Machine Learning , some thoughts
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,
Biomedical Engineering Sciences (Master) ->
Medical Engineering & eHealth (Master) in September 2018!
University of Applied Sciences Technikum Wien
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E: stefan.sauermann at technikum-wien.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
> 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.
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