Science of Machine Learning (was Machine Learning , some thoughts)

Philippe Ameline philippe.ameline at free.fr
Tue Jul 3 07:02:08 EDT 2018


BTW, is someone aware of this project by Google?
https://ai.googleblog.com/2018/05/deep-learning-for-electronic-health.html


Le 03/07/2018 à 12:40, Birger Haarbrandt a écrit :
> Hi Philippe,
>
> I completely agree with your view. This is why data stewardship is
> needed before we can make real use of the data:
> https://en.wikipedia.org/wiki/Data_steward
>
> As we use this approach in HiGHmed, I might be able to report in 2020
> about lessons learned :)
>
> Best,
>
> -- 
> *Birger Haarbrandt, M. Sc.
> Peter L. Reichertz Institut for Medical Informatics (PLRI)
> Technical University Braunschweig and Hannover Medical School
> Software Architect HiGHmed Project *
> Tel: +49 176 640 94 640, Fax: +49 531/391-9502
> birger.haarbrandt at plri.de
> www.plri.de
>
>
>
> Am 03.07.2018 um 12:21 schrieb Philippe Ameline:
>> Le 02/07/2018 à 11:31, Bert Verhees a écrit :
>>
>>> On 30-06-18 17:16, Philippe Ameline wrote:
>>>> (improperly labeling images or adding images of objects that are not
>>>> plants) could probably make the whole app plainly crappy.
>>> Of course Philippe, but that would be vandalism. Most sensible people
>>> don't do that when they stand behind the goal, and a little bit of
>>> dirt, therefor it is Machine Learning, it can filter it out. It is
>>> part of the learning process.
>> If a culture of data quality is properly installed, then it is possible
>> to name improper use "vandalism".
>> In medicine, since such a culture has never existed, we could name it
>> "don't carisme", "no time for thisisme" or "was never thaughtisme".
>>
>> My point, and what the paper I previously pointed out explains, is that
>> trying to get something out of machine learning in a domain of poor data
>> quality is a modern kind of magic thinking.
>> It just means that any such project should first organize for data
>> quality as a first step.
>>
>> When considering it in hindsight, it makes sense since machine learning
>> involves statistics and data quality is paramount in this domain.
>>
>>
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>
>
>

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