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From "Masanz, James J." <Masanz.Ja...@mayo.edu>
Subject RE: Concept annotation questions
Date Thu, 29 Aug 2013 14:18:40 GMT
Hi Dennis,

Thanks for explaining why you are interested in finding out which words in the original text
cause a particular concept to be annotated.  We are currently working on getting Apache cTAKES
3.1 out.  Depending on your timeline, after that is done, perhaps I could create a patch for
you that would help with determining which words from the text matched a dictionary entry,
rather than just the begin offset of the first word and the end offset of the last word.

As far as the chunking, the fact “liver” and “and” are being tagged as O-chunks explains
why the dictionary lookup component is not finding liver cancer or lung cancer in “cancer
of colon, liver and lung”

I’ll try that sentence with the latest chunker model (which will be in cTAKES 3.1) and see
if it assigns correct chunk tags for that sentence.

-- James

From: user-return-257-Masanz.James=mayo.edu@ctakes.apache.org [mailto:user-return-257-Masanz.James=mayo.edu@ctakes.apache.org]
On Behalf Of Dennis Lee Hon Kit
Sent: Wednesday, August 28, 2013 2:33 PM
To: user@ctakes.apache.org
Subject: Re: Concept annotation questions

Hi James & Pei,

Thank you for your replies and sorry for my late reply as I have been away.

Q1 – The longest span could work and is one of the options we are looking at but when there
are overlaps it can get complicated.  In the following example, the longest would work.  We
can take start with 01, and ignore 02 and 03 because their start positions overlap the end
position of 01, and then continue with 04.  But I don’t think it will always be this straight
forward as the being/end string positions may not always be a good indicator of what exactly
in the original text was coded.

00 Invasive ductal carcinoma of the left breast with bone metastases.
01 Invasive ductal carcinoma of the left breast                       408643008|Infiltrating
duct carcinoma of breast (disorder)|
02                                       breast with bone             56873002|Bone structure
of sternum (body structure)|
03                                       breast with bone metastases  94297009|Secondary malignant
neoplasm of female breast (disorder)|
04                                                   bone metastases  94222008|Secondary malignant
neoplasm of bone (disorder)|

Q2 – As we are beginners, we are not at the level where we are comfortable with modifying
cTakes or even know where to begin modifying cTakes but that would be an option in the future.
 Going back to the example of “cancer of liver” and using the begin/end position of the
string that was used to identify the concept, the original string would be “cancer of colon,
lung and liver.”  The CUI that was identified was C0345904, which has 209 (137 unique) descriptions
for all languages.  Examples of English terms include:

  *   CA - Liver cancer
  *   Cancer of Liver
  *   cancer of the liver
  *   Cancer, Hepatic
  *   CANCER, HEPATOCELLULAR
  *   Malignant hepatic neoplasm
  *   Malignant liver tumor
  *   Malignant liver tumour
  *   Malignant neoplasm of liver
  *   malignant neoplasm of liver (diagnosis)
  *   Malignant neoplasm of liver unspecified
  *   Malignant neoplasm of liver unspecified (disorder)
  *   Malignant neoplasm of liver, not specified as primary or secondary
  *   Malignant neoplasm of liver, NOS
  *   Malignant neoplasm of liver, unspecified
  *   malignant neosplasm of the liver
  *   Malignant tumor of liver
  *   Malignant tumor of liver (disorder)
  *   Malignant tumour of liver
It would seem suboptimal to go through each of the descriptions to try and determine which
was the UMLS term that was used in the coding.  It is important for us to know which part
of the string is matched because something like “Invasive ductal carcinoma of the left breast”
will be matched to the SNOMED CT concept “408643008|Infiltrating duct carcinoma of breast
(disorder)|”, but we would like to know that “left” was not matched and would like to
post-coordinate the expression to indicate the left breast, i.e.: 408643008|Infiltrating duct
carcinoma of breast (disorder)|:363698007|Finding site (attribute)|=80248007|Left breast structure
(body structure)|.  When there are other qualifiers like severity, chronicity and episodicity
that may be ignored when matching, we would like to capture it at the level of granularity
specified in the original text.

