JP4132839B2 - Infectious disease system - Google Patents

Description

[0001]
BACKGROUND OF THE INVENTION
The present invention relates to a medical information system, and more particularly to an infectious disease system in the field of infectious diseases.
[0002]
[Prior art]
In recent years, EBM (Evidence Based Medicine) has been demanded in place of empirical medical treatment in the medical field in general. In particular, in the field of infectious diseases, doctors often have to determine the treatment policy without identifying the causative bacteria causing the onset of infection. Yes.
[0003]
One of the information that can be considered as the basis for treatment judgment is the idea of presenting the patient's pathology as an objective index. For example, in the document “A PREDICTION RULE TO IDENTIFY LOW-RISK PATIENTS WITH COMMITITY-ACQUIRED PEUMONIA (New England Journal of Medicine, Vol. Based on the factors composed of the patient's sex, basic disease, physical findings, and clinical laboratory data, the pathology of patients suffering from infectious diseases is classified into five stages, and the subsequent treatment policy is judged Is.
[0004]
This risk class determination is composed of two steps. In Step 1, determine whether a) whether you are under 50 years old, b) presence of basic diseases such as malignant tumor, heart failure, stroke, etc. c) presence or absence of abnormal physical findings such as pulse rate, respiratory rate, etc. Is determined to be risk class 1. If even one of them is applicable, the process proceeds to the next step (Step 2). In step 2, a factor consisting of the patient's age, sex, underlying disease, physical findings, and clinical laboratory data is scored, and risk classes 2 to 5 are determined based on these total scores. For example, in a 70-year-old man, when the underlying diseases are malignant tumor (score 30) and liver disease (score 10), the respective scores are added and the age is added up to +110, and the total score is 91 to 130 The corresponding risk class 4 is determined.
[0005]
[Problems to be solved by the invention]
As described above, in the conventional technology, the risk class is determined based on a certain score defined for each factor such as the underlying disease, but in reality, the risk for each factor is not always constant. . For example, liver diseases include diseases such as liver dysfunction, chronic hepatitis, and cirrhosis, but the risk is greatly different between liver dysfunction with relatively low severity and cirrhosis with relatively high severity. However, since the conventional technology treats it as the same risk, it is judged as the same risk class, and there is a problem that the risk class is not judged correctly.
[0006]
In addition, it is possible to define these diseases as new factors using conventional technology, but in that case, there may be multiple synonymous factors, and the total score will not be calculated correctly. The problem was that the risk class was not judged correctly.
[0007]
The present invention has been made paying attention to such a point, and an object of the present invention is to provide an infectious disease system that enables medical judgment more reliably and objectively.
[0008]
[Means for Solving the Problems]
In order to achieve the above object, in the present invention, a risk factor that is an item that affects resistance to an infection source such as bacteria that causes infection of a subject (patient or healthy person), and infection of the item A knowledge DB (database) that stores a score, which is a coefficient that can be changed according to the degree of influence on the subject, and the medical information of the subject based on the knowledge DB And a risk level determination unit that determines a risk level that is an index in which resistance to an infection source is graded.
[0009]
In the present invention, the knowledge DB stores a risk factor DB that stores the risk factor and the score in association with each other, and the resistance to the infection source calculated from the score of the risk factor corresponding to the patient information. And a risk level determination DB that stores a knowledge base for determining the risk level based on a risk score that is a score that is indicated manually, so that the user can determine the score and risk level for each risk factor Since the logic can be changed by changing the standard, it can be solved more easily.
[0010]
In addition, the present invention is configured such that the knowledge DB has a score that varies according to an option selected from a plurality of options indicating the degree of influence of the item on the resistance, thereby allowing the user to have a degree of sensitivity. Can be selected directly, which can be solved more reliably.
[0011]
In addition, the present invention can be solved more easily because the user can select the option without considering the degree of sensitivity by configuring the option to be a disease name.
