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From Kim Ebert <kim.eb...@perfectsearchcorp.com>
Subject Re: cTakes Annotation Comparison
Date Fri, 19 Dec 2014 15:21:34 GMT

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

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
kim.ebert@imatsolutions.com <mailto:greg.hubert@imatsolutions.com>
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!
> --Guergana
> -----Original Message-----
> From: David Kincaid [mailto:kincaid.dave@gmail.com] 
> Sent: Friday, December 19, 2014 9:02 AM
> To: 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>
>> 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>  Bruce Tietjen 
>> Senior Software Engineer
>> [image: Mobile:] 801.634.1547
>> bruce.tietjen@imatsolutions.com
>> On Thu, Dec 18, 2014 at 3:37 PM, Chen, Pei 
>> <Pei.Chen@childrens.harvard.edu
>> wrote:
>>> Bruce,
>>> 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.
>>> --Pei
>>> -----Original Message-----
>>> From: Bruce Tietjen [mailto:bruce.tietjen@perfectsearchcorp.com]
>>> Sent: Thursday, December 18, 2014 1:45 PM
>>> To: 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
>> cTakes
>>> 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 
>>> community.
>>> 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'
>> is
>>> 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
>> and
>>> 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 
>>> https://github.com/perfectsearch/cTAKES-compare

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