JP4901226B2 - Medical simulation system and computer program thereof - Google Patents

Description

  The present invention relates to a medical simulation system used for supporting treatment of diabetes and the like and a computer program thereof.

When treating a disease, in addition to a doctor's inquiry, various tests are generally performed on patients.
The current situation is that doctors select treatment methods based on their own experiences and intuitions based on judgment materials such as patient test results and clinical findings.

Therefore, if information useful for medical care is provided by a computer, it can be expected that medical treatment by a doctor will be performed more accurately.
As systems that support medical care, there are systems that predict blood glucose levels, as described in Patent Document 1 and Patent Document 2.
These systems support medical care by predicting a change in blood glucose level of a patient and providing the predicted blood glucose level to a doctor.

Japanese Patent Laid-Open No. 10-332704 JP-A-11-296598

When selecting an appropriate treatment method, it is desired that a doctor appropriately grasps factors that constitute the causes of various symptoms of the disease. If the factors can be properly grasped, more appropriate treatment can be expected by performing treatment to improve the factors.
For example, in the case of diabetes, “blood glucose level” is used as an index indicating the degree of the disease. However, the “blood glucose level” is only a result, and doctors can accurately grasp the factors that caused this result, such as insulin secretion failure, peripheral insulin resistance, decreased hepatic glucose uptake, and increased hepatic glucose release. Important in clinical practice.

  And when a doctor performs an appropriate treatment, it is desirable if the degree of treatment effect can be expected when a certain factor is treated.

Moreover, in diseases such as diabetes, a plurality of factors are often combined, for example, both insulin secretion failure and peripheral insulin resistance appear.
When a plurality of factors appear at the same time, it may be impossible to treat all the medical conditions because of the difficulty in combining the drugs.
In such a case, it is necessary for a doctor to determine which factor should be treated to achieve an efficient treatment.

In view of the above problems, an object of the present invention is to provide a medical simulation system capable of confirming in advance the therapeutic effect on the cause of diabetes compared to the state before treatment.

The present invention includes a biological model expressed by a mathematical model including an internal parameter set relating to a biological function, and uses a biological model of a patient constructed by inputting test data of a diabetic patient, and uses a simulated biological response. A display unit and a processing unit, wherein the processing unit accepts input of time series data of an oral glucose tolerance test of a patient, and the time series data input A process for acquiring an internal parameter set forming a biological model that outputs a simulated biological response as an internal parameter set of the patient's biological model, a parameter value included in the internal parameter set of the patient's biological model, Processing to display on the display means as the biological function state value, displayed on the display means Processing of accepting a selection input of the replaced parameters among the parameters of the biological model of business, the of the parameters of the biological model of the patient displayed on the display unit, the value of the other value of the selected parameter as the replacement target The graph showing the temporal change in the post-replacement pseudo-biological response, which is the output of the biological model reflecting the replaced value, and the replacement, which is the output of the biological model reflecting the parameter value before replacement A process of superimposing graphs showing temporal changes in the pre-pseudo biological response and displaying them on the display means is executed.

According to the present invention, it is possible to generate a biological model in which a therapeutic effect is produced by replacing a parameter value corresponding to a disease factor with, for example, a value when the biological function is restored by treatment. Since the post-substitution pseudo-response obtained by simulation based on this biological model simulates the biological response after treatment, the doctor can confirm the therapeutic effect. Note that the replacement may be a change in a direction in which the biological function deteriorates.
Moreover, in the present invention, by displaying the biological response before and after the replacement, it is possible to easily confirm how the biological response has changed due to the replacement.

In the replacement process, it is preferable that the value of the parameter to be replaced is replaced with a value that a normal living body should have. In this case, it is possible to obtain a state where treatment has been performed by replacement, and confirm the treatment effect.

The processing means performs a process of creating determination support information for supporting determination of a therapeutic effect based on each post-substitution pseudo-biological response that is an output of a plurality of biological models with different parameter value replacement methods. Preferably it is performed. In this case, a plurality of post-substitution pseudo-biological responses can be obtained by changing the way of substitution. By creating determination support information based on the plurality of post-substitution pseudo-biological responses, more reliable determination support information can be obtained.

  The medical simulation system can be realized by mounting a computer program for causing a computer to function as the medical simulation system.

According to the system and computer program according to the present invention, the biological biological function state values corresponding to the factors of diabetes, for example, by substituting the value when the biological function is restored by the treatment, the therapeutic effect occurs A model can be generated. Since the post-substitution pseudo-response obtained by the simulation based on this biological model mimics the biological response after treatment, the doctor can confirm the therapeutic effect of diabetes in comparison with the state before treatment.

DESCRIPTION OF EMBODIMENTS Hereinafter, embodiments of a medical simulation system (hereinafter also simply referred to as a system) of the present invention will be described in detail with reference to the accompanying drawings.
[Entire system configuration]
FIG. 1 shows a system configuration diagram when the medical simulation system SS is configured as a server-client system.

This system SS includes a server S including the function of the Web server S1 and a client terminal C connected to the server S via a network. The client terminal C is used by a user such as a doctor.
The client terminal C includes a web browser C1. The web browser C1 functions as a user interface of the system SS, and the user can perform input and necessary operations on the web browser C1. In addition, the screen generated and transmitted by the server S is output to the Web browser C1.

