DISTRIBUTED RELATIONSHIP REASONING ENGINE FOR GENERATING ANALYTICS FOR HEMODYNAMIC INSTABILITY
A reasoning engine is disclosed. Contemplated reasoning engines acquire data relating to one or more aspects of various environments. Inference engines within the reasoning engines review the acquire data, historical or current, to generate one or more hypotheses about how the aspects of the environments might be correlated, if at all. The reasoning engine can attempt to validate the hypotheses through controlling acquisition of the environment data.
1 - 30 . (canceled)
31 . A remote patient data monitoring engine system, comprising:
at least one computer-readable memory storing rules engine software instructions;
a data interface coupled with at least one remote sensor associated with a patient; and
at least one processor coupled with the data interface and the at least one computer-readable memory, and upon execution of the rules engine software instructions performs operations to:
acquire, over a network, environmental data associated with the patient and at least partially including sensor data from the at least one remote sensor acquired via the data interface;
recognize aspects from the environmental data as target objects;
generate at least one hypothesis regarding a correlation among the target objects with respect to an outcome; and
cause an output device to present the at least one hypothesis as relating to the outcome.
32 . The system of claim 31 , wherein the remote patient data monitoring engine comprises a virtual machine in which the operations are performed.
33 . The system of claim 31 , wherein the environmental data represents at least in part the patient's body.
34 . The system of claim 31 , wherein the environmental data further comprises multiple data modalities.
35 . The system of claim 31 , wherein the environmental data further comprises at least one of the following data modalities: biometric data, medical data, demographic data, a trend in a body temperature, wave data, and identity data.
36 . The system of claim 31 , wherein the environmental data further comprises at least one observed change with respect to the patient.
37 . The system of claim 36 , wherein the at least one observed change with respect to the patient includes a body change with respect at least one of the following: a pressure point, airway patency, respiration, blood pressure, perspiration, a body fluid, and perfusion.
38 . The system of claim 31 , wherein the environmental data further comprises at least one of the following observed changes with respect an electrical characteristic: a resistance, a current, a voltage, capacitance, a reactance, an inductance, a permeability, and a permittivity.
39 . The system of claim 31 , wherein the operation to cause the output device to present the at least one hypothesis occurs in real-time relative to acquiring the environmental data.
40 . The system of claim 31 , wherein the environmental data comprises ambient data associated with the patient.
41 . The system of claim 31 , wherein the environmental data comprises a digital representation of a scene associated with the patient.
42 . The system of claim 31 , further comprising a knowledge base storing object information for known target objects, wherein the knowledge base is coupled with the at least one processor.
43 . The system of claim 42 , wherein the knowledge base is personalized to the patient.
44 . The system of claim 31 , wherein the step of generating the at least one hypothesis further includes forming the at least one hypothesis according to a least one rules set encoded in the rules engine software instructions.
45 . The system of claim 44 , wherein the operations further include operations to select the rules set based on the environmental data.
46 . The system of claim 45 , wherein the rules set includes at least one of the following types of rules sets: deductive rules, abductive rules, inductive rules, ampliative reasoning rules, case-based reasoning rules, metaphorical mapping rules, forward chaining rules, backward chaining rules, analogical reasoning rules, cause-and-effect reasoning rules, defeasible or indefeasible reasoning rules, comparative reasoning rules, conditional reasoning rules, decompositional reasoning rules, exemplar reasoning, modal logic rules, set-based reasoning rules, systemic reasoning rules, syllogistic reasoning rules, probabilistic reasoning rules, paraconsistent reasoning rules and fuzzy logic rules.
47 . The system of claim 31 , wherein the outcome comprises a negative outcome.
48 . The system of claim 31 , wherein the outcome comprises a positive outcome.
49 . The system of claim 31 , wherein the operation to present the at least one hypothesis comprises offering a recommendation on a change to a treatment protocol related to the outcome.
50 . A computer-readable memory (CRM) storing rules engine software instructions that, when executed by at least one processor, cause the at least one processor to:
acquire, over a network, environmental data associated with a patient and at least partially including sensor data from at least one remote sensor acquired via a data interface;
recognize aspects from the environmental data as target objects;
generate at least one hypothesis regarding a correlation among the target objects with respect to an outcome; and
cause an output device to present the at least one hypothesis as relating to the outcome.
51 . A method for remotely monitoring patient, the method comprising:
acquiring, over a network, environmental data associated with a patient and at least partially including sensor data from at least one remote sensor acquired via a data interface;
recognizing aspects from the environmental data as target objects;
generating at least one hypothesis regarding a correlation among the target objects with respect to an outcome; and
causing an output device to present the at least one hypothesis as relating to the outcome.