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US 8,751,416 B2Granted
Method and apparatus for deriving probabilistic models from deterministic ones
AssigneeKoninklijke Philips N.V.
InventorsNicolas W. Chbat, Kees Van Zon
Life of this patent
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Abstract
A Dynamic Bayesian Network provides models that provides emulation of patient data.
Claims8
A system for deriving a probabilistic model from a deterministic model, comprising:
- a validated deterministic model (VDM), wherein the VDM is a dynamic Bayesian Network (DBN) which is derived from a system of ordinary differential equations, the DBN being provided as a file representing variables and parameters, probabilistic relationships of the variables and strengths of the probabilistic relationships as defined by the parameters;
- and
- a machine learning algorithm (MLA) operative to receive data from the VDM and to generate the probabilistic model, wherein the probabilistic model receives the data, selects an appropriate class of parameters for the DBN, and emulates a patient according to a patient class.
A system for emulating variables in a person, comprising:
- a validated deterministic model (VDM) operative to provide patient data, wherein the VDM is a dynamic Bayesian Network (DBN) which is derived from a system of ordinary differential equations, the DBN being provided as a file representing variables and parameters, probabilistic relationships of the variables and strengths of the probabilistic relationships as defined by the parameters;
- and
- a machine learning algorithm (MLA) operative to receive patient data from the VDM and to generate the probabilistic model, wherein input variables are provided to the probabilistic model to provide the emulating variables and wherein the probabilistic model receives the patient data, selects an appropriate class of parameters for the DBN, and emulates a patient according to a patient class.
Dependencies are inferred from the claim text.