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From Zuo Yiming <yiming...@gmail.com>
Subject Re: Best combination of analysis engines to consider negation, family history, uncertainty, etc.
Date Wed, 19 Oct 2016 16:21:31 GMT
Hi Sean and Timothy,

Thanks for your clarification about ClearTK tools. I'm amazed by the power
of cTAKES and the resource and community you guys take efforts to built. I
will certainly be happy to provide more feedback as my project moves on.

For Timothy,

By rule-based system, do you refer to the assertion annotator? How about
the old negation annotator and the status annotator, are they also
ruled-based system? I got a feeling that assertion annotator and ClearTK
system are more favored than negation annotator and the status
annotator for some reason in cTAKES right now.

Regarding ClearTK system on my test files, the negation, history,
uncertainty modules work just fine as the assertion annotator. My test
files are only a few, so it's really hard to tell which one is better. The
main difference comes when detecting subject and generic property. On my
limited test files, ClearTK system doesn't work at all. It will assign
patient as the subject for all detected phrases when it's the patient's
family member who have diabetes. The same problem goes to the generic
property, ClearTK system assigns false as the generic property for all
detected phrases. The paper mentioned by you and Sean seems interesting, I
will take a look later.

As for further questions, can you guys give me some suggestions where to
find public golden standard datasets so I can actually conduct some
independent evaluation of cTAKES by metrics like precision/recall and F1
score?

At last, a minor suggestion from the user perspective will be to add the
preferred words property to the AggregatePlaintextUMLSProcessor. Like I
pointed out briefly in my first email,
using AggregatePlaintextFastUMLSProcessor we can get the preferred words
for detected phrases but not AggregatePlaintextUMLSProcessor. This is very
helpful when the detected phrases are acronyms such as pt for patient. From
my experience, AggregatePlaintextUMLSProcessor tend to detect more clinical
relevant phrases compared with AggregatePlaintextFastUMLSProcessor. It will
be really nice if we can have the same preferred words property in
AggregatePlaintextUMLSProcessor in future cTAKES release.

Best,
Yiming

On Wed, Oct 19, 2016 at 11:11 AM, Miller, Timothy <
Timothy.Miller@childrens.harvard.edu> wrote:

