JP6932956B2 - Generation program, generation method and generation device - Google Patents

Description

The present invention relates to a generation program and the like.

In a conventional medical system, patient information is collected and stored in a database. For example, patient information includes patient profiles, treatment histories, test content, free text information written to healthcare professionals, and the like. The patient profile contains information about the patient's gender, age, and allergies. Doctors access the patient information stored in the database and make comprehensive decisions when deciding the patient's condition and future treatment policy.

Japanese Unexamined Patent Publication No. 2004-157623 Japanese Unexamined Patent Publication No. 2012-198846 International Publication No. 2013/001584 Japanese Unexamined Patent Publication No. 2015-116319

However, the above-mentioned conventional technique has a problem that it is not possible to provide information that can confirm the characteristics of the patient.

The patient information includes a wide variety of information, and in particular, when referring to all the texts described in the free text, not only is it time-consuming, but also the burden on the doctor is increased.

In one aspect, it is an object of the present invention to provide a generation program, a generation method and a generation device capable of providing information that can confirm the characteristics of a patient.

In the first plan, the computer is made to perform the following processing. The computer refers to a storage device that stores character string information indicating the patient's condition for each of a plurality of items. The computer analyzes the meaning of the character string information and specifies a value corresponding to a specific parameter type for a specific patient for each specific parameter type. The computer generates graphic data in which a graphic is arranged based on the value of each specific parameter type.

It is possible to provide information that can confirm the characteristics of the patient.

FIG. 1 is a functional block diagram showing a configuration of a generator according to this embodiment. FIG. 2 is a diagram showing an example of the data structure of the treatment history table. FIG. 3 is a diagram showing an example of a data structure of treatment history information. FIG. 4 is a diagram showing an example of the data structure of the score table. FIG. 5 is a diagram showing an example of a data structure of correction information. FIG. 6 is a diagram showing an example of the data structure of the heat map table. FIG. 7 is a diagram showing an example of a patient heat map. FIG. 8 is a diagram showing a part of the heat map in detail. FIG. 9 is a diagram showing an example of a specific result generated by the specific unit. FIG. 10 is a diagram for explaining other processes of the search unit. FIG. 11 is a flowchart showing a processing procedure of the generator according to the present embodiment. FIG. 12 is a diagram showing an example of a hardware configuration of a computer that realizes the same function as the generator.

Hereinafter, examples of the generation program, generation method, and generation apparatus disclosed in the present application will be described in detail with reference to the drawings. The present invention is not limited to this embodiment.

FIG. 1 is a functional block diagram showing a configuration of a generator according to this embodiment. As shown in FIG. 1, the generation device 100 includes a communication unit 110, an input unit 120, a display unit 130, a storage unit 140, and a control unit 150.

The communication unit 110 is a processing unit that executes communication with an external device (not shown) via a network. The communication unit 110 corresponds to a communication device.

The input unit 120 is an input device for inputting various information to the generation device 100. The input unit 120 corresponds to an input device such as a keyboard, a mouse, and a touch panel.

The display unit 130 is a display device that displays information output from the control unit 150. The display unit 130 corresponds to a display device such as a liquid crystal display or a touch panel.

The storage unit 140 has a treatment history table 141, a score table 142, correction information 143, and a heat map table 144. The storage unit 140 corresponds to semiconductor memory elements such as RAM (Random Access Memory), ROM (Read Only Memory), and flash memory (Flash Memory), and storage devices such as HDD (Hard Disk Drive).

The treatment history table 141 is a table that holds various information about the patient. FIG. 2 is a diagram showing an example of the data structure of the treatment history table. As shown in FIG. 2, the treatment history table 141 has patient identification information, a patient profile, and treatment history information.

The patient identification information is information that uniquely identifies the patient. The patient profile contains information about the patient's gender, age, allergies, etc. The treatment history information is information that retains various types of information related to the patient's treatment content, test results, and the like. Hereinafter, treatment history information regarding a patient with certain patient identification information will be described.

FIG. 3 is a diagram showing an example of a data structure of treatment history information. As shown in FIG. 3, this treatment history information has a date, an affected area, a disease name, a doctor's comment, and a drug. The date indicates the date of treatment or examination. The affected area indicates where the treatment or examination was performed. The disease name indicates the name of the disease that the patient suffered. Physician comments indicate the comments of the physician who treated or tested the patient. Drugs indicate the drugs used to provide treatment. Here, as an example, only the drug is shown, but the treatment history information may also include the content of surgery as the content of treatment.

