Patent Publication Number: US-7914449-B2

Title: Diagnostic support system for diabetes and storage medium

Description:
This application claims priority under 35 U.S.C. §119 to Japanese Patent Application No. 2004-076397 filed Mar. 17, 2004, the entire content of which is hereby incorporated by reference. 
     FIELD OF THE INVENTION 
     The present invention relates to a diagnostic support system for diabetes and storage medium, and specifically relates to a diagnostic support system for analyzing the condition of diabetic patients and providing diagnostic support information which considers treatment methods and the like, and a storage medium for recording a computer program providing a computer with the functionality of the diagnostic support system for diabetes. 
     BACKGROUND 
     Diabetes is one of the most common lifestyle related disease. The pathologies of diabetes are classified as either type-1 diabetes or type-2 diabetes. Pathophysiologic conditions of type 2 diabetes are mainly classified into several subtypes from “hepatic glucose production”, “insulin secretion capability”, and “insulin resistance”. The majority of diabetics are type 2 characterized by reduced insulin sensitivity, that is, an increase in insulin resistance and impaired insulin secretion from pancreatic β cells. In many cases, type 2 diabetes progresses without any subjective symptom, and a serious complication will develop if diabetes is left as it is. 
     Terribly, diabetes develops complications of peculiar angiopathy and neuropathy. Such complications occur when blood glucose control has not been satisfactory during progress of the disease for a long period, such as 5 years, 10 years or 20 years. For example, diabetic retinopathy and cataract, which are typical complications, cause vision disorder, and nephropathy causes proteinuria, swelling, and in course of time, leads to uremia. Neuropathy such as feeling of numbness in hands and legs and nerve pain may develop all over the body. Diabetes also accelerates arteriosclerosis, causing angina pectoris, myocardial infarction, cerebral apoplexy and cerebral thrombosis direct to the cause of death. Therefore, primary objects of treatment of diabetes are to prevent the complications and to inhibit the progress. In order to prevent complications, control of blood glucose is a very important factor. 
     For the treatment of type 2 diabetes, dietary therapy and exercise therapy are performed, which are intended to normalize blood glucose. However, when the above two treatments are not sufficient to normalize blood glucose, oral medicament or insulin injection is employed as medical treatment, so that blood glucose is desirably controlled. 
     Medicaments used for the treatment of diabetes are as follows: 
     (1) “sulfonylurea (SU) type drug” acting on pancreatic .beta. cells for promoting secretion of insulin; 
     (2) “biguanide (BG) type drug” acting mainly on the liver for elevating glucose disposal capacity in the liver and inhibiting release of glucose from the liver; 
     (3) “.alpha.-glucosidase inhibitors (AGI)” for depressing hyperglycemia after meals by inhibiting .alpha.-glucosidase (disaccharide hydrolysate enzyme) in the intestinal tract and holding up absorption of glucose through the intestinal tract; 
     (4) “insulin sensitizer (Thiazolidinedione, TZD)” for assisting a decrease of blood glucose by promoting the effects of insulin in the cells and reducing insulin resistance; and 
     (5) Insulin. 
     The most suitable treatment program combining the dietary therapy, exercise therapy, and medication is prepared for controlling blood glucose depending on the state of the individual diabetic patients. 
     The determination of treatment programs is largely dependent on the knowledge and empirical rule of specialists. When analyzing the knowledge and experience of physicians who specialize in diabetes, it was found that treatment policy and programs are determined based on detailed management of the diseases of individual diabetic patients by physicians specializing in diabetes and their clinical findings, laboratory test results and the like. 
     For example, based on such clinical findings and laboratory results, when the pathophysiologic condition of a diabetic patient is classified from four factors of “excessive hepatic glucose production”, “insulin secretion capability”, “insulin resistance”, and “glucose toxicity” which qualifies these factors, the pathophysiologic condition of diabetes is classified as follows. 
     A. Type 1 Diabetes 
     B. Type 2 Diabetes (Peripheral Insulin Resistance); Utilization of glucose in muscles or peripheries is lowered. Most of the patients are obese. 