The server S has a function of the Web server S1 that accepts access from the Web browser C1 of the client terminal C.
In addition, a user interface program S2 for generating a user interface screen displayed on the Web browser C1 is mounted on the server S so that the computer can be executed. The user interface program S2 has a function of generating a screen to be displayed on the Web browser C1 and transmitting it to the client terminal C, and receiving information input on the Web browser C1 from the client terminal C.
The client terminal C downloads a program such as a Java (registered trademark) applet for realizing a part or all of the screen displayed on the Web browser C1 from the server S, and displays the screen on the Web browser C1. May be displayed.

Further, the server S is loaded with a disease state simulator program S3 so that the computer can be executed. The pathological condition simulator program S3 is for performing a simulation regarding a disease based on a biological model as will be described later.
In addition, the server S is provided with a database S4 having various data such as patient examination results. Data input to the system SS, data generated by the system, and other data are stored in the database S4. ing.

As described above, the server S has a function as a Web server, an interface (screen) generation function, and a function as a disease state simulator.
In FIG. 1, a server-client system connected to a network is shown as an example of the configuration of the medical simulation system. However, this system may be configured on a single computer.

  FIG. 2 is a block diagram showing a hardware configuration of the server S. As shown in FIG. The server S is composed of a computer mainly composed of a main body S110, a display S120, and an input device S130. The main body S110 mainly comprises a CPU S110a, a ROM S110b, a RAM S110c, a hard disk S110d, a reading device S110e, an input / output interface S110f, and an image output interface S110h. The S110c, the hard disk S110d, the reading device S110e, the input / output interface S110f, and the image output interface S110h are connected via a bus S110i so that data communication is possible.

The CPU S110a can execute a computer program stored in the ROM S110b and a computer program loaded in the RAM S110c. Then, when the CPU S110a executes the application program 140a such as the programs S2 and S3, each functional block as described later is realized, and the computer functions as the system SS.
The ROM S110b is configured by a mask ROM, PROM, EPROM, EEPROM, or the like, in which computer programs executed by the CPU S110a, data used for the same, and the like are recorded.

The RAM S110c is configured by SRAM, DRAM, or the like. The RAM S110c is used for reading computer programs recorded in the ROM S110b and the hard disk S110d. Further, when these computer programs are executed, they are used as a work area of the CPU S110a.
The hard disk S110d is installed with various computer programs to be executed by the CPU S110a, such as an operating system and application programs, and data used for executing the computer programs. Programs S2 and S3 are also installed in the hard disk S110d.

  The reading device S110e is configured by a flexible disk drive, a CD-ROM drive, a DVD-ROM drive, or the like, and can read a computer program or data recorded on the portable recording medium S140. In addition, the portable recording medium S140 stores an application program S140a (S2, S3) for causing the computer to function as the system of the present invention. It is possible to read S140a and install the application program S140a in the hard disk S110d.

Note that the application program S140a is not only provided by the portable recording medium S140, but also from an external device that is communicably connected to a computer via an electric communication line (whether wired or wireless) through the electric communication line. It is also possible to provide. For example, the application program is stored in a hard disk of an application program providing server computer on the Internet. The server computer can be accessed to download the computer program and install it on the hard disk S110d. .
In addition, an operating system that provides a graphical user interface environment such as Windows (registered trademark) manufactured and sold by US Microsoft Co. is installed in the hard disk S110d. In the following description, the application program S140a (S2, S3) according to the present embodiment is assumed to operate on the operating system.

The input / output interface S110f includes, for example, a serial interface such as USB, IEEE 1394, and RS-232C, a parallel interface such as SCSI, IDE, and IEEE1284, and an analog interface including a D / A converter and an A / D converter. ing. An input device S130 including a keyboard and a mouse is connected to the input / output interface S110f, and the user can input data to the computer by using the input device S130.
The image output interface S110h is connected to a display S120 configured by an LCD, a CRT, or the like, and outputs a video signal corresponding to the image data given from the CPU S110a to the display S120. The display S120 displays an image (screen) according to the input video signal.

  The hardware configuration of the client terminal C is substantially the same as the hardware configuration of the server S.

[Biological model in simulation system]
FIG. 3 is a block diagram showing an overall configuration of an example of a biological model used in the pathological condition simulator program S3 of the system SS. This biological model imitates a biological organ (biological function) particularly related to diabetes, and includes a pancreas block 1, a liver block 2, an insulin dynamic block 3, and a peripheral tissue block 4.

Each block 1, 2, 3, 4 has an input and an output, respectively. That is, the pancreas block 1 has the blood glucose concentration 6 as an input and the insulin secretion rate 7 as an output.
The liver block 2 receives glucose absorption 5 from the gastrointestinal tract, blood glucose concentration 6 and insulin secretion rate 7, and outputs net glucose release 8 and insulin 9 after passing through the liver.
In addition, the insulin dynamic block 3 has the insulin 9 after passing through the liver as an input and the insulin concentration 10 in the peripheral tissue as an output.
Further, the peripheral tissue block 4 has the net glucose release 8 and the insulin concentration 10 in the peripheral tissue as inputs, and the blood glucose concentration 6 as an output.

The glucose absorption 5 is data given from outside the biological model. In the present embodiment, as data relating to glucose absorption, a predetermined value is stored according to the type of test data (biological response) to be input.
Each of the functional blocks 1 to 4 is realized by executing a simulator program by the CPU of the server S.

