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From "Miller, Timothy" <Timothy.Mil...@childrens.harvard.edu>
Subject Re: cTakes Annotation Comparison
Date Fri, 19 Dec 2014 16:54:50 GMT
Thanks Kim,
This sounds interesting though I don't totally understand it. Are you saying that extraction
performance for a given note depends on which order the note was in the processing queue?
If so that's pretty bad! If you (or anyone else who understands this issue) has a concrete
example I think that might help me understand what the problem is/was.

Even though, as Pei mentioned, we are going to try moving the community to the faster dictionary,
I would like to understand better just to help myself avoid issues of this type going forward
(and verify the new dictionary doesn't use similar logic).

Also, when we finish annotating the sample notes, might we use that as a point of comparison
for the two dictionaries? That would get around the issue that not everyone has access to
the datasets we used for validation and others are likely not able to share theirs either.
And maybe we can replicate the notes if we want to simulate the scenario Kim is talking about
with thousands or more notes.


On 12/19/2014 10:24 AM, Kim Ebert wrote:

I'm curious to the number of records that are in your gold standard sets, or if your gold
standard set was run through a long running cTAKES process. I know at some point we fixed
a bug in the old dictionary lookup that caused the permutations to become corrupted over time.
Typically this isn't seen in the first few records, but over time as patterns are used the
permutations would become corrupted. This caused documents that were fed through cTAKES more
than once to have less codes returned than the first time.

For example, if a permutation of 4,2,3,1 was found, the permutation would be corrupted to
be 1,2,3,4. It would no longer be possible to detect permutations of 4,2,3,1 until cTAKES
was restarted. We got the fix in after the cTAKES 3.2.0 release. https://issues.apache.org/jira/browse/CTAKES-310
Depending upon the corpus size, I could see the permutation engine eventually only have a
single permutation of 1,2,3,4.

Typically though, this isn't very easily detected in the first 100 or so documents.

We discovered this issue when we made cTAKES have consistent output of codes in our system.

[IMAT Solutions]<http://imatsolutions.com>
Kim Ebert
Software Engineer
[Office:] 801.669.7342
On 12/19/2014 07:05 AM, Savova, Guergana wrote:

We are doing a similar kind of evaluation and will report the results.

Before we released the Fast lookup, we did a systematic evaluation across three gold standard
sets. We did not see the trend that Bruce reported below. The P, R and F1 results from the
old dictionary look up and the fast one were similar.

Thank you everyone!

-----Original Message-----
From: David Kincaid [mailto:kincaid.dave@gmail.com]
Sent: Friday, December 19, 2014 9:02 AM
To: dev@ctakes.apache.org<mailto:dev@ctakes.apache.org>
Subject: Re: cTakes Annotation Comparison

Thanks for this, Bruce! Very interesting work. It confirms what I've seen in my small tests
that I've done in a non-systematic way. Did you happen to capture the number of false positives
yet (annotations made by cTAKES that are not in the human adjudicated standard)? I've seen
a lot of dictionary hits that are not actually entity mentions, but I haven't had a chance
to do a systematic analysis (we're working on our annotated gold standard now). One great
example is the antibiotic "Today". Every time the word today appears in any text it is annotated
as a medication mention when it almost never is being used in that sense.

These results by themselves are quite disappointing to me. Both the UMLSProcessor and especially
the FastUMLSProcessor seem to have pretty poor recall. It seems like the trade off for more
speed is a ten-fold (or more) decrease in entity recognition.

Thanks again for sharing your results with us. I think they are very useful to the project.

- Dave

On Thu, Dec 18, 2014 at 5:06 PM, Bruce Tietjen < bruce.tietjen@perfectsearchcorp.com<mailto:bruce.tietjen@perfectsearchcorp.com>>

Actually, we are working on a similar tool to compare it to the human
adjudicated standard for the set we tested against.  I didn't mention
it before because the tool isn't complete yet, but initial results for
the set (excluding those marked as "CUI-less") was as follows:

Human adjudicated annotations: 4591 (excluding CUI-less)

Annotations found matching the human adjudicated standard
UMLSProcessor                  2245
FastUMLSProcessor           215

 [image: IMAT Solutions] <http://imatsolutions.com><http://imatsolutions.com>
 Bruce Tietjen
Senior Software Engineer
[image: Mobile:] 801.634.1547

On Thu, Dec 18, 2014 at 3:37 PM, Chen, Pei


Thanks for this-- very useful.
Perhaps Sean Finan comment more-
but it's also probably worth it to compare to an adjudicated human
annotated gold standard.


-----Original Message-----
From: Bruce Tietjen [mailto:bruce.tietjen@perfectsearchcorp.com]
Sent: Thursday, December 18, 2014 1:45 PM
To: dev@ctakes.apache.org<mailto:dev@ctakes.apache.org>
Subject: cTakes Annotation Comparison

With the recent release of cTakes 3.2.1, we were very interested in
checking for any differences in annotations between using the
AggregatePlaintextUMLSProcessor pipeline and the
AggregatePlanetextFastUMLSProcessor pipeline within this release of


with its associated set of UMLS resources.

We chose to use the SHARE 14-a-b Training data that consists of 199
documents (Discharge  61, ECG 54, Echo 42 and Radiology 42) as the
basis for the comparison.

We decided to share a summary of the results with the development

Documents Processed: 199

Processing Time:
UMLSProcessor           2,439 seconds
FastUMLSProcessor    1,837 seconds

Total Annotations Reported:
UMLSProcessor                  20,365 annotations
FastUMLSProcessor             8,284 annotations

Annotation Comparisons:
Annotations common to both sets:                                  3,940
Annotations reported only by the UMLSProcessor:         16,425
Annotations reported only by the FastUMLSProcessor:    4,344

If anyone is interested, following was our test procedure:

We used the UIMA CPE to process the document set twice, once using
the AggregatePlaintextUMLSProcessor pipeline and once using the
AggregatePlaintextFastUMLSProcessor pipeline. We used the
WriteCAStoFile CAS consumer to write the results to output files.

We used a tool we recently developed to analyze and compare the
annotations generated by the two pipelines. The tool compares the
two outputs for each file and reports any differences in the
annotations (MedicationMention, SignSymptomMention,
ProcedureMention, AnatomicalSiteMention, and
DiseaseDisorderMention) between the two output sets. The tool
reports the number of 'matches' and 'misses' between each annotation set. A 'match'


defined as the presence of an identified source text interval with
its associated CUI appearing in both annotation sets. A 'miss' is
defined as the presence of an identified source text interval and
its associated CUI in one annotation set, but no matching identified
source text interval


CUI in the other. The tool also reports the total number of
annotations (source text intervals with associated CUIs) reported in
each annotation set. The compare tool is in our GitHub repository at

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