chris jermaine computer science department rice university ...cmj4.web.rice.edu/forlydia.pdf · 1...
TRANSCRIPT
1
Som
e A
lgor
ithm
s fo
r Det
ectin
g C
hang
es a
nd T
rend
s in
Dat
a...
With
A
pplic
atio
ns to
Ant
imic
robi
al
Res
ista
nce
Chr
is Je
rmai
neC
ompu
ter S
cien
ce D
epar
tmen
tR
ice
Uni
vers
itycm
j4@
cs.ri
ce.e
du
2
Dru
g R
esis
tant
“Sup
erbu
gs”
•N
osoc
omia
l “su
perb
ugs”
are
a m
ajor
wor
ry-H
osp.
res.
bugs
that
hav
e de
velo
ped
antim
icro
bial
resi
stan
ce
•A
re m
any
head
line-
grab
bing
exa
mpl
es...
-MR
SA (M
ethi
cilli
n-re
sist
ant S
taph
yloc
occu
s aur
eus)
; sta
ph
resi
stan
t to
peni
cilli
ns a
nd c
epha
losp
orin
s
3
Dru
g R
esis
tant
“Sup
erbu
gs”
•N
osoc
omia
l “su
perb
ugs”
are
a m
ajor
wor
ry-B
acte
ria th
at h
ave
deve
lope
d an
timic
robi
al re
sist
ance
•
Are
man
y he
adlin
e-gr
abbi
ng e
xam
ples
...-M
RSA
(Met
hici
llin-
resi
stan
t Sta
phyl
ococ
cus a
ureu
s)-R
esis
tant
Pse
udom
onas
aer
ugin
osa
4
Dru
g R
esis
tant
“Sup
erbu
gs”
•N
osoc
omia
l “su
perb
ugs”
are
a m
ajor
wor
ry-B
acte
ria th
at h
ave
deve
lope
d an
timic
robi
al re
sist
ance
•
Are
man
y he
adlin
e-gr
abbi
ng e
xam
ples
...-M
RSA
(Met
hici
llin-
resi
stan
t Sta
phyl
ococ
cus a
ureu
s)-R
esis
tant
Pse
udom
onas
aer
ugin
osa
-ND
M-1
(New
Del
hi m
etal
lo-b
eta-
lact
amas
e); e
nzym
e, n
ot a
bu
g; fi
rst f
ound
in K
lebs
iella
pne
umon
iae
in In
dia
5
Dru
g R
esis
tant
“Sup
erbu
gs”
•N
osoc
omia
l “su
perb
ugs”
are
a m
ajor
wor
ry-B
acte
ria th
at h
ave
deve
lope
d an
timic
robi
al re
sist
ance
•
Are
man
y he
adlin
e-gr
abbi
ng e
xam
ples
...-M
RSA
(Met
hici
llin-
resi
stan
t Sta
phyl
ococ
cus a
ureu
s)-R
esis
tant
Pse
udom
onas
aer
ugin
osa
-ND
M-1
(New
Del
hi m
etal
lo-b
eta-
lact
amas
e)-K
PC (K
lebs
iella
pne
umon
iae
carb
apen
amas
e); r
esis
tanc
e to
ne
arly
eve
ry d
rug,
requ
ires n
asty
trea
tmen
ts (p
olym
yxin
s)
Stat
es w
here
KPC
has b
een
foun
d
6
Dru
g R
esis
tant
“Sup
erbu
gs”
•N
osoc
omia
l “su
perb
ugs”
are
a m
ajor
wor
ry-B
acte
ria th
at h
ave
deve
lope
d an
timic
robi
al re
sist
ance
•
Are
man
y he
adlin
e-gr
abbi
ng e
xam
ples
...-M
RSA
(Met
hici
llin-
resi
stan
t Sta
phyl
ococ
cus a
ureu
s)-R
esis
tant
Pse
udom
onas
aer
ugin
osa
-ND
M-1
(New
Del
hi m
etal
lo-b
eta-
lact
amas
e)-K
PC (K
lebs
iella
pne
umon
iae
carb
apen
amas
e)•
Wha
t hap
pens
whe
n 98
% o
f bug
s hav
e N
DM
-1, K
PC, .
..?-N
ot to
o fa
r fet
ched
-Will
it ta
ke u
s bac
k to
192
8?
7
How
Do
Sup
erbu
gs A
ppea
r?•
Rel
ated
to m
is-/o
veru
se o
f ant
imic
robi
als
•Se
lect
ive
pres
sure
cau
ses b
ugs t
o “l
earn
to”
cope
with
toxi
ns
8
How
Do
Sup
erbu
gs A
ppea
r?•
Rel
ated
to m
is-/o
veru
se o
f ant
imic
robi
als
•Se
lect
ive
pres
sure
cau
ses b
ugs t
o “l
earn
to”
cope
with
toxi
ns•
Mos
t ofte
n as
soci
ated
with
hea
lth-c
are
faci
litie
s-M
icro
bes/
antim
icro
bial
s/im
mun
e-co
mpr
omis
ed p
eopl
e/hy
per-
activ
e do
ctor
s all
toge
ther
in sm
all s
pace
-Ofte
n th
ese b
ugs a
re n
ot su
ited
to th
e “re
al w
orld
”; le
t’s h
ope
it st
ays t
hat w
ay•
Are
CA
ver
sion
s, bu
t eve
n th
ese
tend
to b
e ce
nter
ed a
roun
d fa
cil-
ities
not
unl
ike
heal
thca
re (e
x: lo
cker
room
s!)
9
Trag
edy
of th
e C
omm
ons
•Ev
eryo
ne in
the
hosp
ital k
now
s its
the
docs
’ fau
lt•
Even
the
docs
But t
hat d
oesn
’t m
ean
we
can
actu
ally
bla
me
them
...
10
Trag
edy
of th
e C
omm
ons
•Ev
eryo
ne in
the
hosp
ital k
now
s its
the
docs
’ fau
lt•
Even
the
docs
But t
hat d
oesn
’t m
ean
we
can
actu
ally
bla
me
them
...•
No
one
was
eve
r sue
d be
caus
e th
ey c
rush
a ti
ny b
ug w
ith th
e bi
g-ge
st h
amm
er a
vaila
ble
-Or b
ecau
se th
ey (t
ry to
) cru
sh a
viru
s with
an
antib
iotic
•A
nd th
ere’
s alw
ays a
smal
l cha
nce
that
if th
e do
c ta
kes t
he ti
me
to
do th
ings
righ
t, di
sast
er c
an re
sult
•H
ence
the
prob
lem
get
s wor
se a
nd w
orse
...
11
How
Do
Hos
pita
ls C
ope?
•Ty
pica
lly h
ave
an e
pide
mio
logi
st•
Usu
ally
an
MD
trai
ned
in in
fect
ious
dis
ease
s-M
ay a
lso
have
a d
egre
e in
pub
lic h
ealth
, sta
tistic
s, et
c.•
Spen
ds so
me
frac
tion
of ti
me
look
ing
at d
ata,
con
nect
ing
dots
•O
ne ta
sk is
mon
itorin
g em
ergi
ng a
ntim
icro
bial
resi
stan
ce
12
How
Do
Hos
pita
ls C
ope?
