- From: Daniel Rubin <rubin@med.stanford.edu>
- Date: Tue, 12 Jun 2007 07:03:59 -0700
- To: Matt Williams <matthew.williams@cancer.org.uk>, "Kashyap, Vipul" <VKASHYAP1@PARTNERS.ORG>,public-semweb-lifesci@w3.org
At 07:15 AM 6/11/2007, Matt Williams wrote: >I changed the subject line to make it more specific. > >I think that Evidence is a tricky, slippery subject. It seems to be >both traces (i.e. records of something) and in many cases, >inferences. Those inferences probably shouldn't be called evidence, >but they are the reason that some data are considered evidence, and >others not, and hence often get included. Actually, sometimes the interpretation *is* part of the evidence--best example is medical imaging wherein the radiologist interpretation of the images are part of the primary evidence (the image is the "raw" evidence, but you have no result without the radiology interpretation of the image). Interpretation also transforms raw data into recoded variables that is also used as evidence, for example in interpreting raw EKG tracings to give the label of "ventricular tachycardia" or recording a sodium of 150 as "high sodium." >To take the radiology example below > >>>So evidence is a function of the facts, the >>>analysis method, the method of inference, and perhaps even the >>>observer (e.g., if the evidence is a radiology image or physical >>>exam, there is inter-observer variation). >>>And it's definitely necessary to relate the hypotheses to the >>>evidence with probabilities >>> >I would suggest that the interpretation of the evidence is a >function of the facts (plus other things). However, the facts are >not stable (e.g. with a physical examination) and may conflict with >each other; therefore inconsistency is not a just a matter of which >inference procedure you choose, it is also a matter of which facts >(your premises) you start from. > >It is also not "definitely necessary to relate hypotheses to >evidence with probability" (although it may be useful). There are a >load of other techniques that don't use probability: e.g. Wigmore >Charts (from 1930's onwards) and more recently, non-monotonic >logical techniques. I suggested the importance of probabilities because of their utility in the biomedical domain. Have the other methods you cite been used in biomedicine? If so, I'd be very interested in looking at the citations. >For a good intro. I would recommend David's Schum's book "The >Evidential Foundations of Probabilistic Reasoning". Also,a look at >the evidence science website might be good: http://www.evidencescience.org/ Thanks for the pointers. Daniel >HTH, > >Matt
Received on Tuesday, 12 June 2007 14:04:03 UTC