Awesome Mike that is so cool to hear. Thank you for building it off our software system. Really awesome.
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ Chris Mattmann, Ph.D. Principal Data Scientist, Engineering Administrative Office (3010) Manager, NSF & Open Source Projects Formulation and Development Offices (8212) NASA Jet Propulsion Laboratory Pasadena, CA 91109 USA Office: 180-503E, Mailstop: 180-503 Email: chris.a.mattm...@nasa.gov WWW: http://sunset.usc.edu/~mattmann/ ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ Director, Information Retrieval and Data Science Group (IRDS) Adjunct Associate Professor, Computer Science Department University of Southern California, Los Angeles, CA 90089 USA WWW: http://irds.usc.edu/ ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ From: Michael Trepanier <m...@metistream.com> Reply-To: "user@ctakes.apache.org" <user@ctakes.apache.org> Date: Friday, August 11, 2017 at 11:08 AM To: "user@ctakes.apache.org" <user@ctakes.apache.org> Subject: Re: Implementation Improvements for cTAKES on top of Spark Chris, We actually were basing our implementation off of https://github.com/selinachu/SparkStreamingCTK which I believe came from your team, but updated it for cTAKES 4.0. For those trying to do this, you'll likely run into issues tied to the lvg annotator outlined here: https://issues.apache.org/jira/browse/CTAKES-445 The comments provide a solution (essentially, ensure the cTAKES resources zip is on your classpath). In a cluster environment, this means putting them on every node at that particular classpath location. Fingers crossed that in some future implementations cTAKES can just be zipped up in a fat-jar with no issues. Mike On Fri, Jul 28, 2017 at 1:17 PM, Mattmann, Chris A (3010) <chris.a.mattm...@jpl.nasa.gov<mailto:chris.a.mattm...@jpl.nasa.gov>> wrote: FYI for interest, my JPL team implemented a prototype of this in 2015: https://www.mail-archive.com/user@ctakes.apache.org/msg01082.html ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ Chris Mattmann, Ph.D. Principal Data Scientist, Engineering Administrative Office (3010) Manager, NSF & Open Source Projects Formulation and Development Offices (8212) NASA Jet Propulsion Laboratory Pasadena, CA 91109 USA Office: 180-503E, Mailstop: 180-503 Email: chris.a.mattm...@nasa.gov<mailto:chris.a.mattm...@nasa.gov> WWW: http://sunset.usc.edu/~mattmann/ ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ Director, Information Retrieval and Data Science Group (IRDS) Adjunct Associate Professor, Computer Science Department University of Southern California, Los Angeles, CA 90089 USA WWW: http://irds.usc.edu/ ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ On 7/28/17, 11:19 AM, "Michael Trepanier" <m...@metistream.com<mailto:m...@metistream.com>> wrote: That's an excellent suggestion! In a Spark implementation, build the pipeline outside of the map function and pass the aed in as an input. Then just ensure the jcas object persists between each mapping iteration and leverage the reset method. On Fri, Jul 28, 2017 at 11:11 AM, Abramowitsch, Peter <pabramowit...@hearst.com<mailto:pabramowit...@hearst.com>> wrote: > About your second question with UMLS, You can build the pipeline > initially and it will verify the license info, then just reuse the > pipeline on each call. > > > > On 7/25/17, 4:53 PM, "Michael Trepanier" <m...@metistream.com<mailto:m...@metistream.com>> wrote: > >>Hi, >> >>I am currently leveraging cTAKES inside of Apache Spark and have >>written a function that takes in a single clinical note as as string >>and does the following: >> >>1) Sets the UMLS system properties. >>2) Instantiates JCAS object. >>3) Runs the default pipeline >>4) (Not shown below) Grabs the annotations and places them in a JSON >>object for each note. >> >> def generateAnnotations(paragraph:String): String = { >> System.setProperty("ctakes.umlsuser", "MY_UMLS_USERNAME") >> System.setProperty("ctakes.umlspw", "MY_UMLS_PASSWORD") >> >> var jcas = >>JCasFactory.createJCas("org.apache.ctakes.typesystem.types.TypeSystem") >> var aed = ClinicalPipelineFactory.getDefaultPipeline() >> jcas.setDocumentText(paragraph) >> SimplePipeline.runPipeline(jcas, aed) >> ... >> >>This function is being implemented as a UDF which is applied to a >>Spark Dataframe with clinical notes in each row. I have two >>implementation questions that follow: >> >>1) When cTAKES is being applied iteratively to clinical notes, is it >>necessary to instantiate a new JCAS object for every annotation? Or >>can the same JCAS object be utilized over and over with the document >>text being changed? >>2) For each application of this function, the >>UmlsDictionaryLookupAnnotator has to connect to UMLS using the >>provided UMLS information. This Is there any way to instead perform >>this step locally? Ie. ingest UMLS and place it in either HDFS or just >>mount it somewhere on each node? I'm worried about spamming the UMLS >>server in this step, and about how long this seems to take. >> >>Thanks, >> >>Mike >> >> >>-- >> >>Mike Trepanier| Big Data Engineer | MetiStream, Inc. | >>m...@metistream.com<mailto:m...@metistream.com> | 845 - 270 - 3129<tel:845%20-%20270%20-%203129> (m) | www.metistream.com<http://www.metistream.com> > -- Mike Trepanier| Big Data Engineer | MetiStream, Inc. | m...@metistream.com<mailto:m...@metistream.com> | 845 - 270 - 3129<tel:845%20-%20270%20-%203129> (m) | www.metistream.com<http://www.metistream.com> -- [etiStream Logo - 500] Mike Trepanier| Big Data Engineer | MetiStream, Inc. | m...@metistream.com<mailto:m...@metistream.com> | 845 - 270 - 3129 (m) | www.metistream.com<http://www.metistream.com/>
