Science of Machine Learning (was Machine Learning , some thoughts)
a.anastasiou at swansea.ac.uk
Tue Jul 3 07:13:19 EDT 2018
Initially, I thought that it would have been this one: https://arstechnica.com/tech-policy/2017/07/google-deepmind-nhs-deal-broke-uk-data-law/
But it seems to be entirely US based.
“Events of importance worth recording in the BOOK OF LIFE are frequently put on record in difference places since the person moves about
the world throughout his lifetime. This makes it difficult to assemble this BOOK into a single compact volume.
Yet, sometimes it is necessary to examine all of an individual’s important records simultaneously. No one would read a novel, the pages of which were not assembled.
Just so, it is necessary at times to link the various important records of a person’s life”.
Getting there ☺
All the best
From: openEHR-clinical <openehr-clinical-bounces at lists.openehr.org> On Behalf Of Philippe Ameline
Sent: 03 July 2018 12:02
To: Birger Haarbrandt <birger.haarbrandt at plri.de>; For openEHR clinical discussions <openehr-clinical at lists.openehr.org>
Subject: Re: Science of Machine Learning (was Machine Learning , some thoughts)
BTW, is someone aware of this project by Google?
Le 03/07/2018 à 12:40, Birger Haarbrandt a écrit :
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 :)
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<mailto:birger.haarbrandt at 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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