Drug recommendation device, control method, and program

Abstract

A medication recommending device (2000) acquires disease information (20), genetic mutation information (30), and in-use medication information (40). The disease information (20) is information related to a disease a subject has. The genetic mutation information (30) is information related to genetic mutation in the subject. The in-use medication information (40) is information related to a medication the subject is using. The medication recommending device (2000) utilizes the disease information (20), the genetic mutation information (30), and the in-use medication information (40) to generate candidate medication information (10). The candidate medication information (10) is information related to a medication candidate to be recommended or prescribed for use by the subject.

Description

[Technical field]

[0001] The present disclosure relates to a technology that supports drug selection. [Background technology]

[0002] For humans and other animals, the act of prescribing or recommending (hereinafter, "prescribing") suitable medicines is being carried out. Systems that support such medicine prescribing have been developed. For example, Patent Document 1 discloses a system that can take side effects into consideration when prescribing medicines. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Publication 2002-245172 Summary of the Invention [Problem to be solved by the invention]

[0004] Side effects are not the only thing to be considered when prescribing a drug. The present invention has been made in view of this problem, and an object of the present invention is to provide a technology that enables more appropriate prescription and recommendation of drugs. [Means for solving the problem]

[0005] The drug recommendation device of the present disclosure has an acquisition unit that acquires disease information on a disease suffered by a subject, genetic mutation information on genetic mutations of the subject, and previously used drug information on drugs being used by the subject, and a generation unit that uses the disease information, the genetic mutation information, and the previously used drug information to generate candidate drug information, which is information on candidate drugs to be recommended or prescribed for use by the subject.

[0006] The control method of the present disclosure is executed by a computer and includes an acquisition step of acquiring disease information on a disease suffered by a subject, genetic mutation information on genetic mutations of the subject, and previously used drug information on drugs used by the subject, and a generation step of generating candidate drug information, which is information on candidates of drugs recommended or prescribed for use by the subject, using the disease information, the genetic mutation information, and the previously used drug information.

[0007] The computer-readable medium of the present disclosure stores a program for causing a computer to execute the control method of the present disclosure. Effect of the Invention

[0008] According to the present disclosure, a technique is provided that enables more appropriate prescription and recommendation of medications. [Brief description of the drawings]

[0009] [Figure 1] FIG. 2 is a diagram illustrating an example of an outline of the operation of the drug recommendation device of the first embodiment. [Diagram 2] 1 is a block diagram illustrating a functional configuration of a drug recommendation device according to a first embodiment. [Diagram 3] FIG. 2 is a block diagram illustrating an example of a hardware configuration of a computer that realizes the drug recommendation device. [Figure 4] 4 is a flowchart illustrating a process flow executed by the medicine recommendation device of the first embodiment. [Diagram 5] FIG. 2 is a diagram illustrating an example of first knowledge information in a table format. [Figure 6] 11 is a diagram illustrating an example of first knowledge information further including information related to attributes. FIG. [Figure 7] FIG. 13 is a diagram illustrating an example of second knowledge information in a table format. [Figure 8] FIG. 13 is a diagram illustrating an example of second knowledge information further including information related to attributes. [Figure 9] FIG. 13 is a diagram illustrating an example of third knowledge information in a table format. [Figure 10]FIG. 13 is a diagram illustrating an example of third knowledge information further including information related to attributes. [Figure 11] FIG. 13 is a diagram conceptually showing a process for generating candidate drug information. [Figure 12] 13 is a diagram illustrating a case in which candidate drug information indicates a rank. [Figure 13] FIG. 1 is a first diagram illustrating candidate drug information including information on resistance, etc. and side effects. [Figure 14] FIG. 2 is a second diagram illustrating candidate drug information including information on resistance, etc. and side effects. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] Hereinafter, an embodiment of the present disclosure will be described in detail with reference to the drawings. In each drawing, the same or corresponding elements are given the same reference numerals, and duplicated explanations will be omitted as necessary for clarity of explanation. Furthermore, unless otherwise specified, predetermined values ​​such as predetermined values ​​and threshold values ​​are stored in advance in a storage device accessible from a device that uses the values.