[0012]
In addition, the present invention uses a more objective index by configuring the knowledge DB to have a score that varies according to at least one medical information among vital signs, clinical test results, and image test results. Since the score can be calculated, it can be solved more accurately.
[0013]
Further, according to the present invention, since the knowledge DB is configured to have a score that varies according to a plurality of risk factors, a score reflecting the interrelationship between the risk factors can be calculated, which can be solved more accurately. .
[0014]
In addition, since the present invention has a configuration in which the knowledge DB has the variable score and a standard score when the variable score cannot be calculated, an approximate value can be calculated even when the score cannot be calculated. Can be solved universally.
[0015]
The present invention also provides a knowledge base reading unit that reads a knowledge base stored in the knowledge DB, a knowledge base editing unit that can edit the knowledge base read by the knowledge base reading unit, and a knowledge base after editing By configuring with the knowledge base writing unit stored in the knowledge DB, editing can be performed while confirming the knowledge base, so that the problem can be solved more efficiently.
[0016]
The present invention also includes a simulation unit that calculates a risk level based on the knowledge base edited by the knowledge base editing unit, thereby confirming the risk level after the user has changed the logic. Can be solved more reliably.
[0017]
DETAILED DESCRIPTION OF THE INVENTION
Embodiments of the present invention will be described below with reference to the drawings.
[0018]
In FIG. 1, the block diagram of the infectious disease system 100 which is an Example of this invention is shown. The system 100 includes a control unit 101, a communication unit 102, a patient information acquisition unit 103, a risk level determination knowledge reading unit 104, a risk level determination unit 105, a case data writing unit 106, and a case data reading unit 153. And a risk level determination knowledge editing unit 154, a simulation unit 155, a risk level determination knowledge writing unit 156, a case DB 112, and a risk level determination knowledge DB 111.
[0019]
The system 100 can communicate with the input / output terminal 120 via the network 130. The system 100 can communicate with other medical information systems 140 such as a clinical examination system, a medical accounting system, an order entry system, and an electronic medical record system via the network 130.
[0020]
The network 130 is assumed to be an in-hospital network, but may be a regional network or a wider area network. The medical information system 140 may be a regional medical system.
[0021]
The input / output terminal 120 is assumed to be an information device such as a personal computer having a keyboard or mouse as an input function and a CRT display as an output function, but may have other input / output functions. The input / output terminal 120 may be a terminal equipped with a web browser function. Further, although the system 100 and the input / output terminal 120 are described as different hardware, the input / output function of the input / output terminal 120 may be installed in the system 100.
[0022]
Moreover, although this system is described as a hardware configuration, the function of this system may be configured by software.
[0023]
Although this system is described as one hardware configuration, it may be configured as another hardware such as the risk level knowledge editing unit 154 and the simulation unit 155.
[0024]
The risk level determination knowledge DB 111 includes a risk factor DB 310 that stores risk factors that are items that affect resistance to infection sources such as bacteria that cause infection of patients or healthy persons, and a risk level determination reference DB 320. , Is composed. The risk factor DB 310 quantifies a field 311 for storing a risk ID that is an identifier of the risk factor, a field 312 for storing the name of the risk factor, and the degree of influence of the risk factor on the resistance to an infection source. And a field 313 for storing a score which is a coefficient obtained by the above operation. The field 313 can store a conditional score that can be changed by the weight of the risk factor and a standard score that is used when the conditional score cannot be calculated.
[0025]
The risk factors include basic information such as age, lifestyle information such as alcohol consumption, smoking, hypertension, hyperlipidemia, liver disease, diabetes, respiratory disease, collagen disease, cerebrovascular disorder, kidney disease, pancreatitis, Information on basic diseases such as anemia, malnutrition, nephrosis, heart disease, burns, hypoglobulinemia, hypocomplementemia, hematological malignancy, solid malignancy, AIDS, neutrophil dysfunction, low birth weight infant, etc. Stomach drugs, antihypertensive drugs, wide-area antibacterial drugs, anti-ulcer drugs, steroid drugs, immunosuppressive drugs, anticancer drugs, medication information such as insulin, treatment device information such as catheters, tracheostomy, respirators, drains, radiation therapy, thoracotomy -Information on treatment such as laparotomy, transplantation, splenectomy, blood transfusion, bedridden, ICU management status, SIRS status, and other status information may be included.
