Good
morning.
Good
afternoon.
Good
evening,
everyone.
My
name
is
Stan
Saranovich,
and
I
am
the
principal
analyst
at
Crucial
Connection,
LLC.
And
I
am
located
in
Jeffersonville,
Indiana,
right
across
the
river
from
Louisville,
Kentucky,
United
States
of
America.
And
today
I'm
going
to
talk
about
sepsis
predictions
from
clinical
data
using
JMP
Pro
16.
But
almost
all
of
what
I
am
going
to
do
here
will
be
available
in
the
standard
version
of
JMP.
Now
let's
talk
about
sepsis
for
a
minute.
To
start
off,
sepsis
is
a
life
threatening
condition
which
occurs
when
the
body's
response
to
infection
causes
tissue
damage
and
cause
organ
failure
or
even
death.
In
fact,
sepsis
costs
United
States
hospital
more
than
any
other
health
condition.
So
if
we
could
predict
sepsis
and
detect
it
early,
we
could
improve
the
outcome
of
critical
care
patients
and
also
lower
the
cost
of
health
care.
So
we're
going
to
look
at
a
data
set
today,
and
this
is
an
actual
data
set
of
clinical
data.
It
was
collected
from
two
hospitals
in
Boston,
Massachusetts,
area
in
the
United
States.
And
these
two
data
tables
were
published
as
a
contest
on
Kaggle
by
a
Cardiology
group,
and
the
results
were
eventually
published
in
the
Cardiology
Journal.
Now,
there
were
three
units
involved
in
this
study,
and
in
this
context,
I
have
the
data
for
what
we'll
call
unit
one
and
unit
two,
which
is
data
from
two
ICU
intensive
care
units.
The
third
group
of
data
was
not
made
publicly
available
and
was
held
back
for
the
contest,
and
it's
still
not
publicly
available.
So
what
we'll
do
now
is
examine
the
data
and
see
if
we
can
predict
sepsis
and
what
variables
we
should
be
following
to
avoid
sepsis,
which,
of
course,
can
be
a
life
threatening
condition.
Now
I
have
a
data
set
in
front
of
me,
and
this
was
downloaded
from
the
Kaggle
site
and
imported
into
JMP.
And
let's
take
a
closer
look
at
it.
Usually
I
like
to
JMP
right
in
to
the
data
analysis,
but
in
this
particular
case,
that's
not
going
to
be
a
good
idea.
And
after
we
look
over
the
data,
you'll
see
why.
First
of
all,
let's
look
over
at
the
left
of
the
JMP
data
table,
and
we
have
the
columns
window.
I
prefer
to
do
a
lot
of
the
work
from
here
and
here
we
see
a
number
of
variables.
So
let's
start
with
heart
rate
right
here.
That's
going
to
be
an
important
predictor,
probably,
but
we
don't
know
that.
And
we
have
the
rest
of
the
predictors
over
here.
And
there
are
actually
40
of
them.
Well,
no,
38,
if
you
don't
count
the
units,
two
saturation,
et
cetera.
And
we
could
go
through
the
list
right
here
temperature.
But
what
you'll
notice
and
for
this,
I'm
going
to
have
to
scroll
is
that
the
first
six
or
eight
columns
are
just
clinical
data.
We
have
systolic
blood
pressure.
We
have
respirations
diastolic
blood
pressure.
And
as
we
go
across
our
data
table,
we
see
some
lab
data.
We're
talking
about
glucose
and
lactate
levels,
magnesiums,
phosphate,
potassium,
bilirubin,
et
cetera,
et
cetera.
And
finally,
we
have
a
set
of
columns,
which
I
guess
we
could
call
it
demographic
data.
We
have
the
age
right
here,
gender,
and
what
unit
they
belong
to.
Now,
while
we're
on
units,
let's
take
a
look
here.
We
have
unit
one
and
unit
two.
And
we
know
from
doing
our
background
research
and
we
all
do
background
research.
Right
before
we
start
the
analysis,
we
know
that
one
is
a
Cardiology
unit
and
the
other
one
is
a
surgery
unit.