  Next, details of each block in the above-described example will be described. Note that FGB and Ws indicate the fasting blood glucose level (FGB = BG (0)) and the assumed body weight, respectively, and DVg and DVi indicate the distribution volume volume for glucose and the distribution volume volume for insulin, respectively.

[Pancreatic block of biological model]
The input / output relationship of the pancreas block 1 can be described using the following differential equation (1). It can also be expressed using a block diagram equivalent to the differential equation (1) shown in FIG.
Differential equation (1):
dY / dt = −α {Y (t) −β (BG (t) −h)}
(However, BG (t)> h)
= −αY (t) (where BG (t) <= h)
dX / dt = −M · X (t) + Y (t)
SR (t) = M · X (t)
variable:
BG (t): Blood glucose level X (t): Total amount of insulin secretable from pancreas Y (t): Insulin supply rate newly supplied to X (t) in response to glucose stimulation SR (t): Insulin from pancreas Secretion rate parameters:
h: threshold of glucose concentration capable of stimulating insulin supply α: followability to glucose stimulation β: sensitivity to glucose stimulation M: secretion rate per unit concentration Here, blood glucose level as input to pancreatic block 1 in FIG. 6 corresponds to BG (t), and the output insulin secretion rate 7 corresponds to SR (t).

  In the block diagram of FIG. 4, 6 is a blood glucose level BG (t), 7 is an insulin secretion rate SR (t) from the pancreas, 12 is a glucose concentration threshold value h that can stimulate insulin supply, and 13 is a glucose stimulus. Sensitivity β, 14 is a follow-up α to glucose stimulation, 15 is an integral element, 16 is an insulin supply rate Y (t) newly supplied for glucose stimulation, 17 is an integral element, and 18 is insulin that can be secreted from the pancreas The total amount X (t), 19 indicates the secretion rate M per unit concentration.

[Liver model liver block]
The input / output relationship of the liver block 2 can be described using the following differential equation (2). It can also be expressed using the block diagram shown in FIG. 5, which is equivalent to the differential equation (2).
Differential equation (2):
dI ₄ (t) / dt = α2 {−A ₃ I ₄ (t) + (1−A ₇ ) · SR (t)}
Goff (FGB) = f1 (where FGB <f3)
= F1 + f2 · (FGB-f3)
(However, FGB> = f3)
Func1 (FGB) = f4−f5 · (FGB−f6)
Func2 (FGB) = f7 / FGB
b1 (I ₄ (t)) = f8 {1 + f9 · I4 (t)}
HGU (t) = r · Func1 (FGB) · b1 (I 4 (t)) · RG (t) + (1-r) · Kh · BG (t) · I 4 (t) ( where HGU (t) > = 0)
_{HGP (t) = I 4off ·} Func2 (FGB) · b2 + G off (FGB) -I 4 (t) · Func2 (FGB) · b2 ( where HGP (t)> = 0)
SGO (t) = RG (t) + HGP (t) -HGU (t)
SRpost (t) = A ₇ SR (t)
variable:
BG (t): blood glucose level (glucose concentration per unit volume of blood)
SR (t): rate of insulin secretion from the pancreas SRpost (t): insulin after passing through the liver RG (t): glucose absorption from the gastrointestinal tract HGP (t): liver glucose release HGU (t): liver glucose uptake SGO (T): Net glucose from the liver I ₄ (t): Liver insulin concentration parameter:
Kh: Insulin-dependent glucose uptake rate in the liver per unit insulin, unit glucose A ₇ : Insulin passage rate in the liver Goff: Glucose release rate relative to basal metabolism b2: Adjustment term relating to liver glucose release inhibition rate r: Insulin-independent Distribution ratio to hepatic glucose uptake α2: followability to insulin stimulation I _4off : threshold function of insulin concentration at which hepatic glucose release is suppressed:
Goff (FGB): Glucose release rate for basal metabolism Func1 (FGB): liver glucose uptake rate for glucose stimulation from the gastrointestinal tract Func2 (FGB): liver glucose release inhibition rate for insulin stimulation f1-f9: Expression of the above three elements Constant b1 (I ₄ (t)) used in the adjustment: Here, the adjustment term relating to the liver glucose uptake rate, where glucose absorption 5 from the digestive tract, which is an input to the liver block in FIG. 3, is RG (t), blood glucose level 6 BG (t), insulin secretion rate 7 corresponds to SR (t), output net glucose release 8 corresponds to SGO (t), and post-liver insulin 9 corresponds to SRpost (t) .

In the block diagram of FIG. 5, 5 is glucose absorption RG (t) from the digestive tract, 6 is blood glucose level BG (t), 7 is insulin secretion rate SR (t) from the pancreas, and 8 is net glucose from the liver. SGO (t), 9 is insulin SRpost (t) after passing through the liver, 24 is the insulin uptake rate of the liver (1-A7), 25 is the follow-up α2 to the insulin stimulation, 26 is the rate of insulin disappearance A3, 27 in the liver Is an integral factor, 28 is a liver insulin concentration I ₄ (t), 29 is an insulin-dependent hepatic glucose uptake distribution rate (1-r), 30 is a unit insulin, an insulin-dependent glucose uptake rate Kh in the liver per unit glucose, 31 is the distribution rate r for insulin-independent liver glucose uptake, 32 is the liver glucose uptake rate for glucose stimulation from the digestive tract Func1 (FGB) 33 adjustment term _b1 (I 4 (t)) relating to hepatic glucose uptake rate, 34 is hepatic glucose output uptake HGU (t), 35 is insulin concentration hepatic glucose release is suppressed threshold _{I 4off,} 36 is insulin Inhibition rate of liver glucose release to stimulus Func2 (FGB), 37 is an adjustment term b2 relating to the inhibition rate of liver glucose release, 38 is an endogenous glucose release rate relative to basal metabolism, 39 is liver glucose release HGP (t), 40 is liver It indicates insulin passage rate a _7.

