Hi Sean, Would love to help you out if you think I can be handy in any area. Do keep me posted.
On Mon, Jun 29, 2020 at 8:33 PM Finan, Sean < sean.fi...@childrens.harvard.edu> wrote: > Hi all, > > > General admission to ApacheCon 2020 is free: > https://hopin.to/events/apachecon-home > > > I think that price of admission and travel costs have held back ctakes > users from attending past conferences, and lack of a sizable audience has > diminished the comparative value of ctakes presentations in the eyes of > ApacheCon planners. Because of the "at home" nature of this year's > conference, an app with smaller presence and less hip buzz has a better > chance of grabbing some time on the schedule. > > > The predetermined tracks are still an ill fit when it comes to the nature > of ctakes. https://apachecon.com/acah2020/cfp.html > > However, I think that we can still use this opportunity to deliver some > powerful introduction and training videos, as well as user stories and > clinical project application. Perhaps we can argue for a NLP track and do > some coordination with projects like OpenNLP and UIMA. > > > There are a scant two weeks to come up with presentations, and less time > to propose a track/topic. The call for presentations ends July 13th. That > is a deadline that requires immediate attention by anybody who wants to > show off their project or expertise. > > > Apache wants to have a single point of contact for each project, and I am > volunteering to be that person for ctakes. I am volunteering, not laying > claim, so if you think that you are a better fit for the position please > let me know. > > > I have written some ideas for presentations below. If you want to take > one (modify as you like) then please write me and post to the devlist. If > you have ideas for another presentation topic, please let me and the > devlist know - even if you aren't volunteering to do the presentation > yourself perhaps somebody else will. Again ... two weeks. > > > Thank you, > > Sean > > > > * The following talk ideas are by and large directed toward training. > That does not mean that topics should stay within that scope. > > > ================================================================= > > > Customizing cTAKES: First Principles > > Built using Apache UIMA, cTAKES is modular and extensible. Why is it > frequently treated as a black box? Is it lack of need, sparsity of > resources, or simply fear of the unknown? > > This is a quick start tutorial on adding custom elements to cTAKES. We > illustrate creating simple classes to input, process and output data. This > involves a concise overview of Apache uimaFIT and the cTAKES type system, > as well as building a UIMA pipeline using piper files. > > > ================================================================= > > > Loading a shippable with cTAKES DockHand > > Customizing a simple pipeline need not be left to cTAKES experts. Making > a cTAKES installation need not be confined to source code checkouts or > lengthy multi-stage binary downloads. > > We introduce cTAKES DockHand, a compact single-file installation tool that > allows one to construct custom pipelines as well as local installations, > Rest Services and Dockerfiles. > > > ================================================================== > > > Secret Engines of cTAKES > > The cTAKES default natural language processing pipeline is a standard in > the clinical research community. What is past that standard? While the > default clinical pipeline uses almost 20 engines, there are dozens more in > various cTAKES modules. > > We present and discuss the top 10 annotation engines you never knew you > had. > > > ==================================================== > > > Does cTAKES Know "The Best Words"? > > Named Entity Recognition is at the core of all complete natural language > processing tools. Out of the box cTAKES uses a dictionary containing part > of the Unified Medical Language System (UMLS) that covers most common > clinical terms. But it also comes with a custom dictionary creator. > > If you think that your clinical research is directed, then you should > probably have a directed dictionary. UMLS subsets, non-english > dictionaries and novel custom dictionaries have all been successfully used > with cTAKES. > > This is an overview of cTAKES named entity recognition with the essential > what, why and how of custom dictionaries as the centerpiece. > > > ==================================================== > > Academic Software: Performance or Performance? > > A conundrum faced by all academic software projects is how to make the > best of a small amount of resources. Clinical natural language processing > projects that use cTAKES are not exempt, and balancing accuracy of results > against speed of processing often becomes central when it is time to put > things into production (or just please the boss). > > More than a history of cTAKES and its evolutionary efforts in precision, > speed and usability, this presentation contains examples of how to best > utilize each aspect. > > > ================================================================ > > > Diet cTAKES > > One reason cTAKES is a popular framework in clinical natural language > processing tools is its use of Apache Maven for project management. > Navigating cTAKES dependencies can be difficult, leading to a common > practice of consuming the whole project. Much of what ends up in your > system may lead to unnecessary bloat. > > Going piecemeal through the values and weights of cTAKES modules and > resources, this presentation will assist any cTAKES user in trimming > project bulk from gigabytes to megabytes. > > > ================================================================ > > > cTAKES Saved my Life > > The title is inappropriate when it comes to healthcare in practice. > However, I used Apache cTAKES for my clinical research project on ________, > and its [versatile / comprehensive / speedy / ?] nature was important in > completing things [on time / accurately / ?]. > > We share our real-world experiences with using cTAKES, discuss why we > chose it, issues we faced and how we overcame unexpected problems. > > > ================================================================ > > > Large-scale cTAKES, an Installation Story > > At our _____ facility, we needed to process _____ [patients / notes / term > lists / ?] on a ______ system. > > We present a real-world application of cTAKES on a large scale, our needs > for _____ input and ____ output. We compare and contrast cTAKES with other > [clinical] NLP platforms that we tried and explain why we chose [it / > another] in the end. > > We will also share the novel [techniques / code / integration] that we > used for the success of our installation. > > > ================================================================ > > > My Engine is Faster than Yours > > We have created a cTAKES annotation engine that performs the task of > _____. This is [newer / faster / more comprehensive] than existing > engines in [cTAKES / other]. > > We will present [numbers , usage , capabilities / i/o ] of the engine and > its [model / data ]. > We will also commit the code and documentation to Apache cTAKES. > > > ================================================================ > > > cTAKES on the Catwalk > > We have created a Machine Learning model that can be used in cTAKES for > ______. The model uses the third party ______ for [newer / faster / more > comprehensive] results. > > We will present the essentials of model creation as well as [numbers , > usage , capabilities / i/o ] of our model. We will also advocate for the > third party _____ and how we integrated it with cTAKES. > We will also commit the code [model] and documentation to Apache cTAKES. > > > > > > > > > -- Regards, Gandhi "The best way to find urself is to lose urself in the service of others !!!"