In terms of the chunking, here is what I see for “cancer of colon, lung and liver”:

  *   NP: cancer of colon, lung and liver
  *   PP: of
  *   NP: colon, lung and liver
For “cancer of colon, liver and lung” here is what I see:

  *   NP: cancer of colon,
  *   PP: of
  *   NP: colon
  *   O: liver
  *   O: and
  *   NP: lung
Q3 – To answer Pei’s question, we are not looking at the preferred name from the UMLS,
just which term was used.

Regards,
Dennis

From: Chen, Pei<mailto:Pei.Chen@childrens.harvard.edu>
Sent: Thursday, August 22, 2013 12:27 PM
To: user@ctakes.apache.org<mailto:user@ctakes.apache.org>
Subject: RE: Concept annotation questions

Also,
> 3)… or the exact description that was returned in the UMLS?
I presume you mean to save the preferred name from UMLS?  If so, this seems to be a common
request- see: https://issues.apache.org/jira/browse/CTAKES-224

--Pei

From: Masanz, James J. [mailto:Masanz.James@mayo.edu]
Sent: Thursday, August 22, 2013 3:24 PM
To: 'user@ctakes.apache.org'
Subject: RE: Concept annotation questions


Welcome to the cTAKES community.

Q1 – some people use the longest span.
Q2 &Q3 – can you just use the text from the dictionary “Malignant neoplasm of liver
(disorder)“.  Alternatively you could modify cTAKES to save the text of the words that it
matches when it is performing dictionary lookup. I would guess there is a term in the UMLS
dictionary with the same code as Malignant neoplasm of liver (disorder) that just has the
words “cancer of liver”, but there isn’t anything in cTAKES to give that to you just
through a configuration change.

For “cancer of colon, liver and lung“, can you look at the chunk  tag for liver.  If it’s
in a separate noun phrase (NP) from “cancer of colon” that would account for why cancer
is not getting tied to liver in that case (but wouldn’t account for why the chunker is creating
as a separate noun phrase)

-- James

From: user-return-248-Masanz.James=mayo.edu@ctakes.apache.org<mailto:user-return-248-Masanz.James=mayo.edu@ctakes.apache.org>
[mailto:user-return-248-Masanz.James=mayo.edu@ctakes.apache.org] On Behalf Of Dennis Lee Hon
Kit
Sent: Wednesday, August 21, 2013 1:10 PM
To: user@ctakes.apache.org<mailto:user@ctakes.apache.org>
Subject: Concept annotation questions

Hi Everyone,

We are new to cTakes so please bear with our questions.  We are using cTakes to annotate things
like encounter diagnoses and referral notes and are especially interested with the SNOMED
CT encodings.  But we are not sure how to make sense of all the outputs.

Example #1

In the example below, “cancer of colon, lung and liver” has been encoded with SNOMED CT
and additional concepts that do not apply have been removed (e.g., general “cancer” concept,
lung, colon and liver structures, etc).   They have been plotted out by the begin/end positions.
 If the terms to do not align, its probably because the email only accepts plain text and
a mono-spaced font is not the default.

cancer of colon, lung and liver
cancer of colon, lung and liver   93870000|Malignant neoplasm of liver (disorder)|
cancer of colon, lung             363358000|Malignant tumor of lung (disorder)|
cancer of colon                   363406005|Malignant tumor of colon (disorder)|

Question (1) – We had to do quite a bit of post-processing to remove inactive concepts,
subtype concepts, concepts that are part of the defining attributes, etc.  Are there a set
of guidelines to help sort out the CUI or SNOMED CT codes that have been identified?
Question (2) – How can we determine that “93870000|Malignant neoplasm of liver (disorder)|”
refers to “cancer of liver” as opposed to using the begin/end string, which points to
“cancer of colon, lung and liver”?  Certainly we can try to do additional parsing but
there are a lot of different scenarios to take into account.
Question (3) – This relates to question 2, are we able to identify the original terms that
were used for the concept matching or the exact description that was returned in the UMLS?
 While the CUI is helpful, the CUI can refer to tens or even hundreds of descriptions.

________________________________
Example #2

Switching the position of colon, lung and liver can result in different encodings.  Once again,
after removing additional concepts not needed (i.e., “cancer” and “colon structure”),
we get the following.  What happened to liver and lung cancer?

cancer of colon, liver and lung
cancer of colon                   363406005|Malignant tumor of colon (disorder)|
                           lung   39607008|Lung structure (body structure)|

We have more questions but will start with these.  Thank you in advance.

Regards,
Dennis
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