Data processing: structural design – modeling – simulation – and em – Simulating nonelectrical device or system – Biological or biochemical
Reexamination Certificate
1999-09-20
2001-05-15
Teska, Kevin J. (Department: 2123)
Data processing: structural design, modeling, simulation, and em
Simulating nonelectrical device or system
Biological or biochemical
C600S300000, C600S301000
Reexamination Certificate
active
06233539
ABSTRACT:
BACKGROUND
1. Field of the Invention
The present invention relates generally to disease simulation systems, and in particular to a system and method for simulating a disease control parameter and for predicting the effect of patient self-care actions on the disease control parameter.
2. Description of Prior Art
Managing a chronic disease or ongoing health condition requires the monitoring and controlling of a physical or mental parameter of the disease. Examples of these disease control parameters include blood glucose in diabetes, respiratory flow in asthma, blood pressure in hypertension, cholesterol in cardiovascular disease, weight in eating disorders, T-cell or viral count in HIV, and frequency or timing of episodes in mental health disorders. Because of the continuous nature of these diseases, their corresponding control parameters must be monitored and controlled on a regular basis by the patients themselves outside of a medical clinic.
Typically, the patients monitor and control these parameters in clinician assisted self-care or outpatient treatment programs. In these treatment programs, patients are responsible for performing self-care actions which impact the control parameter. Patients are also responsible for measuring the control parameter to determine the success of the self-care actions and the need for further adjustments. The successful implementation of such a treatment program requires a high degree of motivation, training, and understanding on the part of the patients to select and perform the appropriate self-care actions.
One method of training patients involves demonstrating the effect of various self-care actions on the disease control parameter through computerized simulations. Several computer simulation programs have been developed specifically for diabetes patients. Examples of such simulation programs include BG Pilot™ commercially available from Raya Systems, Inc. of 2570 E
1
Camino Real, Suite 520, Mountain View, Calif. 94040 and AIDA freely available on the World Wide Web at the Diabetes UK website http://www.pcug.co.uk/diabetes/aida.htm.
Both BG Pilot™ and AIDA use mathematical compartmental models of metabolism to attempt to mimic various processes of a patient's physiology. For example, insulin absorption through a patient's fatty tissue into the patient's blood is represented as a flow through several compartments with each compartment having a different flow constant. Food absorption from mouth to stomach and gut is modeled in a similar manner. Each mathematical compartmental model uses partial differential equations and calculus to simulate a physiological process.
This compartmental modeling approach to disease simulation has several disadvantages. First, understanding the compartmental models requires advanced mathematical knowledge of partial differential equations and calculus which is far beyond the comprehension level of a typical patient. Consequently, each model is an unfathomable “black box” to the patient who must nevertheless trust the model and rely upon it to learn critical health issues.
A second disadvantage of the compartmental modeling approach is that a new model is needed for each new disease to be simulated. Many diseases involve physiological processes for which accurate models have not been developed. Consequently, the mathematical modeling approach used in BG Pilot™ and AIDA is not sufficiently general to extend simulations to diseases other than diabetes.
A further disadvantage of the modeling approach used in BG Pilot™ and AIDA is that the mathematical models are not easily customized to an individual patient. As a result, BG Pilot™ and AIDA are limited to simulating the effect of changes in insulin and diet on the blood glucose profile of a typical patient. Neither of these simulation programs may be customized to predict the effect of changes in insulin and diet on the blood glucose profile of an individual patient.
OBJECTS AND ADVANTAGES OF THE INVENTION
In view of the above, it is an object of the present invention to provide a disease simulation system which is sufficiently accurate to teach a patient appropriate self-care actions and sufficiently simple to be understood by the average patient. It is another object of the invention to provide a disease simulation system which may be used to simulate many different types of diseases. A further object of the invention is to provide a disease simulation system which may be easily customized to an individual patient.
These and other objects and advantages will become more apparent after consideration of the ensuing description and the accompanying drawings.
SUMMARY OF THE INVENTION
The invention presents a system and method for simulating a disease control parameter and for predicting the effect of patient self-care actions on the disease control parameter. According to the method, 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
. In the preferred embodiment, the differentials are multiplied by corresponding scaling factors K
M
and the future disease control parameter value X(t
j
) is calculated according to the equation:
X
(
t
j
)
=
R
(
t
j
)
+
(
X
(
t
i
)
-
R
(
t
i
)
)
+
Σ
M
K
M
(
S
M
(
t
i
)
-
O
M
(
t
i
)
)
.
A preferred system for implementing the method includes an input device for entering the patient self-care values S
M
(t
i
). The system also includes a memory for storing 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.
In the preferred embodiment, the system further includes a recording device in communication with the processor for recording an actual control parameter value A(t
i
) at time t
i
, an actual control parameter value A(t
j
) at time t
j
, and actual self-care parameters having actual self-care values C
M
(t
i
) at time t
i
. The processor adjusts the scaling factors K
M
based on the difference between the actual control parameter value A(t
j
) and the optimal control parameter value R(t
j
), the difference between the actual control parameter value A(t
i
) and the optimal control parameter value R(t
i
), and the difference between the actual self-care values C
M
(t
i
) and the optimal self-care values O
M
(t
i
). Thus, the scaling factors K
M
are customized to an individual patient to predict the effect on the disease control parameter of self-care actions performed by the individual patient.
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patent: 5730124 (1998-03-01), Yamauchi
patent: 5730654 (1998-03-01), Brown
patent: 5822715 (1998-10-01), Worthington et al.
patent: 5956501 (1999-09-01), Brown
patent: 0813155 (1997-12-01), None
patent: 0558975 (1997-12-01), None
patent: 5266002 (1993-10-01), None
patent: WO 96/36923 (199
Black Lowe & Graham PLLC
Health Hero Network, Inc.
Jones Hugh
Teska Kevin J.
LandOfFree
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