[0026]
Here, a unique code is used as the risk ID. However, an international disease classification code such as ICD10, a disease classification code of the Ministry of Health, Labor and Welfare Hospital Infection Control Surveillance, or the like may be used.
[0027]
The risk level determination standard DB 320 stores a field 321 that stores a determination ID that is an identifier of a risk level determination standard, fields 322 and 323 that store lower limit values and upper limit values that serve as determination standards, and a field that stores risk levels. 324.
[0028]
FIG. 2 is a flowchart showing the operation of the system 100 when determining the risk level. First, when the present system is activated, the control unit 101 activates the patient information acquisition unit 103 and executes step 201 of acquiring patient information input by a doctor using the input / output terminal 120.
[0029]
FIG. 4 shows a screen example 500 of the input / output terminal 120 when a doctor inputs patient information. First, the doctor identifies a patient ID 501 for identifying the patient, basic information such as age and lifestyle, disease information such as past illness and current medical history, patient status, medication information, iatrogenic treatment information, and treatment information applied to the patient. For example, the information 503 necessary for determining the risk level and the date and time 502 when the doctor can determine the information 503 are input, and the determination button 504 is selected.
[0030]
Next, the control unit 101 activates the risk level determination knowledge reading unit 104 to execute a step 202 for reading out risk factors and scores from the risk level determination knowledge DB 111 and a step 203 for reading out risk level determination criteria.
[0031]
Next, the control unit 101 activates the risk level determination unit 105, and based on the patient information acquired in the step 201, the risk factor and score read in the step 202, Step 204 for calculating a risk score that is a numerical value indicating resistance is executed, and Step 205 for determining a risk level that is an index that grades resistance to an infection source based on the calculated risk score. To do.
[0032]
For example, in the case of the patient with the patient ID “P1234567” shown in FIG. 4, “diabetes”, “liver disease”, “neutrophil dysfunction”, and “SIRS status” correspond to the risk factors.
[0033]
If there is no information regarding the weight of the risk factor, the standard scores “10”, “10”, “80”, “80”, based on the scores stored in the risk factor DB 312 shown in FIG. 50 ”is added to calculate the risk score“ 150 ”. At this time, if there is a risk factor “anemia”, “20” is further added. Further, the risk level “3” corresponding to the risk score “150” is determined based on the criterion stored in the risk level criterion DB 320 shown in FIG.
[0034]
In FIG. 4, with respect to the weight of the risk factor, the risk factor “diabetes” is “good control”, the risk factor “liver disease” is “liver dysfunction”, the risk factor “neutrophil dysfunction” is “WBC = 200”, Therefore, the conditional scores “10”, “5”, “120”, “50” are calculated based on the scores stored in the risk factor DB 312 shown in FIG. Add the risk score “185”. At this time, if there is a risk factor “anemia”, “20” is further added as in the case of the standard score. Further, the risk level “4” corresponding to the risk score “185” is determined based on the criterion stored in the risk level criterion DB 320 shown in FIG.
[0035]
In this embodiment, the scores are all positive scores, but may be negative scores.
[0036]
Thus, the risk level can be determined even when there is no information regarding the weight of the risk factor. Furthermore, if there is information about the weight of the risk factor, you can use the score according to the weight of the risk factor,
The risk level can be determined more accurately.
[0037]
In the above example, the example in which the risk score is calculated by adding the scores in step 204 has been shown, but the risk score may be calculated using another arithmetic expression. Further, the risk score may be calculated using a neural network, fuzzy theory, or the like. Moreover, you may combine them.