So
if
they're
in
unit
two,
we
have
a
one
standard
practice,
and
if
they're
in
unit
one,
which
they're
not,
we
have
a
zero.
Also
standard
practice.
But
notice
something
else
here.
We
have
a
lot
of
rows
where
there
is
no
unit.
Now,
we
don't
know
where
that
data
came
from,
not
unit
two.
And
the
background
that
was
published
on
the
cable
site
tells
us
that
the
third
unit
is
going
to
be
held
back
to
score
the
model.
So
it
wasn't
made
public.
So
we
don't
know
where
that
data
came
from.
And
I'll
discuss
that
in
further
detail
in
a
little
bit.
Now
let's
scroll
back
over
and
look
at
some
other
things
about
this
data
table.
We
have
a
whole
lot
of
missing
data
and
some
of
them
let's
take
a
look
at
some
of
the
columns
here,
and
we
can
just
scroll
down.
Here
the
billerobin
direct.
There's
one
we
had
to
go
down
63
rows
before
we
found
one
set
of
data.
Here's
another
one
at
row
113.
So
there's
not
a
whole
lot
of
data
in
there.
As
a
matter
of
fact,
that
column
is
only
3%
populated,
and
there's
a
whole
lot
of
other
columns
that
are
populated
at
a
similar
rate.
That
was
the
worst
example,
but
they're
a
whole
lot
of
five
and
ten.
And
we
also
have
the
problem
with
submission
unit
assignment.
So
let
me
close
that
data
table
and
I
will
open
another
one.
There
we
are.
Now,
I
made
some
modifications
to
that
first
table
and
I
decided
to
save
some
time
and
not
make
you
sit
through
and
just
watch
me
clicking
on
columns.
Notice
over
here
in
the
columns
area,
we
have
these
two
symbols
right
here.
One
hides
columns
and
the
other
one
excludes
them
from
the
analysis.
And
if
you'll
notice
this
one
particular
one,
et
CO2,
it
was
right
about
here
in
the
first
data
set
and
it's
missing.
So
we
hid
them
and
we're
going
to
exclude
them
from
the
analysis.
I
also
did
two
things
which
it's
just
personal
to
me.
Number
one,
I
moved
our
target
variable.
What
we
want
to
predict
is
sepsis
to
the
left,
so
that
when
I
view
the
data
table,
I
can
just
scan
across
the
rows
and
see
some
relationships
if
there's
something
I
want
to
see.
And
they
also
moved
this
one
over
because
I
knew
this
just
from
all,
for
lack
of
a
better
term,
general
intuition
that
this
was
going
to
be
an
important
variable
and
that's
ICU,
Los,
which
is
intensive
care,
unit
length
of
stay.
Now
let's
look
at
some
other
things
around
here.
I
want
to
note
one
other
thing
I
excluded.
Where
was
it?
Right
here.
You
can't
see
it
hospital
admission
time.
And
what
that
is,
we
think,
is
the
time
between
they
were
admitted
to
the
hospital
and
the
time
they
were
admitted
to
the
ICU.
And
a
lot
of
those
numbers
are
negative,
but
there's
nothing
in
the
documentation
that
tells
us
how
you
can
have
a
negative
time.
So
I
excluded
those
also.
And
the
other
rows
that
I
excluded,
like
the
bridge
in
there
right
above
it.
It's
the
same
deal
with
those.
There's
a
lot
of
missing
data,
so
I
just
excluded
those.
And
let's
see,
is
there
anything
else
I
need
to
do
here?
No,
it's
time
to
do
the
analysis.
But
before
we
do
that,
let
me
tell
you
what
the
overall
plan
is
going
to
be.
And
that
is
after
we
examine
the
data
and
clean
it
and
prep
it,
which
we've
already
done,
we're
going
to
look
at
the
individual
units.
We're
going
to
look
at
is
sepsis,
in
other
words,
whether
or
not
the
people
develop
sepsis,
and
we're
going
to
do
some
database
management.