•Ty
pica
lly h
ave
an e
pide
mio
logi
st•
Usu
ally
an
MD
trai
ned
in in
fect
ious
dis
ease
s-M
ay a
lso
have
a d
egre
e in
pub
lic h
ealth
, sta
tistic
s, et
c.•
Spen
ds so
me
frac
tion
of ti
me
look
ing
at d
ata,
con
nect
ing
dots
•O
ne ta
sk is
mon
itorin
g em
ergi
ng a
ntim
icro
bial
resi
stan
ce•
My
impr
essi
on-T
hey
all a
gree
(bad
doc
s) =
(ant
imic
robi
al re
sist
ance
)-B
ut th
ey c
an’t
tell
docs
wha
t to
do-T
hey
see
thei
r job
as d
isco
verin
g/pr
ovid
ing
data
to d
ocs
-Hop
eful
ly, g
et d
ocs t
o th
ink
twic
e be
fore
they
act
13
Wha
t’s O
ur G
oal?
•W
ant t
o gi
ve th
at e
pide
mio
logi
st a
set o
f too
ls to
hel
p ou
t•
Goa
l is t
o he
lp h
im/h
er “
min
e” th
e da
ta-T
ools
to a
utom
atic
ally
dis
cove
r im
porta
nt tr
ends
-Fin
d th
ings
not
obv
ious
thro
ugh
“tra
ditio
nal m
etho
ds”.
.. ta
bu-
latin
g th
e da
ta, r
unni
ng re
gres
sion
s, et
c.
14
Wha
t’s O
ur G
oal?
•W
ant t
o gi
ve th
at e
pide
mio
logi
st a
set o
f too
ls to
hel
p ou
t•
Goa
l is t
o he
lp h
im/h
er “
min
e” th
e da
ta-T
ools
to a
utom
atic
ally
dis
cove
r im
porta
nt tr
ends
-Fin
d th
ings
not
obv
ious
thro
ugh
“tra
ditio
nal m
etho
ds”.
.. ta
bu-
latin
g th
e da
ta, r
unni
ng re
gres
sion
s, et
c.•
DIS
CL
AIM
ER
-I’m
a c
ompu
ter s
cien
tist,
not a
clin
icia
n-S
o m
y fo
cus f
rom
her
e is
mos
tly o
n m
etho
ds, n
ot re
sults
!
15
1st P
rob:
Spa
tial A
nom
aly
Det
ectio
n•
Imag
ine
you
are
a H
E•
Hav
e re
sist
ance
dat
a fo
r you
r hos
pita
l, hu
ndre
ds o
f oth
ers
-For
eac
h bu
g/dr
ug p
air,
for e
ach
of la
st 1
0 ye
ars,
you
have
:(a
) Num
ber o
f pat
ient
s who
had
bug
s cul
ture
d(b
) Num
ber o
f cul
ture
s tha
t cam
e ba
ck “
susc
eptib
le”
•R
esis
tanc
e go
es u
p ov
er ti
me.
But
you
won
der:
-Are
thin
gs g
ettin
g w
orse
fast
er a
t my
hosp
ital c
ompa
red
to th
e su
rrou
ndin
g ho
spita
ls?
-Are
thin
gs g
ettin
g w
orse
fast
er in
my
imm
edia
te a
rea
com
-pa
red
to th
e re
gion
?
16
1st P
rob:
Spa
tial A
nom
aly
Det
ectio
n•
Imag
ine
you
are
a H
E•
Hav
e re
sist
ance
dat
a fo
r you
r hos
pita
l, hu
ndre
ds o
f oth
ers
-For
eac
h bu
g/dr
ug p
air,
for e
ach
of la
st 1
0 ye
ars,
you
have
:(a
) Num
ber o
f pat
ient
s who
had
bug
s cul
ture
d(b
) Num
ber o
f cul
ture
s tha
t cam
e ba
ck “
susc
eptib
le”
•R
esis
tanc
e go
es u
p ov
er ti
me.
But
you
won
der:
-Are
thin
gs g
ettin
g w
orse
fast
er a
t my
hosp
ital c
ompa
red
to th
e su
rrou
ndin
g ho
spita
ls?
-Are
thin
gs g
ettin
g w
orse
fast
er in
my
imm
edia
te a
rea
com
-pa
red
to th
e re
gion
?
us
Com
bina
toria
lpr
oble
m!
17
1st P
rob:
Spa
tial A
nom
aly
Det
ectio
n•
Imag
ine
you
are
a H
E•
Hav
e re
sist
ance
dat
a fo
r you
r hos
pita
l, hu
ndre
ds o
f oth
ers
-For
eac
h bu
g/dr
ug p
air,
for e
ach
of la
st 1
0 ye
ars,
you
have
:(a
) Num
ber o
f pat
ient
s who
had
bug
s cul
ture
d(b
) Num
ber o
f cul
ture
s tha
t cam
e ba
ck “
susc
eptib
le”
•R
esis
tanc
e go
es u
p ov
er ti
me.
But
you
won
der:
-Are
thin
gs g
ettin
g w
orse
fast
er a
t my
hosp
ital c
ompa
red
to th
e su
rrou
ndin
g ho
spita
ls?
-Are
thin
gs g
ettin
g w
orse
fast
er in
my
imm
edia
te a
rea
com
-pa
red
to th
e re
gion
?
18
1st P
rob:
Spa
tial A
nom
aly
Det
ectio
n•
Imag
ine
you
are
a H
E•
Hav
e re
sist
ance
dat
a fo
r you
r hos
pita
l, hu
ndre
ds o
f oth
ers
-For
eac
h bu
g/dr
ug p
air,
for e
ach
of la
st 1
0 ye
ars,
you
have
:(a
) Num
ber o
f pat
ient
s who
had
bug
s cul
ture
d(b
) Num
ber o
f cul
ture
s tha
t cam
e ba
ck “
susc
eptib
le”
•R
esis
tanc
e go
es u
p ov
er ti
me.
But
you
won
der:
-Are
thin
gs g
ettin
g w
orse
fast
er a
t my
hosp
ital c
ompa
red
to th
e su
rrou
ndin
g ho
spita
ls?
-Are
thin
gs g
ettin
g w
orse
fast
er in
my
imm
edia
te a
rea
com
-pa
red
to th
e re
gion
?
19
1st P
rob:
Spa
tial A
nom
aly
Det
ectio
n•
Imag
ine
you
are
a H
E•
Hav
e re
sist
ance
dat
a fo
r you
r hos
pita
l, hu
ndre
ds o
f oth
ers
-For
eac
h bu
g/dr
ug p
air,
for e
ach
of la
st 1
0 ye
ars,
you
have
:(a
) Num
ber o
f pat
ient
s who
had
bug
s cul
ture
d(b
) Num
ber o
f cul
ture
s tha
t cam
e ba
ck “
susc
eptib
le”
•R
esis
tanc
e go
es u
p ov
er ti
me.
But
you
won
der:
-Are
thin
gs g
ettin
g w
orse
fast
er a
t my
hosp
ital c
ompa
red
to th
e su
rrou
ndin
g ho
spita
ls?
-Are
thin
gs g
ettin
g w
orse
fast
er in
my
imm
edia
te a
rea
com
-pa
red
to th
e re
gion
?
20
Spat
ial A
nom
aly
Det
ectio
n•
Long
-stu
died
pro
blem
in st
atis
tics,
epid
emio
logy
, dat
a m
inin
g•
Usu
al c
onte
xt is
find
ing
spat
ial “
hot s
pots
” (d
isea
se, s
ales
, etc
.)•
Cla
ssic
exa
mpl
e: 1
854
Bro
ad S
treet
cho
lera
out
brea
k
21
Spat
ial A
nom
aly
Det
ectio
n•
Obv
ious
ly, t
here
is a
lot m
ore
mat
h th
ese
days
in S
AD
•A
nd th
ere’
s a lo
t mor
e da
ta, t
oo -
plot
s by
hand
are
not
pra
ctic
al!
