Machine Learning , some thoughts

Stefan Sauermann sauermann at technikum-wien.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

Stefan Sauermann

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Am 23.06.2018 um 18:11 schrieb Bert Verhees:
> Today my wife showed me Plantnet.
>
> https://plantnet.org/en/
>
> 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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