So
let's
get
started.
The
first
thing
I'd
like
to
do
is
go
up
here
to
Tables
and
then
go
to
Subset,
and
we
get
the
pop
up
window
from
JMP.
It
says
create
a
new
data
table,
et
cetera,
et
cetera.
So
let
me
click
on
that.
And
of
course,
I
want
to
check
this
box
here
that
says
Subset,
Buy.
And
I'll
Scroll
down
because
we
knew
there
were
two
units
and
there
was
also,
in
effect,
a
third
unit,
which
was
neither
unit
one
or
unit
two.
So
let
me
just
click
on
unit
one,
and
I
want
to
pull
in
all
the
rows
from
that
one.
And
I'd
like
to
keep
a
Buy
column
just
as
a
safety
check,
and
you'll
find
out
why
in
just
a
minute
here.
And
we
could
keep
the
dialogue
open.
But
I've
run
through
this
analysis
before,
so
right
now,
hopefully
I
won't
make
a
mistake.
It
won't
change
my
mind.
And
there'll
be
no
need
to
keep
the
dialogue
box
open.
And
for
right
now,
we'll
skip
the
output
table
name,
and
I
don't
want
to
link
it
to
the
original
data
table,
but
I'll
come
down
here
to
this
box
and
save
the
script
to
the
source
table
and
take
one
last
look
over
this
and
everything
looks
okay.
And
I'll
click
the
okay.
Now
let
me
separate
these
a
little
bit.
Remember,
we
had
unit
one,
unit
two,
and
missing,
which
was
neither
unit
one
or
unit
two.
And
I've
got
three
data
tables
here.
And
at
the
titles,
it
says
unit
one,
JMP
probe.
Well,
let's
just
look
at
that.
It
says
here
unit
one.
And
if
I
Scroll
over,
it
says
unit
one
and
unit
two.
And
this
is
the
reason
I
kept
the
Buy
columns
right
here.
It's
all
missing,
dad.
And
we
scroll
down
a
little
bit
just
to
double
check.
And
yeah,
it
looks
like
they're
missing.
So
it
says
unit
one.
So
what
we
want
to
do
is
relabel
that
right
click,
hit,
edit,
and
we
can
change
that
to
missing.
And
I'll
type
it
in
and
we'll
hit
okay,
and
we
now
know
what
that
is.
It
says
unit
one
equals
I
mistyped
it
should
be
missing,
but
I'll
leave
it
go
for
right
now
because
we
can't
analyze
that
because
we
don't
know
where
it
came
from
or
rather,
we
don't
want
to
analyze
it.
So
I'll
just
close
that
out
and
I
don't
want
to
save
the
changes.
Now,
this
one
says
unit
one
equals
zero.
And
I'll
come
over
here
and
yeah,
sure
enough,
there's
zero
in
unit
one,
which
means
it
does
not
belong
to
unit
one.
And
over
here
it
says
unit
two
and
there's
one
in
the
columns.
Scroll
down
a
little
bit
just
to
make
sure.
And
yeah,
it
says
unit
two.
Okay,
so
what
we'll
want
to
do
is
go
up
here
and
we
can
click
that
and
we
could
edit
one,
edit
it.
And
now
I
want
to
change
that
and
we'll
do
that.
And
here
where
it
says
subset
script.
We
can
go
up
here
and
I
want
to
change
this.
I
can
edit
that
and
I'll
change
that
to
unit,
too,
but
it
won't
do
it
right
now.
And
it
says
right
here,
if
we
want
to
check,
we're
looking
at
unit
and
right
here
it
says
keep
by
column
one,
unit
one,
and
that's
the
column
it
is.
So
I'll
just
cancel
out
of
that
for
now.
And
it's
the
same
with
this
data
table
over
here.
We
can
go
to
source
code,
or
rather
the
source
edit.
It
says
keep
by
columns
one,
columns
one,
and
we'll
cancel
out
of
that.
And
that's
it
for
the
units
for
right
now.
Now,
what
we
could
do
is
go
up
here
and
do
another
table
platform.
