Disease simulation system and method

• US 20070118348A1
• Filed: 01/22/2007
• Published: 05/24/2007
• Est. Priority Date: 01/10/1997
• Status: Active Grant

• Current Assignee: Health Hero Network Incorporated (Robert Bosch GmbH)
• Original Assignee: Health Hero Network Incorporated (Robert Bosch GmbH)
• Inventors: Brown, Stephen
• Granted Patent: US 7,912,688 B2
• Time in Patent Office: Days
• US Class Current: 703/11
• CPC Class Codes: A61B 5/0002 Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network A61B5/07 takes precedence; transmission systems for measured values G08C; transmission H04B; transmission of digital information H04L; wireless communication networks H04W
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61B 5/411 Detecting or monitoring allergy or intolerance reactions to an allergenic agent or substance
• CPC Class Codes: A61B 5/6896 Toys
• CPC Class Codes: A61B 5/7275 Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: G01N 2035/00891 Displaying information to the operator
• CPC Class Codes: G01N 33/48792 Data management, e.g. communication with processing unit for in vivo diagnostics A61B5/0002; transmission systems for measured values G08C
• CPC Class Codes: G09B 19/00 Teaching not covered by other main groups of this subclass teaching or practice apparatus for gun-aiming or gun-laying F41G3/26
• CPC Class Codes: G16H 10/40 for data related to laboratory analysis, e.g. patient specimen analysis
• CPC Class Codes: G16H 20/13 delivered from dispensers
• CPC Class Codes: G16H 20/17 delivered via infusion or injection
• CPC Class Codes: G16H 20/30 relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
• CPC Class Codes: G16H 20/60 relating to nutrition control, e.g. diets
• CPC Class Codes: G16H 40/63 for local operation
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders
• CPC Class Codes: G16H 70/20 relating to practices or guidelines
• CPC Class Codes: Y10S 128/92 Computer assisted medical diagnostics

Abstract

A system and method for predicting the effect of patient self-care actions on a disease control parameter. A future disease control parameter value X(t_j) at time t_j is determined from a prior disease control parameter value X(t_i) at time t_i based on an optimal control parameter value R(t_j) at time t_j, the difference between the prior disease control parameter value X(t_i) and an optimal control parameter value R(t_i) at time t_i, and a set of differentials between patient self-care parameters having patient self-care values S_M(t_i) at time t_i and optimal self-care parameters having optimal self-care values O_M(t_i) at time t_i. The differentials are multiplied by corresponding scaling factors K_M. The system includes an input device for entering the patient self-care values S_M(t_i). A memory stores the optimal control parameter values R(t_i) and R(t_j), the prior disease control parameter value X(t_i), the optimal self-care values O_M(t_i), and the scaling factors K_M. A processor in communication with the input device and memory calculates the future disease control parameter value X(t_j). A display is connected to the processor to display the future disease control parameter value X(t_j) to a patient.

Claims

1. A system configured to determine a future disease control parameter value comprising:

a computing device; and

a memory comprising (i) an optimal disease control parameter value, (ii) one or more optimal self-care values, (iii) one or more scaling factors for weighting an impact of one or more self-care actions by a patient on a disease control parameter value of the patient and (iv) one or more actual self-care values of the patient, wherein the memory is readable by the server and the server is configured to calculate the future disease control parameter value based upon (i) the optimal disease control parameter value, (ii) the one or more optimal self-care values, (iii) the one or more scaling factors, (iv) the one or more actual self-care values of a patient and (v) the prior disease control parameter value.