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.haarbra...@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. >> >> >> _______________________________________________ >> openEHR-clinical mailing list >> openEHR-clinical@lists.openehr.org >> http://lists.openehr.org/mailman/listinfo/openehr-clinical_lists.openehr.org > > >
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