We've
got
summary
and
subset
and
whatnot
we
could
click
on
summary,
and
we
could
drag
what
we
want
to
summarize
in
here
and
we
could
pick
our
statistics,
whether
we
want
to
count
in
the
mean,
standard
deviation,
the
medium,
excuse
me
and
whole
lot
of
other
stuff,
but
we'll
skip
that
for
now
and
let
me
cancel
that
out
and
I'll
close
these
two
tables
and
I
won't
save
them
because
I
have
a
couple
of
tables
down
here
where
I
already
did
this.
So
let
me
take
come
up
here
and
I'll
Select
unit
one
and
unit
two
and
I'll
open
them.
Now,
if
we
wanted
to
see
the
difference
in
outcomes,
for
example,
between
unit
one
and
unit
two,
we
could
analyze
both
of
these
status
sets
separately,
but
in
the
interest
of
time,
we
will
not
do
that.
What
we
will
do,
we
combine
them
and
analyze
them
together.
Now,
let's
show
another
feature
of
JMP
that
makes
the
data
handling
part
of
our
job
easy.
And
let
me
go
up
to
tables
and
what
we
want
to
do
here
is
concatenate
and
the
helper
window
shows
up.
It
says
combines
rows
from
several
data
tables.
We
have
a
number
of
other
selections
we
could
have
made
to
avoid
us
having
to
write
some
SQL
code,
some
SQL
code.
But
here
we
want
to
concatenate
it.
And
unit
one
showed
up
on
top
and
that's
good.
And
what
we
want
to
do
is
concatenate
unit
two.
So
I'll
click
on
that
and
I'll
click
that
and
let's
give
this
a
name.
Let's
call
this.
How
about
both
this
or
not?
Check
it.
I'll
just
check
it
here
now.
And
we
could
create
a
source
column
and
again
as
a
check
just
to
make
sure
everything
is
proceeding
like
we
want
it
to
proceed.
I'll
create
the
source
column
and
I
could
keep
the
dialogue
box
open.
That's
right
here.
But
I
will
close
it
for
now
since
hopefully
I
didn't
make
any
mistakes.
Won't
have
to
go
back
and
we'll
click
the
run
button
and
let's
see.
It
didn't
come
up.
Let
me
try
that
again.
Let
me
close
that
window.
We'll
leave
it
like
that
and
let's
see
what
happens.
There
we
go.
Must
have
fat
fingers
this
morning.
And
this
is
a
combined
data
table
and
as
I
mentioned,
I'd
like
to
keep
that
open.
It's
our
source
table.
Normally
what
I
do
is
drag
that
somewhere
off
the
edge
of
the
table.
But
for
right
now,
we'll
leave
it
there
and
we'll
just
scroll
down
a
little
bit
and
we
note
that
we
have
unit
one,
unit
one.
There
we
go.
Unit
two.
So
that
serves
as
a
check.
So
we
have
that
there.
Now
it's
time
finally
to
start
the
analysis.
We
can
do
a
number
of
things
here.
First
of
all,
we
note
that
most
of
our
variables
are
continuous
except
for
our
target
variable,
which
is
binary
on
or
off,
yes
or
no,
et
cetera.
But
let's
just
take
a
look
at
the
distributions.
I
always
like
to
point
this
out
in
the
analysis.
This
is
one
of
my
favorite
features
to
JMP.
You
can
do
a
quick
inspection
right
here
to
see
if
there's
anything
weird
and
let's
see.
Okay,
scroll
back
over.
I
don't
see
anything
that
pops
out
at
me.
It
looks
like
unit
one
and
unit
two.
We're
right
in
there.
One
thing
to
note
right
here
is
sepsis
one
there's.
There's
a
lot
fewer
rows
where
the
patient
went
into
sepsis.
And
let's
see,
I
think
it
was
about
7%.
So
we're
looking
at
93
here
and
7%
here.
So
let
me
get
rid
of
that.
That's
what
I
like
to
do.
