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From "Finan, Sean" <Sean.Fi...@childrens.harvard.edu>
Subject RE: Sundry; Problem Lists
Date Mon, 04 Nov 2013 17:15:14 GMT
Excellent!  By the by, I know next to nothing about nlp - I'm just a software developer that
(for some reason) jumped down this (nlp) particular rabbit hole.  When it comes to nlp background,
research, state and direction I'm hoping that somebody much more knowledgable than I will
jump in.

>after a thorough pubmed search, no one seems to have tried to build problem lists for
ACUTE encounters, only as extensions to a past medical history
I''m really glad that we have a truly novel road on which to travel.

> I seem to be interested in a current encounter (the now) [as opposed to]  the longitudinal
problem list (the ever).
I think that is a great as both a challenge and possible tool, as well as your thought on
> prioritization, eg enumeration from most important to least, as well as clumping

I briefly discussed the first idea (acute vs. historical) with another physician (after you
brought it up) and there was concurrency that such a feature would be extremely useful - if
not completely necessary for any real clinical use of nlp.  I think that if temporal parsing
ever becomes finite enough with respect to the time of an event relative to the time of the
note (DocTimeRel) or with proper narrative containers, then this becomes a possible use case.
 I mention this in a weak attempt to pull the nlpers into the discussion ...

> This is probably well known stuff
Bad assumption ... insert emoticon here ...

>working back from the known natural history of diseases would possibly be a route to a
solution.
Now that is a challenge!

Cheers for the inspiration and enthusiasm,
Sean


________________________________
From: John Green [john.travis.green@gmail.com]
Sent: Monday, November 04, 2013 10:45 AM
To: Finan, Sean
Subject: RE: Sundry; Problem Lists

Oh goodness no, I didnt think that at all! Im so new to the field of NLP, anything and everything
helps and is appreciated. Heck, im just now learning to understand Markov chains.

An additional thought: after a thorough pubmed search, no one seems to have tried to build
problem lists for ACUTE encouters, only as extensions to a past medical history. I think this
would be a very fruitful avenue. It could easily be scored against a gold standard medical
resident list for a few hundred patients across depth and acuity.

Just thinkin out loud, bouncing ideas off those who know more than I!

Jg
—
Sent from Mailbox<https://www.dropbox.com/mailbox> for iPhone



On Mon, Nov 4, 2013 at 9:24 AM, Finan, Sean <Sean.Finan@childrens.harvard.edu<mailto:Sean.Finan@childrens.harvard.edu>>
wrote:

Hi John,

I hope that you didn't think that I was belittling your ideas or saying that anything has
been done (and done). I was just throwing in two resources for further thought. You have brought
forward some great applications for cTakes and nlp!

Sean
________________________________________
From: John Green [john.travis.green@gmail.com]
Sent: Thursday, October 31, 2013 7:26 PM
To: dev@ctakes.apache.org
Subject: RE: Sundry; Problem Lists

Last point: I seem to be interested in a current encounter (the now) and diagnosis, the article
seems to be interested in an arguably just as useful tool, the longitudinal problem list (the
ever), though very different I would think in approach.




Thoughts?

Jg







—
Sent from Mailbox for iPhone

On Thu, Oct 31, 2013 at 7:22 PM, John Green <john.travis.green@gmail.com>
wrote:

