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From samir chabou <samir...@yahoo.com>
Subject Re: Concept annotation questions
Date Thu, 29 Aug 2013 18:51:12 GMT
Thanks Tim,
it looks a better and cleaner way. It means the List l = JCasUtil.selectCovered(jcas, BaseToken.class,
i) will give me the intersection between the BaseTokens and IdentifiedAnnotations. If my base
token is in the list so the base token is also an IdentifiedAnnotation. I'll give it a try
some time next week and let you know. 

 From: Tim Miller <timothy.miller@childrens.harvard.edu>
To: user@ctakes.apache.org 
Sent: Thursday, August 29, 2013 1:07:58 PM
Subject: Re: Concept annotation questions

You may be able to use the JCasUtil class from Uimafit to do
    something like the following:

for each IdentifiedAnnotation i:
    List l = JCasUtil.selectCovered(jcas, BaseToken.class, i)

(this is java-ish pseudocode obviously). Then the list you get of
    tokens will all have the same type as the IdentifiedAnnotation i.
    Would that solve your problem?

On 08/29/2013 12:29 PM, samir chabou wrote:

Hi James and Pei,
>I also need to know what is the medical type (Sympto, Drug , procedure, relation) of
a given word token. Since in the typeystem hierarchy wordtoken is not under the same inheritance
tree than identifiedAnnotation . I’m currently iterating on all wordTokens and compare each
wordToken.CoveredText to the annotations.CovredText in the identifiedAnnotation. I found this
a long process. James, do you think the patch  <<I could create a patch for you that
would help with determining which words from the text matched a dictionary entry >>
that you are planning to create will permit also this requirement ? or can you suggest me
some thing better than I’m currently doing.
> From: "Masanz, James J." <Masanz.James@mayo.edu>
>To: "'user@ctakes.apache.org'" <user@ctakes.apache.org> 
>Sent: Thursday, August 29, 2013 10:18:40 AM
>Subject: RE: Concept annotation questions
>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
>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)|
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 
>	* 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.
>From:Chen, Pei 
>Sent:Thursday, August 22, 2013 12:27 PM
>Subject:RE: Concept annotation questions
>>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
>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]
On Behalf Of Dennis Lee Hon Kit
>Sent: Wednesday, August 21, 2013 1:10 PM
>To: 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
>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
>                           lung   39607008|Lung structure
(body structure)|
>We have more questions but will start with these.  Thank you in advance.
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