[0011] Fig. 1 is a diagram illustrating an example of an outline of the operation of the drug recommendation device 2000 of the embodiment 1. Here, Fig. 1 is a diagram for facilitating understanding of the outline of the drug recommendation device 2000, and the operation of the drug recommendation device 2000 is not limited to that shown in Fig. 1.

[0012] The drug recommendation device 2000 is used to generate candidate drug information 10. The candidate drug information 10 is information on candidate drugs that are prescribed or recommended for use (hereinafter, "prescribed") for humans or other animals. Hereinafter, humans or other animals for whom a drug is prescribed are referred to as "subjects." For example, the candidate drug information 10 indicates one or more drugs that are recommended for prescription, etc. for a subject, and the degree to which prescription, etc. of each drug is recommended.

[0013] For example, the candidate drug information 10 is used as information to be referred to when a doctor prescribes a drug for a patient or an animal. However, the situation in which the candidate drug information 10 is used is not necessarily limited to the situation in which a doctor prescribes a drug. For example, the candidate drug information 10 may be used when a salesperson at a drug store selects a drug to recommend to a customer.

[0014] The drug recommendation device 2000 acquires disease information 20, gene mutation information 30, and previously used drug information 40, and generates candidate drug information 10 using these pieces of information. The disease information 20 indicates, among the diseases suffered by a subject, diseases that are targets of the drugs indicated by the candidate drug information 10. For example, in a case where a drug for stomach cancer is to be prescribed to a subject with two diseases, stomach cancer and lower back pain, the disease information 20 indicates the disease "stomach cancer."

[0015] The gene mutation information 30 is information indicating a gene mutation possessed by the subject. For example, gene mutations include BRAF V600E and BRAF V600K. The already used drug information 40 indicates drugs already used by the subject. For example, in the above-mentioned case where a drug for stomach cancer is to be prescribed to a subject with two diseases, stomach cancer and lower back pain, the subject is already using a drug for lower back pain. In this case, the already used drug information 40 indicates drugs used by the subject for lower back pain. Note that drugs that can be indicated by the already used drug information 40 are not limited to drugs prescribed by a doctor, and may be drugs purchased by the subject at a drugstore or the like without being prescribed by a doctor (for example, drugs classified as quasi-drugs).

[0016] <Example of action and effect> According to the drug recommendation device 2000 of this embodiment, the candidate drug information 10 is generated using the disease information 20, the genetic mutation information 30, and the used drug information 40. This makes it possible to recommend a drug to be prescribed for the disease of the subject, taking into consideration the genetic mutation of the subject and the drug being used by the subject. Therefore, it is possible to recommend a drug more suitable for the subject, compared to a case where the genetic mutation and the drug being used are not taken into consideration.

[0017] The drug recommendation device 2000 of this embodiment will be described in more detail below.

[0018] <Example of functional configuration> 2 is a block diagram illustrating a functional configuration of the drug recommendation device 2000 of embodiment 1. The drug recommendation device 2000 includes an acquisition unit 2020 and a generation unit 2040. The acquisition unit 2020 acquires disease information 20, genetic mutation information 30, and previously used drug information 40. The generation unit 2040 generates candidate drug information 10 using the disease information 20, genetic mutation information 30, and previously used drug information 40.

[0019] <Example of hardware configuration> Each functional component of the drug recommendation device 2000 may be realized by hardware (e.g., hardwired electronic circuitry, etc.) that realizes each functional component, or may be realized by a combination of hardware and software (e.g., a combination of an electronic circuitry and a program that controls it, etc.). Below, a further description will be given of the case where each functional component of the drug recommendation device 2000 is realized by a combination of hardware and software.