•B
ut th
e ba
sic
idea
is u
ncha
nged
...
22
Spat
ial A
nom
aly
Det
ectio
n•
Und
erly
ing
stat
istic
al m
odel
is ty
pica
lly q
uite
sim
ple
Hav
e so
me
spat
ial r
egio
n R
R
23
Spat
ial A
nom
aly
Det
ectio
n•
Und
erly
ing
stat
istic
al m
odel
is ty
pica
lly q
uite
sim
ple
Div
ided
into
subr
egio
ns th
at a
re
smal
l eno
ugh
that
insi
de-r
egio
n va
riatio
n is
not
inte
rest
ing
R
24
Spat
ial A
nom
aly
Det
ectio
n•
Und
erly
ing
stat
istic
al m
odel
is ty
pica
lly q
uite
sim
ple
BTW
: Poi
sson
wid
ely
used
her
e be
caus
e it
give
s you
(und
er c
erta
in a
ssum
ptio
ns) t
he p
roba
bilit
y th
at w
ith a
ver
y la
rge
popu
latio
n, y
ou’d
see
n ev
ents
in a
tim
e in
terv
al (f
or e
xam
ple,
sick
peo
ple)
, giv
en th
at h
isto
rical
ly th
ere
wer
e la
mbd
a ev
ents
exp
ecte
d
Ass
ume
data
in e
ach
cell
are
gen-
erat
ed u
sing
sim
ple
mod
el (e
x:
Pois
son)
with
kno
wn
para
ms
R λ15
=
λ11
=λ
19=
λ12
=
λ22
=
λ9
=λ
5=
λ3
=
25
Spat
ial A
nom
aly
Det
ectio
n•
Und
erly
ing
stat
istic
al m
odel
is ty
pica
lly q
uite
sim
ple
Then
whe
n yo
u ob
serv
e th
e ac
tual
da
ta...
R λ15
=
λ11
=λ
19=
λ12
=λ
22=
λ9
=n
35=
λ3
=n
14=
n12
=
n22
=n
13=
n20
=
n13
=
26
Spat
ial A
nom
aly
Det
ectio
n•
Und
erly
ing
stat
istic
al m
odel
is ty
pica
lly q
uite
sim
ple
...yo
u try
to fi
nd a
con
t., re
ason
-ab
ly-s
hape
d re
gion
whe
re o
bs.
data
are
ext
rem
ely
unlik
ely
R λ15
=
λ11
=λ
19=
λ12
=λ
22=
λ9
=n
35=
λ3
=n
14=
n12
=
n22
=n
13=
n20
=
n13
=
27
Spat
ial A
nom
aly
Det
ectio
n•
Mos
t rel
ated
wor
k in
the
stat
istic
al li
tera
ture
beg
ins w
ith th
e so
-ca
lled
“spa
tial s
can
stat
istic
” (P
oiss
on m
odel
)...
-Ide
a is
to se
arch
all
poss
ible
con
tiguo
us re
gion
s of a
cer
tain
sh
ape
(usu
ally
a c
ircle
, squ
are,
rect
angl
e)-F
ind
top
k th
at re
ject
a P
oiss
on-b
ased
LRT
-May
be u
se si
mul
atio
n to
dea
l with
MH
T-R
etur
n th
ose
regi
ons t
hat s
urvi
ve to
the
user
28
Mor
e C
ompl
icat
ed M
odel
s•
But
wha
t if y
our p
robl
em is
mor
e co
mpl
icat
ed?
•O
ur m
otiv
atin
g pr
oble
m:
-Ano
mal
ies i
n no
soco
mia
l ant
imic
robi
al re
sist
ance
tren
ds-W
e kn
ow tr
end
is g
ener
ally
upw
ard
-But
is th
e up
war
d tre
nd u
nifo
rm, o
r is t
here
spat
ial v
aria
tion?
29
Mor
e C
ompl
icat
ed M
odel
s•
But
wha
t if y
our p
robl
em is
mor
e co
mpl
icat
ed?
•O
ur m
otiv
atin
g pr
oble
m:
-Ano
mal
ies i
n no
soco
mia
l ant
imic
robi
al re
sist
ance
tren
ds-W
e kn
ow tr
end
is g
ener
ally
upw
ard
-But
is th
e up
war
d tre
nd u
nifo
rm, o
r is t
here
spat
ial v
aria
tion?
•A
reas
onab
le m
odel
for a
hos
pita
l’s re
sist
ance
tren
d:
01
Resistancerate
At s
tart
of ti
me,
resi
stan
ce
prob
abili
ty fo
r a b
ug in
an
arbi
trary
pat
ient
is p
0
p 0
time
30
Mor
e C
ompl
icat
ed M
odel
s•
But
wha
t if y
our p
robl
em is
mor
e co
mpl
icat
ed?
•O
ur m
otiv
atin
g ex
ampl
e:-A
nom
alie
s in
noso
com
ial a
ntim
icro
bial
resi
stan
ce tr
ends
-Due
to (m
is-)
use
of a
ntim
icro
bial
s, bu
gs d
evel
op re
sist
ance
-But
is th
e up
war
d tre
nd u
nifo
rm, o
r is t
here
spat
ial v
aria
tion?
•A
reas
onab
le m
odel
for a
hos
pita
l’s re
sist
ance
tren
d:
01
Resistancerate
Each
yea
r, th
ere
is a
ch
ange
in
resi
stan
ce ra
te
ΔΔ
1 ye
ar
31
Mor
e C
ompl
icat
ed M
odel
s•
But
wha
t if y
our p
robl
em is
mor
e co
mpl
icat
ed?
•O
ur m
otiv
atin
g ex
ampl
e:-A
nom
alie
s in
noso
com
ial a
ntim
icro
bial
resi
stan
ce tr
ends
-Due
to (m
is-)
use
of a
ntim
icro
bial
s, bu
gs d
evel
op re
sist
ance
-But
is th
e up
war
d tre
nd u
nifo
rm, o
r is t
here
spat
ial v
aria
tion?
•A
reas
onab
le m
odel
for a
hos
pita
l’s re
sist
ance
tren
d:
01
Resistancerate
An
infe
cted
pat
ient
at t
ime
t has
a re
sist
ant b
ug if
Ber
-no
ulli
trial
with
pro
be
is tr
uep
p 0t
t 0–
()Δ
+=
t
p
32
Mor
e C
ompl
icat
ed M
odel
s•
But
wha
t if y
our p
robl
em is
mor
e co
mpl
icat
ed?
•O
ur m
otiv
atin
g ex
ampl
e:-A
nom
alie
s in
noso
com
ial a
ntim
icro
bial
resi
stan
ce tr
ends
-Due
to (m
is-)
use
of a
ntim
icro
bial
s, bu
gs d
evel
op re
sist
ance
-But
is th
e up
war
d tre
nd u
nifo
rm, o
r is t
here
spat
ial v
aria
tion?