The score table 142 is a table used when calculating a score (value) corresponding to a parameter. Here, the parameter is a combination of each item included in the treatment history information. In this embodiment, as an example, the parameter is a combination of the items "affected part" and "disease name", but the present invention is not limited to this.

FIG. 4 is a diagram showing an example of the data structure of the score table. As shown in FIG. 4, the score table 142 associates the parameter type with the basic score. In this embodiment, the parameter type is a combination of the affected area and the disease name, but other combinations may be used. For example, the basic score corresponding to the affected area "head", name of disease "disease name a" is "S _A". As will be described later, the score specified based on the basic score indicates the degree of severity, and the higher the score, the worse the patient's condition.

The correction information 143 is weight information used when correcting the score described with reference to FIG. FIG. 5 is a diagram showing an example of a data structure of correction information. As shown in FIG. 5, the correction information 143 has a table 143a and a table 143b. Table 143a is information that defines the weights corresponding to the drugs. Table 143b is information that defines weights corresponding to a predetermined keyword.

In the example shown in FIG. 5, the table 143a and the table 143b are shown, but the correction information 143 may further have another table. For example, the correction information 143 may have a table in which the content of the operation and the weight are associated with each other.

The heat map table 144 holds heat map information corresponding to each patient. FIG. 6 is a diagram showing an example of the data structure of the heat map table. As shown in FIG. 6, this heat map table associates the patient identification information with the heat map. The patient identification information is information that uniquely identifies the patient. The heat map is graphic data in which figures whose shading values are adjusted according to the scores of parameters related to patients are arranged in a matrix.

FIG. 7 is a diagram showing an example of a patient heat map. In FIG. 7, the vertical axis is the axis corresponding to the date, and the horizontal axis is the axis corresponding to the parameter. In this embodiment, the vertical axis is divided into predetermined periods. The horizontal axis is divided for each parameter.

FIG. 8 is a diagram showing a part of the heat map in detail. The example shown in FIG. 8 has figures 10a, 11a, 12a, 10b, 11b, 12b, 10c, 11c, 12c. The figure 10a is a figure corresponding to the period "April 2015; April 1st to April 30th, 2015" and the parameter A. The figure 11a is a figure corresponding to the period “May 2015; May 1st to May 31st, 2015” and the parameter A. The figure 12a is a figure corresponding to the period "June 2015; June 1st to June 30th, 2015" and the parameter A.

The figure 10b is a figure corresponding to the period "April 2015; April 1st to April 30th, 2015" and the parameter B. The figure 11b is a figure corresponding to the period "May 2015; May 1st to May 31st, 2015" and the parameter B. The figure 12b is a figure corresponding to the period "June 2015; June 1st to June 30th, 2015" and the parameter B.

The figure 10c is a figure corresponding to the period "April 2015; April 1st to April 30th, 2015" and the parameter C. The figure 11c is a figure corresponding to the period “May 2015; May 1st to May 31st, 2015” and the parameter C. The figure 12c is a figure corresponding to the period "June 2015; June 1st to June 30th, 2015" and the parameter C.

The darkness of the figures 10a to 12c varies depending on the score of the parameter type in the corresponding period. For example, the higher the score, the darker the color of the figure.

Returning to the description of FIG. The control unit 150 includes a reception unit 151, a specific unit 152, a generation unit 153, and a search unit 154. The control unit 150 can be realized by a CPU (Central Processing Unit), an MPU (Micro Processing Unit), or the like. The control unit 150 can also be realized by hard-wired logic such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array).

The reception unit 151 is a processing unit that receives various types of information via the communication unit 110 or the input unit 120, and updates the information stored in the storage unit 140 based on the received information. For example, when the reception unit 151 receives the patient profile and the treatment identification information associated with the patient identification information, the reception unit 151 updates the treatment history table 141 with the received information.

The identification unit 152 is a processing unit that specifies the score of the patient parameter type for each parameter type based on the treatment history table 141, the score table 142, and the correction information 143. The specific unit outputs the specific result to the generation unit 153. For example, it is assumed that the parameter type is defined in the score table 142 in advance.

The identification unit 152 describes an example of the process of specifying the score of the parameter type. For example, the identification unit 152 specifies the score of the parameter type based on the equation (1). In the formula (1), the basic score is a basic score corresponding to the parameter type defined in the score table 142. Wa and Wb are weights specified based on the correction information 143.