[0038]
In step 201, when the system 100 can communicate with the medical information system 140, when the patient ID 501 and the date 502 are input, information necessary for risk level determination is acquired from the medical information system. May be. Further, the system 100 may manage information necessary for determining the risk level.
[0039]
Further, for example, (1) body temperature is 38 degrees or more or 36 degrees or less, (2) heart rate 90 / min, (3) respiration rate 20 / min, or PaCO, which are conditions of SIRS state ₂ (Blood carbon dioxide concentration) <32 Trr, (4) White blood cell count> 12000 / mm ^Three Or <4000mm ^Three Alternatively, when the immature granulocytes satisfy 10%, the risk factor “SIRS state” may be automatically selected.
[0040]
Next, the control unit 101 uses the communication unit 102 to execute step 206 in which the risk level determined in step 205 is output to the input / output terminal 120.
[0041]
FIG. 5 shows an example screen 600 displayed on the input / output terminal 120 after the execution of step 206. It can be seen that “risk level 4 (risk score 185)” is output to the risk level output area 601 as the determination result.
[0042]
In this way, by outputting the risk level, the user can objectively know the degree of resistance of the patient to the infection source, so that it is possible to determine a treatment policy suitable for the patient. Further, by outputting the risk score, the user can know the degree of sensitivity in detail.
[0043]
Next, the control unit 101 executes Step 207 that allows the user to determine whether or not to save the patient information acquired in Step 201.
[0044]
If the user presses the save button 603 on the screen 600 and selects to save, the control unit 101 determines the patient information acquired in step 201, the risk level determined in step 205, and the user. Step 208 is executed to store the risk level determined by the in the case DB 112.
[0045]
FIG. 3 shows an example of the case DB 112. The previous case DB 112 stores a field 401 for storing a case ID for identifying case data, a field 402 for storing a patient ID for identifying a patient, a field 403 for storing a risk factor, and a risk level as an output of this system. A field 404 for storing, a field 405 for storing the date and time, and a field 406 for storing the risk level determined by the user. For example, in the case of the screen 600, since the risk level for the patient ID “P1234567” is “4” and the risk level of the patient determined by the user is “5”, case data is stored as in the case DB 112. At this time, the case ID is automatically added so as to be unique in the case DB 112.
[0046]
If it is determined in step 207 that saving is not selected, the operation for determining the risk level of the present system ends.
[0047]
FIG. 6 is a flowchart showing the operation of the system 100 when supporting the risk level determination knowledge DB editing.
[0048]
First, when the system 100 starts an operation at the time of editing support, the control unit 101 starts the operation, starts the risk level determination knowledge reading unit 152, and reads a knowledge base from the risk level determination knowledge DB 111 701. Execute.
[0049]
Next, the control unit 101 executes Step 702 for reading out case data from the case DB 112.
[0050]
Next, the control unit 101 executes Step 703 for supporting editing of the knowledge base.
[0051]
FIG. 7 shows a screen example 800 when the step 703 is executed. The knowledge base of the risk level determination knowledge DB 111 is displayed on the left side of the screen, and the case data of the case DB 112 is displayed on the right side of the screen. In the screen example 800, the risk factor and score read in step 701 are displayed on the upper left side of the screen, and the risk level criteria are displayed on the lower left side of the screen. Here, when the user inputs a case ID into the case ID input box 811 and presses the patient selection button 812, case data corresponding to the case ID is displayed.
[0052]
FIG. 8 shows a screen example 900 when the user selects the case ID “00000004” in the screen example 800. Here, it can be seen that the case data of the case ID “00000004” stored in the case DB 112, the risk level as the determination result, and the risk level determined by the user are displayed.
[0053]
FIG. 9 shows a screen example 1000 when the user edits the knowledge base. In the screen example 1000, the score of the risk level criterion “level 5” is edited as “180”.