•A
reas
onab
le m
odel
for a
hos
pita
l’s re
sist
ance
tren
d:
01
Resistancerate
An
infe
cted
pat
ient
at t
ime
t has
a re
sist
ant b
ug if
Ber
-no
ulli
trial
with
pro
b is tr
uep
p 0t
t 0–
()Δ
+=
t
p
*Cou
ld im
agin
e ev
en m
ore
real
istic
mod
els:
allo
w fo
r cor
rela
tion
acro
ss tr
ials
(pat
ient
s),
go lo
gist
ic ra
ther
than
line
ar...
the
poin
t is t
hat a
sim
ple
Pois
son
not e
noug
h!
33
Find
ing
a R
egio
n of
Uni
que
Tren
ds•
Giv
en th
is, w
e ha
ve m
any
hosp
itals
in a
larg
e sp
atia
l are
a
34
Find
ing
a R
egio
n of
Uni
que
Tren
ds•
Each
has
its o
wn
set o
f dat
a as
soci
ated
with
it
35
Find
ing
a R
egio
n of
Uni
que
Tren
ds•
Dat
a ar
e us
ed to
lear
n a
loca
l val
ue fo
r Δ
36
Find
ing
a R
egio
n of
Uni
que
Tren
ds•
Then
we
find
a lo
cal r
egio
n w
ith a
n ab
norm
al
-Thi
s res
ult i
ndic
ates
that
yes
, thi
ngs a
re w
orse
loca
lly th
en
they
are
in th
e w
ider
are
a
Δ
37
Two
Key
Pro
blem
s to
Add
ress
Giv
es ri
se to
two
key
ques
tions
:1.
How
doe
s one
bui
ld a
gen
eric
softw
are
that
allo
ws s
patia
l ano
m-
aly
sear
ch w
ith v
irtua
lly a
ny u
ser-s
peci
fied
stat
istic
al m
odel
?-T
hat w
ay, c
an e
asily
giv
e a
who
le su
ite o
f mod
els t
o H
E-I
s the
re a
prin
cipl
ed w
ay to
def
ine
the
gene
ral d
etec
tion
prob
-le
m?
-How
can
a u
ser s
peci
fy/c
ode
his/
her s
peci
fic m
odel
?2.
How
doe
s one
ens
ure
that
the
sear
ch is
reas
onab
ly fa
st?
-We
are
com
pute
r sci
entis
ts, a
fter a
ll
38
Bui
ldin
g a
Gen
eral
Pur
pose
Sof
twar
e -
Pro
blem
Def
initi
on•
Act
ually
qui
te e
asy
to c
ome
up w
ith a
n ap
prop
riate
pro
blem
def
i-ni
tion,
bas
ed o
n a
gene
ric L
RT•
LRT: -G
iven
like
lihoo
d fu
nc.
L
θX
()
Thin
k of
L a
s def
inin
g yo
ur m
odel
...Ta
kes i
n a
set o
f par
amet
ers (
e.g.
slop
e, in
terc
ept).
..R
etur
ns th
e lik
elih
ood
they
pro
duce
d th
e da
ta se
t X...
Para
ms m
atch
dat
a cl
osel
y? T
hen
L re
turn
s lar
ge v
al
39
Bui
ldin
g a
Gen
eral
Pur
pose
Sof
twar
e -
Pro
blem
Def
initi
on•
Act
ually
qui
te e
asy
to c
ome
up w
ith a
n ap
prop
riate
pro
blem
def
i-ni
tion,
bas
ed o
n a
gene
ric L
RT•
LRT: -G
iven
like
lihoo
d fu
nc.
-Let
b
e th
e fu
ll pa
ram
eter
spac
e,
the
“nul
l” o
r uni
nter
est-
ing
part
of th
e pa
ram
eter
spac
e (e
.g.,
all
s are
iden
tical
)
Lθ
X(
)
ΘΘ
0
ΔTh
at is
, the
ta is
cho
sen
from
the
(infin
ite) s
et
Wan
t to
see
if th
eta
is m
ore
likel
y in
or o
ut o
f If
in, t
hen
noth
ing
to se
e he
re!
Θ Θ0
40
Bui
ldin
g a
Gen
eral
Pur
pose
Sof
twar
e -
Pro
blem
Def
initi
on•
Act
ually
qui
te e
asy
to c
ome
up w
ith a
n ap
prop
riate
pro
blem
def
i-ni
tion,
bas
ed o
n a
gene
ric L
RT•
LRT: -G
iven
like
lihoo
d fu
nc.
-Let
b
e th
e fu
ll pa
ram
eter
spac
e,
the
“nul
l” o
r uni
nter
est-
ing
part
of th
e pa
ram
eter
spac
e (e
.g.,
all
s are
iden
tical
)-W
ant t
o co
mpa
re
vs.
Lθ
X(
)
ΘΘ
0
ΔH
0:θ
Θ0
∈H
a:θ
ΘΘ
0–
∈
41
Bui
ldin
g a
Gen
eral
Pur
pose
Sof
twar
e -
Pro
blem
Def
initi
on•
Act
ually
qui
te e
asy
to c
ome
up w
ith a
n ap
prop
riate
pro
blem
def
i-ni
tion,
bas
ed o
n a
gene
ric L
RT•
LRT: -G
iven
like
lihoo
d fu
nc.
-Let
b
e th
e fu
ll pa
ram
eter
spac
e,
the
“nul
l” o
r uni
nter
est-
ing
part
of th
e pa
ram
eter
spac
e (e
.g.,
all
s are
iden
tical
)-W
ant t
o co
mpa
re
vs.
-Can
use
-Cla
ssic
resu
lt; u
nder
,
is c
hi-s
quar
ed
Lθ
X(
)
ΘΘ
0
ΔH
0:θ
Θ0
∈H
a:θ
ΘΘ
0–
∈
ΛX(
)2
sup θ
Θ0
∈ L
θX
()
sup θ
Θ∈
Lθ
X(
)----
--------
--------
--------
--------
--lo
g–
=
H0
ΛX(
)
42
Bui
ldin
g a
Gen
eral
Pur
pose
Sof
twar
e -
Pro
blem
Def
initi
on•
Then
, lay
out
dat
a in
a g
rid:
43
Bui
ldin
g a
Gen
eral
Pur
pose
Sof
twar
e -
Pro
blem
Def
initi
on•
Then
, lay
out
dat
a in
a g
rid:
•O
ut o
f all
regi
ons c
onta
inin
g ho
spita
l, fin
d th
e k
that
hav
e th
e gr
eate
st
val
ues
•C
an u
se c
hi-s
quar
ed d
ist t
o de
term
ine
sign
ifica
nce,
or e
lse
do
sim
ulat
ion
k =
3
ΛX(
)
44
Bui
ldin
g a
Gen
eral
Pur
pose
Sof
twar
e -
Pro
blem
Def
initi
on•
Then
, lay
out
dat
a in
a g
rid:
•O
ut o
f all
regi
ons c
onta
inin
g ho
spita
l, fin
d th
e k
that
hav
e th
e gr
eate
st
val
ues
•C
an u
se c
hi-s
quar
ed d
ist t
o de
term
ine
sign
ifica
nce,
or e
lse
do
sim
ulat
ion
k =
3
ΛX(
)
45
Bui
ldin
g a
Gen
eral
Pur
pose
Sof
twar
e -
Pro
blem
Def
initi
on•
Then
, lay
out
dat
a in
a g
rid:
•O
ut o
f all
regi
ons c
onta
inin
g ho
spita
l, fin
d th
e k
that
hav
e th
e gr
eate
st
val
ues
•C
an u
se c
hi-s
quar
ed d
ist t
o de
term
ine
sign
ifica
nce,
or e
lse
do
sim
ulat
ion
k =
3
ΛX(
)
46
“Tem
plat
izin
g” th
e S
oftw
are
•Th
at’s
fine
, but
we
wan
t to
build
a so
ftwar
e th
at m
akes
it e
asy
to
impl
emen
t suc
h a
test
for a
ny p
artic
ular
like
lihoo
d fu
nctio
n•
So th
at’s
wha
t we
did!