[0020] 3 is a block diagram illustrating a hardware configuration of a computer 500 that realizes the drug recommendation device 2000. The computer 500 is any computer. For example, the computer 500 is a stationary computer such as a PC (Personal Computer) or a server machine. In addition, for example, the computer 500 is a portable computer such as a smartphone or a tablet terminal. The computer 500 may be a dedicated computer designed to realize the drug recommendation device 2000, or may be a general-purpose computer.

[0021] For example, by installing a specific application on the computer 500, each function of the drug recommendation device 2000 is realized by the computer 500. The application is configured with a program for realizing each functional component of the drug recommendation device 2000. The method of acquiring the program is arbitrary. For example, the program can be acquired from a storage medium (such as a DVD disk or a USB memory) in which the program is stored. Alternatively, the program can be acquired by downloading the program from a server device that manages the storage device in which the program is stored.

[0022] The computer 500 has a bus 502, a processor 504, a memory 506, a storage device 508, an input / output interface 510, and a network interface 512. The bus 502 is a data transmission path for the processor 504, the memory 506, the storage device 508, the input / output interface 510, and the network interface 512 to transmit and receive data to and from each other. However, the method of connecting the processor 504 and other components to each other is not limited to a bus connection.

[0023] The processor 504 is a processor such as a central processing unit (CPU), a graphics processing unit (GPU), or a field-programmable gate array (FPGA). The memory 506 is a main storage device realized using a random access memory (RAM) or the like. The storage device 508 is an auxiliary storage device realized using a hard disk, a solid state drive (SSD), a memory card, or a read only memory (ROM) or the like.

[0024] The input / output interface 510 is an interface for connecting the computer 500 to an input / output device. For example, the input / output interface 510 is connected to an input device such as a keyboard and an output device such as a display device.

[0025] The network interface 512 is an interface for connecting the computer 500 to a network. This network may be a LAN (Local Area Network) or a WAN (Wide Area Network).

[0026] The storage device 508 stores a program (a program for realizing the above-mentioned application) for realizing each functional component of the drug recommendation device 2000. The processor 504 reads the program into the memory 506 and executes it to realize each functional component of the drug recommendation device 2000.

[0027] The drug recommendation device 2000 may be realized by one computer 500 or may be realized by a plurality of computers 500. In the latter case, the configurations of the computers 500 do not need to be the same, and may be different from each other.

[0028] <Processing flow> 4 is a flowchart illustrating a process executed by the drug recommendation device 2000 of the first embodiment. The acquisition unit 2020 acquires the disease information 20, the gene mutation information 30, and the used drug information 40 (S102). The generation unit 2040 uses the disease information 20, the gene mutation information 30, and the used drug information 40 to generate the candidate drug information 10 (S104).

[0029] <Information Acquisition: S102> The acquisition unit 2020 acquires the disease information 20, the gene mutation information 30, and the previously used drug information 40 (S102). There are various methods for the acquisition unit 2020 to acquire this information. For example, this information is stored in advance in a storage device accessible from the drug recommendation device 2000. The acquisition unit 2020 acquires this information by accessing this storage device. As a more specific example, this information may be included in data representing the subject's medical record (so-called electronic medical record). In this case, the acquisition unit 2020 acquires each of the above information from the electronic medical record. Note that existing technology can be used as a technology for acquiring desired information from a specific person's electronic medical record.

[0030] Alternatively, for example, the disease information 20, the gene mutation information 30, and the used drug information 40 may be input by a user. Alternatively, for example, the disease information 20, the gene mutation information 30, and the used drug information 40 may be transmitted to the drug recommendation device 2000 from another device.

[0031] <Generation of candidate drug information 10: S104> The generating unit 2040 generates the candidate drug information 10 by using the disease information 20, the genetic mutation information 30, and the previously used drug information 40. For example, the disease information 20, the genetic mutation information 30, and the previously used drug information 40 are each used to identify one or more drugs that can be prescribed to a subject, to identify the presence or absence and the magnitude of resistance or sensitivity to a drug, and to identify side effects that can occur when used in combination with a drug being used. This will be described in detail below.

