Obviously one requires data in order to define a set but so often we fail to
use that data.
If, for example, one knows the value of a urea one can define a set bounded
by that urea value plus or minus the measurement uncertainty (say the sum of
the analytical and biological variations).
The new set may or may not differ in proportionate composition from the
parent set from which is was derived (that is there may or may not be
information gain) but it is the new set that defines prior probability.
Extracting relevant sets is not that difficult provided the data is properly
structured and the process can be made very fast.
Most of the expert systems that I have seen described are, in truth, little
more than risk algorithms using pre-defined data alone.
Trevor Tickner
> -----Original Message-----
> From: Dr Henry Chandler [SMTP:[log in to unmask]]
> Sent: 12 December 2001 19:42
> To: [log in to unmask]
> Subject: Re: a challenge for interpretative comments ....
>
> In message <[log in to unmask]
> norwich.thenhs.com>, TICKNER TREVOR (RM1) Norfolk and Norwich NHS Trust
> <[log in to unmask]> writes
> >
> >Similar objections can be raised to many 'expert systems'. Only rarely do
> >such systems make any attempt to divide patient into prior-data sets
> except
> >where that data is known to correlate with diagnostic assignment and that
> >'gold standard' assignment is no more than the opinion of an expert
> panel.
> >Trevor Tickner
> >Norwich
>
> The trouble in designing "expert systems" that divide patients into
> prior data sets is that you need a test to make that division which, of
> course, has a likelihood ratio ...
>
> Do I hear the Reverend Dr Bayes thundering up behind me with a problem
> of ever decreasing circles, but again perhaps Pascal was right.
> --
> Dr Henry Chandler
>
>
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