47
“Tem
plat
izin
g” th
e S
oftw
are
•To
app
ly o
ur so
ftwar
e, u
ser n
eed
only
dev
elop
an
appr
opria
te
gene
rativ
e st
atis
tical
mod
el (b
inom
ial w
ith ti
me-
depe
nden
t p in
ou
r exa
mpl
e), l
oad
up th
e da
ta, c
hoos
e a
few
opt
ions
, and
pre
ss
<ret
urn>
•W
e ca
n pr
ovid
e a
libra
ry o
f com
mon
mod
els
48
Key
Impl
emen
tatio
n C
halle
nge
- Spe
ed
•D
epen
ding
on
exac
t pro
blem
, are
~n4 re
ctan
gles
to c
ompu
te
for
•W
hat’s
a re
alis
tic b
ut re
lativ
ely
larg
e va
lue
of n
? M
aybe
100
?
•G
rant
ed, w
hen
I’ve
got
Am
azon
’s c
loud
, 100
4 is n
ot th
at b
ig•
But
nai
ve im
plem
enta
tion
invo
kes t
wo
MLE
s for
eac
h re
gion
•M
LEs o
ften
need
non
-line
ar o
ptim
izat
ion;
at t
en se
cond
s eac
h,
that
’s st
ill 3
2 ye
ars o
f com
pute
tim
e - b
ig e
ven
by c
loud
stan
dard
s•
Our
softw
are
uses
pre
-com
puta
tion
plus
a se
t of t
ricks
to u
pper
-bo
und
with
out e
ver c
ompu
ting
an M
LE
ΛX A(
) ΛX(
)
49
How
Wel
l Doe
s it
Wor
k?•
We
have
app
lied
this
to m
any
prob
lem
s•
In g
ener
al, s
peed
up is
in th
e ra
nge
of 5
tim
es to
100
tim
es•
On
the
antim
icro
bial
resi
stan
ce e
xam
ple
mod
el (3
00+
hosp
itals
):
Grid
size
Our
tim
ePr
unin
g ra
teN
aive
tim
eSp
eedu
p16
x 1
60.
15 d
ays
96.5
%2.
6 da
ys17
.332
x 3
21.
1 da
ys97
.6%
36 d
ays
31.8
64 x
64
11.9
day
s98
.0%
544
days
45.7
50
2nd
Pro
b: D
escr
ibin
g R
esis
tanc
e Tr
ends
•Wan
t to
allo
w H
E to
und
erst
and
the
trend
in h
is/h
er h
ospi
tal
•Key
dat
a: m
icro
biol
ogy
lab
repo
rtpa
tient
: Chr
is J
erm
aine
bug:
e. C
oli
date
: Aug
ust 2
2, 2
010
•U
sual
repo
rt: ta
ble
w. b
ug v
s. dr
ug, g
ive
perc
enta
ge in
eac
h ce
ll-D
oes n
ot sh
ow tr
end
over
tim
e (O
K, c
an fi
x th
at)
Dru
g 1
Dru
g 2
Dru
g 3
Dru
g 4
Dru
g 5
Sus
cept
ible
Sus
cept
ible
Res
ista
ntR
esis
tant
Und
eter
min
ed
51
2nd
Pro
b: D
escr
ibin
g R
esis
tanc
e Tr
ends
•B
igge
r iss
ue: d
oes n
ot a
ccur
atel
y de
scib
e co
rrel
atio
ns...
-Sim
ple
tabl
e ca
nnot
des
crib
e th
e di
ffere
nce
betw
een
this
-And
this
-Bot
h ha
ve 5
0% re
sist
ance
for e
very
thin
g!
Dru
g 1
Dru
g 2
Dru
g 3
Dru
g 4
Dru
g 5
Sus
cept
ible
Sus
cept
ible
Res
ista
ntR
esis
tant
Sus
cept
ible
Dru
g 1
Dru
g 2
Dru
g 3
Dru
g 4
Dru
g 5
Res
ista
ntR
esis
tant
Sus
cept
ible
Sus
cept
ible
Res
ista
nt
Dru
g 1
Dru
g 2
Dru
g 3
Dru
g 4
Dru
g 5
Sus
cept
ible
Sus
cept
ible
Sus
cept
ible
Sus
cept
ible
Sus
cept
ible
Dru
g 1
Dru
g 2
Dru
g 3
Dru
g 4
Dru
g 5
Res
ista
ntR
esis
tant
Res
ista
ntR
esis
tant
Res
ista
nt
52
Mix
ture
Mod
els
•Ins
tead
of t
abul
ar re
p, u
se a
“M
M”
(clu
ster
ed re
pres
enta
tion)
•MM
are
ubi
quito
us in
stat
s, da
ta m
inin
g, m
achi
ne le
arni
ng...
•Sta
tistic
al m
odel
com
pose
d of
mul
tiple
(sim
ple)
com
pone
nts
–Cla
ssic
exa
mpl
e: G
auss
ian
Mix
ture
Mod
el
•MM
imag
ines
follo
win
g, g
ener
ativ
e pr
o-ce
ss fo
r eac
h da
ta p
oint
:(1
) Rol
l a b
iase
d di
e to
sele
ct c
ompo
nent
(die
det
erm
ines
so-c
alle
d “m
ixin
g pr
op.”
)(2
) Use
sele
cted
com
pone
nt to
gen
erat
e po
int
•Pop
ular
for s
ever
al re
ason
s:–S
impl
e di
strib
utio
ns a
s bui
ldin
g bl
ock
mea
ns le
arne
d m
odel
is e
asy
to u
nder
stan
d–W
ith e
noug
h co
mpo
nent
s, ca
n m
odel
non
-par
amet
ric d
ata
of a
rbitr
ary
com
plex
ity–M
odel
pro
vide
s a n
atur
al se
gmen
tatio
n/gr
oupi
ng o
f dat
a
53
Mix
ture
Mod
els
•Var
iant
on
mul
tinom
ial m
ixtu
re m
odel
mig
ht g
ive
you:
Bug
Cla
ss ID
Pr (
freq)
Dru
g 1
Dru
g 2
Dru
g 3
Dru
g 4
Dru
g 5
10.
8R
: 0.2
S
: 0.6
U
: 0.2
R: 0
.1S
: 0.9
U
: 0.0
R: 0
.0
S: 1
.0U
: 0.0
R: 0
.1
S: 0
.9
U: 0
.0
R: 0
.2
S: 0
.7U
: 0.1
20.
15R
: 0.6
S
: 0.2
U
: 0.2
R: 0
.1S
: 0.9
U
: 0.0
R: 0
.0
S: 1
.0U
: 0.0
R: 0
.3
S: 0
.6
U: 0
.1
R: 0
.7
S: 0
.2U
: 0.1
30.