[0032] <<Identification of drugs that can be prescribed, etc.>> The generating unit 2040 identifies one or more drugs that can be prescribed or the like for the subject, based on the disease of the subject indicated by the disease information 20. Hereinafter, the drugs identified here are referred to as candidate drugs.

[0033] To identify candidate drugs, for example, information that corresponds a disease with a drug that can be used for that disease is used. Hereinafter, this information is referred to as first knowledge information. FIG. 5 is a diagram illustrating an example of first knowledge information in table format. The first knowledge information 50 shown in FIG. 5 has two columns: disease 52 and drug 54. Disease 52 indicates identification information of the disease (such as the name and identification code of the disease). Drug 54 indicates identification information of the drug that can be used for the corresponding disease 52 (such as the name and identification code of the drug).

[0034] For example, the generating unit 2040 identifies a record that indicates a disease indicated in the disease information 20 in the disease 52 from the first knowledge information 50. Then, the generating unit 2040 identifies a drug indicated in the drug 54 of the identified record as a candidate drug.

[0035] The candidate drug may be identified by further considering the attributes of the subject. In this case, the acquisition unit 2020 further acquires attribute information indicating the attributes of the subject. The attributes of the subject indicated by the attribute information include, for example, the subject's age, sex, race, height, weight, or a disease other than the disease indicated by the disease information 20. In this case, the first knowledge information 50 further indicates information related to the attributes.

[0036] Fig. 6 is a diagram illustrating the first knowledge information 50 further including information on attributes. Fig. 6 has a column called attribute 53. The generating unit 2040 identifies a record from the first knowledge information 50 that indicates a disease indicated in the disease information 20 in disease 52 and indicates an attribute matching the attribute of the subject in attribute 53. Then, the generating unit 2040 identifies a drug indicated in the identified record as a candidate drug.

[0037] <<Identification of resistance or susceptibility>> A person with a specific gene mutation may have resistance or sensitivity to a specific drug due to the gene mutation. If a subject has resistance to a certain drug, the drug may be ineffective or less effective for the subject. On the other hand, if a subject has sensitivity to a certain drug, the drug may be effective or more effective for the subject. Therefore, the decision of whether or not to prescribe a certain drug to a subject and the decision of the amount of the drug to be used (prescribed amount) are influenced by the gene mutation that the subject has.

[0038] Therefore, the generating unit 2040 identifies the presence or absence of resistance or sensitivity of the subject to each candidate drug, or the level of the subject's resistance or sensitivity to each candidate drug, based on the genetic mutation of the subject indicated in the genetic mutation information 30. For this identification, for example, second knowledge information that associates the genetic mutation, the drug, and information indicating the resistance or sensitivity of the person having the genetic mutation to the drug is used.

[0039] FIG. 7 is a diagram illustrating an example of the second knowledge information in a table format. The second knowledge information 60 shown in FIG. 7 has three columns: a genetic mutation 62, a drug 64, and a property 66. The genetic mutation 62 indicates identification information of the genetic mutation (such as the name or identification code of the genetic mutation). The drug 64 indicates identification information of the drug. The property 66 indicates information on the resistance or susceptibility of a person having a genetic mutation to a drug indicated by the corresponding genetic mutation 62 and the corresponding drug 64. For example, the property 66 indicates the presence or absence of resistance or susceptibility. In another example, the property 66 indicates the degree of resistance or susceptibility.

[0040] The degree of resistance or susceptibility is expressed, for example, by the recommended dosage of the corresponding drug. The dosage of the drug may be expressed in absolute terms or in relative terms based on the dosage recommended for people who do not have the corresponding gene mutation (i.e., the standard dosage).