Score = basic score x Wa x Wb ... (1)

An example of a process in which the identification unit 152 identifies the score of the patient “parameter A” of the patient identification information “P101” will be described. Parameter A is a combination of the affected area “head” and the disease name “disease name a”. The specific unit 152 acquires the basic score of the parameter A from the score table 142. In the example shown in FIG. 4, the basic score of the parameters A (diseased "head", name of disease "disease name a") is "S _A".

An example of the process of specifying Wa will be described. The specific unit 152 accesses the treatment history table 141, analyzes the treatment identification information of the patient identification information "P101" by characters, and identifies a record having the affected area "head" and the disease name "disease name a" corresponding to the parameter A. do. The record specified by the specific unit 152 is appropriately referred to as a "target record". In the example shown in FIG. 3, the target record is the record on the first row. The target record shall be specified from the record of the date of the period specified in advance.

The specific unit 152 executes character analysis on the "drug" of the target record, and extracts the medicine defined in the table 143a. The identification unit 152 identifies the weight Wa corresponding to the extracted drug with reference to the table 143a.

When the target record contains one drug, the specific unit 152 sets the weight corresponding to one drug as Wa. When there are a plurality of drugs included in the target record, the specific unit 152 sets the average value of the weights corresponding to the plurality of drugs as Wa.

An example of the process for specifying Wb will be described. The specific unit 152 executes character analysis on the "doctor comment" of the target record, and extracts the keywords defined in the table 143b. The identification unit 152 identifies the weight Wb corresponding to the extracted keyword with reference to the table 143b.

When one of the keywords defined in the table 143b is included in the doctor's comment of the target record, the specific unit 152 sets the weight corresponding to one keyword as Wb. When there are a plurality of keywords included in the doctor's comment of the target record among the keywords defined in the table 143b, the identification unit 152 sets the average value of the weights corresponding to the plurality of keywords as Wb.

By executing the above process, the specific unit 152 associates the date of the target record with the score of the parameter A. When a plurality of target records exist, the specific unit 152 associates the score of the parameter A with each date of the target record. The specific unit 152 also executes the same processing as that of the parameter A for the other parameter types to generate information in which the date and the score of the parameter type are associated with each other. Further, the specific unit 152 also executes the above processing for other patients in the same manner.

FIG. 9 is a diagram showing an example of a specific result generated by the specific unit. As shown in FIG. 9, the specific result associates the patient identification information with the parameter type, the date, and the score. The specific unit 152 outputs the specific result shown in FIG. 9 to the generation unit 153.

The generation unit 153 is a processing unit that generates the heat map table 144 based on the information of the specific result generated by the specific unit 152. The generation unit 153 generates a heat map for each patient identification information based on the score of each parameter type associated with the patient identification information, and registers the heat map in the heat map table 144.

The generation unit 153 calculates the average value of the scores for each period for a certain parameter type. The period delimiter shall be set in advance. In the example shown in FIG. 8, the period is separated by month.

The generation unit 153 compares the calculated average value of the score with the shading table to specify the shading value for each period of a certain parameter type. The shading table is a table in which the average value of the score and the shading value are associated with each other, and the larger the average value of the score, the larger (darker) the shading value. For example, it is assumed that the shade value defined in the shade table is set in advance so as to fall between 0 and 255.

The generation unit 153 repeatedly executes the above processing for each parameter type to generate information in which the period, the parameter type, and the shading value are associated with each other. In the following description, information relating the period, the parameter type, and the shading value is appropriately referred to as "graphic information".

The generation unit 153 repeatedly executes a process of specifying a position corresponding to a period and a parameter of the figure information on the heat map and arranging the figure adjusted to the density according to the shading value at the specified position. Then generate a heat map. It is assumed that the order of the periods set on the vertical axis of the heat map and the order of the parameter types set on the horizontal axis are preset.

The generation unit 153 generates a heat map of each patient by executing the above process. The generation unit 153 registers the patient identification information and the heat map in the heat map table 144 in association with each other.

The search unit 154 receives the patient identification information and the designation of the starting date via the communication unit 110 or the input unit 120. The search unit 154 searches for patient identification information having a heat map similar to the heat map of the patient identification information that has received the designation in the period before and after the starting date. In the following description, the designated patient identification information will be referred to as "designated identification information" as appropriate. The starting date may be the date of admission of the designated patient or the date of receiving the prescribed treatment.

