Bert, I don't think that we really disagree there. As you nail it the dataset comes from people agreeing on building it the proper way. And agreeing with Karsten (who is plainly right), doesn't make that process simple.
Means that wether: 1) you can find a bunch of practitioners that agree on working extra hours to comment a big bunch of images, or 2) you expect this process to be(come) part of the usual information recording... and you must instill a culture of data quality and information awareness before the dataset can exist. Le 25/06/2018 à 15:22, Bert Verhees a écrit : > On 25-06-18 14:47, Philippe Ameline wrote: >> Successfully using machine learning demands a prior culture of data >> quality and information awareness. > > Dear Philippe, I read your document later. > > I have to disagree with the word "prior". > > It makes it sound like, is has gone wrong long time ago, and there is > nothing what we can do. > > Big data for machine learning can be build very quick, we have > millions of people in healthcare every day. > > Imagine a GP making a picture of an eye, or a part of skin, and gets > within a second a good explanation about what is there to see. > > It is cheap. If many GP's agree to use an app for classifying viewable > symptoms, the supporting big database will grow fast. > > I also have to agree with Karsten, it is not only a disease which > needs to be cured, but it is a person having that disease. So, age, > weight, gender, ethnicity, profession, social status, country, those > are all factors which limit the search area in which the machine > learning database must find what it sees. > > > _______________________________________________ > openEHR-clinical mailing list > openEHR-clinical@lists.openehr.org > http://lists.openehr.org/mailman/listinfo/openehr-clinical_lists.openehr.org > > _______________________________________________ openEHR-clinical mailing list openEHR-clinical@lists.openehr.org http://lists.openehr.org/mailman/listinfo/openehr-clinical_lists.openehr.org