Now
let's
go
up
to
the
analyze
menu,
and
we're
going
to
make
use
of
this
pretty
much
exclusively
from
now
until
the
end
of
the
presentation.
So
I'm
going
to
go
down
here,
I'm
going
to
choose
multivariate
methods
from
the
drop
down,
and
I
get
another
drop
down.
And
what
I'm
going
to
do,
come
on,
is
choose
multivariate.
And
this
window
pops
up
and
wants
to
know
the
why
call.
By
the
way,
this
is
the
reason
why
I
like
to
put
another
reason
I
like
to
put
the
target
variable
over
on
the
left.
It's
right
here,
and
we
can
pop
it
into
the
Y
column
and
we
don't
have
to
scroll
and
Hunt
for
it.
So
let's
see,
we've
got
everything
in
there.
We
don't
want
unit
one
or
unit
two.
What
else
do
we
have?
Gender,
age?
I'll
tell
you
what,
let's
put
them
all
in
there
and
click
my
call
hit.
Okay.
And
here's
what
we
get.
We
get
a
correlation
matrix.
Now
let's
take
a
little
closer
look
at
this.
It's
a
little
confusing
because
we
put
a
whole
lot
of
variables
in.
But
again,
that's
one
of
the
advantages
to
JMP.
You
can
pop
them
all
in
there
and
don't
have
to
write
extra
code
for
it.
So
we
have
our
diagonal
here.
Our
matrix
is
reflected
along
the
diagonal.
So
it's
the
same
data
top
and
bottom
and
some
different
colors
here.
One
means
a
high
correlation.
It's
statistically
significant,
which
is
what
we
expect.
The
blood
pressure,
for
example,
should
be
correlated
fairly
well
with
itself.
But
we
also
note
that
right
here,
right
under
it,
it's
correlated
with
something
called
Map,
which
is
mean
arterial
pressure.
So
that's
mean
between
a
systolic
and
a
diastolic.
So
that
makes
sense.
And
we
have
DBP,
which
is
diastolic
blood
pressure.
So
that
makes
sense.
And
there's
not
a
whole
lot
we
can
see
here.
Here's
another
correlation.
Okay.
That's
bun
for
the
urea
content.
Blood
urea
nitrogen
is
what
it
stands
for.
That's
the
urea
content
of
the
blood,
which
is
a
byproduct
of
cellular
physiology.
And
that
looks
like
it's
correlated
with
something
over
here,
potassium,
but
pretty
much
that's
about
it.
Here's
a
Hema
crit,
which
looks
like
it's
correlated
with
right
over
here,
the
HGB.
If
we
read
over,
everybody
see
that
square
here?
Take
a
second
to
look
at
it.
And
HCT
is
hemacrit,
and
HGB
is
hemoglobin.
And
hemoglobin
is
a
direct
measure
of
hemacrit
or
excuse
me,
the
hemoglobin.
And
the
hemochret
is
the
I
believe
it's
the
volume
fraction
of
red
blood
cells.
So
you
would
expect
them
to
be
correlated.
So
not
a
whole
lot
to
see
here
outside
of
that.
May
as
well
close
that.
Next,
we're
going
to
go
back
up
to
the
analyze
menu.
We're
going
to
go
to
analyze.
And
from
the
drop
down,
we're
going
to
go
to
screening.
And
again,
we
have
another
drop
down.
And
let's
Hover
over
this.
It's
called
predictor
screening.
It
screens
many
predictions
for
their
ability
to
predict
an
outcome.
So
we
want
to
be
able
to
predict
whether
or
not
a
particular
patient
is
going
to
develop
sepsis
so
that
it
looks
like
a
good
choice.
So
we
click
it
and
this
is
the
window
that
we
get.
And
again,
we
want
to
know
is
sepsis.
One
is
yes,
zero
is
no.
So
we
click
that
and
we're
presented
with
the
range
again,
the
same
range
we
had
before.
And
let's
do
what
we
did
before.
Start
here.
We're
going
to
go
down
here
to
gender,
ignoring
the
units
again,
hit
the
shift
button,
click
and
we
select
all
of
them
and
we're
going
to
hit
the
X
button.