[Insulin dynamics block of biological model]
The relationship between input and output of insulin dynamic secretion can be described using the following differential equation (3). It can also be expressed using the block diagram shown in FIG. 6, which is equivalent to the differential equation (3).
Differential equation (3):
dI ₁ (t) / dt = −A ₃ I ₁ (t) + A ₅ I ₂ (t) + A ₄ I ₃ (t) + SRpost (t)
dI ₂ (t) / dt = A ₆ I ₁ (t) −A ₅ I ₂ (t)
dI ₃ (t) / dt = A ₂ I ₁ (t) −A ₁ I ₃ (t)
variable:
SRpost (t): insulin after passing through the liver I ₁ (t): blood insulin concentration I ₂ (t): insulin concentration in non-insulin-dependent tissue I ₃ (t): insulin concentration parameter in peripheral tissue:
A ₁ : Insulin disappearance rate in peripheral tissue A ₂ : Insulin distribution rate to peripheral tissue A ₃ : Insulin disappearance rate after passing through liver A ₄ : Insulin outflow rate after passing through peripheral tissue A ₅ : In insulin independent tissue Insulin disappearance rate A ₆ : Insulin distribution rate to non-insulin-dependent tissues Here, the insulin 9 after passing through the liver, which is the input of the insulin dynamic block in FIG. 3, corresponds to SRpost (t) and is the peripheral tissue that is the output The insulin concentration of 10 corresponds to I ₃ (t).

In the block diagram of FIG. 6, 9 is the insulin SR post (t) after passing through the liver, 10 is the insulin concentration I ₃ (t) in the peripheral tissue, 50 is the integral element, 51 is the insulin disappearance rate A ₃ after passing through the liver , 52 is the blood insulin concentration I ₁ (t), 53 is the insulin distribution ratio A ₂ to the peripheral tissue, 54 is the integral element, 55 is the insulin disappearance rate A1 in the peripheral tissue, 56 is the insulin efflux after passing through the peripheral tissue The rate A ₄ , 57 is the insulin distribution rate A ₆ to the insulin-independent tissue, 58 is an integral factor, 59 is the insulin concentration I ₂ (t) in the insulin-independent tissue, and 60 is the rate of insulin disappearance in the insulin-independent tissue shows a _5, respectively.

[Peripheral tissue block of biological model]
The input / output relationship of the peripheral tissue block 4 can be described using the following differential equation (4). It can also be expressed using the block diagram shown in FIG. 7 equivalent to the differential equation (4).
Differential equation (4):
dBG ′ / dt = SGO (t) −u * Goff (FGB) −Kb (BG ′ (t) −FBG ′) − Kp · I ₃ (t) · BG ′ (t)
variable:
BG ′ (t): blood glucose level (glucose concentration per unit body weight)
(However, BG [mg / dl], BG '[mg / kg])
SGO (t): Net glucose from the liver I ₃ (t): Insulin concentration in peripheral tissues FBG ′: Fasting blood glucose (however, FBG ′ = BG ′ (0))
Parameters:
Kb: Insulin-independent glucose consumption rate in peripheral tissues Kp: Insulin-dependent glucose consumption rate in peripheral tissues per unit insulin, unit glucose u: Insulin-independent glucose consumption for basal metabolism out of glucose release rates for basal metabolism Ratio function:
Goff (FGB): Glucose release rate for basal metabolism f1 to f3: constants used in expressing Goff Here, the insulin concentration 10 in the peripheral tissue, which is the input to the peripheral tissue block in FIG. 3, is I ₃ (t), Net glucose 8 from the liver corresponds to SGO (t), respectively, and the output blood glucose level 6 corresponds to BG (t).

In the block diagram of FIG. 7, 6 is the blood glucose level BG (t), 8 is the net glucose SGO (t) from the liver, 10 is the insulin concentration I ₃ (t) in the peripheral tissues, and 70 is the insulin non-basic for basal metabolism. Dependent glucose consumption rate u * Goff (FGB), 71 is an integral factor, 72 is insulin-independent glucose consumption rate Kb in peripheral tissues, 73 is unit insulin, insulin-dependent glucose consumption rate Kp in peripheral tissues per unit glucose, Reference numeral 74 denotes a unit conversion constant Ws / DVg.

  As shown in FIG. 3, since the input and output between the blocks constituting this system are connected to each other, by giving glucose absorption 5 from the digestive tract, the blood glucose level and the insulin concentration are given as a simulated biological response. Can be calculated and simulated based on mathematical expressions.

  For example, E-CELL (Keio University public software) and MATLAB (product of Massworks) can be used to calculate the differential equation of this system, but other calculation systems may be used.