     C. Type 2 Diabetes (Excessive Hepatic Glucose Production); The promotion effect of hepatic glycogen synthesis and inhibitory action of gluconeogenesis are lowered. Even though patients are not obese, visceral fat is accumulated in many cases. 
     D. Type 2 Diabetes (Impaired Insulin Secretion); Secretion of insulin is incomplete because of exhausted pancreatic .beta. cells. Patients are not obese, but are rather emaciated. 
     It is difficult to have adequate control of blood glucose levels in the standard treatment programs for these conditions, and an optimum treatment program must combine diet, exercise, and medications which correspond to the individual condition. General practitioners and general internists who are not specialists in diabetes may not necessarily be able to realize an optimum treatment program for an individual patient, nor achieve a desired blood glucose level. However, even a general practitioner or general internist may provide treatment suited for an individual diabetic patient if the non-specialist general practitioner or general internist is provided with diagnostic support information to accurately and quantitatively manage the diabetic condition. 
     Although a number of diagnostic support systems for diabetes exist, most such systems simply monitor the measurements of the patient&#39;s blood glucose level, and simply determine an insulin dose from the measurements such as the patient&#39;s blood glucose level, and do not provide adequate support information for the non-specialist (for example, refer to Japanese Laid-Open Patent Publication Nos. 10-332704 and No. 11-296598). Furthermore, Diagnostic Criteria for diabetes of the Japan Diabetes Society are well known. Patients are classified as either normal type, borderline diabetic type, or diabetic type based on the presence/absence of a typical diabetic condition and the results of an oral glucose tolerance test, and patients who are classified as diabetic type based on two tests are diagnosed as diabetic. 
     Existing computer systems which perform diabetes diagnostic support often provide automated determinations based on this diagnostic standard. For example, such systems will input the results of the oral glucose tolerance test and the like, and automatically compare the input data with predetermined standard values, classify the patient as either normal type, borderline diabetic type, or diabetic type, and output the result. 
     High precision systems also exist which determine whether or not a patient is obese by inputting the patient&#39;s combined height and weight, and adding functions to automatically select the medication to be administered. 
     Since these conventional systems cannot perform detailed analysis of the condition of an individual patient, however, physicians using these conventional systems are not able to provide detailed management of the patient&#39;s condition. Accordingly, the patient&#39;s condition cannot be properly managed without the subjectivity and broad experience of a specialist, and physicians who are not diabetes specialists and physicians of lesser experience in diagnosing diabetes are unable to make accurate determinations. Even the determinations of specialists are dependent on subjective factors and varying degrees of experience, such that some divergence is inevitable. 
     BRIEF SUMMARY 
     The scope of the present invention is defined solely by the appended claims, and is not affected to any degree by the statements within this summary. 
     In consideration of the above information, an object of the present invention is to provide a diagnostic support system capable of estimating the cause of disease from input testing values, and providing support information which aids management of the patient&#39;s condition and diagnosis by non-diabetes specialist physicians. 
     The first aspect of the present invention relates to a diagnostic support system for diabetes comprising an input device used for inputting diagnostic data including clinical testing data; a biological model having parameters and which represents a organ function related to diabetes as a numerical model; an predicting means for predicting parameter values suited for a patient based on the diagnostic data and the biological model; a pathological condition analyzing means for analyzing a diabetic pathological condition of a patient based on the parameter values predicted by the predicting means; a diagnostic support information generating means for generating diagnostic support information based on the analyzed pathological condition; and a diagnostic support information outputting means for outputting information obtained by the diagnostic support information generating means. 
     The second aspect of the present invention relates to a diagnostic support system for diabetes comprising an input device used for inputting diagnostic data including clinical testing data; a biological model having parameters and which represents a organ function related to diabetes as a numerical model; an predicting means for predicting parameter values suited for a patient based on the diagnostic data and the biological model; and a treatment simulating means for inputting the parameter values predicted by the predicting means to the biological model, and simulating the effects of the treatment by simulated execution of the treatment based on a hypothetical treatment policy. 
