PNM and Adverse Effects in Medicine

PNM pattern-based characterization of the clinical profiles opens the “missing half” of the information content in standard data used for clinical prognostication: the information supporting clinical decisions can be based both on the PRESENCE as well as on the ABSENCE of characteristic clinical patterns:

Adverse-effects analysis — presence vs absence of clinical patterns (3)

The information gain from using PNM stems from preserving the coherence of all the data collected together for an individual patient. This is the largest information resource that PNM can utilize, in contrast to conventional methods which treat every individual clinical variable independently. After capturing the patient’s personalized clinical-profile pattern into the PNM graph, it is “just” mathematics. Please note that this mathematics has serious foundations (proved theorems, information-theory arguments, an active component through entropy maximization, and more) which contribute to the non-empirical, novel, but still directly clinically interpretable results.

PNM adverse-effects analysis — figure F3

PNM adverse-effects analysis — figure F4_0

PNM adverse-effects analysis — figure F5

PNM adverse-effects analysis — figure F6

PNM adverse-effects analysis — figure F7

PNM adverse-effects analysis — figure F8

PNM adverse-effects analysis — figure F8b_0

PNM adverse-effects analysis — figure F9

PNM adverse-effects analysis — figure F10

PNM adverse-effects analysis — figure F11

PNM adverse-effects analysis — figure F12

PNM adverse-effects analysis — figure F13

PNM adverse-effects analysis — figure F14_0

PNM adverse-effects analysis — figure F15

PNM adverse-effects analysis — figure F16

PNM adverse-effects analysis — figure F17

PNM adverse-effects analysis — figure F18

PNM adverse-effects analysis — figure F19

PNM adverse-effects analysis — figure F20