[Overall procedure]
FIG. 8 shows a processing procedure until the present system SS generates and displays determination support information for supporting the determination of the therapeutic effect by the doctor.
The process of FIG. 8 roughly includes a biological response input process STP1, a pre-replacement process STP2, a replacement process STP3, a post-replacement process STP4, and a determination support information creation process STP5.
Note that the processing of FIG. 8 is realized by the user interface program S2 or the disease state simulator program S3 being executed by a computer.

[Biological response input process STP1]
First, input processing of OGTT (Oral Glucose Tolerance Test) time-series data as biological response information (measured clinical data) is performed.
OGTT time-series data is OGTT, which is a test actually performed on a patient to be simulated by a living body model (measures temporal changes in blood glucose level and blood insulin concentration by orally loading a predetermined amount of glucose solution) The system receives an input from the client terminal C as an actual biological response (actual test value). Here, two blood glucose level data and insulin concentration data are input as OGTT time-series data.
The biological response information input to the client terminal C is transmitted to the server S, and the server S receives the information. Thus, the server S has a function as a biological response input unit.
The biological response input may be performed by transmitting biological response information from another computer outside the system to the system SS.

FIG. 9 shows an example in which the input blood glucose level and the actually measured value (temporal change) of the insulin concentration are displayed on a graph. The screen of FIG. 9 can be displayed on the client terminal C (also on the server S if necessary).
The blood glucose level data in FIG. 9 is actually measured data corresponding to temporal changes in the blood glucose level BG (t), which is one of the output items in the biological model shown in FIGS.
9 is actually measured data corresponding to a temporal change in blood insulin concentration I ₁ (t) which is one of output items in the biological model shown in FIGS.

[Pre-replacement process STP2]
Subsequently, as a pre-replacement process, a biological model parameter set acquisition process STP2-1, a pathological condition simulation process STP2-2, and a display process STP2-3 are performed.

[Biometric Model Parameter Set Acquisition Processing (Biological Function State Value Generation Processing) STP2-1]
In order to simulate a living organ of an individual patient using the above-described living body model shown in FIGS. 3 to 7, it is necessary to generate a living body model having characteristics corresponding to the individual patient. Specifically, the parameters of the biological model and the initial values of the variables are determined according to the individual patient, and the determined parameters and initial values are applied to the biological model to obtain a biological model corresponding to the individual patient. (In the following, unless otherwise distinguished, initial values of variables are also included in the parameters to be generated).

For this reason, the server S of the present system SS obtains an internal parameter set (hereinafter simply referred to as “parameter set”) that is a set of internal parameters of the biological model, and determines the biological model to which the obtained parameter set is applied. It has a function to generate. This function is realized by the disease state simulator program S3.
By providing the biological model with the parameter set generated by the biological model generation unit, the biological model calculation unit simulates the function of the biological organ and generates a pseudo response that simulates the actual biological response (test result). Can be output.

  Hereinafter, a parameter set acquisition process for generating a set of parameters (biological function state values) for forming a living body model imitating a living body organ of the patient based on a test result (biological response) of an actual patient (living body) Details will be described.

[Template matching STP2-1-1]
As shown in FIG. 10, the present system SS performs matching between the input OGTT time-series data and the template of the template database DB1. The template database DB1 is one database included in the database S4 of the server S.
As shown in FIG. 11, the template database DB1 includes reference output values T1, T2,... Of a biological model to be a template, and parameter sets PS # 01, PS # 02,. Are stored in advance. To create a set of reference output values and parameter sets, assign an appropriate parameter set to any reference output value, or conversely, output the biometric model when any parameter set is selected. It may be obtained with a simulation system.

FIG. 12 shows an example of a template (reference output value) T1. FIG. 12A shows blood glucose level data as a template, and a reference time series corresponding to a temporal change in blood glucose level BG (t), which is one of output items in the biological model shown in FIGS. It is data. FIG. 12B shows blood insulin concentration data as a template, corresponding to temporal changes in blood insulin concentration I ₁ (t), which is one of the output items in the biological model shown in FIGS. Time series data for reference.

The system SS calculates the similarity between each reference time series data in the template database DB1 and the OGTT time series data. The similarity is obtained by calculating the sum of errors. The error sum is obtained by the following equation, for example.
Error sum = αΣ | BG (0) −BGt (0) | + βΣ | PI (0) −PIt (0) |
+ ΑΣ | BG (1) −BGt (1) | + βΣ | PI (1) −PIt (1) |
+ ΑΣ | BG (2) −BGt (2) | + βΣ | PI (2) −PIt (2) |
+ ...
= Α {Σ | BG (t) −BGt (t) |} + β {Σ | PI (t) −PIt (t) |}
here,
BG: Blood glucose level of input data [mg / dl]
PI: Blood insulin concentration in input data [μU / ml]
BGt: Blood glucose level of template [mg / dl]
PIt: Template blood insulin concentration [μU / ml]
t: Time [minutes]
Α and β are coefficients used for normalization.
α = 1 / Average {ΣBG (t)}
β = 1 / Average {ΣPI (t)}
The average of the formula indicates an average value for all templates stored in the template database DB1.

The smaller the error sum, the more similar the template and the OGTT time-series data. Moreover, it can be said that the template parameter set similar to the OGTT time-series data well represents the state of the biological function.
Therefore, the CPU S110a calculates a sum of errors for each template in the template database DB1, and determines a template that minimizes the sum of errors (similarity), that is, a template that most closely approximates OGTT time-series data.