     The third aspect of the present invention relates to a computer-readable storage medium for recording a computer program which enables a computer provided with an input device and an output device to function as a diagnostic support system for diabetes, wherein the computer program comprises a step of receiving the input of diagnostic data which includes clinical testing data to the computer through the input device; a step of, in the computer, predicting parameter values which are suited for a patient based on diagnostic data and a biological model having parameters and which represents a organ function related to diabetes as a numerical model; a step of, in the computer, analyzing a diabetic pathological condition of a patient based on the predicted parameter values; a step of, in the computer, generating diagnostic support information based on the analyzed pathological condition; and a step of, in the computer, outputting the generated diagnostic support information through the output device. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a function block diagram briefly showing the structure of an embodiment of the diagnostic support system for diabetes of the present invention; 
         FIG. 2  is a block diagram showing the hardware structure of the embodiment of the diagnostic support system for diabetes of  FIG. 1 ; 
         FIG. 3  is a flow chart showing all the processing of the embodiment of the diagnostic support system for diabetes of the present invention; 
         FIG. 4  is a flow chart showing the diagnostic support information generating process of the embodiment of the diagnostic support system for diabetes of the present invention; and 
         FIG. 5  is a flow chart showing the disease condition simulation process of the embodiment of the diagnostic support system for diabetes of the present invention. 
     
    
    
     DETAILED DESCRIPTION OF THE DRAWINGS AND THE PRESENTLY PREFERRED EMBODIMENTS 
     The embodiment of the present invention is described hereinafter with reference to the drawings. 
       FIG. 1  is a function block diagram briefly showing the structure of the embodiment of the diagnostic support system for diabetes of the present invention. As shown in  FIG. 1 , a diagnostic support system  10  of the present embodiment has various function blocks including a diagnostic data input unit  1 , biological model  2 , biological model drive unit  4 , biological model generator  3 , pathological condition analyzer  5 , diagnostic support information generator  6 , pathological condition simulator  7 , and diagnostic support information output unit  8 . The respective function blocks are realized by cooperative operation of the following hardware structures and computer programs. 
       FIG. 2  is a block diagram showing the hardware structure of the embodiment of the diagnostic support system for diabetes of the present invention. The diagnostic support system  10  of the present embodiment of the invention is a computer  10   a  which mainly includes a body  11 , display  12  and input device  13 . The body  11  mainly includes a CPU  11   a , ROM  11   b , RAM  11   c , hard disk  11   d , reading device  11   e , input/output (I/O) interface  11   f , communication interface  11   g , and image output interface  11   h ; the CPU  11   a , ROM  11   b , RAM  11   c , hard disk  11   d , reading device  11   e , I/O interface  11   f , and image output interface  11   h  are connected by a bus  11   i  to enable data communications. 
     The CPU  11   a  is capable of executing computer programs recorded in ROM  11   b , and computer programs loaded in the RAM  11   c . Each of the previously mentioned function blocks can be realized and the computer  10   a  can function as the diagnostic support system  10  when the CPU  11   a  executes an application program  14   a  described later. 
     The ROM  11   b  may be formed by a mask ROM, PROM, EPROM, EEPROM and the like, and is used to store the computer programs excited by the CPU  11   a  and data and the like used by these programs. 
     The RAM  11   c  is formed by an SRAM, DRAM and the like. The RAM  11   c  is used to read the computer programs stored in the ROM  11   b  and hard disk  11   d . Furthermore, the RAM  11   c  is used as a work area by the CPU  11   a  when these computer programs are being executed. 
     The hard disk  11   d  contains various types of installed computer programs executed by the CPU  11   a , such as an operating system and application programs and the like, as well as the data used when executing these computer programs. The data used when executing the application program  14   a  described later is also installed on the hard disk  11   d.    
     The reading device  11   e  is formed by a flexible disk drive, CD-ROM drive, DVD-ROM drive or the like, and is capable of reading computer programs and data recorded on a transportable storage medium  14 . Furthermore, the transportable storage medium  14  stores the application program  14   a  which enables a computer to function as the diagnostic support system for diabetes of the present invention. The computer  10   a  is capable of reading the application program  14   a  of the present invention from the transportable storage medium  14 , and installing the application program  14   a  on the hard disk  11   d.    
