Johnsd11 commented on issue #56:
URL: https://github.com/apache/ctakes/issues/56#issuecomment-2835961166
> I am looking for a NLP to read pathology reports and extract cancer
> related site, histology, stage and any other DX/RX data available. In
> looking at CTakes, I have a few questions;
>
> - Is CTakes an appropriate tool to automate this task?
I wrote a commercial surgical-pathology coding module some years ago, and
could imagine doing it in cTAKES.
Here's my two cents to add to the wealth of information Peter has already
provided.
Best luck.
> Where can I find an "executive overview" (30,000 foot view) of how the
CTakes works?
As Peter said, there's a lot of documentation out there!
Videos here: https://ctakes.apache.org/tutorials.html
Key point: it's built on top of UIMA https://uima.apache.org//
which ingests and annotates data from any source, letting you mix, match
and create your own annotators to build chains of analyses.
The cTAKES value-adds include a clinical type system and a spiffy
dictionary (see below).
> My ignorance regarding NLP algorithms like CTakes is whether it is
keyword driven, or it is self learning.
cTAKES is *not* "self-learning"; you have to tell it exactly what
information you want to extract from where.
Pro: High precision; explainable; you won't get the right answer for the
wrong reason.
Con: Low recall; brittle; you may not get answers at all! If you're
processing unpredictable document formats from many different facilities,
it can be hard to generalize over them.
> I currently have a homegrown application which looks for keywords and
negation modifiers within a certain distance from the keywords
cTAKES can certainly help with that.
-
*Keywords *cTAKES lets you use the NLM's UMLS Metathesaurus
<https://www.nlm.nih.gov/research/umls/knowledge_sources/metathesaurus/index.html>,
using the dictionary framework Peter mentioned:
https://cwiki.apache.org/confluence/display/CTAKES/cTAKES+4.0+-+Fast+Dictionary+Lookup
These sources may be useful in building your custom dictionary:
- the NCI Thesaurus:
https://www.nlm.nih.gov/research/umls/sourcereleasedocs/current/NCI/index.html
- CPT, if you want codes from there:
https://www.nlm.nih.gov/research/umls/sourcereleasedocs/current/CPT/index.html
- For anatomy, I'm not familiar with the "anatomical site annotator"
Peter alludes to, but the FMA is better structured than SNOMED:
https://www.nlm.nih.gov/research/umls/sourcereleasedocs/current/FMA/index.html
- *Negation*
Several annotators available:
https://cwiki.apache.org/confluence/display/CTAKES/cTAKES+4.0+-+Negation+Annotators
Distance-from-keywords is a start, but sentence detection and shallow
parsing both help.
I like the ctakes-ytex-uima NegexAnnotator and SentenceDetector.
-
*Document structure *I found header detection to be crucial in processing
pathology reports:
tracking specimens through a document, extracting tumor info from
tables, etc.
The cTAKES RegexSectionizer might work for you.
https://ctakes.apache.org/apidocs/4.0.0/org/apache/ctakes/core/ae/RegexSectionizer.html
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