Comput. Biol. Med. - Hyperbolic Dirac Nets for medical decision support. Theory, methods, and comparison with Bayes Nets.

Tópicos

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Resumo

We recently introduced the concept of a Hyperbolic Dirac Net (HDN) for medical inference on the grounds that, while the traditional Bayes Net (BN) is popular in medicine, it is not suited to that domain: there are many interdependencies such that any "node" can be ultimately conditional upon itself. A traditional BN is a directed acyclic graph by definition, while the HDN is a bidirectional general graph closer to a diffuse "field" of influence. Cycles require bidirectionality; the HDN uses a particular type of imaginary number from Diracs quantum mechanics to encode it. Comparison with the BN is made alongside a set of recipes for converting a given BN to an HDN, also adding cycles that do not usually require reiterative methods. This conversion is called the P-method. Conversion to cycles can sometimes be difficult, but more troubling was that the original BN had probabilities needing adjustment to satisfy realism alongside the important property called "coherence". The more general and simpler K-method, not dependent on the BN, is usually (but not necessarily) derived by data mining, and is therefore also introduced. As discussed, BN developments may converge to an HDN-like concept, so it is reasonable to consider the HDN as a BN extension.

Resumo Limpo

recent introduc concept hyperbol dirac net hdn medic infer ground tradit bay net bn popular medicin suit domain mani interdepend node can ultim condit upon tradit bn direct acycl graph definit hdn bidirect general graph closer diffus field influenc cycl requir bidirect hdn use particular type imaginari number dirac quantum mechan encod comparison bn made alongsid set recip convert given bn hdn also ad cycl usual requir reiter method convers call pmethod convers cycl can sometim difficult troubl origin bn probabl need adjust satisfi realism alongsid import properti call coher general simpler kmethod depend bn usual necessarili deriv data mine therefor also introduc discuss bn develop may converg hdnlike concept reason consid hdn bn extens

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