And
there's
nothing
else
there
for
us
to
take
note
of.
Doesn't
look
like
anything
else
to
click.
So
do
that.
We'll
click.
Okay.
And
there
we
go.
And
JMP
tells
us
that
it's
doing
a
bootstrap
forest.
We
could
do
a
whole
presentation
on
bootstrap
forest,
but
we
don't
have
time.
In
fact,
we
could
probably
do
two
or
three
or
four.
And
we
are
getting
there.
It's
scoring
the
results
and
we
just
have
to
wait.
It's
taking
a
while
for
some
reason.
And
there
we
are.
Let's
look
at
what
we
have
here.
We
have
the
contribution,
which
is
the
net
contribution,
not
scaled,
not
scaled
to
the
model.
I
have
to
use
the
word
again
portion
that
contributes
to
the
model.
And
you
can
think
of
this
as
a
weight
fraction
or
if
you
prefer,
multiply
by
100
in
your
head
to
make
it
a
percent.
And
if
we
just
take
a
quick
look
at
that,
we
see
you
at
zero,
61
and
three.
So
we're
at
zero,
74,
six.
Looks
like
it
takes
us
up
to
zero,
eight
or
80%
explanation.
So
that's
what
that
is.
So
all
those
make
sense.
Now
let's
look
at
that
Iculos
before,
I
talked
about
excluding
that
well
intensive
care
unit
length
of
stay
that
predicts
what
looks
like
more
than
the
others
combined,
probably.
However,
if
we
use
that,
that
would
be
a
little
bit
of
circular
reasoning.
If
people
develop
sepsis,
they're
almost
certainly
going
to
end
up
in
the
ICU.
If
they
are
really
sick,
they
may
be
at
higher
risk
of
developing
sepsis.
So
they
are
going
to
end
up
in
the
ICU.
So
if
they're
in
their
ICU,
they're
probably
pretty
sick
to
begin
with.
They're
already
developing
sepsis
and
they're
going
to
be
in
there
for
a
while.
So
let's
go
up
here
in
Excel.
That
really
doesn't
help
us
too
much
because
it's
not
something
we
can
measure
like
blood
pressure.
I
mean,
they're
already
in
there
or
they're
not
in
there.
So
let's
exile
to
that
and
we'll
go
up
here
to
analyze
back
down
to
screening,
predictor,
screening.
Hit
is
sepsis
for
the
Y
response.
And
let's
leave
that
one
out.
Let's
leave
the
ICU
Los
out
and
we'll
do
everything
exactly
the
same
as
before.
Hit,
shift,
gender,
select
everything.
Hit
the
X
button.
Nothing
else
for
us
to
do
there.
It
doesn't
look
like
Hit.
Okay.
And
we'll
just
wait
for
a
little
while.
Again,
looks
like
it's
running
a
little
bit
faster
this
time.
And
here
we
go.
Now
we
had
the
ICU
Los
completely
out
of
the
picture
and
we
see
something
else
here.
The
Bun
blood
urea
nitrogen
looks
like
it's
in
the
running
for
a
significant
predictor
after
that
temperature,
which
makes
sense.
If
you
develop
sepsis,
you
have
an
infection.
So
you're
probably
going
to
have
a
temperature.
Creatinine
is
a
byproduct
of
muscle
breakdown.
So
that
makes
sense.
And
remember,
we
did
our
research
before
we
started
the
analysis
and
after
that
respirations,
that
make
sense.
Shallow,
rapid
breathing,
hemoglobin
content,
Hema,
crit.
Okay,
they're
highly
correlated
blood
pressure.
And
this
WBC
I
didn't
point
out
before,
but
that's
white
blood
cell
count.
So
that
makes
sense
too.
Now
we
have
a
decision
to
make.
This
is
obviously
the
most
prominent
don't
want
to
say
important.
We're
not
sure
that
yet,
but
it's
the
most
prominent.
After
that
comes
temperature
and
creatinine.
