Complex text parsing task

From: Joshua Wiley <jwiley.psych at gmail.com>
Subject: Re: [R] Complex text parsing task
To: "Paul Miller" <pjmiller_57 at yahoo.com>
Cc: "Nick Gayeski" <nick at wildfishconservancy.org>, r-help at r-project.org
Received: Monday, May 21, 2012, 11:01 AM
Hi Paul,

I do not think that Nick's comment was really meant to be
directed at
you.? He is probably just tired of getting so many
emails from R-help.

Nick, to stop getting emails if you no longer want them, try
following
the link at the bottom of every single email you have
received from
R-help...you can unsubscribe yourself from there if you
want.? If you
like R-help but just do not like the quantity of emails, you
could
consider switching your subscription to a daily digest so
you just get
one email.? Alternately, you could create a special
folder in your
email for R-help messages, and create a filter that
automatically
sends all message from R-help to that special folder so you
still have
them all but they do not clutter up your inbox.

Cheers,

Josh

On Mon, May 21, 2012 at 8:53 AM, Paul Miller <pjmiller_57 at yahoo.com>
wrote:

Hi Nick,

Can you elaborate (hopefully in a constructive way) on

what it is that you find objectionable about my post?

Thanks,

Paul

--- On Mon, 5/21/12, Nick Gayeski <nick at wildfishconservancy.org>

wrote:

From: Nick Gayeski <nick at wildfishconservancy.org>
Subject: RE: [R] Complex text parsing task
To: "'Paul Miller'" <pjmiller_57 at yahoo.com>,

r-help at r-project.org

Received: Monday, May 21, 2012, 10:36 AM
Please stop sending these emails!


-----Original Message-----
From: r-help-bounces at r-project.org
[mailto:r-help-bounces at r-project.org]
On
Behalf Of Paul Miller
Sent: Monday, May 21, 2012 8:32 AM
To: r-help at r-project.org
Subject: [R] Complex text parsing task

Hello Everyone,

I have what I think is a complex text parsing task.

I've

provided some
sample data below. There's a relatively simple

version of

the coding that
needs to be done and a more complex version. If

someone

could help me out
with either version, I'd greatly appreciate it.

Here are my sample data.

haveData <-
structure(list(profile_key = structure(c(1L, 1L,

2L, 2L, 2L,

3L, 3L, 4L, 4L,
5L, 5L, 5L, 6L, 6L, 7L, 7L), .Label = c("001-001

",

"001-002 ", "001-003 ", "001-004 ", "001-005 ",

"001-006 ",

"001-007 "
), class = "factor"), encounter_date =

structure(c(9L, 10L,

11L, 12L, 13L,
5L, 6L, 7L, 8L, 1L, 2L, 3L, 4L, 4L, 7L, 7L), .Label

= c("

2009-03-01 ", "
2009-03-22 ", " 2009-04-01 ", " 2010-03-01 ", "

2010-10-15

", " 2010-11-15
", " 2011-03-01 ", " 2011-03-14 ", " 2011-10-10 ",

"

2011-10-24 ", "
2012-09-15 ", " 2012-10-05 ", " 2012-10-17 "
), class = "factor"), raw = structure(c(9L, 12L,

16L, 13L,

10L, 7L, 6L, 3L,
2L, 4L, 14L, 15L, 1L, 5L, 8L, 11L), .Label = c("

... If

patient KRAS result
is wild type, they will start Erbitux. ... (Several

lines of

material) ...
Ordered KRAS mutation test 11/11/2011. Results are

still not

available. ...
", " ... KRAS (mutated). Therefore did not

prescribe

Erbitux. ... ", " ...
KRAS (mutated). Will not prescribe Erbitux due to

mutation.

... ", " ...
KRAS (Wild). ...", " ... KRAS results are in.

Patient has

the mutation. ...
", " ... KRAS results still pending. Note that

patient was

negative for
Lynch mutation. ...", " ... KRAS test results

pending. Note

that patient was
negative for Lynch mutation. ...", " ... Ordered

KRAS

mutation testing on
02/15/2011. Results came back negative. ...