The search unit 154 refers to the heat map table 144, compares the heat map corresponding to the designated identification information with the heat map corresponding to other patient identification information within a certain period width based on the starting date, and makes the same. Calculate the difference value of the shade value of the figure at the position. For example, one year before and after the month including the starting date is set as a fixed period width, but it may be changed as appropriate. When the total of the difference values is less than the threshold value, the search unit 154 outputs the patient identification information of the heat map to be compared to the display unit 130 as a search result.

Here, the case where the search unit 154 searches for the patient identification information which is a heat map similar to the heat map of the designated identification information by obtaining the difference value has been described, but the present invention is not limited to this. For example, the search unit 154 obtains a correlation value between the heat map of the designated identification information and the heat map of the patient identification information, and when the correlation value is equal to or greater than the threshold value, the search unit 154 displays the patient identification information as a search result. It may be output to 130.

Further, the search unit 154 may narrow down the patient identification information based on various conditions, and execute the above processing for the heat map of the narrowed down patient identification information. For example, the search unit 154 acquires the information of the keyword list via the communication unit 110 or the input unit 120, and refers to the patient identification information including the keywords defined in the keyword list in the treatment history information with reference to the patient history table 141. Identify by doing. The search unit 154 searches the heat map of the identified patient identification information for the patient identification information whose difference value from the heat map of the designated identification information is less than the threshold value.

The search unit 154 may narrow down the patient identification information by combining the designation by the patient profile, the condition of the test result, the keyword list, and the like.

Further, the search unit 154 may execute the following processing when searching for a heat map similar to the heat map corresponding to the designated identification information. In the following description, patient identification information corresponding to similar heat maps will be referred to as "similar identification information".

FIG. 10 is a diagram for explaining other processes of the search unit. The search unit 154 compares the heat map 30 of the designated identification information with the heat map 20 of the similar identification information, and finds the position of the figure whose difference value is equal to or more than the threshold value among the difference values of the shade values of the figures at the same position. Identify.

In the example shown in FIG. 10, the difference value is less than the threshold value for the set of figures 21 and 31, the set of figures 22 and 32, the set of figures 23 and 33, and the set of figures 24 and 34. The difference value is also less than the threshold value for the set of the figures 26 and 36, the set of the figures 27 and 37, the set of the figures 28 and 38, and the set of the figures 29 and 39. On the other hand, for the set of figures 25 and 35, the difference value is equal to or larger than the threshold value. Therefore, the search unit 154 identifies the figures 25 and 35, and outputs the parameter type and the period corresponding to the specified figure to the display unit 130.

Since it can be said that the patient with the designated identification information and the patient with the similar identification information have a similar medical history, the disease that does not occur in the patient with the designated identification information and develops in the patient with the similar identification information is In the future, it may occur in patients with designated identification information. Therefore, by displaying the above parameter type and the period on the display unit 130, it is possible to notify a doctor or the like of a medical condition that may occur in a patient with designated identification information. For example, in the example shown in FIG. 10, it is possible to notify that the medical condition corresponding to the parameter B may occur in the patient with the designated identification information.

Next, the processing procedure of the generator 100 according to this embodiment will be described. FIG. 11 is a flowchart showing a processing procedure of the generator according to the present embodiment. The process shown in FIG. 11 is executed for each patient identification information and each parameter type.

As shown in FIG. 11, the specific unit 152 of the generation device 100 acquires the treatment history information of each date included in the designated period from the treatment history table 141 (step S101). The specific unit 152 selects treatment history information on an unselected date (step S102).

The specific unit 152 character-analyzes the treatment history information corresponding to the parameter type and extracts a keyword (step S103). The specific unit 152 calculates the score corresponding to the parameter type (step S104). In step S104, the specific unit 152 calculates the score corresponding to the parameter type based on the equation (1).

The specific unit 152 determines whether or not the treatment history information of all dates included in the designated period has been selected (step S105). If the specific unit 152 is not selected (step S106, No), the process proceeds to step S102. When the specific unit 152 is selected (step S106, Yes), the specific unit 152 proceeds to step S107.

The generation unit 153 of the generation device 100 specifies the shading value of the figure based on the score corresponding to the parameter type of each date included in the designated period (step S107). When another designated period is selected (step S108, Yes), the generation device 100 proceeds to step S101. If the generator 100 does not select another designated period (steps S108, No), the generator 100 proceeds to step S109.

The generation unit 153 generates a heat map by arranging a figure at a position corresponding to the period and the parameter type (step S109).