> I can second Sean's thank you, it is good to have this feedback. The
> ClearTK machine learning models were made the default after we ran some
> experiments that found it performed better across a range of standard
> datasets than rule-based algorithms or the existing cTAKES module (
> http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0112774).
> Since making them the default, though, we have heard from people and had
> our own experience conflict with those experiments. And certainly the
> errors in the rule-based system are easier to understand.
>
> Just curious, are you able to characterize the errors you see from the
> ClearTK system? I did some experiments recently on a new dataset comparing
> negex with the cleartk negation module and found that there was a
> precision/recall tradeoff but almost identical F1 scores. But for that
> dataset the tradeoff negex provided was preferred by our collaborators. (I
> think negex had better recall of negated terms but worse precision).
>
> Tim
>
>
>
> ________________________________________
> From: Finan, Sean <Sean.Finan@childrens.harvard.edu>
> Sent: Wednesday, October 19, 2016 10:53 AM
> To: dev@ctakes.apache.org
> Subject: RE: Best combination of analysis engines to consider negation,
> family history, uncertainty, etc.
>
> Hi Yiming,
>
>
>
> Thank you very much for letting the community know what has and has not
> worked for you.  I have also had better results with the Assertion
> annotators than the ClearTk alternatives, but that could be because of the
> note types/formats that I am using.
>
>
>
> Regarding the "Clear" in names, it is because ClearTk (Clear ToolKit) is
> used to train machine learning models for detection of the indicated
> property.  You can find information on ClearTk starting here:
> https://urldefense.proofpoint.com/v2/url?u=http-3A__clear.
> colorado.edu_compsem_&d=DQIGaQ&c=qS4goWBT7poplM69zy_
> 3xhKwEW14JZMSdioCoppxeFU&r=Heup-IbsIg9Q1TPOylpP9FE4GTK-
> OqdTDRRNQXipowRLRjx0ibQrHEo8uYx6674h&m=aRk0CH-2UrNpH0F4PgdnzixY-
> xVsh8OYTCP8mhe27Gw&s=0mEmiKK5adFN2YCkYyNCNM3Cv4FNWlMbN8XU6GtcQP4&e=
>
>
>
> If you prefer to read a paper, you can check out
> https://urldefense.proofpoint.com/v2/url?u=http-3A__www.
> lrec-2Dconf.org_proceedings_lrec2014_pdf_218-5FPaper.pdf&
> d=DQIGaQ&c=qS4goWBT7poplM69zy_3xhKwEW14JZMSdioCoppxeFU&r=
> Heup-IbsIg9Q1TPOylpP9FE4GTK-OqdTDRRNQXipowRLRjx0ibQrHEo8uYx6674h&m=aRk0CH-
> 2UrNpH0F4PgdnzixY-xVsh8OYTCP8mhe27Gw&s=T-pZCKB6BckhHzvYc9gyutCmKQlhitdO
> _-i4e387tjM&e=
>
>
>
> Others no the devlist can provide much more information than can I, so you
> could post a question if you like.
>
>
>
> Cheers,
>
> Sean
>
>
>
> -----Original Message-----
>
> From: Zuo Yiming [mailto:yimingzuo@gmail.com]
>
> Sent: Wednesday, October 19, 2016 10:04 AM
>
> To: user@ctakes.apache.org; dev@ctakes.apache.org
>
> Subject: Best combination of analysis engines to consider negation, family
> history, uncertainty, etc.
>
>
>
> Hi everyone,
>
>
>
> I've spent the last a few months working on a clinical NLP project using
> cTAKES. It's a very complex system to me and every time I dig into it some
> new discoveries will come out. Since last week, I tried to figure out which
> analysis engine can help to do a good job to consider cases like negation,
> family history, uncertainty, etc. By now, I had some experience and would
> like to share with the community.
>
>
>
> The best combination for me is to use assertionMiniPipelineAnalysisEngine
>
> for negation, uncertainty, generic and subject detection, and
> HistoryCleartkAnalysisEngine for history detection. Both engines are in
> desc/ctakes-assertion folder. The assertionMiniPipelineAnalysisEngine
> also claims to be useful for conditional detection, which I haven't
> verified using my test files yet.
>
>
>
> I'm using the AggregatePlaintextFastUMLSProcessor on the higher level.
> The default analysis engines in AggregatePlaintextFastUMLSProcessor for
> negation, uncertainty, generic, etc. are StatusAnnotator +
> NegationAnnotator + PolarityCleartkAnalysisEngine +
> SubjectCleartkAnalysisEngine + UncertaintyCleartkAnalysisEngine +
> GenericCleartkAnalysisEngine + HistoryCleartkAnalysisEngine. It looks like
> in the node part, StatusAnnotator and NegationAnnotator are commented out,
> so only the remaining five analysis engines are actually used and all of
> them are in the same desc/ctakes-assertion folder. These five analysis
> engines were not effective in my test files and I'm still confused by their
> relationship to the assertionaAnalysisEngine, conceptConverterAnalysisEngine,
> GenericAttributeAnalysisEngine and SubjectAttributeAnalysisEngine used in
> assertionMiniPipelineAnalysisEngine.
>
> It looks to me the Clear in their names indicate something but I couldn't
> figure it out without going through the java code, which I intend not to do
> at this level.
>
>
>
> That's pretty much all of it for now. Anyone familiar with this topic are
> welcome to jump in to provide my insights or correction. Hopefully, we can
> have a nice discussion that can be useful to other users and developers.
>
>
>
> ps. The reason for using AggregatePlaintextFastUMLSProcessor rather than
> AggregatePlaintextProcessor is that I find the preferred words property in
> the former very useful while it can't be detected using the latter.
>
>
>
> Best,
>
> Yiming
>
> --
>
> Yiming Zuo <https://urldefense.proofpoint.com/v2/url?u=https-
> 3A__sites.google.com_site_yimingzuo_&d=DQIBaQ&c=qS4goWBT7poplM69zy_
> 3xhKwEW14JZMSdioCoppxeFU&r=fs67GvlGZstTpyIisCYNYmQCP6r0bcpKGd4f7d4gTao&m=
> 4at7fOO27JCueBfJFn7Hv2vKWlUAK-nuYYdmMyGRJPQ&s=vSmSOvLXuCa-
> Pwp8qu05VTzZgGA0P3Y2CL8q3JBhppQ&e=> Georgetown U. Medical Center:
>
> Dr. Ressom's Omics Lab <https://urldefense.proofpoint.com/v2/url?u=http-
> 3A__omics.georgetown.edu_&d=DQIBaQ&c=qS4goWBT7poplM69zy_
> 3xhKwEW14JZMSdioCoppxeFU&r=fs67GvlGZstTpyIisCYNYmQCP6r0bcpKGd4f7d4gTao&m=
> 4at7fOO27JCueBfJFn7Hv2vKWlUAK-nuYYdmMyGRJPQ&s=yNsVaS7s20e-
> 125SmdmQqKHvQ0lAQ7si98GefPRDxT0&e=> ECE Department of Virginia Tech:
>
> Computational Bioinformatics & Bio-imaging Laboratory <https://urldefense.
> proofpoint.com/v2/url?u=http-3A__www.cbil.ece.vt.edu_&d=
> DQIBaQ&c=qS4goWBT7poplM69zy_3xhKwEW14JZMSdioCoppxeFU&r=
> fs67GvlGZstTpyIisCYNYmQCP6r0bcpKGd4f7d4gTao&m=
> 4at7fOO27JCueBfJFn7Hv2vKWlUAK-nuYYdmMyGRJPQ&s=DpORI1TH9yITkdlRX_
> RLjxejH2jMJUq8yFaTPjWAar4&e=>
>
>


-- 
Yiming Zuo <https://sites.google.com/site/yimingzuo/>
Georgetown U. Medical Center:
Dr. Ressom's Omics Lab <http://omics.georgetown.edu/>
ECE Department of Virginia Tech:
Computational Bioinformatics & Bio-imaging Laboratory
<http://www.cbil.ece.vt.edu/>

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