     The application program  14   a  is not only provided on the transportable storage medium  14 , inasmuch as it may also be provided by transmission over an electrical communication line from an external device connected to the computer  10   a  so as to allow communication through an electrical communication line (whether wired or wireless). For example, the application program  14   a  may be stored on the hard disk of a server computer on the internet, such that the computer  10   a  can access the server computer and download the computer program and install the computer program on the hard disk  11   d.    
     Also installed on the hard disk  11   d  is an operating system which provides a graphical user interface environment, such as, for example, Windows (registered trademark), a commercial product of Microsoft Corporation, USA. In the following description, the application program  14   a  of the present embodiment performs the operations of an operating system. 
     The I/O interface  11   f  includes a serial interface, such as, for example, USB, IEEE1394, RS-232C or the like, parallel interface, such as, for example, SCSI, IDE, IEEE1284 or the like, and analog interface such as, for example, a digital-to-analog (D/A) converter, analog-to-digital (A/D) converter or the like. An input device  13 , which includes a keyboard and mouse, is connected to the I/O interface  11   f , and a user can input data to the computer  10   a  by using the input device  13 . 
     The image output interface  11   h  is connected to the display  12  formed by an LCD, CRT or the like, and image signals corresponding to the image data from the CPU  11   a  are output to the display  12 . The display  12  displays images (screens) in accordance with the input image signals. 
     The diagnostic data input unit  1  shown in  FIG. 1  is used to input the values of clinical testings of blood glucose level and so on, information on findings obtained from doctor&#39;s question, and other information into the system; and this input unit  1  is formed by the input device  13  shown in  FIG. 2 , I/O interface  11   f , and computer programs related to receiving information input from the input device  13 . Although the diagnostic data input unit  1  is formed by the input device  13  in the present embodiment, the present invention is not limited to this mode inasmuch as various input device other than the input device  13  may be provided, for example, optical character recognition (OCR), scanner and the like; furthermore, when various types of information are recorded beforehand in a database or the like accessible to the computer  10   a , the information may be input by the computer  10   a  accessing the database. The input information is stored on the hard disk  11   d  so as to be usable by the biological model generator  4  and the like. 
     In the present embodiment, input diagnostic data includes, at least, an oral glucose tolerance test insulin value (μu/ml), blood glucose level (mg/dl), insulin values a after fasting and 2 hr after eating (μu/ml), blood glucose level b (mg/dl), HOMA-IR value (=a times b/405), quantitative 24 hours urine C-peptide (μg), glycated hemoglobin index HbA 1c , presence/absence of weight loss, BMI value, ΔIRI/ΔBS, whether or not the patient is urine ketone body-positive and the like. However, the information is not limited to that listed above, and other testing values and the like may be input as necessary. Examples of other such information include the state of obesity, the state of fasting and postprandial blood glucose level, the state of dietary intake of carbohydrate and the like. 
     The biological model  2  is a model which represents the organ functions related to diabetes as a numerical model, for example, insulin secretion function in the pancreas, glucose uptake and production function in the liver, and glucose metabolism function using insulin in peripheral tissue can be represented. An example of a method capable of representing the model is a differential equation in which substance concentrations are variables. However, the present invention is not limited to the above examples inasmuch as other methods may be used to represent the model, including other organ functions, and other functions as necessary. The biological model  2  has structural components common to each patient, and parameters, that is, variables, which differ for each patient. 
     Examples of the biological model  2  are described by Bergman, who uses a model which mathematically represents a minimal model, as disclosed in Bergman et al.,  American Journal of Physiology , vol. 236(6), p. E-667-77 (1979), and Bergman et al.,  Journal of Clinical Investigation , vol. 68(8), p. 1456-67 (1981). 
     This minimal model mathematically represents plasma glucose concentration, plasma insulin concentration, and amount of acting insulin at the insulin action site of peripheral tissue, that is, remote insulin, as variables. In this case, when the plasma glucose concentration at time t is designated G(t), plasma insulin concentration is designated  1 ( t ), and remote insulin is designated X(t), then G(t), I(t), and X(t) the respective time differentials can be described on the left side of the differential equations below. 
