Machine Learning , some thoughts

Stefan Sauermann sauermann at
Mon Jun 25 06:21:26 EDT 2018

82% of correct recognition rate is a desaster in healthcare.
74% is even worse.

My evidence based feeling is that we still will need to sort it out 
manually for some years to come.

Hope this helps,

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
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E: stefan.sauermann at

Am 23.06.2018 um 18:11 schrieb Bert Verhees:
> Today my wife showed me Plantnet.
> It recognizes over 6000 plants from showing a flower or a leaf to your 
> phone. It has learned from machine-learning 700.000 pictures, and its 
> knowledge every day grows stronger, because it keeps on learning. And 
> not only the looks of a flower, but if it takes location (biotope) and 
> date in consideration, the certainty of recognizing gets stronger.
> Now you can imagine that it must be hard to recognize a plant from a 
> picture, without seeing the dimensions and showed in many possible 
> angles, in sunlight, cloudy or twilight.
> I was impressed how good it already was. Very advanced 
> computer-knowledge for free in the hands of the millions.
> There is also an app, I did not try it, which recognizes birds from 
> audio. You walk somewhere, hear a bird and want to know what kind of 
> bird that is.
> The Berlin Natural History Museum leads a contest of 29 teams using 23 
> different methods, with more than 82% good identifications for 
> isolated bird recordings, and more than 74% correct identifications 
> for recordings mixing several bird songs.
> I often notice there is a trend in thinking that Machine Learning 
> cannot be much help, see how miserable google-translate translates. 
> But then we for get to see how much progress is made in other areas.
> Why am I writing this? Just to let you think about it.
> I wonder, Is OpenEhr usable for recognizing pattern in diseases over 
> Machine Learning, isn't behind every diagnosis a small cloud of 
> archetypes which forms a pattern? The features of recognizing/learning 
> should not be found in archetypes ID's, although, that can help a lot, 
> but it should also look to datatypes, their semantics and relations.
> Isn't OpenEhr better for recognizing pattern then whichever classic 
> storage structure, because the data-structures in OpenEhr are in 
> semantic models, this instead of some weird Codd-structure, which only 
> has technical reasons to exist.
> (Classic data stored in classic SQL schema's could be brought over to 
> archetyped structures, to make the base of machine-learning larger.)
> I think, when this is developed, we should be able to get to at least 
> two advantages.
> 1) We don't need CKM anymore, computers can understand archetypes, we 
> don't need to restrict ourselves to a limited number. We can also use 
> archetypes we do not know, and maybe we never know. Even, we wouldn't 
> need archetypes anymore, just as reminder/instruction. But the 
> computer could create the archetypes on the fly, when seeing the kind 
> of data, the relations, the diagnosis.
> 2) We could use the pattern to recognize healthcare situations, and 
> maybe treat/handle/cure on base of instructions coming from machine 
> learning.
> Some thoughts when walking with my wife through the wonderful dunes, 
> and its special vegetation. Maybe I must write a blog about it.
> Have a nice day.
> Bert
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