It is a truth that, in the case of GP’s, almost always they deal with Complaints, Tentative diagnosis or estimation of the Seriousness (good/bad feeling), some trial Therapies and some addition investigations/tests, plus finally some time to observe the evolution of the complaints over time; await the test results, and after a while get an idea of a possible diagnosis or a complaint free patient.
Rarely one consultation resulted in a distinct diagnosis. The patient population of GP’s is very amorf. There are a lot of haystacks and a few needles. In hospitals it is somewhat different. It is much more finding a diagnosis and treatment or proving that there some diagnosis do not apply. Hospital patient are a highly selected group of patients. Gerard Freriks +31 620347088 gf...@luna.nl Kattensingel 20 2801 CA Gouda the Netherlands > On 25 Jun 2018, at 14:56, Anastasiou A. <a.anastas...@swansea.ac.uk> wrote: > > Hello Bert and all > > I am a little bit "worried" with "micro-archetypes" the way you describe them. > > I think that what you are probably referring to is "Disease Specific > Templates", which I really hope is what we are all working towards :) > > So, archetypes do indeed describe one conceptual quantity, or aspect of a > person's healthcare and then a template describes a multidimensional "Point" > which characterises the patient journey within the disease. > > Consider for example "Total Brain Volume". You can use it to track cognitive > decline in AD > (https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(04)15441-X/abstract) > and this gives you one "explanatory variable". But, still, there are patients > whose brain volume is abnormal (for their age) and they still perform well in > other tests, so you need more > "data points" (a richer template) around the phenomenon to understand it > better. > > I think that what you are describing is something like "An automated approach > to constructing disease specific 'Minimal Clinical Datasets'". > > Once you have this minimal dataset discovered, THEN you could compose the > template or automatically create the archetypes. > > And yes, this CAN be done today, definitely. > > All the best > Athanasios Anastasiou > > > > > > > -----Original Message----- > From: Bert Verhees <bert.verh...@rosa.nl> > Sent: 25 June 2018 12:31 > To: Anastasiou A. <a.anastas...@swansea.ac.uk>; For openEHR clinical > discussions <openehr-clinical@lists.openehr.org> > Subject: Re: Machine Learning , some thoughts > > On 25-06-18 12:44, Anastasiou A. wrote: >> The time scales for doing this would be enormous. We can probably work >> out a lower limit by looking at the lifecycle of archetypes in the current >> CKM. > > Thanks, for your answer, I agree with you and others, and already wrote that, > that an EHR will not be good enough for machine learning. > > I was too optimistic and to much impressed by some results of machine > learning. It will do very good things in healthcare, but only on very > specific cases. > > But while writing this > > What would be good, however, an improvement. I suggested to my wife (a GP), > and she agreed (partly) > > Classic EHR software only has few datapoints on a screen, and many > particularities come into free text, and if the GP is really motivated, maybe > he finds some ICPC code. > > Archetypes do not really change this practice. A GP is a busy person. > > What could help is modularity. A GP should be able to add datapoints to his > screen. For example, beside all the normal things, the GP sees that there are > red eyes, but how can he make this available to the system in a way that it > can be found back? > > What about micro-archetypes which describe only one datapoint? And the GP > should be able to invoke them by voice. He says "red eyes" and magic happens, > there is a datapoint on the screen which offers a possibility to click on a > checkbox. Eventually a choice, A bit red, medium red, very red. > > This kind of software does not have to be something for the far future, but > can be available already now. > > Also thanks to machine learning, a limited form of NLP (natural language > expression (machine learning helping with NLP) can be used, and that was my > idea of generating archetypes, last Saturday. A computer could, in some cases > of simple datapoints, also even generate micro-archetypes for them, and with > templates or container-archetypes, generate evaluation-archetypes > > Maybe, when it is so easy to create datapoints, and store them, maybe then > machine learning in diagnostic can come closer, also in some cases for a GP, > or machine learning can do suggestion: look to the tongue of the patient, but > the fact remains, a good GP needs experience for diagnotics. > > Bert > > _______________________________________________ > openEHR-clinical mailing list > openEHR-clinical@lists.openehr.org > http://lists.openehr.org/mailman/listinfo/openehr-clinical_lists.openehr.org
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