Machine Learning , some thoughts
sauermann at technikum-wien.at
Tue Jun 26 08:35:48 EDT 2018
Dear Bert, all!
Sorry if this consumes excess bandwith, feel free to delete.
The case you describe clearly provides a sound reason why "generic
archetypes will remain necessary".
I agree completely. This use case must always be satisfied.
It does not include automated processing at the receiving end. The
receiving party must read the information and decide what to do, using
their human brain cells, no 100% reliable computer aided decision
support (as in medical devices).
In this use case, I see no difference between:
- transmitting information within a "generic archetype"
- transmitting the same information in unstructured free text.
Both technologies provide a useful solution for the use case.
- So (in my humble view) this specific use case does not demand a
"generic archetype". In other words, it needs no archetype at all.
- A generic archetype does not hurt, of course.
- If a community decides to store ALL information in archetyped ways,
then so be it. There then MUST be structured and unstructured archetypes.
My engineering mind puts up behavioural resistance, if unstructured
information is stored in a structure, like an archetype. I can happily
educate my mind to stop this, and get acquainted with the view that:
- all medical information can be stored in archetypes
- some archetypes are more restrained than others
- the "generic archetype" does not restrain at all
- decision support and advanced, automated analysis will only be
available for information stored in the more restrained archetypes, if
reliability is needed
- information contained in the "generic archetype" must be interpreted
by human experts
I am very sorry if I missed basic truth, that everybody else is fully
All the best,
greetings from Vienna,
Biomedical Engineering Sciences (Master) ->
Medical Engineering & eHealth (Master) in September 2018!
University of Applied Sciences Technikum Wien
Hoechstaedtplatz 6, 1200 Vienna, Austria
P: +43 1 333 40 77 - 988
M: +43 664 6192555
E: stefan.sauermann at technikum-wien.at
Am 25.06.2018 um 23:32 schrieb Bert Verhees:
> > Therefore I conclude for myself that I will not trust (and recommend to
> > trust) automatically found archetypes, because you can not derive
> > reliable conclusions from them at a defined level of reliability.
> Stefan, I give a short reply, I have already given much input in this
> discussion and want others to let give their opinion.
> Suppose an IoT device gives an output which is not covered by a CKM
> archetype. Suppose someone is treated in Georgia with bacteriaphages
> therapy. Someone having strange skin marks which do not fit in the CKM
> evaluation archetype, but which is recognized by a machine learning
> app. What to do, not accept this medical relevant information, or
> create an a on-the-fly archetype, or let a computer create it, so the
> information can be stored?
> Suppose we had a situation like In the eighties, it would be difficult
> to enter in an EHR that someone having AIDS, because no software would
> support that, it was a new disease. All those rare symptoms coming
> together. How would we handle that? It is clear that generic
> archetypes will remain necessary, and generic flat archetypes are
> perfect to be used by computers to store generated datasets. That is
> in fact a good possibility to store unexpected datapoints.
> Today we have in the Netherlands rare diseases caused by chemical
> substances where people worked with thirty years ago. It is so
> complex, so many kinds of poison, all kind of symptoms and treatments
> can be necessary, how to handle this without generic archetypes?
> I wanted to keep it short. So best regards
> Bert Verhees
> Op ma 25 jun. 2018 18:31 schreef Stefan Sauermann
> <sauermann at technikum-wien.at <mailto:sauermann at technikum-wien.at>>:
> Dear Bert!
> Sorry, I did not want to be a nuisance.
> I agree that AI makes sense in healthcare! I also agree that progress
> has been made.
> My experience especially in information modelling (10 years within
> electronic health record in Austria, leading role in designing the
> implementation guides for laboratory report, infection report,
> antibiotics resistance report, cancer statistics report) tells me
> information modelling better is not done by a machine.
> Therefore I conclude for myself that I will not trust (and
> recommend to
> trust) automatically found archetypes, because you can not derive
> reliable conclusions from them at a defined level of reliability.
> My feeling is that you can not (today) put an "automated archetype
> design machine" through medical device regulations. This is a "must
> have" if anybody uses results in clinical practice. That was my few
> cents, I am sorry if I put it into the discussion in a cumbersone way.
> If you are interested in going deeper, we are definitely
> interested. We
> do a lot of work in information modelling, and also in sharing and
> making use of information.
> My feeling is that the reflector is not an appropriate tool to
> accomodate this discussion, and structured cooperations. We will be
> happy to engage in a structured discussion elsewhere.
> Looking forward to hear more,
> greetings from Vienna,
> Stefan Sauermann
> Program Director
> Biomedical Engineering Sciences (Master) ->
> Medical Engineering & eHealth (Master) in September 2018!
> University of Applied Sciences Technikum Wien
> Hoechstaedtplatz 6, 1200 Vienna, Austria
> P: +43 1 333 40 77 - 988
> M: +43 664 6192555
> E: stefan.sauermann at technikum-wien.at
> <mailto:stefan.sauermann at technikum-wien.at>
> I: www.technikum-wien.at/mme <http://www.technikum-wien.at/mme>
> I: www.technikum-wien.at/bhse <http://www.technikum-wien.at/bhse>
> I: healthy-interoperability.at <http://healthy-interoperability.at>
> fb: www.facebook.com/uastwMME <http://www.facebook.com/uastwMME>
> portfolio: https://mahara-mr.technikum-wien.at/user/sauermann
> Am 25.06.2018 um 12:53 schrieb Bert Verhees:
> > On 25-06-18 12:21, Stefan Sauermann wrote:
> >> Hope this helps,
> > Not really Stefan, but thanks for trying.
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