.
His
primarily
works
on
research
areas
such
as
data
mining,
machine
learning,
and
artificial
intelligenc
e.
His
work
has
been
published
in
curated
journals
such
a
s
Elsevier
and
Springer
.
He can be
contacted
at email
: taranat
h@
presidency
universi
ty.in.
Evaluation Warning : The document was created with Spire.PDF for Python.