     
       
         
           
             
               
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     Where the parameters in the equations are defined as follows, and each parameter can represent different values for each patient.
         p1: Non-insulin-dependent glucose metabolism rate   Gb: Basal glucose concentration   p2: Insulin uptake at insulin action site   p3: Insulin consumption rate relative to insulin-dependent glucose metabolism   Ib: Basal insulin concentration   n: Insulin consumption per unit time   γ: Insulin secretion sensitivity relative to glucose simulation   h: Blood glucose threshold value at which insulin secretion begins       

     The biological model drive unit  3  performs calculations for reproducing the behavior of a living body using the biological model  2 . The behavior of a living body also may be calculated using, for example, MatLab (The MathWorks, Inc.), and E-Cell (public domain software of Keio University). Other calculation systems may also be used. 
     When the minimal model is used as the biological model  2 , numerical calculation software capable of calculating differential equations by optional parameters and optional time intervals may be used as the biological model drive unit  3 . 
     The biological model generator  4  predicts the parameter values of the biological model  2  so as to produce agreement between the output of the biological model drive unit  3  and the input diagnostic data. Well-known method of least squares, method of steepest descent, and method of genetic algorithms may be used as the parameter values prediction method. The present invention is not limited to these methods, and other methods also may be used as necessary. 
     When the minimal model is used as the biological model  2 , the first step is to estimate p1, p2, p3, and Gb values among the aforesaid parameters using the well-known method of least squares, method of steepest descent, and method of genetic algorithms so as to minimize errors in the G(t) output by the biological model drive unit  3 , using the plasma insulin concentration change data of intravenous glucose tolerance test as the diagnostic data. The next step is to estimate the values γ, n, h, and Ib among the aforesaid parameters using the same well-known methods so as to minimize errors in the I(t) output by the biological model drive unit  3  using the plasma glucose concentration change data from the intravenous glucose tolerance test as the diagnostic data. 
     The pathological condition analyzer  5  analyzes the disease condition by associating the parameter values generated by the biological model generator  4  with the three types of diabetic conditions, that is, excessive hepatic glucose production, insulin secretion capability, and insulin resistance, comparing the data to parameter values of healthy persons determined beforehand, and analyzing the pathological condition by detecting differences in the parameters. For example, among the minimal model parameters, γ can be associated with insulin secretion capability, Gb can be associated with excessive hepatic glucose production, and p3 can be associated with insulin resistance. Furthermore, in regard to specific parameters, upper and lower limit values of healthy persons may be set, so as to determine as abnormal any values outside this range. Moreover, normal and abnormal values may be determined by the ratio of typical values of healthy persons and the generated patient parameter values. 
     The diagnostic support information generator  6  generates support information starting with the optimum considered treatment method based on treatment determination standards described later, using the pathological condition patterns output by the pathological condition analyzer  5 , and a database storing specialist medical software and diagnostic data input from the diagnostic data input unit  1 . 
     The database, which stores the specialized knowledge of specialist physicians, includes specialist knowledge related to medications for treating diabetes, knowledge related to exercise programs, and knowledge related to diet programs, and systematically organizes such knowledge as treatment policies in accordance with the pathological condition patterns, clinical findings of patients, surgical case histories and the like. This information is stored in memory devices, such as the hard disk  11   d  and RAM  11   c  and the like. 
     Knowledge of medications includes candidate drugs which can be administered for each pathological condition, drug selection ranking and dosage corresponding to the clinical findings, and types (contraindication information) of drugs which cannot be administered in a patient in a particular condition. The drug dosage rate is included as a function of the intensity of the parameter such as whether or not there is organic disorder of the kidneys, for example, and the influence of disease etiology on diabetes in each patient. 
     For example, policies for drug administration combining AGI and TZD provide the maximum effect of improved insulin resistance among the etiology of diabetes for a particular patient, and of course consider conditions such as the absence of cardiac insufficiency, absence of electrolyte anomalies, and absence of past surgery of the gastrointestinal track. 
