Machine Learning , some thoughts

Heather Leslie heather.leslie at
Fri Jun 29 01:38:11 EDT 2018

I totally endorse what Thomas is saying here.

Let's be realistic here! CKM is not some automagic process. There is no archetype fairy. 

We need candidate archetypes proposed by openEHR members and we need the process to be modestly funded.

What we have achieved with largely volunteer labour so far is absolutely extraordinary - let's not lose sight of that and we should be eternally grateful to those who have contributed long unpaid hours to create what we have today. 

Lots of crappy archetypes won't help interoperability. Only a few perfect ones won't either.

We're working hard to get a balance between the two with a minimum of resourcing.

Until there is dedicated, ongoing funding for managing CKM, and potentially additional funding for modellers to create new archetypes at the request of the community, the status quo will remain indefinitely. 

BTW Bert - here's a project that has some archetypes that might be useful for your diet app scenario: They were volunteered by some of our Portuguese colleagues and refined by CKM Editors.



-----Original Message-----
From: openEHR-clinical <openehr-clinical-bounces at> On Behalf Of Thomas Beale
Sent: Friday, 29 June 2018 12:13 AM
To: openehr-clinical at
Subject: Re: Machine Learning , some thoughts

On 27/06/2018 16:57, Bert Verhees wrote:
> I have sport-app which tells me the power I produce, and it tells me 
> that in Watt/kg That is more important then BMI, because athletes can 
> have a BMI above thirty (muscles are heavier then fat) and be very 
> healthy, so important is to know what they can do with all that 
> weight.
> I didn't see that one in CKM. When do you expect that to be there? 
> Will it make the next Olympics (in 2020 in Tokyo) And in the meantime, 
> we tell those athletes to be patient?

or... someone who is working on an application or system to be used for sports can just create drafts of the archetypes and upload them to CKM now. The review might take a bit of time, but not that long, if the archetypes are not complicated.

> For boxers, weight is also very important, if the grow into an higher 
> class, they are the lightest person in that class and become from 
> winner a loser.
> So they watch very carefully what they eat. They could use a 
> machine-learning program which tells them how many sandwiches to eat.
> Because every person reacts different on food, the one gets fat from 
> the same amount of food where another stays the same.
> They need tables which tell, the bread with cheese has so much 
> calories, and bread with fish so much. How would these tables come 
> alive. In archetypes?

no because this is reference knowledge, in the same sense as references ranges of path results, or formal drug descriptions. Archetypes are models of data about instances (individuals). You would probably want to create archetypes for recording meals / ingredients however, then an application can compute for you your calorific intake.

> Exactly, and it can be a micro-archetype, which makes it modular. Not 
> a cluster, because it is only one data-item. It will be an ELEMENT. A 
> CLUSTER with only one datapoint looks a bit stupid.
> Better is in CKM that they replace all CLUSTER slots with ITEM slots 
> so that it can be a CLUSTER or ELEMENT, what is appropriate.

it is immaterial, as far as I can see whether it is a CLUSTER (= a data
group) or an ELEMENT (= a data point). If some device outputs treadmill speed, treadmill incline, heart rate, vO2, work rate etc as a group, then you will have an archetype for this. With a bit of study and review of typical devices, it will be fairly clear what kinds of things go together in what ways. For example, input variables such as treadmill speed and incline could be anything, depending on the machine in use, but the physiological variables are all going to be pretty standard ones.

If you have customers wanting this stuff, I suggest making some initial proposals for CKM.

- thomas

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