05R
: 1.0
S
: 0.0
U
: 0.0
R: 1
.0S
: 0.0
U
: 0.0
R: 0
.7
S: 0
.3U
: 0.0
R: 0
.5
S: 0
.4
U: 0
.1
R: 0
.8
S: 0
.1U
: 0.1
54
MM
+ E
volv
ing
Mix
ing
Pro
porti
ons
•Wha
t if w
e co
uld
give
the
epi.
som
ethi
ng li
ke th
is?
•Sho
ws a
cle
ar d
ecre
ase
in “
good
” su
bpop
ulat
ion
over
tim
e–A
lso
show
s sig
nific
ant i
ncre
ase
in w
orris
ome
“cla
ss 3
”
•Far
mor
e us
eful
than
the
stat
ic p
ictu
re v
ia c
lass
ical
MM
Bug
Cla
ss ID
Dru
g 1
Dru
g 2
Dru
g 3
Dru
g 4
Dru
g 5
1R
: 0.2
S
: 0.6
U
: 0.2
R: 0
.1S
: 0.9
U
: 0.0
R: 0
.0
S: 1
.0U
: 0.0
R: 0
.1
S: 0
.9
U: 0
.0
R: 0
.2
S: 0
.7U
: 0.1
2R
: 0.6
S
: 0.2
U
: 0.2
R: 0
.1S
: 0.9
U
: 0.0
R: 0
.0
S: 1
.0U
: 0.0
R: 0
.3
S: 0
.6
U: 0
.1
R: 0
.7
S: 0
.2U
: 0.1
3R
: 1.0
S
: 0.0
U
: 0.0
R: 0
.1S
: 0.9
U
: 0.0
R: 0
.0
S: 1
.0U
: 0.0
R: 0
.5
S: 0
.4
U: 0
.1
R: 0
.8
S: 0
.1U
: 0.1
Pr =
0
Pr =
1
clas
s 1cl
ass 2
clas
s 3
Year
= ‘0
0Ye
ar =
‘08
Freq
.
Tim
e
55
MM
+ E
volv
ing
Mix
ing
Pro
porti
ons
•To
obta
in su
ch a
mod
el, i
mag
ine
a ne
w g
ener
ativ
e pr
oces
s for
eac
h da
ta p
oint
:(1
) Che
ck th
e “c
lock
” to
det
erm
ine
time
t whe
n th
e da
ta p
oint
is to
be
gene
rate
d
(2) C
ompu
te
to o
btai
n m
ixin
g pr
opor
tions
at t
ime
t
(3) R
oll a
n ap
prop
riate
ly b
iase
d di
e to
sele
ct c
ompo
nent
(4) U
se se
lect
ed c
ompo
nent
to g
ener
ate
poin
t
•PD
F is
πt()
bt
t b–
t et b
–----
--------
--e
b–
()
×+
=
fy
Θt,
()
π it()
f iy
θ i(
)ik
∑=
56
Why
Lin
ear E
volu
tion?
•Tw
o m
ain
reas
ons
•Firs
t, pe
ople
(use
rs) l
ove
linea
r mod
els
–Eas
y to
und
erst
and,
vis
ualiz
e, e
spec
ially
for n
on-m
ath
peop
le–L
et’s
face
it: l
inea
r reg
ress
ion
is b
y FA
R th
e m
ost p
opul
ar “
data
min
ing
algo
rithm
”!
•Sec
ond,
no
need
to n
orm
aliz
e w
eigh
ts u
nder
line
ar m
odel
–Kno
w th
at
is a
lway
s one
for a
ny t
–But
use
a d
iffer
ent f
unct
ion
(e.g
., al
low
ing
for a
“pe
ak”)
and
you
may
nee
d to
nor
m–C
an d
isto
rt or
igin
al fu
nctio
n an
d pr
ovid
e fo
r ver
y st
rang
e re
sults
π it()
i1
=k∑
57
No
Rea
son
To E
volv
e O
nly
Mix
ing
•In
fact
, AN
Y li
near
ly in
terp
rabl
e pa
ram
can
be
hand
led
in th
is w
ay
–Mea
ns, c
ovar
ianc
es, h
yper
-par
amet
ers,
etc.
•“Li
near
ly in
terp
rabl
e” m
eans
that
if
and
a
re v
alid
par
amet
er
valu
es, t
hen
is a
lway
s val
id
•Exa
mpl
e: li
near
com
bina
tions
of t
wo
cova
rianc
e mat
rices
(mus
t be
posi
tive,
sem
i-def
inite
) are
als
o po
sitiv
e, se
mi-d
efin
ite
θ bθ e
θθ b
tt b
–t e
t b–
--------
-----θ e
θ b–
()
×+
=
58
Can
Eve
n A
llow
Mul
tiple
Cut
s...
allo
ws m
ore
deta
il in
tren
d in
form
atio
n (a
t cos
t of e
xtra
par
ams)
Pr =
0
Pr =
1
clas
s 1cl
ass 2
clas
s 3
Year
= ‘0
0Ye
ar =
‘08
Freq
.
Tim
eP
r = 0
Pr =
1
clas
s 1cl
ass 2
clas
s 3
Year
= ‘0
0Ye
ar =
‘08
Freq
.
Tim
e
59
Gen
erat
ive
Bay
esia
n M
M•W
e as
sum
e th
e fo
llow
ing
gene
rativ
e pr
oces
s for
a d
ata
poin
t:
– p
aram
eter
izes
the
Inve
rse
Gam
ma
and
is u
ser-s
uppl
ed
– p
aram
eter
izes
a D
irich
let,
whi
ch p
rodu
ces t
he m
ixin
g pa
ram
s at t
he b
egin
ning
an
d en
d of
tim
e
–, w
hen
then
incl
udes
as
wel
l as t
he m
ixtu
re c
ompo
nent
par
ams,
is th
en a
ra
ndom
var
iabl
e
α α
η b η e
� b �e
�πt
zy
IGR
IGR
Diri
chle
t
Diri
chle
t
Mul
tinom
ial
f z
α η Θη b
η eb
e,
,,
60
Gen
erat
ive
Bay
esia
n M
M•W
e as
sum
e th
e fo
llow
ing
gene
rativ
e pr
oces
s for
a d
ata
poin
t:
– p
aram
eter
izes
the
Inve
rse
Gam
ma
and
is u
ser-s
uppl
ed
– p
aram
eter
izes
a D
irich
let,
whi
ch p
rodu
ces t
he m
ixin
g pa
ram
s at t
he b
egin
ning
an
d en
d of
tim
e
–, w
hen
then
incl
udes
as
wel
l as t
he m
ixtu
re c
ompo
nent
par
ams,
is th
en a
ra
ndom
var
iabl
e
α α
η b η e
� b �e
�πt
zy
IGR
IGR
Diri
chle
t
Diri
chle
t
Mul
tinom
ial
f z
α η Θη b
η eb
e,
,,
In m
ulti-
cut c
ase,
can
add
man
y of
thes
e, p
ositi
ons
dete
rmin
ed b
yD
irich
let
61
Gen
erat
ive
Bay
esia
n M
M•W
e as
sum
e th
e fo
llow
ing
gene
rativ
e pr
oces
s for
a d
ata
poin
t:
– p
aram
eter
izes
the
Inve
rse
Gam
ma
and
is u
ser-s
uppl
ed
– p
aram
eter
izes
a D
irich
let,
whi
ch p
rodu
ces t
he m
ixin
g pa
ram
s at t
he b
egin
ning
an
d en
d of
tim
e
–, w
hen
then
incl
udes
as
wel
l as t
he m
ixtu
re c
ompo
nent
par
ams,
is th
en a
ra
ndom
var
iabl
e
α α
η b η e
� b �e
�πt
zy
IGR
IGR
Diri
chle
t
Diri
chle
t
Mul
tinom
ial
f z
α η Θη b
η eb
e,
,,
Can
hav
ea
sim
ilar
proc
ess f
oran
y ot
her
para
met
er
62
Gen
erat
ive
Bay
esia
n M
M•W
e as
sum
e th
e fo
llow
ing
gene
rativ
e pr
oces
s for
a d
ata
poin
t:
•The
n c
an b
e “l
earn
ed”
via
a G
ibbs
sam
pler
–Gib
bs sa
mpl
er d
raw
s sam
ples
from
the
“pos
terio
r dis
tribu
tion”
for
–Tha
t is,
sam
ples
from
w
here
–Tec
hnic
ally
(som
ewha
t) ch
alle
ngin
g: u
se fa
ct D
irich
let r
elie
s on
Gam
ma
α α
η b η e
� b �e
�πt
zy
IGR
IGR
Diri
chle
t
Diri
chle
t
Mul
tinom
ial
f z
Θ
ΘF
ΘX
()
Xz 1
t 1,
()
z 2t 2
,(
)…
,,
{}
=
63
Is T
his
Use
ful?