[Parameter set acquisition STP2-1-2]
Furthermore, the system SS acquires a parameter set corresponding to the template determined in STP2-2-1 from the template database DB1. Hereinafter, the acquired parameter set is referred to as a “pre-replacement parameter set”, and individual parameters constituting the pre-replacement parameter set are referred to as “pre-replacement parameters”.

Note that the method of generating the parameter set (biological model) is not limited to the template matching as described above. For example, the parameter set may be generated by a genetic algorithm. In other words, a genetic algorithm that randomly generates an initial population of parameter sets and performs a selection / crossover / mutation process on the parameter sets (individuals) included in the initial population to generate a new child population. Can be applied. Of the parameter sets generated by this genetic algorithm, a parameter set that outputs a pseudo-response that approximates the input biological response (test result) can be employed.
As described above, the specific generation method is not particularly limited as long as the biological model generation unit can generate a biological model that can input and output a pseudo-response that simulates a biological response.

[Simulation before replacement STP2-2]
The system SS gives the pre-replacement parameter set obtained by the parameter set acquisition process STP2-1 to the biological model, performs an operation based on the biological model, and simulates the biological response information (blood glucose level) imitating the input OGTT time-series data. Value and a graph showing the time change of insulin concentration). This function is realized by the disease state simulator program S3.
Hereinafter, the pseudo response generated in the simulation (before replacement) STP2-2 is referred to as “pre-replacement pseudo biological response”. The pre-substitution pseudo-biological response mimics OGTT time-series data.

[Display processing STP2-3]
[Display of pre-replacement bioresponse (input bioresponse and pre-replacement pseudo bioresponse)]
FIG. 13 shows a screen display that is generated by the user interface program S2 of the server S based on the processing result of the STP 2-2, transmitted from the server S2 to the client C, and displayed on the Web browser C1 of the client C. ing.
The screen of FIG. 13 displays both the input OGTT time-series data and the pseudo-biological response before replacement. By outputting both the OGTT time-series data and the pre-replacement pseudo-biological response, the user confirms whether the pre-replacement pseudo-biological response well reproduces the OGTT time-series data (input bio-response). Can do.
In addition, when there is no particular distinction between the OGTT time-series data and the pseudo-biological response before replacement that reproduces the OGTT time-series data, they are collectively referred to as “pre-substitution biological response”.

[Biological function status value (parameter set) display]
FIG. 14 shows a screen display that is generated by the user interface program S2 of the server S based on the processing result of the STP2-1, transmitted from the server S2 to the client C, and displayed on the Web browser C1 of the client C. ing.
The screen in FIG. 14 displays the pre-replacement parameter set as the state value of the biological function indicated by the biological model. More specifically, the screen of FIG. 14 shows a biological model parameter name (BETA, H,...) Display unit 100, a pre-replacement parameter set display unit 101, and a normal type average display showing average parameter values of healthy subjects. Unit 102, a boundary type average display unit 103 indicating the average parameter value of the boundary type diabetic patient, and a diabetes type average display unit 104 indicating the average parameter value of the diabetic patient.

As shown in FIG. 14, by displaying values such as normal type average, boundary type average, diabetes type average, and the like as comparison target values useful for grasping the disease state together with the parameter set before replacement, the user (doctor) This makes it easier to understand the patient's condition.
For example, the parameter “BETA” in FIG. 14 corresponds to “β: sensitivity to glucose stimulation (also referred to as“ insulin secretion sensitivity ”)” in the pancreatic block 1 of the biological model. The value of “BETA” in the pre-replacement parameter set is far from the value of “BETA” of the normal average value, and the doctor can easily determine that the insulin secretion sensitivity is deteriorated as a disease state.

  The parameter “Kp” in FIG. 14 corresponds to “Kp: Insulin-dependent glucose consumption rate in peripheral tissues per unit glucose (peripheral insulin sensitivity)” in the peripheral tissue block 4 of the biological model. Yes. The value of “Kp” in the parameter set before replacement is far from the value of “Kp” of the normal type average, and the doctor can easily determine that peripheral insulin sensitivity is deteriorated as a disease state.

As described above, the display in FIG. 14 provides information that helps the doctor to grasp the patient's condition.
Note that the display of FIG. 14 shows only some of the parameters constituting the parameter set, but other parameters may be displayed. In FIG. 14, the parameter value is displayed as it is as the biological function state value, but a value calculated based on one or a plurality of parameter values may be used as the biological function state value.

[Replacement processing STP3]
[Replacement Object Selection Process STP3-1]
Subsequently, the user selects a parameter (biological function state value) as a value replacement target while referring to the screen display of FIG. This selection can be performed using a general selection input function adopted in a computer, such as clicking a parameter name to be replaced with a mouse pointer on the screen of FIG.
The replacement target is not limited to input from the user, but may be automatically performed by the system SS. For example, a parameter that is a predetermined value or more away from the normal average may be automatically selected as a replacement target.
Further, the parameter that the user wants to replace may be selected from the replacement candidates listed in the automatic selection by the system SS.