Next, the effect of the generator 100 according to this embodiment will be described. The generation device 100 identifies the score corresponding to the parameter type from the treatment history information of the patient, and arranges the figure whose shading value is adjusted according to the score in a matrix in association with the date to generate the heat map information. Generate. Since the heat map graphically represents the characteristics of the patient, the doctor can grasp the characteristics of the patient without referring to the treatment history information. That is, the doctor can easily confirm the characteristics of the patient by referring to the heat map.

The generation device 100 generates a heat map for each patient and searches for a patient corresponding to graphic data similar to the graphic data of a specific patient. In this way, it is possible to easily identify a specific patient and a patient having a similar medical history only by performing a comparison using a heat map.

The generation device 100 compares the values of the positions of similar heat maps and notifies the positions of the graphic data having different values. As described with reference to FIG. 10, since it can be said that the patient with the designated identification information and the patient with the similar identification information have similar medical histories, the patient with the designated identification information does not have the disease and the patient with the similar identification information. The illness that develops in the disease may develop in patients with designated identification information in the future. Therefore, by notifying the position of the graphic data having different values, it is possible to notify the doctor or the like of the medical condition that may occur in the patient with the designated identification information.

Next, an example of a computer hardware configuration that realizes the same functions as the generator 100 shown in the above embodiment will be described. FIG. 12 is a diagram showing an example of a hardware configuration of a computer that realizes the same function as the generator.

As shown in FIG. 12, the computer 200 includes a CPU 201 that executes various arithmetic processes, an input device 202 that receives data input from a user, and a display 203. Further, the computer 200 has a reading device 204 for reading a program or the like from a storage medium, and an interface device 205 for exchanging data with another computer via a network. Further, the computer 200 has a RAM 206 for temporarily storing various information and a hard disk device 207. Then, each of the devices 201 to 207 is connected to the bus 208.

The hard disk device 207 has a specific program 207a, a generation program 207b, and a search program 207c. The CPU 201 reads out the specific program 207a, the generation program 207b, and the search program 207c and deploys them in the RAM 206.

The specific program 207a functions as a specific process 206a. The generation program 207b functions as the generation process 206b. The search program 207c functions as a search process 206c. The process of the specific process 206a corresponds to the process of the specific unit 152. The processing of the generation process 206b corresponds to the processing of the generation unit 153. The process of the search process 206c corresponds to the process of the search unit 154.

The programs 207a to 207c do not necessarily have to be stored in the hard disk device 207 from the beginning. For example, each program is stored in a "portable physical medium" such as a flexible disk (FD), a CD-ROM, a DVD disk, a magneto-optical disk, or an IC card inserted into the computer 200. Then, the computer 200 may read and execute each of the programs 207a to 207c.

The following additional notes will be further disclosed with respect to the embodiments including each of the above embodiments.

(Appendix 1) To the computer
Refer to the storage device that stores the character string information indicating the patient's condition for each of multiple items.
By character-analyzing the character string information, a value corresponding to a specific parameter type for a specific patient is specified for each specific parameter type.
A generation program characterized by executing a process of generating graphic data in which a graphic is arranged based on the value of each specific parameter type.

(Appendix 2) The generated process is characterized in that graphic data is generated for each patient and a computer is further executed to search for a patient corresponding to graphic data similar to the graphic data of a specific patient. The generation program described in Appendix 1.

(Appendix 3) In the process of specifying the value, the table associated with the keyword and the score is compared with the character string information of the item related to the specific parameter type, and the value corresponding to the specific parameter type is calculated. The generation program according to Appendix 1 or 2, characterized in that

(Appendix 4) The storage device stores the date, the item, and the character string information in association with each other, and the process of specifying the value specifies a value corresponding to a specific parameter type for each date. The generation program according to Appendix 3, wherein the generation process generates graphic data by arranging a graphic whose shading is adjusted based on the value and a date in a matrix.

(Appendix 5) Based on the search result of the process to be searched, the value of each position of similar graphic data is compared, and the computer is further executed to notify the position of the graphic data having different values. The generation program described in Appendix 2.

(Appendix 6) This is a generation method executed by a computer.
Refer to the storage device that stores the character string information indicating the patient's condition for each of multiple items.
By character-analyzing the character string information, a value corresponding to a specific parameter type for a specific patient is specified for each specific parameter type.
A generation method characterized by executing a process of generating graphic data in which a graphic is arranged based on the value of each specific parameter type.