[0054]
Here, when the user presses the determination button 1011, the control unit FIG. 9 activates the simulation unit 155, and executes a step 704 for performing a risk level determination simulation based on the risk factor and score after editing. To do.
[0055]
FIG. 10 shows a screen example 1100 after execution of step 704. As a result of the simulation, it can be seen that the risk level is changed to “5”.
[0056]
If the user is satisfied with the simulation result, the confirmation button 1001 is pressed. Thus, the control unit 101 executes step 706 of writing the edited knowledge base to the risk level determination knowledge DB after executing the confirmation confirmation step 705.
[0057]
If the user is not satisfied with the simulation result, the process can be repeated from step 703.
[0058]
Further, when the user presses a cancel button 1002, it can be terminated without writing.
[0059]
FIG. 11 shows an example 1200 of the risk level determination knowledge DB 111 after the completion of step 706. It can be seen that the values indicated by the risk level criteria 1201 and 1202 are changed to “180”.
[0060]
In this way, the logic for determining the risk level can be easily changed by the user changing the score for each risk factor and the criterion for determining the risk level.
[0061]
Further, since the risk level after the user has changed the logic for determining the risk level can be confirmed by simulation, the logic for determining the risk level can be changed more reliably.
[0062]
Here, editing of the risk level determination reference DB is shown as an example, but the risk factor DB can be similarly edited and simulated.
[0063]
Here, an example of presenting the result of simulation based on case data for one person has been shown as judgment knowledge editing support, but the result of simulation based on case data of multiple persons is presented. May be. At that time, not only results for all case data but also statistical processing results such as average and variance may be presented.
[0064]
The case data may not be selected arbitrarily by the user, but only cases having different judgment levels 404 and user judgment levels 406 stored in the case DB 112 may be extracted and used.
[0065]
In addition, the case data is not arbitrarily selected by the user, but the cases where the determination level 404 and the user determination level 406 stored in the case DB 112 are different, the determination level 404 and the user determination level 406 are The same number of the same cases may be extracted and used.
[0066]
In this example, the subject whose risk level is determined is a patient who has developed a disease, but may be a healthy person who has not developed a disease.
[0067]
With the infectious disease system described above, the user can objectively know the degree of resistance of the patient or healthy person to the infection source, so that it is possible to determine a treatment policy suitable for the patient or the healthy person.
[0068]
Further, the logic for determining the risk level can be easily changed by the user changing the score for each risk factor and the criterion for determining the risk level.
[0069]
Further, since the risk level after the user has changed the logic for determining the risk level can be confirmed by simulation, the logic for determining the risk level can be changed more reliably.
[0070]
Further, the basic concept of the present invention is not limited to the infectious disease system in the embodiment described above, but can be applied to a medical information system such as a clinical examination system, a medical support system, a nursing support system, and the like.
[0071]
The present invention is summarized as follows.
[0072]
(1) An item including a factor that affects the resistance to an infection source that causes an infectious disease of a subject, and the degree of resistance of the item to the infection source are quantified to determine the degree of influence on the subject. A storage unit that stores a coefficient that can be changed as a knowledge database, and based on the knowledge database, the level of resistance to the infection source of the subject is graded as an index according to the medical information of the subject An infectious disease system comprising: a determination unit for determining.
[0073]
(2) In the configuration of the item (1), the knowledge database is an infection source calculated from an item database that stores the item and the coefficient in association with each other, and a coefficient of an item corresponding to the medical information of the subject. An infectious disease system comprising: an index determination database that stores a knowledge base for determining the index based on a score that numerically indicates resistance to the disease.
[0074]
(3) In the configuration of the item (1), the knowledge database has a coefficient that varies according to a selected option among a plurality of options indicating the degree of influence of the item on the resistance force. Infectious disease system.
[0075]
(4) In the configuration of item (1), the knowledge database has a coefficient that varies according to at least one medical information among vital signs, clinical test results, and image test results. .