[Replacement processing STP3-2]
When the system SS accepts the selection of the replacement target (replacement target parameter), the replacement target parameter value is set to the value of the normal living body (healthy person), that is, the value displayed on the normal type average display unit 102 in FIG. Replace with.
For example, when the replacement process is performed using “BETA” as a replacement target parameter in the pre-replacement parameter set shown in FIG. 14 (hereinafter, this process is referred to as “BETA replacement”), the post-replacement parameter set has a BETA value of The normal type average value is “0.39939”, and the other parameter values are the same as the values of the parameter set before replacement. In FIG. 14, the parameter set after BETA replacement is displayed on the BETA replacement display unit 105.

  Further, in FIG. 14, when the replacement process is performed using “Kp” as a replacement target parameter in the pre-replacement parameter set (hereinafter, this process is referred to as “Kp replacement”), the post-replacement parameter set has a Kp value of The normal type average value is “0.000161”, and the other parameter values are the same as the values of the parameter set before replacement. In FIG. 14, the parameter set after Kp replacement is displayed on the Kp replacement display unit 105.

  As described above, in the present system SS, a plurality of post-replacement parameter sets having different replacement methods can be generated for one pre-replacement parameter set. If these post-substitution parameter sets are applied to the biological model, a plurality of post-substitution biological models with different replacement methods can be generated.

In the above description, in one replacement process, there is only one parameter to be replaced (for example, in case of BETA replacement, the replacement target parameter is only BETA). It may be replaced.
In the above description, the replacement to the normal type average value is performed, but the replacement to the boundary type average value or the diabetes type average value may be performed. That is, you may perform the replacement of the direction which worsens a medical condition.

Furthermore, the parameter value after replacement may be an arbitrary value. That is, the parameter value after replacement may be input by the user and set to an arbitrary value.
Further, the process of generating a plurality of post-replacement parameter sets (post-replacement biological models) having different replacement methods includes replacing the same parameter with a different value. For example, the post-replacement parameter set in which the BETA of the pre-replacement parameter set is replaced with the normal average value and the post-replacement parameter set in which the boundary type average value is replaced can be regarded as different parameter sets.

[Post-replacement processing STP4]
[Post-replacement simulation processing STP4-1]
Similar to the simulation process STP2-2 before replacement, the system SS gives the post-replacement parameter set obtained by the replacement process STP3 to the biological model, performs calculation based on the biological model, and performs post-substitution pseudo-biological response information (blood glucose level). And a graph showing the change over time in the insulin concentration). This function is realized by the disease state simulator program S3.
Hereinafter, the pseudo response generated in the post-substitution simulation STP4-1 is referred to as “post-substitution pseudo biological response”. The post-substitution pseudo-biological response simulates the OGTT time-series data of a patient whose disease state corresponding to the substituted parameter is improved.

[Replacement Bioresponse Display Processing STP4-2]
15 and 16 are generated by the user interface program S2 of the server S based on the simulation processing result of STP4-1, transmitted from the server S2 to the client C, and displayed on the Web browser C1 of the client C. The screen display is shown.
The screen of FIG. 15 displays both the pre-substitution pseudo-biological response and the post-substitution pseudo-biological response after BETA substitution. By outputting both the pre-substitution pseudo-biological response and the post-substitution pseudo-biological response, the user can easily understand how the biometric response before BETA replacement and the bioresponse after BETA replacement have changed. be able to.
That is, by looking at the screen of FIG. 15, it is possible to confirm before treatment the OGTT data when the etiology of BETA (insulin secretion sensitivity) is treated by drug treatment or the like. That is, the screen of FIG. 15 is determination support information for supporting the determination of the treatment effect, and the screen display function of FIG. 15 also functions as a determination support information creation unit and a determination support information display unit. .

  Further, the screen of FIG. 16 displays both the pre-substitution pseudo-biological response and the post-substitution pseudo-biological response after Kp substitution. Looking at the screen of FIG. 16, it is possible to confirm before treatment the OGTT data when the etiology of Kp (peripheral insulin sensitivity) is treated by drug treatment or the like. That is, the screen of FIG. 16 is determination support information for supporting the determination of the treatment effect, and the screen display function of FIG. 16 also functions as a determination support information creation unit and a determination support information display unit. .

  Furthermore, since the post-substitution pseudo-biological responses of the plurality of post-substitution biological models shown in FIG. 15 and FIG. 16 are displayed together on the screen, the user can compare the plurality of post-substitution pseudo-biological responses with each other. It is also possible to obtain information for determining which etiology treatment is effective.

[Judgment support information creation process STP5]
The display of FIG. 15 and FIG. 16 also functions as information that supports the determination of the treatment effect, but here, determination support information that is easier to understand for the user is generated. Specifically, in step STP5, information is generated that makes it easy to determine which replacement (treatment) is effective for a plurality of replacements (treatments) with different replacement methods.

[Calculation of blood glucose level STP5-1]
First, for a plurality of substitutions (BETA substitution and Kp substitution), the blood glucose lowering level before and after each substitution is calculated. The blood glucose lowering degree is obtained by calculating the difference (difference area in the graph) between the blood glucose level before replacement and the blood glucose level after replacement.

[Etiology Occupancy Ratio Calculation / Display Processing STP5-2]
Subsequently, the occupation ratio of the etiology is calculated from the ratios of the blood glucose lowering levels of the BETA substitution and Kp substitution. In the case of FIGS. 15 and 16, the occupation ratio of the etiology “BETA” is 24%, whereas the occupation ratio of the etiology “Kp” is as high as 76%. That is, as the etiology of this patient, “Kp” is relatively dominant, and treatment that corrects “Kp” is more effective.
FIG. 17 shows the cause-occupancy ratio as judgment support information displayed in a pie chart. The display in FIG. 17 is performed on the Web browser C1 of the client C. The doctor can determine that the treatment for correcting Kp has a higher therapeutic effect by viewing the display of FIG.