•Exa
mpl
e ap
p: a
pplie
d th
e m
odel
/lear
ner t
o ho
spita
l dat
a–A
roun
d 10
K d
iffer
ent l
ab re
sults
for E
. col
i; 27
ant
imic
robi
als
–Fou
r yea
rs o
f dat
a–F
rom
seve
ral d
iffer
ent h
ospi
tals
–Lea
rned
five
clu
ster
s
64
Is T
his
Use
ful?
•Exa
mpl
e ap
p: a
pplie
d th
e m
odel
/lear
ner t
o re
al-li
fe h
ospi
tal d
ata
12
34
56
78
910
1112
1314
1516
1718
1920
2122
2324
2526
270
0.51
patte
rn 1
12
34
56
78
910
1112
1314
1516
1718
1920
2122
2324
2526
270
0.51
patte
rn 2
12
34
56
78
910
1112
1314
1516
1718
1920
2122
2324
2526
270
0.51
patte
rn 3
12
34
56
78
910
1112
1314
1516
1718
1920
2122
2324
2526
270
0.51
patte
rn 4
00.
51pa
ttern
5
Amp/SulbactamAmox/K Clav
ImipenemCefepime
Ticar/K ClavMeropenem
AmikacinTobramycin
CefazolinCefotetan
LevofloxacinCeftriaxone
CiprofloxacinMoxifloxacinCefotaximeCeftazidimeCefuroxime
NitrofurantoinPip/Tazo
GentamicinAztreonam
CefoxitinErtapenem
AmpicillinTrimeth/SulfaTetracyclineCephalothin
0
0.2
0.4
0.6
year
clus
ter
2
clus
ter
4
clus
ter
1cl
uste
r 3
clus
ter
5
65
Is T
his
Use
ful?
•Exa
mpl
e ap
p: a
pplie
d th
e m
odel
/lear
ner t
o re
al-li
fe h
ospi
tal d
ata
12
34
56
78
910
1112
1314
1516
1718
1920
2122
2324
2526
270
0.51
patte
rn 1
12
34
56
78
910
1112
1314
1516
1718
1920
2122
2324
2526
270
0.51
patte
rn 2
12
34
56
78
910
1112
1314
1516
1718
1920
2122
2324
2526
270
0.51
patte
rn 3
12
34
56
78
910
1112
1314
1516
1718
1920
2122
2324
2526
270
0.51
patte
rn 4
00.
51pa
ttern
5
Amp/SulbactamAmox/K Clav
ImipenemCefepime
Ticar/K ClavMeropenem
AmikacinTobramycin
CefazolinCefotetan
LevofloxacinCeftriaxone
CiprofloxacinMoxifloxacinCefotaximeCeftazidimeCefuroxime
NitrofurantoinPip/Tazo
GentamicinAztreonam
CefoxitinErtapenem
AmpicillinTrimeth/SulfaTetracyclineCephalothin
0
0.2
0.4
0.6
year
clus
ter
2
clus
ter
4
clus
ter
1cl
uste
r 3
clus
ter
5
Obs
erva
tions
From
a c
linic
al st
and-
poin
t, re
ason
to su
spec
t “e
volu
tion”
from
clus
ter 2
to
4 to
1 to
3 to
5. W
hy?
66
Is T
his
Use
ful?
•Exa
mpl
e ap
p: a
pplie
d th
e m
odel
/lear
ner t
o re
al-li
fe h
ospi
tal d
ata
12
34
56
78
910
1112
1314
1516
1718
1920
2122
2324
2526
270
0.51
patte
rn 1
12
34
56
78
910
1112
1314
1516
1718
1920
2122
2324
2526
270
0.51
patte
rn 2
12
34
56
78
910
1112
1314
1516
1718
1920
2122
2324
2526
270
0.51
patte
rn 3
12
34
56
78
910
1112
1314
1516
1718
1920
2122
2324
2526
270
0.51
patte
rn 4
00.
51pa
ttern
5
Amp/SulbactamAmox/K Clav
ImipenemCefepime
Ticar/K ClavMeropenem
AmikacinTobramycin
CefazolinCefotetan
LevofloxacinCeftriaxone
CiprofloxacinMoxifloxacinCefotaximeCeftazidimeCefuroxime
NitrofurantoinPip/Tazo
GentamicinAztreonam
CefoxitinErtapenem
AmpicillinTrimeth/SulfaTetracyclineCephalothin
0
0.2
0.4
0.6
year
clus
ter
2
clus
ter
4
clus
ter
1cl
uste
r 3
clus
ter
5
Obs
erva
tions
From
a c
linic
al st
and-
poin
t, re
ason
to su
spec
t “e
volu
tion”
from
clus
ter 2
to
4 to
1 to
3 to
5. W
hy?
Ear
ly g
ener
atio
n C
epha
-lo
spor
ins (
key
drug
s)
67
Is T
his
Use
ful?
•Exa
mpl
e ap
p: a
pplie
d th
e m
odel
/lear
ner t
o re
al-li
fe h
ospi
tal d
ata
12
34
56
78
910
1112
1314
1516
1718
1920
2122
2324
2526
270
0.51
patte
rn 1
12
34
56
78
910
1112
1314
1516
1718
1920
2122
2324
2526
270
0.51
patte
rn 2
12
34
56
78
910
1112
1314
1516
1718
1920
2122
2324
2526
270
0.51
patte
rn 3
12
34
56
78
910
1112
1314
1516
1718
1920
2122
2324
2526
270
0.51
patte
rn 4
00.
51pa
ttern
5
Amp/SulbactamAmox/K Clav
ImipenemCefepime
Ticar/K ClavMeropenem
AmikacinTobramycin
CefazolinCefotetan
LevofloxacinCeftriaxone
CiprofloxacinMoxifloxacinCefotaximeCeftazidimeCefuroxime
NitrofurantoinPip/Tazo
GentamicinAztreonam
CefoxitinErtapenem
AmpicillinTrimeth/SulfaTetracyclineCephalothin
0
0.2
0.4
0.6
year
clus
ter
2
clus
ter
4
clus
ter
1cl
uste
r 3
clus
ter
5
Obs
erva
tions
From
a c
linic
al st
and-
poin
t, re
ason
to su
spec
t “e
volu
tion”
from
clus
ter 2
to
4 to
1 to
3 to
5. W
hy?