[0076]
(5) In the configuration of item (3), the knowledge database has the variable coefficient and a standard coefficient when the variable coefficient cannot be calculated.
[0077]
(6) Items that affect resistance to infection sources such as bacteria that cause infectious diseases of patients or healthy individuals, and the degree of influence that varies depending on the degree of resistance of the items to the infection source A knowledge DB that stores the converted coefficients, and a determination unit that determines resistance to an infection source as a stepped index according to the personal information of a patient or a healthy person based on the knowledge DB. Infectious disease system characterized by that.
[0078]
(7) In the infectious disease system having the configuration of the item (6), the knowledge DB is calculated from an item DB that stores the item and the coefficient in association with each other, and a coefficient of the item corresponding to the patient information. An infectious disease system comprising: an index determination DB that stores a knowledge base for determining the index based on a numerical value indicating resistance to an infection source numerically.
[0079]
(8) In the infectious disease system configured as described in item (6) or (7), according to the selected option among a plurality of options indicating the degree of influence on the resistance of the item in the knowledge DB An infectious disease system characterized by having a variable coefficient.
[0080]
(9) An infectious disease system configured as described in item (8) above, wherein the option is a disease name.
[0081]
(10) The infectious disease system configured as described in the items (6) to (9), wherein the knowledge DB has a coefficient that varies according to one or more vital signs, clinical test results, and image test results. Infectious disease system characterized by that.
[0082]
(11) The infectious disease system configured as described in the items (6) to (10), wherein the knowledge DB has a coefficient that varies according to a plurality of risk factors.
[0083]
(12) The infectious disease system configured as described in the items (6) to (11), wherein the knowledge DB includes the variable coefficient and a standard coefficient when the variable coefficient cannot be calculated. Infectious disease system.
[0084]
(13) An infectious disease system configured as described in (6) to (12) above, wherein a knowledge base reading unit that reads a knowledge base stored in the knowledge DB and a knowledge base read by the knowledge base reading unit An infectious disease system comprising: an editable knowledge base editing section; and a knowledge base writing section for storing the edited knowledge base in the knowledge DB.
[0085]
(14) The infectious disease system configured as described in (13) above, further comprising a simulation unit that calculates the index based on the knowledge base edited by the knowledge base editing unit. system.
[0086]
【The invention's effect】
According to the present invention, an infectious disease system that enables more reliable and objective medical judgment in the field of infectious diseases is realized.
[Brief description of the drawings]
FIG. 1 is a diagram illustrating a configuration of an infectious disease system according to an embodiment of the present invention.
FIG. 2 is a flowchart showing an operation at the time of risk level determination of the system.
FIG. 3 is a diagram for explaining an example of a case DB.
FIG. 4 is a diagram showing a screen example of an input / output terminal when patient information is input.
FIG. 5 is a diagram showing a screen example of an input / output terminal when a risk level is output.
FIG. 6 is a flowchart showing an operation at the time of determination knowledge editing support of the system.
FIG. 7 is a diagram showing an example of an editing support screen.
FIG. 8 is a diagram showing an example of an editing support screen after selecting case data.
FIG. 9 is a diagram showing an example of an editing support screen when editing a knowledge base.
FIG. 10 is a diagram showing an example of an editing support screen after simulation.
FIG. 11 is a diagram showing an example of a risk level judgment knowledge DB after editing judgment knowledge.