FIGS. 18-23 has shown the example of a screen display at the time of performing the process shown in FIG. 8 about another patient.
18 is a graph display screen of the input OGTT time-series data, and FIG. 19 is a graph display screen showing the input OGTT time-series data and its pseudo-biological response before replacement). FIG. 20 is a display screen for other parameter sets before replacement, and also displays values when BETA replacement and Kp replacement are performed.

FIGS. 21 and 22 are graph display screens showing the pre-substitution biological response and the post-substitution pseudo-biological response, respectively. From the comparison of FIGS. 21 and 22, treatment for correcting BETA, that is, insulin secretion sensitivity is shown. The improvement can be considered to have a higher therapeutic effect.
Further, FIG. 23 is a graph display screen of the etiology occupancy calculated from the results of FIGS. 21 and 22, and it is highly effective that a treatment to correct insulin secretion sensitivity is performed according to the display of FIG. 23. Can be more easily determined.

  The present invention is not limited to the above embodiment, and various modifications can be made. For example, the disease targeted by the system of the present invention is not limited to diabetes, and may be other diseases such as hypertension. In the case of hypertension, it is preferable to use blood pressure as the biological response output from the biological model.

1 is a system configuration diagram of a medical simulation system. It is a block diagram which shows the hardware constitutions of a server. It is a whole biological model block diagram. It is a block diagram which shows the structure of the pancreas model of a biological model. It is a block diagram which shows the structure of the liver model of a biological model. It is a block diagram which shows the structure of the insulin dynamic model of a biological model. It is a block diagram which shows the structure of the peripheral tissue model of a biological model. It is a whole process flowchart of a system. It is a display screen of measured OGTT time series data. It is a flowchart of the parameter set acquisition process of a biological model. It is a figure which shows a template database. It is a figure which shows the OGTT time series data of a template, (a) is a blood glucose level, (b) is an insulin concentration. It is a display screen which shows the input OGTT data and the pseudo-biological response before replacement. It is a display screen for a parameter set before replacement. It is a biological response display screen before and after replacement when BETA replacement is performed. It is a biological response display screen before and after replacement when Kp replacement is performed. It is a pie chart display which shows etiology occupation rate. It is another example of the display screen of measured OGTT time series data. It is another example of the display screen which shows the input OGTT data and the pseudo-biological response before replacement. It is another example of display screens, such as a parameter set before substitution. It is another example of the biological response display screen before and after replacement when BETA replacement is performed. It is another example of the biological response display screen before and after replacement when Kp replacement is performed. It is another example of the pie chart display which shows the etiology occupation rate.

Explanation of symbols

SS Medical simulation system 1 Pancreas block 2 Liver block 3 Insulin dynamic block 4 Peripheral tissue block 5 Glucose absorption from the gastrointestinal tract 6 Blood glucose level 7 Insulin secretion rate 8 Net glucose release 9 Insulin concentration in peripheral tissue STP1 after passing through the liver Biological response input processing (biological response input unit)
STP2-1 Parameter set acquisition process (biological function state value generation unit)
STP2-3 display processing (biological function state value display unit)
STP3 replacement process (replacement unit)
STP3-1 Replacement target selection acceptance process (selection unit)
STP4-1 Simulation process after replacement (simulation unit)
STP4-2 Substitution biological response display processing (substitution biological response display section)
STP5 Decision support information creation processing (judgment support information creation unit)
STP5-2 Pathogenesis occupation ratio calculation display processing (judgment support information display section)

Claims (4)

Medical that has a biological model expressed by a mathematical model that includes an internal parameter set related to the function of the living body, and generates a simulated biological response using the patient's biological model constructed by inputting test data of a diabetic patient Simulation system for
Display means;
A processing means,
The processing means includes
A process that accepts input of time series data of the patient's oral glucose tolerance test,
A process of acquiring an internal parameter set that forms a biological model that outputs a simulated biological response that imitates the input time-series data, as an internal parameter set of the biological model of the patient;
A process for displaying a value of a parameter included in an internal parameter set of the patient's biological model as a biological function state value on the display means;
A process of receiving selection input of a parameter to be replaced among parameters of a patient's biological model displayed on the display unit;
Of the parameters of the patient's biological model displayed on the display means, processing for replacing the value of the parameter selected as the replacement target with another value , and
A graph showing temporal changes in the post-substitution pseudo-biological response, which is an output of the biometric model reflecting the replaced value, and a pre-substitution pseudo-biological response, which is the output of the biometric model, in which the parameter value before the substitution is reflected A process of displaying a graph showing a change in the display means on the display means,
A medical simulation system characterized by executing Wherein in the process of replacement, medical simulation system according to claim 1 Symbol mounting, characterized in that the value of the parameter to be the replacement target is replaced with the value should have the normal living body. The processing means performs a process of creating determination support information for supporting determination of a therapeutic effect based on each post-substitution pseudo-biological response that is an output of a plurality of biological models with different parameter value replacement methods. The medical simulation system according to claim 1, wherein the medical simulation system is executed. The computer program for functioning a computer as a medical simulation system in any one of Claims 1-3 .

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