[0041] For example, the generating unit 2040 identifies, for each candidate drug, a record from the second knowledge information 60 that indicates the gene mutation indicated in the gene mutation information 30 in gene mutation 62 and indicates the candidate drug in drug 64. Then, the generating unit 2040 identifies the presence or absence of resistance or sensitivity of the subject to the candidate drug, or the degree of resistance or sensitivity, by referring to the property 66 indicated in the identified record.

[0042] In identifying resistance or susceptibility, the attributes of the subject may be further taken into consideration. In this case, the acquiring unit 2020 acquires the above-mentioned attribute information. In this case, the second knowledge information 60 further indicates information related to the attributes.

[0043] FIG. 8 is a diagram illustrating the second knowledge information 60 further including information on attributes. The second knowledge information 60 in FIG. 8 has a column called attribute 65. The generating unit 2040 identifies, for each candidate drug, a record from the second knowledge information 60 that indicates in gene mutation 62 the gene mutation indicated in the gene mutation information 30, indicates the candidate drug in drug 64, and indicates in attribute 65 an attribute that matches the attribute of the subject. Then, the generating unit 2040 identifies the presence or absence of resistance or susceptibility of the subject to the candidate drug, or the degree of resistance or susceptibility, by referring to the property 66 indicated in the identified record.

[0044] <<Identifying Side Effects>> The generating unit 2040 identifies side effects that may occur when each candidate drug is used in combination with a drug indicated by the previously used drug information 40 (i.e., a drug currently being used by the subject). To identify side effects, for example, information indicating two drugs to be used in combination in association with side effects that may occur when these drugs are used in combination is used. Hereinafter, this information is referred to as third knowledge information.

[0045] Fig. 9 is a diagram illustrating an example of the third knowledge information in a table format. The third knowledge information 70 shown in Fig. 9 has three columns: a first drug 72, a second drug 74, and a side effect 76. The first drug 72 indicates identification information of a candidate drug. The second drug 74 indicates identification information of a drug that is already being used. The side effect 76 indicates a side effect that may occur when the two drugs indicated in the corresponding first drug 72 and second drug 74 are used together.

[0046] For example, the generating unit 2040 identifies, for each candidate drug, a record from the third knowledge information 70 in which the candidate drug is indicated in the first drug 72 and the drug indicated in the previously used drug information 40 is indicated in the second drug 74. Then, the generating unit 2040 identifies side effects that may occur by combining the candidate drug with the drug indicated in the previously used drug information 40 by referring to the side effects 76 of the identified record.

[0047] The third knowledge information 70 may further indicate side effects of the candidate drug alone. A record indicating side effects of the candidate drug alone can be realized, for example, by leaving the second drug 74 blank.

[0048] In identifying the side effect, the attributes of the subject may be further taken into consideration. In this case, the acquiring unit 2020 acquires the above-mentioned attribute information. In this case, the third knowledge information 70 further indicates information related to the attributes.

[0049] FIG. 10 is a diagram illustrating the third knowledge information 70 further including information on attributes. The third knowledge information 70 in FIG. 10 has a column called attribute 75. The generating unit 2040 identifies, for each candidate drug, a record from the third knowledge information 70 in which the candidate drug is indicated in the first drug 72, the drug indicated in the used drug information 40 is indicated in the second drug 74, and an attribute matching the attribute of the subject is indicated in the attribute 75. Then, the generating unit 2040 identifies side effects that may occur by using the candidate drug in combination with the drug indicated in the used drug information 40 by referring to the side effects 76 of the identified record.

[0050] <<Generation of Candidate Drug Information 10>> The generating unit 2040 generates the candidate drug information 10 based on the above-mentioned identification result. The candidate drug information 10 indicates one or more of the above-mentioned candidate drugs as drugs recommended for prescription or the like for the subject. For example, the generating unit 2040 calculates, for each candidate drug, the degree to which the candidate drug is recommended (hereinafter, a recommendation score) based on the subject's resistance or sensitivity to the candidate drug and side effects that may occur when the candidate drug is used in combination with other drugs. The generating unit 2040 generates the candidate drug information 10 based on the recommendation score calculated for each candidate drug. A method for calculating the recommendation score will be described later.