     Knowledge of exercise programs includes exercise intensity, amount of exercise, and recommended type of exercise corresponding to each disease condition. 
     Knowledge of dietary programs includes allowed calorie intake, and allowed intake of each nutrient corresponding to each disease condition. 
     The pathological condition simulator  7  uses the biological model characteristic of a patient generated by the biological model generator  4  to predict the disease condition after treatment of the patient. For example, regarding a patient for whom the maximal influencing etiology is impaired secretion of insulin before treatment, when the simulation result hypothesizes administration of insulin and observations include elevated insulin secretion and decreasing glucose production, but a rise in utilization of glucose is not observed, the maximal influencing etiology is insulin resistance after treatment, and a result signifying this outcome is output. 
     The diagnostic support information output unit  8  outputs support information, such as treatment methods generated by the diagnostic support information generator  6 , condition or treatment result predicted by the pathological condition simulator  7  and the like. 
     As shown in  FIG. 2 , the diagnostic support information output unit  8  is formed by the display  12 , image output interface  11   h , and computer program relating to the output of information to the display  12 . In the present embodiment, the diagnostic support information output unit  8  is formed by the display  12 , however, the present invention is not limited to this mode inasmuch as, other than the display  12 , a device such as, for example, a printer or the like may also be provided. 
     The contents of processes performed by the diagnostic support system of the present invention are described below.  FIGS. 3 ,  4 , and  5  are flow charts showing the flow of the processing performed by the diagnostic support system of the present invention. First, in step S 1 , the CPU  11   a  receives from the input device  13  the input of diagnostic data for a patient as previously described. 
     Then, the CPU  11   a  starts the biological model predicting process (step S 2 ). In this step, the CPU  11   a  sets predetermined initial values as the parameter values of the biological model  2  (step S 2 - 1 ), and reproduces the behavior of a living body in the biological model drive unit  3  (step S 2 - 2 ). 
     Then, the CPU  11   a  compares the output of the biological model drive unit  3  with the input diagnostic data, and determines whether or not both are in substantial agreement (steps S 2 - 3 , S 2 - 4 ). When the data are not in substantial agreement, the CPU  11   a  updates the parameter values, and returns to step S 2 - 2  and repeats the process. 
     When the output of the biological model generator  3  and the input diagnostic data are in substantial agreement, the CPU  11   a  outputs the parameter values at this time, and the process continues to the disease condition analysis step of S 3 . 
     Next, the CPU  11   a  starts the disease condition analysis process (step S 3 ). The disease condition analysis process mainly performed the following three determination processes. 
     Step S 3 - 1 : Determine peripheral insulin resistance 
     Step S 3 - 2 : Determine excessive hepatic glucose production 
     Step S 3 - 3 : Determine impaired insulin secretion 
     For example, when p3 in the minimal model is parameters expressing the amount of glucose metabolized per unit time relative to the insulin concentration in peripheral tissue, then a patient for whom this parameter is reduced can be determined to have peripheral insulin resistance. 
     When Gb in the minimal model is parameters expressing a blood glucose value as a base value at which glucose production is suppressed, then a patient for whom this parameter is increased can be determined to have excessive production in the liver. 
     When γ in the minimal model is parameters expressing the amount of insulin produced per unit time by the pancreas relative to glucose stimulation, then a patient for whom this parameter is reduced can be determined to have impaired insulin secretion capability. 
     When each step of the determination process is executed, then in each step the CPU  11   a  calculates a score, and the scores are temporarily stored in the RAM  11   c  or hard disk  11   d . For example, in step S 3 - 1 , the CPU  11   a  executed the peripheral insulin resistance determination process top determine an evaluation value (designated score A) which represents the degree of influence insulin resistance has in the etiology of diabetes. The method of calculating the score A may be, for example, to divide p3 in the minimal model, that is, the insulin consumption rate relative to the insulin-dependent glucose metabolism, by the normal subject standard value. The score A is temporarily stored, and used in the subsequent diagnostic support information generating process (step S 4 ). 
