Hi Sekhar The predictions item in your list of objectives is very tricky and cTakes, or indeed any software system will only get you part of the way there. CDS (clinical decision support) researchers have been on this path for many years and it is clear that even an hybrid human/computational system is limited in its accuracy & predictive ability. And with medicine, a miss is as good as a mile - as the saying goes.
As to your vocabularies question - if you don't already know the SNOMED clinical ontology, and RxNorm resources I suggest you have a look. cTakes can fish out the appropriate CUIs and SNOMED term ids, and the ontologies will help you draw the lateral links through common parents - or in your specific example, therapeutic classes. - Peter On Tue, May 7, 2019 at 6:47 PM Hari, Sekhar <sekhar.h...@cgi.com> wrote: > Hi there - > > I'm trying to predict a few things from clinical notes as follows: > > > 1. Look at the notes and discharge summaries, and predict the > re-admissions data, cardiac arrests, diabetes, and pre-term birth. > > 2. Understand the vocabulary of doctors and pharmacies. For example, > recognize that Tylenol and Acetaminophen refer to the same item. Have a > good understanding of body parts and diseases. The vocabulary is > domain-specific. > > 3. The data is loaded from Cerner and EPIC. > > Can somebody help with suggestions on the list of pipelines that can be > used to achieve (1) and (2) above? Should I also develop a machine-learning > model along with cTAKES to get the desired results? > > Thanks > Sekhar Hari | AI Program Lead | Health Sciences R&D | Asia Pacific > Solutions Delivery Center > +91 814 7027 779 (C) > >
