Hi Mahesh,
While there is a subject annotator and trained model, we found there
were very few non-patient instances in our training data, and so the
model has a difficult time finding any non-patient subjects.
Unfortunately, modifying the machine learning model is not feasible, so
my recommendation is, if this is important to your application, to
write a rule-based annotator and add to the end of your pipeline that
sets the subject attribute.

On Tue, 2018-04-10 at 09:39 +0000, Mahesh Kanthaswamy wrote:
> Hi Team,
> I tried running Assertion pipeline file for subject extraction.
> Its correctly annotate subject which for patient related instances
> from the input narration , however it does annotate Patient’s father
>  as ‘Patient’ which is wrong .
> Can you please let me know what will be change I would need to
> implement to correct the issue?
> Piper:
> // Add the Dependency parser for use by assertion
> addDescription ClearNLPDependencyParserAE
> // Add the Semantic Role Labeler parser for use by assertion
> addLogged ClearNLPSemanticRoleLabelerAE
> // Add the assertion packages for class lookups
> package org.apache.ctakes.assertion.medfacts
> package org.apache.ctakes.assertion.attributes
> add ConceptConverterAnalysisEngine
> add AssertionAnalysisEngineFit
> add GenericAttributeAnalysisEngine
> add SubjectAttributeAnalysisEngine
> Input Narration:
> The patient had cancer and it was detected with MRI on 26th May 2017.
> The patient's father had diabetes. But Patient has been prescribed
> DrugXXXX for last five years. CT-SCAN of prefrontal lobe confirmed
> patient had lesions in brain from last 3 months. The patient was
> discharged from hospital after chemotherapy for 4 months.
> Thanks & Regards,
> Technical Lead | LIFE SCIENCES | Pune - SEZ
> Email : mahesh_kanthasw...@infosys.com
> Mobile : +91 9970406008
> Upcoming OOO:

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