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Posted to user@ctakes.apache.org by "Hari, Sekhar" <se...@cgi.com> on 2019/05/07 16:47:37 UTC
Reading clinical notes for specific predictions
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)
Re: Reading clinical notes for specific predictions
Posted by Peter Abramowitsch <pa...@gmail.com>.
Ctakes can detect many forms for each "identified annotation", and it can
be trained with further dictionary development, to handle more acronyms,
more idiosyncratic speech etc, but it is not perfect. For example, It is
not that good at the moment, for detection of temporal context to
distinguish medical history from current observation or from family
history. It is better, but still not perfect at detecting negated
concepts which can occur in many different forms depending on the
linguistic patterns of the specific physicians whose notes you are
reading. What makes clinical notes particularly tricky as an NLP task is
that physicians are rushed - they abbreviate, they misspell, they create
staccato phrases instead of sentences, etc. It is not like parsing
well-formed published text.
I have not tried all the available annotators, so you may want to
experiment and see what works best for you.
I hope you were joking about "a couple of algorithms". Prediction is one
of the problems that has been addressed by thousands of highly trained
experts in diagnostics and clinical informatics. I have found interesting
work that was done years ago by some people working on Inference engines
using Deontic logic. Prediction is only partially an information handling
problem -- it is also a capture problem. It is something that only highly
trained observers can get right part of the time, and by observations that
do not always become part of the clinical record. If you want to know
more about what I'm talking about, try reading "Cutting for Stone" . It
is written by one of the world's most distinguished diagnosticians, Abraham
Verghese who now teaches at Stanford University.
Peter
On Wed, May 8, 2019 at 2:10 PM Hari, Sekhar <se...@cgi.com> wrote:
> Thanks Peter for your insights. Agree, this kind of predictions will need
> a couple of algorithms to be trained and work together to get to level of
> acceptable accuracy. I'm familiar with the RXNORM and SNOMED contents; but
> will dig deeper.
>
> Do you know if cTAKES can identify events such as "cardiac arrest",
> "diabetes" and "pre-term birth"? Likely these are mentioned with different
> text representations in the clinical notes.
>
> Thanks
> Sekhar Hari | AI Program Lead | Health Sciences R&D | Asia Pacific
> Solutions Delivery Center
> +91 814 7027 779 (C)
>
> -----Original Message-----
> From: Peter Abramowitsch <pa...@gmail.com>
> Sent: Wednesday, May 8, 2019 2:50 PM
> To: dev@ctakes.apache.org
> Subject: Re: Reading clinical notes for specific predictions
>
> 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 <se...@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)
> >
> >
>
RE: Reading clinical notes for specific predictions
Posted by "Hari, Sekhar" <se...@cgi.com>.
Thanks Peter for your insights. Agree, this kind of predictions will need a couple of algorithms to be trained and work together to get to level of acceptable accuracy. I'm familiar with the RXNORM and SNOMED contents; but will dig deeper.
Do you know if cTAKES can identify events such as "cardiac arrest", "diabetes" and "pre-term birth"? Likely these are mentioned with different text representations in the clinical notes.
Thanks
Sekhar Hari | AI Program Lead | Health Sciences R&D | Asia Pacific Solutions Delivery Center
+91 814 7027 779 (C)
-----Original Message-----
From: Peter Abramowitsch <pa...@gmail.com>
Sent: Wednesday, May 8, 2019 2:50 PM
To: dev@ctakes.apache.org
Subject: Re: Reading clinical notes for specific predictions
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 <se...@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)
>
>
Re: Reading clinical notes for specific predictions
Posted by Peter Abramowitsch <pa...@gmail.com>.
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 <se...@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)
>
>