[0051] For example, the candidate drug information 10 is generated to show only candidate drugs whose recommendation scores are equal to or greater than a predetermined threshold. In this case, the generating unit 2040 identifies candidate drugs whose recommendation scores are equal to or greater than the threshold by comparing the recommendation score of each candidate drug with the threshold. The generating unit 2040 then generates the candidate drug information 10 showing each candidate drug whose recommendation score is equal to or greater than the threshold. Note that the candidate drug information 10 may further show the recommendation score of each candidate drug whose recommendation score is equal to or greater than the threshold together with the candidate drug.

[0052] Fig. 11 is a diagram conceptually showing a process for generating the candidate drug information 10. In the example of Fig. 11, the candidate drugs are candidate drugs M1, M2, and M3. The recommendation scores of the candidate drugs M1, M2, and M3 are 30, 70, and 90, respectively. Furthermore, the threshold value of the recommendation score that determines the candidate drugs to be included in the candidate drug information 10 is 50. In other words, only candidate drugs with a recommendation score of 50 or more are included in the candidate drug information 10.

[0053] In this example, the drugs M2 and M3 have recommendation scores of 50 or more. Therefore, the candidate drug information 10 shows the candidate drugs M2 and M3 and their recommendation scores. It is preferable that the candidate drugs shown in the candidate drug information 10 are sorted by the magnitude of the recommendation score.

[0054] The candidate drug information 10 may indicate a rank based on the magnitude of the recommendation score together with or instead of the recommendation score. In this case, the range of the recommendation score is divided into multiple non-overlapping numerical ranges, and a rank is assigned to each numerical range. The generating unit 2040 identifies the numerical range to which the recommendation score belongs for each candidate drug included in the candidate drug information 10. Then, the generating unit 2040 generates the candidate drug information 10 so as to indicate each candidate drug together with the identified rank for that candidate drug.

[0055] 12 is a diagram illustrating a case where the candidate drug information 10 indicates a rank. In this example, the range of the recommendation score is 0 or more and 100 or less. Ranks 1, 2, 3, and 4 are assigned to the ranges of 0 or more and less than 25, 25 or more and less than 50, 50 or more and less than 75, and 75 or more and less than 100, respectively. Other assumptions are the same as the example of FIG. 11.

[0056] As described above, the candidate drugs included in the candidate drug information 10 are candidate drugs M2 and M3. Since the recommendation score of candidate drug M2 is 70, its rank is rank 3. Since the recommendation score of candidate drug M3 is 90, its rank is rank 4. Therefore, the candidate drug information 10 in FIG. 12 further indicates ranks of rank 3 and rank 4 for candidate drugs M2 and M3, respectively.

[0057] The candidate drug information 10 may show all candidate drugs, not just those with recommendation scores equal to or greater than a threshold. In this case, for example, the generating unit 2040 generates the candidate drug information 10 showing all candidate drugs together with their recommendation scores. Note that, in this case as well, as described above, a rank based on the magnitude of the recommendation score may be shown together with or instead of the recommendation score.

[0058] Furthermore, the candidate drug information 10 may include various information used to generate candidate drugs and their recommendation ranks, in addition to information about the candidate drugs. For example, the candidate drug information 10 may include information that associates a genetic mutation possessed by a subject with resistance or sensitivity to a candidate drug, information that associates a drug used by a subject with side effects that may occur when the drug is used in combination, and the like.

[0059] 13 is a diagram illustrating an example of candidate drug information 10 including information on resistance and side effects. In this example, the subject has a disease D1. The subject has a genetic mutation C1. Candidate drugs that can be prescribed for the disease D1 are candidate drugs M1, M2, and M3.