[Explanation of symbols]
DESCRIPTION OF SYMBOLS 100 ... Infectious disease system, 101 ... Control part, 102 ... Communication part, 103 ... Patient information acquisition part, 104 ... Risk level determination knowledge reading part, 105 ... Risk level determination part, 106 ... Case data writing part, 153 ... Case data Reading unit, 154 ... risk level determination knowledge editing unit, 155 ... simulation unit, 156 ... risk level determination knowledge writing unit, 112 ... case DB, 111 ... risk level determination knowledge DB, 120 ... input / output terminal, 130 ... network, 140 ... medical information system, 310 ... risk factor DB, 320 ... risk level judgment reference DB, 311 ... field storing risk ID, 312 ... field storing risk factor name, 313 ... field storing score, 321 ... determination Field for storing ID, 322-3 DESCRIPTION OF SYMBOLS 3 ... The field which stores the minimum value used as a criterion, the upper limit, 324 ... The field which stores a risk level, 201 ... The step which acquires patient information, 202 ... The step which reads a risk factor and a score, 203 ... Risk level criterion 204, calculating the risk score, 205, determining the risk level, 206, outputting the risk level to the input / output terminal 120, 207, allowing the user to determine whether to save the patient information, 500: Screen example of input / output terminal 120 when patient information is input by doctor, 501 ... Patient ID, 502 ... Date and time, 503 ... Information necessary for determination of risk level, 504 ... Determination button, 600 ... After execution of step 206, Example of screen displayed on input / output terminal 120, 601 ... Risk Bell output area, 603 ... Save button, 401 ... Field for storing case ID, 402 ... Field for storing patient ID, 403 ... Field for storing risk factor, 404 ... Field for storing risk level as output of this system 405, a field for storing the date and time, 406, a field for storing the risk level determined by the user, 701, a step for reading the knowledge base from the risk level determination knowledge DB 111, 702, a step for reading the case data from the case DB 112, 703 ... Knowledge Step for supporting editing of base, 704... Simulating risk level determination, 705... Confirmation step, 706... Writing knowledge base to risk level determination knowledge DB, 800. Example: 811 ... Case ID input box, 812 ... Patient selection button, 900 ... Example screen when the user selects the case ID "00000004" in the above screen example 800, 1000 ... Example screen when the user edits the knowledge base 1011 ... Determination button, 1001 ... Confirm button, 1002 ... Cancel button, 1100 ... Example of screen after execution of step 704, 1200 ... Example of the risk level determination knowledge DB 111 after completion of step 706, 1201 to 1202 ... Changed by step 706 Decision criteria.

Claims (5)

An item including a factor that affects the resistance to the infection source causing the infectious disease of the subject, and the degree of the resistance of the item to the infection source are quantified and changed according to the degree of the influence on the subject. A storage for storing possible coefficients as a knowledge database;
Based on the knowledge database, in accordance with the medical information of the subject, a determination unit that determines the level of resistance to the infection source of the subject as an index , and
In an infectious disease system comprising a display unit capable of displaying at least the determination result in the determination unit,
The knowledge database is
An item database for associating and storing the items and the coefficients;
An index determination database for storing a knowledge base for determining the index based on a score numerically indicating resistance to an infection source calculated from a coefficient of an item corresponding to the medical information of the subject; Have
The determination unit
An editing unit capable of editing the score that numerically indicates the coefficient of the item corresponding to the medical information of the subject or the resistance to the infection source;
A simulation unit that determines the index based on a score after the score that numerically represents the coefficient of the item corresponding to the medical information of the subject or the resistance to the infection source is edited by the editing unit And
An infectious disease system characterized in that a result determined by the simulation unit can be displayed on the display means .   The determination unit includes a knowledge base writing unit capable of storing a coefficient of an item corresponding to medical information of the subject edited by the editing unit or a score numerically indicating resistance to the infection source in the knowledge database. The infectious disease system according to claim 1, comprising:   The infectious disease system according to claim 1, wherein the knowledge database has a coefficient that varies according to a selected option among a plurality of options indicating the degree of influence of the item on the resistance.   The infectious disease system according to claim 1, wherein the knowledge database has a coefficient that varies according to at least one medical information among vital signs, clinical test results, and image test results.   The infectious disease system according to claim 3, wherein the knowledge database includes the variable coefficient and a standard coefficient when the variable coefficient cannot be calculated.

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