> Sean - quick note: after looking at the above two resources, a couple of points. The
first resource confirms what I expected, that the vocabulary exists in ctakes. The second
confirms what I suspected: that novel approaches to ordering and identification of top members
of a problem list are needed. Namely, that the vocabulary may be there, but thats only a tenth
of the battle. Your second great resource you sent me acknowledges this - that prioritization,
eg enumeration from most important to least, as well as clumping, are the true battle.
> A point of clarification on my end: it would be interesting to see what could be added
on top of existing ctakes in order to facilate a solution to the second problem - clumping
and prioritizing. (For instance, from the second article, an acute process may have nothing
todo with the past medical history and if an algorithm were concerned with all members as
equals, it would miss the issue at hand).
> Just as a thought: working back from the known natural history of diseases would possibly
be a route to a solution.
> This is probably well known stuff, so please forgive my ignorance if its all been done/thought
of before.
> Again, the two links were very helpful, thank you.
> Jg
> —
> Sent from Mailbox for iPhone
> On Thu, Oct 31, 2013 at 2:04 PM, Finan, Sean
> <Sean.Finan@childrens.harvard.edu> wrote:
>> I don't know if what I write below truly applies to the discussion, but here it is.
>>>much of a problem list definition may already be contained to varying degrees
>>> in existing cTakes databases.
>> The UMLS does provide a problem list, but I haven't looked at it.
>> http://www.nlm.nih.gov/research/umls/Snomed/core_subset.html
>> This might be a paper of interest to you:
>> http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2655994/
>> It discusses the use of nlp to create something like a problem list.
>> Sean
>> ________________________________________
>> From: John Green [john.travis.green@gmail.com]
>> Sent: Thursday, October 31, 2013 12:02 PM
>> To: dev@ctakes.apache.org
>> Subject: Re: Sundry
>> Pei and Tim - Good questions.
>> The bottom line is that OPQRST is the algorithm that every clinician uses
>> to characterize the history of a sign, symptom or constellation of
>> symptoms. Each letter has multiple meanings, but generally they're grouped.
>> O for onset, was it quick or slow in onset, P for palliative or provoking
>> phenomenon, that is, does tylenol make it better? Does it feel better when
>> you lean forward? Is it worse with standing? Q is the quality, generally,
>> though I could give more examples of each Ill keep it brief from here, R is
>> generally region or radiation of the pain and or sign, S is the severity,
>> and T is the time course, is it intermittent? When it happens, how long
>> does it last for? I could send documents used to teach new clinicians to
>> better comprehend for anyone interested.
>> OPQRST, while most residents would assume it is only for teaching new
>> clinicians, as Tim said, is a useful tool at all levels. Great clinicians,
>> and I work with some great senior folks, use this everyday. The idea that
>> it is only for teaching is founded on two things: one, that it doubles as a
>> structured mnemonic for characterizing signs and symptoms and two, that
>> everyone so far ingrains this into their clinical skill set, unless they
>> are geared toward teaching, they, after the basic level, never think about
>> it again! Caveat: many good clinicians will tell you to keep it algorithmic
>> so that you're systematic and do not overlook details.
>> What is it's application to ML? Obviously the furthest desired end-state
>> for NLP like cTakes would be understanding a clinical encounter to such a
>> nuanced level that detailed diagnoses could be considered along with
>> treatment plans. While I only know what I've read in Artificial
>> Intelligence: A Modern Approach and picked up from friends over the years
>> who were good knowledgeable in this field, I feel that OPQRST would be a
>> huge benefit toward beginning to outline the problem of more rigorous ML
>> characterization of the clinical narrative.
>> The utility of OPQRST may not still be entirely clear to those who have
>> never been presented with a clinical encounter. Let me try one more stab:
>> Take the classic example of chest pain. A man comes to the ER with chest
>> pain. Is the onset quick? Yes doc, it was all of a sudden. This might
>> support a diagnosis of, say, MI, aortic dissection, pulmonary embolism, but
>> less likely someone would call GERD sudden. Palliative or provoking
>> features? Well, when I take 8 antacids it gets better (GERD), or, When I
>> take my wifes nitroglycerine it got better for a little while (angina), or,
>> when I took my wifes nitroglycerine it did nothing (pericarditis?).
>> Quality: Is it stabbing? Ya doc, its stabbing (less likely MI). Is it
>> crushing? Like an elephant on your chest? Ya doc, that's it. (more likely
>> MI), and so on.
>> Now of course, cTakes could be used for a real life encounter like this
>> (middleware) at some point, but likely it would be taking a history and
>> proposing a diagnosis (middleware again Tim, yes). But the point is, the
>> first steps toward knowing what were dealing with at the historical level
>> is centered around OPQRST, and it just occurred to me to ask what we
>> thought about the feasibility of something like that.
>> In retrospect, it may be too tough, but at some point it would need done,
>> just as much as a clinician must learn it.
>> One final point: problem lists. These are absolutely essential to any
>> clinician in making a diagnosis. Again, often times, they dont think about
>> it, but they use them. When writing the above it occurred to me: much of a
>> problem list definition may already be contained to varying degrees in
>> existing cTakes databases. It would be an interesting and worthwhile paper,
>> I think, to see how well cTakes compiled problem lists matched Medical
>> Students, Residents, and Attending physician's problem lists. If anyone is
>> interested in this line of thought, I would be interested in collaborating.
>> It would be very easy, and the data may actually already exist to compare.
>> Forgive me if its already been done, but, if it hasnt, then it would go a
>> long way toward proving cTakes efficacy in regards to high-order processes.
>> And if it hasnt been done and someone does it at a later date, please, send
>> me an email to the paper!
>> JG
>> On Wed, Oct 30, 2013 at 10:08 AM, Tim Miller <
>> timothy.miller@childrens.harvard.edu> wrote:
>>> Thanks for bumping this Pei, it reminds me I meant to respond to it.
>>>
>>> The OPQRST does sound like a great ML project. At a glance I might think a
>>> sequence model over sentences (like a CRF) would be a good model.
>>> But I'm wondering what the end use case is? Is it for teaching OPQRST to
>>> new clinicians? Or maybe as a sort of middleware for other projects where
>>> it might be a useful feature? Without a physician's intuition I sometimes
>>> suffer from a failure of imagination on these things.
>>>
>>> Tim
>>>
>>>
>>>
>>> On 10/30/2013 09:59 AM, Chen, Pei wrote:
>>>
>>>> Hi John,
>>>> I was away for a little bit and finally got a chance to catch up on
>>>> emails...
>>>>
>>>> 2) I work for the DoD and have latched on to several IRB approved
>>>>> projects
>>>>> within that community where Ill be using cTakes, though minimally at
>>>>> first.
>>>>> This is just a statement, a bug in the ear of the community of what
>>>>> people
>>>>> are up to.
>>>>>
>>>> This is really news! Looking forward to hearing more...
>>>>
>>>> has anyone considered (and maybe the components already do this in some
>>>>> way I
>>>>> haven't explored yet - time is ever limited) adding an OPQRST classifier?
>>>>>
>>>> I'm not too familiar on how OPQRST would be determined from the patient's
>>>> record.
>>>> Just curious, how is it currently determined manually now? Is it a
>>>> single score determined by a formula/rule(s)?
>>>> Seems like another good use case for cTAKES output-- clinically focused.
>>>> --Pei
>>>>
>>>
>>>


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