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RE: An argument for bridging information models and ontologies at the syntactic level

From: Ogbuji, Chimezie <OGBUJIC@ccf.org>
Date: Wed, 16 Apr 2008 10:45:00 -0400
Message-ID: <2702D0EBA4F0A749968E52E8644184EA01DE99AB@CCHSCLEXMB59.cc.ad.cchs.net>
To: dan.russler@oracle.com
cc: dirk.colaert@agfa.com, "Dan Corwin" <dan@lexikos.com>, public-hcls-coi@w3.org, public-semweb-lifesci@w3.org, public-semweb-lifesci-request@w3.org, rector@cs.man.ac.uk, "Samson Tu" <swt@stanford.edu>, "Oniki, Tom (GE Healthcare, consultant)" <Tom.Oniki@ge.com>, "Kashyap, Vipul" <VKASHYAP1@PARTNERS.ORG>

Hello, Dan.  Comments inline below.  I'll start with my general
understanding of data and measurements and see if I can't converge on an
answer to your question.

By data, I mean anything that is captured in some (mostly electronic)
medium and typically represents or is a proxy for some phenomenon of
interest.  By measurement_process I mean an assay ("a quantitative or
qualitative test of a thing [sic] to determine its components").  By
measurement_data, I simply mean data that was derived from a
measurement_process.

>-----Original Message-----
>Hi Chimezie,
>
>It may be helpful to examine what "data" means and what 
>"measurement" means.

Absolutely.

>There is a kind of classification system that is used in medicine.
>The "process of living" includes many hundreds of thousands of 
>sub-processes that must work in harmony for the individual to 
>remain alive.
>These sub-processes each have a "status" at a given point in time.
>We can perform measurements that give us data about the status 
>of the sub-processes. 

measurement_processes that result in measurment_data?

> However, the data is meaningless without 
>interpretation:
>Are the values we receive from measurement free of error? Most 
>tests will guarantee that the data is only 95% free of 
>significant error.
>If the value can be assumed to be free of error, does the 
>value represent a significant change in the state underlying 
>process being measured?
>If the state of the underlying process is significantly 
>changed, does the change represent "dis-ease" or does the 
>change just represent a "variant of normal," i.e. a temporary 
>normal abberation in a processes?
>Note that there is not always agreement about when a variance 
>of normal process function actually constitutes a disease or 
>"diseased process." Of course, life is only compatible with 
>relatively minor abnormalities in body processes. Major 
>abnormalities lead to immediate death.

I think of measurement_data as (mostly) objective and so independent of
the interpretation that comes into play when (for example) a clinician
follows-up with an attempt to categorize the change that the
measurement_data reflects.

>So we are left with some important concepts that need to be 
>fit into an ontology that is useful to scientists and clinicians.
>"Living is a set of processes; disease is a slightly abnormal 
>process in the midst of many normal processes. 

So, by *this* classification, the distinction between disease (or
slightly_abnormal_process) and data would follow from:

processes != data

Right?

> We only know 
>about these slightly abnormal processes by making measurements 
>of some sort that proxy for the "state of the process." 

measurement_data that proxy for the "state of the process"?

>Usually, one kind of measurement is not sufficient to tell us 
>much about the status of a processes. From a series of 
>different kinds of measurements, one can make conclusions 
>about whether the state of the process is permanently abnormal 
>or just transiently abnormal. One can make conclusions about 
>whether the process is becoming more abnormal or becoming more normal."

So, the conclusions would be the diagnosis (which is independent from
the measurement_data, measurement_process, abnormal processes, etc.?)

>Now with this perspective, perhaps you can better describe how 
>a disease is different than data?

I've tried to above.  I hope the distinction is clearer than before.

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Received on Wednesday, 16 April 2008 14:46:01 GMT

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