And
then
there's
a
large
drop
in
the
rankings
and
the
importance
of
the
rankings
in
the
portion
here.
So
we
have
a
decision
to
make.
So
let's
start
up
there
with
the
blood
urea
nitrogen
and
let's
go
down.
Let's
pull
in
as
much
as
we
can
because
JMP
is
going
to
make
this,
all
the
repetitive
tasks,
all
the
calculations,
easy
for
us
by
taking
them
away
from
us.
So
Shift
click.
Let's
go
down
to
systolic
blood
pressure.
That's
probably
going
to
play
a
role
because
if
you
have
sepsis,
you
tend
to
have
very
low
blood
pressure.
Dangerous.
And
we
have
some
other
measurements
down
here,
but
we'll
just
skip
those.
By
the
way,
these
two
are
going
to
be
correlated.
This
one
right
here
is
the
partial
pressure
of
the
carbon
dioxide
in
the
blood
and
this
is
the
carbonate
content
of
the
blood.
So
those
are
going
to
be
related.
So
it
doesn't
look
like
there's
anything
else
of
importance.
And
JMP
puts
in
a
handy
link
there.
It
says
copy
selected.
So
let's
do
that.
I
copied
the
selected
and
I'll
just
leave
that
open
for
right
now
and
we'll
go
up
here
to
analyze
once
again.
And
what
we
want
to
do
now
is
fit
the
model.
It
says
fits
a
linear
regression.
So
let's
go
there.
And
since
we
copied
selection
in
the
previous
window,
JMP
remembered
that
for
us.
And
what
we
want
to
do
is
click
the
add
button
and
we'd
like
them
to
construct
the
model
effects.
That
means
we
want
to
use
them
as
a
modeling
variable.
And
what
else
we
have
here.
Notice
this
upper
right
hand
corner
we
have
something
called
personality.
So
focus
on
that
right
hand
corner
for
just
the
next
30
seconds
or
so.
And
I'll
go
over
here
and
hit
his
sepsis
and
we'll
put
that
in
the
wine.
I'll
look
at
the
upper
right
hand
corner
personality.
I
click
the
Y
and
I
get
a
choice
here.
I
get
some
choices
in
the
drop
down
menu
and
I
get
an
emphasis
window
and
I'll
click
the
drop
down
triangle
here
and
I
have
a
whole
lot
of
choices
here
and
we
won't
go
over
them
right
now.
But
probably
what
I
want
is
a
generalized
linear
model.
And
if
I
Hover
over
it,
let
me
try
that
again.
It
gives
us
a
pop
up
window.
It
says
fits
a
generalized
model,
and
try
it
once
again.
And
I
get
to
select
the
distribution
and
the
link
and
all
that
sort
of
thing,
which
I'll
go
over
in
a
couple
of
seconds
here,
and
I
get
another
drop
down
here
a
link
function.
So
let's
start
with
distribution.
And
remember,
we
expanded
that
top
column
and
we
got
our
distributions.
We
took
a
look
at
it
didn't
look
like
there's
anything
weird
there,
at
least
on
a
macro
scale.
So
let's
just
pick
normal.
And
I
want
logic
for
the
link
function
because
we've
got
a
binary
variable
that
we're
trying
to
predict.
And
let's
see,
take
a
closer
look.
Nothing
else
left
for
me
to
do.
It
doesn't
look
like.
So
I'll
hit
the
run
button
and
here
we
go.
Here's
our
generalized
linear
model
fit,
and
it
gives
us
a
summary
up.
Here
what
we
looked
at.
We
got
a
Kai
square.
I
won't
go
over
that
in
any
great
detail,
but
let's
scroll
down
a
little
bit
more,
cut
that
off.
Here
we
have
an
effect
summary.
And
just
by
the
way,
if
we
click
on
these
triangles,
we
can
hide
them
or
make
them
appear
again.
So
depending
on
what
we
want
to
present,
I'll
just
leave
them
all
open
for
right
now.
And
what
we
have
up
here
is
the
source.
And
there
it
is.
Bloody
reinrogen.