(Appendix 7) The generated process is characterized in that graphic data is generated for each patient and a computer is further executed to search for a patient corresponding to the graphic data similar to the graphic data of a specific patient. The generation method according to Appendix 6.

(Appendix 8) In the process of specifying the value, the table associated with the keyword and the score is compared with the character string information of the item related to the specific parameter type, and the value corresponding to the specific parameter type is calculated. The generation method according to Appendix 6 or 7, wherein the method is to be used.

(Appendix 9) The storage device stores the date, the item, and the character string information in association with each other, and the process of specifying the value specifies a value corresponding to a specific parameter type for each date. The generation method according to Appendix 8, wherein the generation process generates graphic data by arranging a graphic whose shading is adjusted based on the value and a date in a matrix.

(Appendix 10) Based on the search result of the process to be searched, the value of each position of similar graphic data is compared, and the computer is further executed to notify the position of the graphic data having different values. The generation method according to Appendix 7.

(Appendix 11) A storage unit that stores character string information indicating the patient's condition for each of a plurality of items, and a storage unit.
A specific part that analyzes the character string information and specifies a value corresponding to a specific parameter type for a specific patient for each specific parameter type.
A generator characterized by having a generator for generating graphic data in which a graphic is arranged based on a value of each specific parameter type.

(Supplementary Note 12) The description in Appendix 11, wherein the generation unit further includes a search unit that generates graphic data for each patient and searches for a patient corresponding to the graphic data similar to the graphic data of a specific patient. Generator.

(Appendix 13) The specific unit compares the table associated with the keyword and the score with the character string information of the item related to the specific parameter type, and calculates the value corresponding to the specific parameter type. The generator according to Appendix 11 or 12, which is characterized.

(Appendix 14) The storage unit stores a date, the item, and the character string information in association with each other, and the specific unit specifies a value corresponding to a specific parameter type for each date and generates the data. The generation device according to Appendix 13, wherein the unit is characterized in that graphic data is generated by arranging graphic data whose shading is adjusted based on the above values and dates in association with each other in a matrix.

(Supplementary Note 15) The generator according to Appendix 12, wherein the search unit compares the values of the positions of similar graphic data and notifies the positions of the graphic data having different values.

100 Generator 140 Storage unit 141 Treatment history table 142 Score table 143 Correction information 144 Heat map table 150 Control unit 151 Reception unit 152 Specific unit 153 Generation unit 154 Search unit

Claims (7)

On the computer
Refer to the storage device that stores the character string information indicating the patient's condition for each of multiple items.
By character-analyzing the character string information, the value corresponding to the parameter type of the combination of the specific affected area and the specific disease name related to the specific patient, and the value indicating the severity of the specific patient is specified. Specify for each parameter type of
A generation program characterized by executing a process of generating graphic data in which a graphic is arranged based on the value of each specific parameter type. The first aspect of the invention is characterized in that the generated process generates graphic data for each patient and causes a computer to further execute a process of searching for a patient corresponding to the graphic data similar to the graphic data of a specific patient. Described generator. The process of specifying the value is characterized in that the table associated with the keyword and the score is compared with the character string information of the item related to the specific parameter type to calculate the value corresponding to the specific parameter type. The generation program according to claim 1 or 2. The storage device stores the date, the item, and the character string information in association with each other, and the process of specifying the value specifies a value corresponding to a specific parameter type for each date and generates the value. The generation program according to claim 3, wherein the processing is to generate graphic data by arranging a graphic whose shading is adjusted based on the above values and a date in a matrix. The claim is characterized in that, based on the search result of the process to be searched, the values of the positions of similar graphic data are compared, and the computer is further executed to notify the positions of the graphic data having different values. The generation program according to 2. It ’s a computer-executed generation method.
Refer to the storage device that stores the character string information indicating the patient's condition for each of multiple items.
By character-analyzing the character string information, the value corresponding to the parameter type of the combination of the specific affected area and the specific disease name related to the specific patient, and the value indicating the severity of the specific patient is specified. Specify for each parameter type of
A generation method characterized by executing a process of generating graphic data in which a graphic is arranged based on the value of each specific parameter type. A storage unit that stores character string information indicating the patient's condition for each of multiple items,
By character-analyzing the character string information, the value corresponding to the parameter type of the combination of the specific affected area and the specific disease name related to the specific patient, and the value indicating the severity of the specific patient is specified. Specific part to be specified for each parameter type of
A generator characterized by having a generator for generating graphic data in which a graphic is arranged based on a value of each specific parameter type.

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