(Several lines

of material) ...
Patient KRAS mutation test is negative. Will start

Erbitux.

...", " ...
Ordered KRAS testing on 10/10/2010. Results not

yet

available. If patient
has a mutaton, will start Erbitux. ...", " ...

Ordered KRAS

testing. Waiting
for results. ...", " ... Patient is KRAS negative.

Started

Erbitux on
03/01/2011. ...", " ... Received KRAS results on

10/20/2010.

Test results
indicate tumor is wild type. Ua Protein positve.

ER/PR

positive. HER2/neu
positve. ...", " ... Still need to order KRAS

mutation

testing. ... ", " ...
Tumor is negative for KRAS mutation. ...", " ...

Tumor is

wild type. Patient
is eligible to receive Eribtux. ...", " ... Will

conduct

KRAS mutation
testing prior to initiation of therapy with

Erbitux. ..."

), class = "factor")), .Names = c("profile_key",
"encounter_date", "raw"),
row.names = c(NA, -16L), class = "data.frame")

The following code displays the results of

so-called

"simple" coding.

#### Simple coding ####

KRASpatient <- c("001-001", "001-002",

"001-003",

"001-004", "001-005",
"001-006",? "001-007") KRAStested <-
c(2,3,2,2,2,3,3) KRASwild <-
c(1,0,2,0,3,1,3) KRASmutant <- c(4,2,2,3,1,2,2)
simpleData <-
data.frame(KRASpatient, KRAStested, KRASwild,

KRASmutant)

simpleData

Here, KRAStested is calculated by summing all

references to

"KRAS" for each
patient. Wild is calculated by summing all

references to

"wild type",
"wild", and "negative" that come within 20 words of

the

closest reference to
KRAS. Mutant is calculated by summing all

references to

"mutant", "mutated",
and "positive" that occur within 20 words of the

closest

reference to KRAS.


The second kind of coding is what I'm referring to

as

"complex coding".? The
following code displays the results of this type of

coding.

#### Complex coding ####

KRAStested <- c(2,1,0,2,2,2,3)
KRASwild <- c(1,0,0,0,3,0,3)
KRASmutant <- c(0,0,0,3,0,1,0)
complexData <- data.frame(KRASpatient,

KRAStested,

KRASwild, KRASmutant)
complexData

The results of "complex coding" differ

substantially from

those obtained
under "simple coding" and I think illustrate the

potential

problems with
that approach. With "complex coding", the goal

would be to

identify and sum
only true references to KRAS testing and true

references to

the result of
that testing (either wild type/negative or
mutant/positive).

True references to KRAS testing would be identified

using a

set of
qualifiers that eliminate the false references. So,

for

example, one of the
patients in my (made up) sample data has the phrase

"Will

conduct KRAS
mutation testing prior to initiation of therapy

with

Erbitux" in their
medical record. In this case, "Will" is a qualifier

that

indicates this is
not a true reference to KRAS testing. For this

exercise,

other qualifiers
related to KRAS testing would include "need",

"order" (but

not the past
tense "ordered"), "wait", "waiting", "await", and
"awaiting".
To be a qualifier, these terms would need to occur

within 12

words of the
closest true reference to KRAS.

True references to the results of testing would

also be

identified using a
set of qualifiers that eliminate false references.

Here the

list of
qualifiers would include "if", "lynch", "kras

mutation

test", "kras mutation
testing" and "for kras mutation". Qualifiers would

need to

come within 12
words of a true reference to KRAS testing.

There's an additional wrinkle for identifying true
references to the results
of testing. One also needs to take into account the

presence

of what I'm
calling "nullifiers". For purposes of this

exercise,

nullfiers include "Ua
Protein", "ER/PR", and "HER2/neu" If "positive" or
"negative" come closer to
one of these words than to a true reference to

KRAS, then

they should not be
used to identify the results of KRAS testing.

Help with either type of coding would be greatly
appreciated.

Thanks,

Paul

reproducible

code.

mailing list

reproducible code.



-- 
Joshua Wiley
Ph.D. Student, Health Psychology
Programmer Analyst II, Statistical Consulting Group
University of California, Los Angeles
https://joshuawiley.com/