     Similarly, in step S 3 - 2 , the CPU  11   a  calculates a score B, which is the evaluation value of excessive glucose production, and in step S 3 - 3 , the CPU  11   a  calculates a score C for impaired insulin secretion. The method of calculating the score B may be, for example, to divide Gb in the minimal model, that is, the glucose concentration base value, by the normal subject standard value. The method of calculating the score C may be, for example, to divide h in the minimal model, that is, the blood glucose threshold value at which insulin secretion starts, by the normal subject standard value. The larger these numerical scores are, the greater the influence on disease etiology. 
     The CPU  11   a  advances to the support information generating process (step S 4 ), and determines whether to continue to the pathological condition simulation (step S 5 ). This determination may initiate a display prompting for user input so as to specify the process to be executed by specific key input determined by the user. Alternatively, when there is input in step S 1 , information specifying which processes may be input by a user beforehand. Furthermore, when this multipoint determination is not necessary, the step S 5  may be sequentially executed after the CPU  11   a  has executed step S 4 . 
     In the diagnostic support information generating process of step S 4 , the CPU  11   a  may execute processes among the four processes described below according to the magnitude relationship of the three scores (A, B, C) determined in step S 2 . 
     Step S 4 - 1 : Executed when the peripheral insulin resistance is determined to be maximum. 
     Step S 4 - 2 : Executed when excessive hepatic glucose production is determined to be maximum. 
     Step S 4 - 3 : Executed when insulin secretion reduction is determined to be maximum. 
     Among these three processes, the CPU  11   a  generates support information, which includes treatment policies, medications to be administered and the like, for the respective pathological conditions based on a predetermined treatment determination standard. 
     For example, when peripheral insulin resistance is determined to be greatest, information specifying the resistance enhancing drug TZD as the most suitable can be generated by the CPU  11   a.    
     When excessive hepatic glucose production is determined to be greatest, information specifying the glucose production suppressing drug BG as most suitable can be generated by the CPU  11   a.    
     Similarly, when insulin secretion reduction is determined to be greatest, information specifying the insulin secretion stimulating drug SU as most suitable can be generated by the CPU  11   a.    
     After step S 4 , the CPU  11   a  displays the generated diagnostic support information on the display  12 , or send the data to the printer (step S 6 ). 
     In the pathological condition simulation process (step S 5 ), the CPU  11   a  uses the determined parameter values to predict the condition of the patient after treatment. The condition prediction is accomplished, for example, by the CPU  11   a  calculating each function value representing the parameter values by inputting information on specific dosages of certain medications, and increasing or decreasing each parameter value to have these parameter values affect the biological model drive unit. Thereafter, the CPU  11   a  advances to the process of step S 6 , and the information serving the diagnostic supports determined in steps S 2 , S 3 , and S 5  is displayed on the display  12 , or sent to a printer. The processes described above are the overall flow of the diagnostic support system of the present invention. 
     After the policies analyzing the pathological condition have been determined, then in step S 6 , diagnostic support information including these determined policies is provided to the physician. Since identical testing and analysis are performed at re-testing and subsequent testing of an individual patient and diagnostic support information is provided together with numerical analysis results (scores), it is possible to objectively and precisely manage the change in the patient&#39;s condition over the time course, and implement treatment based on suitable determination according to the patient&#39;s condition over time. 
     For example, the physician is not only made aware of changes in condition according to simple classifications by viewing the change in scores, the physician can also confirm whether or not the degree of the condition has changed from the quantified scores, thereby making more appropriate determinations and treatment possible. Furthermore, since analysis of a pathological condition and treatment policies can be obtained by uniform treatment determination standards prepared beforehand without reliance on subjective judgment and experience which is rife with indefinite factors, it is possible to arrive at determinations and treatments identical to or very similar to those of specialist physicians without the physician being a specialist in diabetes. 
     The foregoing detailed description and accompanying drawings have been provided by way of explanation and illustration, and are not intended to limit the scope of the appended claims. Many variations in the presently preferred embodiments illustrated herein will be obvious to one of ordinary skill in the art, and remain within the scope of the appended claims and their equivalents.