Adv
ance
d ge
nera
tion
Cep
halo
spor
ins (
resis
-ta
nce
is in
dica
tor
of
EB
SL)
68
Is T
his
Use
ful?
•Exa
mpl
e ap
p: a
pplie
d th
e m
odel
/lear
ner t
o re
al-li
fe h
ospi
tal d
ata
12
34
56
78
910
1112
1314
1516
1718
1920
2122
2324
2526
270
0.51
patte
rn 1
12
34
56
78
910
1112
1314
1516
1718
1920
2122
2324
2526
270
0.51
patte
rn 2
12
34
56
78
910
1112
1314
1516
1718
1920
2122
2324
2526
270
0.51
patte
rn 3
12
34
56
78
910
1112
1314
1516
1718
1920
2122
2324
2526
270
0.51
patte
rn 4
00.
51pa
ttern
5
Amp/SulbactamAmox/K Clav
ImipenemCefepime
Ticar/K ClavMeropenem
AmikacinTobramycin
CefazolinCefotetan
LevofloxacinCeftriaxone
CiprofloxacinMoxifloxacinCefotaximeCeftazidimeCefuroxime
NitrofurantoinPip/Tazo
GentamicinAztreonam
CefoxitinErtapenem
AmpicillinTrimeth/SulfaTetracyclineCephalothin
0
0.2
0.4
0.6
year
clus
ter
2
clus
ter
4
clus
ter
1cl
uste
r 3
clus
ter
5
Obs
erva
tions
The
prev
alen
ce o
f clu
ster
2
+ 4
(“be
nign
” cl
uste
rs)
decr
ease
s sig
nific
antly
69
Is T
his
Use
ful?
•Exa
mpl
e ap
p: a
pplie
d th
e m
odel
/lear
ner t
o re
al-li
fe h
ospi
tal d
ata
12
34
56
78
910
1112
1314
1516
1718
1920
2122
2324
2526
270
0.51
patte
rn 1
12
34
56
78
910
1112
1314
1516
1718
1920
2122
2324
2526
270
0.51
patte
rn 2
12
34
56
78
910
1112
1314
1516
1718
1920
2122
2324
2526
270
0.51
patte
rn 3
12
34
56
78
910
1112
1314
1516
1718
1920
2122
2324
2526
270
0.51
patte
rn 4
00.
51pa
ttern
5
Amp/SulbactamAmox/K Clav
ImipenemCefepime
Ticar/K ClavMeropenem
AmikacinTobramycin
CefazolinCefotetan
LevofloxacinCeftriaxone
CiprofloxacinMoxifloxacinCefotaximeCeftazidimeCefuroxime
NitrofurantoinPip/Tazo
GentamicinAztreonam
CefoxitinErtapenem
AmpicillinTrimeth/SulfaTetracyclineCephalothin
0
0.2
0.4
0.6
year
clus
ter
2
clus
ter
4
clus
ter
1cl
uste
r 3
clus
ter
5
Obs
erva
tions
On
the
othe
r han
d, p
reva
-le
nce
of c
lust
er 1
(the
“b
ridge
” ov
er to
EB
SL)
incr
ease
s ove
r tim
e
70
Is T
his
Use
ful?
•Exa
mpl
e ap
p: a
pplie
d th
e m
odel
/lear
ner t
o re
al-li
fe h
ospi
tal d
ata
12
34
56
78
910
1112
1314
1516
1718
1920
2122
2324
2526
270
0.51
patte
rn 1
12
34
56
78
910
1112
1314
1516
1718
1920
2122
2324
2526
270
0.51
patte
rn 2
12
34
56
78
910
1112
1314
1516
1718
1920
2122
2324
2526
270
0.51
patte
rn 3
12
34
56
78
910
1112
1314
1516
1718
1920
2122
2324
2526
270
0.51
patte
rn 4
00.
51pa
ttern
5
Amp/SulbactamAmox/K Clav
ImipenemCefepime
Ticar/K ClavMeropenem
AmikacinTobramycin
CefazolinCefotetan
LevofloxacinCeftriaxone
CiprofloxacinMoxifloxacinCefotaximeCeftazidimeCefuroxime
NitrofurantoinPip/Tazo
GentamicinAztreonam
CefoxitinErtapenem
AmpicillinTrimeth/SulfaTetracyclineCephalothin
0
0.2
0.4
0.6
year
clus
ter
2
clus
ter
4
clus
ter
1cl
uste
r 3
clus
ter
5
Obs
erva
tions
Fortu
nate
ly, t
his i
s not
yet
le
d to
an
incr
ease
of t
he
“nas
ty”
bugs
in c
lust
ers 3
+
5, b
ut th
e ep
idem
iolo
-gi
st sh
ould
be
wor
ried?
?
71
Is T
his
Use
ful?
•Exa
mpl
e ap
p: a
pplie
d th
e m
odel
/lear
ner t
o re
al-li
fe h
ospi
tal d
ata
12
34
56
78
910
1112
1314
1516
1718
1920
2122
2324
2526
270
0.51
patte
rn 1
12
34
56
78
910
1112
1314
1516
1718
1920
2122
2324
2526
270
0.51
patte
rn 2
12
34
56
78
910
1112
1314
1516
1718
1920
2122
2324
2526
270
0.51
patte
rn 3
12
34
56
78
910
1112
1314
1516
1718
1920
2122
2324
2526
270
0.51
patte
rn 4
00.
51pa
ttern
5
Amp/SulbactamAmox/K Clav
ImipenemCefepime
Ticar/K ClavMeropenem
AmikacinTobramycin
CefazolinCefotetan
LevofloxacinCeftriaxone
CiprofloxacinMoxifloxacinCefotaximeCeftazidimeCefuroxime
NitrofurantoinPip/Tazo
GentamicinAztreonam
CefoxitinErtapenem
AmpicillinTrimeth/SulfaTetracyclineCephalothin
0
0.2
0.4
0.6
year
clus
ter
2
clus
ter
4
clus
ter
1cl
uste
r 3
clus
ter
5
Obs
erva
tions
Als
o in
tere
stin
g th
at c
lus-
ter 1
is th
e bi
gges
t “m
over
”; sh
ows g
reat
est
resi
stan
ce to
Flu
oroq
uino
-lo
nes (
pow
erfu
l, bu
t now
w
idel
y ov
erus
ed a
ntim
i-cr
obia
ls)
72
Con
clus
ions
•W
e ha
ve w
orke
d on
man
y ot
her p
robl
ems i
n th
is d
omai
n•
Ex: h
ow to
aut
omat
ical
ly d
etec
t cha
nge
of d
istri
butio
n?•
The
oppo
rtuni
ties f
or c
ompu
tatio
nally
-orie
nted
peo
ple
to h
elp
are
wid
e-ra
ngin
g•
In p
ract
ice:
the
bigg
est p
robl
em is
get
ting
the
data
•In
oth
er d
omai
ns, I
T pe
ople
acc
ept n
eed
for “
DW
” an
d “B
A”.
..•
But
thes
e ar
e st
ill to
ugh
sells
in h
ealth
care
dom
ain
•Se
lf-as
sess
men
t: ho
w d
id o
ur 4
-yea
r gra
nt g
o? C
-/D+
•M
aybe
tim
e w
ill c
hang
e th
is?
We
can
only
hop
e!