The
bun
is
up
there
very
high
again,
followed
by
temp,
creatinine,
white
blood
cell
content,
heart
rate,
and
some
blood
pressure
measurements.
And
all
this
makes
sense
from
what
we
know
about
sepsis
and
log
worth
is
contribution.
And
over
here
we
have
P
value
and
we
see
that
they're
all
very
highly
significant
up
to
right
about
here,
the
white
blood
cell
content.
And
we
see
a
blue
line
here.
And
what
that
is
the
log
worth
of
two.
And
the
reason
we
take
the
log
worth
is
because
we'd
like
to
be
able
to
Doe
things
on
the
graph.
So
we
put
it
on
a
log
skill
and
that
just
makes
it
easier.
Otherwise
the
spray
bar
up
here
would
be
off
the
edge
of
my
screen.
And
this
blue
line
here
is
a
significance
level
because
it's
the
log
worth
and
zero
one
significance
level
is
log
of
negative
two.
Excuse
me,
is
the
value
of
zero
two,
which
is
log
of
two.
Get
rid
of
the
negative
sign.
And
that's
what
this
blue
line
is.
So
these
are
all
significant
up
to
HR.
And
if
we
come
down
here,
we
have
the
square
results
and
we
see
some
significance
levels
here,
too.
And
basically,
we're
looking
at
the
respiration,
the
bun
and
the
creatinine
and
also
the
temperature.
They're
all
highly
almost
forgot
the
white
blood
cell
content
down
here.
And
I'm
running
out
of
time,
so
I
won't
explain
a
whole
lot
about
that.
But
if
we
scroll
down
a
little
bit
more,
we
can
get
an
estimate
for
our
predicted
variables
here.
This
is
an
estimate
of
the
exponent
and
it
gives
us
more
statistical
data
on
that.
And
I'm
starting
to
run
out
of
time.
So
let
me
just
minimize
those
windows
and
we'll
get
rid
of
all
the
highlights
and
let's
recap
what
we
had
here.
Here
is
our
original,
Highly
cleaned
and
rearranged
data
table.
We
want
to
predict
sepsis,
which
is
binary.
And
we
ruled
out
the
lens
of
stay
in
the
ICU
unit
that
is
right
here
Because
it
didn't
help
us
and
it
was
kind
of
circular
logic.
And
we've
got
our
variables
in
three
separate
groups
up
here.
We
start
off
with
the
clinical
and
then
we
come
over
here
and
we
had
all
our
blood
tests
and
then
we
had
the
demographic
data
and
we
had
two
units
and
we
excluded
all
25
or
30%
of
the
data
Because
the
data
wasn't
assigned
to
either
unit
and
we
don't
know
where
it
came
from.
So
we
got
rid
of
that
and
then
we
subseted
everything.
Remember,
we
got
to
well,
actually,
we
got
three
separate
subsets
Because
the
third
subset
was
the
missing
data
unit
unit
being
in
quotes.
And
we
went
from
there
and
we
went
to
the
multivariate
to
look
for
some
correlations.
Then
we
went
to
the
analyze
screening,
predictor
screening,
and
we
got
what
we
figured
was
going
to
be
our
most
valuable
predictors
to
predict
sepsis.
And
finally
we
went
to
fit
model
and
let
me
reiterate
on
that.
We
went
up
to
analyze
fit
model
and
we
clicked
that
and
we
got
this
window
and
we
dumped
everything
in
there
except
for
what
we
wanted
to
exclude.
And
we
put
sepsis
right
here
in
this
y
variable.
And
remember,
this
is
the
area
that
we
had
to
focus
on
up
here
in
the
upper
right
hand
corner.
We
had
the
personality
and
a
couple
of
other
selections
to
make,
and
we
selected
from
that.
And
we
got
our
results,
which
I
went
over
about
a
minute
ago,
and
that
is
the
end
of
the
presentation.
I
hope
everybody
enjoys
even
learned
a
little
something
from
it.
Thank
you
for
watching,
listening,
and
giving
it
your
attention.
