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From John Green <john.travis.gr...@gmail.com>
Subject Re: Sundry; Problem Lists
Date Mon, 04 Nov 2013 22:29:27 GMT
Thank you Sean for taking the time to respond to me, it was much
appreciated. I'm learning a lot about a lot.

>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 ...

I'd be interested in hearing more of what you meant by: " - if not
completely necessary for any real clinical use of nlp". I may be showing my
lack of knowledge here again, or I may have miscommunicated in the first
instance: a good problem list, whether the physician admits to it or not,
is interpretation problem-number-one. Take this example of a "History of
Present Ilness" in physician lingo: I come in with a cough, I have a sick
child at home with a cough, I'm also 60 years old and a bad diabetic and a
recent lab value showed an A1C of 9. Further, I'm also a traveler and I
just came back from visiting my cousin in (some country endemic with
tuberculosis). Of course, all of the above may be in a narrative that
includes complex story features, that the physician may or may not have
included in the free-text note. "Mr X is a 60 yo man with a known history
of CAD and DMII. Patient states he came home and had a cough. He further
states that his daughter has a cough. He recently returned from a country
in which he had regular contact with people with TB. He expresses concern
and anxiety over this." Well, our problem list is above (Cough, Sick
contact at home (viral), Sick contact abroad (TB), A1C of 9). In the
short-term, any NLP wanting to suggest further workup on this man would
need to a) recognize those features of the HPI and b) prioritize the TB
workup! So the modified by priority problem list would be 1) Cough 2) TB
exposure ... etc. Clumping could ensue. Also, for a "longitudinal" problem
list, one that tracked across clinical encounters, only the TB exposure and
maybe a "history of poorly controlled diabetes" would need to continue on
in the patients history. Certainly a sick child at home would not (what I
meant by acute vs longitudinal problem lists).

Thanks for the conversation Sean,
Sincerely,
John


On Mon, Nov 4, 2013 at 12:15 PM, Finan, Sean <
Sean.Finan@childrens.harvard.edu> wrote:

>  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> 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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