[0060] Between the candidate drug M1 and the genetic mutation C1, a mark "resistant" is shown. This indicates that the subject has resistance to the candidate drug M1 due to the genetic mutation C1. In other words, the candidate drug M1 is a drug that is less effective against the subject.

[0061] 13 also shows drug M10 as a drug that the subject is already using. A side effect S1 is shown between drug M1 and drug M10. This indicates that a side effect S1 occurs when drug M1 and drug M10 are used in combination.

[0062] Furthermore, a side effect S2 is shown for the candidate drug M2 without association with other drugs, indicating that the candidate drug M2 alone can cause the side effect S2.

[0063] When the screen showing the candidate drug information 10 in Fig. 13 is displayed on a display device, it is preferable to be able to access more detailed information regarding each piece of information shown in Fig. 13. Therefore, it is preferable to include a link to the detailed information on the screen. For example, by selecting a mark "resistance", detailed information regarding that resistance can be obtained (for example, a pop-up display). The same applies to information other than resistance.

[0064] By viewing the information shown in FIG. 13, a doctor can not only know to what extent each candidate drug is recommended, but also further understand the basis for the degree to which each candidate drug is recommended.

[0065] 14 is a second diagram illustrating the candidate drug information 10 including information on drug resistance and side effects. The prerequisite conditions are the same as those in the example of FIG.

[0066] In the example of Fig. 14, the candidate drug information 10 shows information in a table format. Specifically, for each candidate drug, the number of pieces of information on resistance and susceptibility obtained from the gene mutation information 30 and the number of pieces of information on side effects obtained from the previously used drug information 40 for that candidate drug are shown. The number of pieces of information is represented, for example, by the number of records of the corresponding gene mutation information 30 or previously used drug information 40. The table in Fig. 14 also shows the score and rank for each candidate drug.

[0067] When the screen showing the candidate drug information 10 in Fig. 14 is displayed on a display device, it is preferable to be able to access more detailed information for each piece of information shown in Fig. 14. Therefore, it is preferable to include a link to the detailed information on the screen. For example, by selecting the display of the number of items such as "1 item" included in the table, the detailed information can be obtained (for example, by displaying it as a pop-up).

[0068] <<<Calculation of recommendation score>>> The generating unit 2040 calculates a recommendation score for each candidate drug, and generates the candidate drug information 10 based on the recommendation score. When the subject has resistance to a certain candidate drug, it is preferable to reduce the recommendation score of the candidate drug. That is, the generating unit 2040 reduces the recommendation score of a candidate drug to which the subject has resistance, compared to when the subject does not have resistance to the candidate drug. Also, the stronger the resistance that the subject has to a certain candidate drug, the smaller the recommendation score of the drug.

[0069] When a subject has sensitivity to a certain candidate drug, it is preferable to increase the recommendation score of the candidate drug. That is, the generating unit 2040 increases the recommendation score of a candidate drug to which the subject has sensitivity compared to a case in which the subject does not have sensitivity to the candidate drug. Also, the greater the sensitivity of the subject to a certain candidate drug, the greater the recommendation score of the candidate drug.

[0070] It is preferable to reduce the recommendation score for a candidate drug that may cause side effects when used by the subject. That is, the generating unit 2040 reduces the recommendation score for a candidate drug identified as being capable of causing side effects when used by the subject, in comparison with a candidate drug not identified as being capable of causing side effects. For example, the recommendation score is reduced as the number of side effects obtained from the third knowledge information 70 increases. In addition, the magnitude of the impact (e.g., the seriousness of the side effect) may be determined in advance for each type of side effect. In this case, the generating unit 2040 calculates the recommendation score according to the magnitude of the impact.

[0071] Taking the above into consideration, the recommendation score is calculated, for example, using the following formula (1).

number

[0072] <Output from Drug Recommender 2000> The drug recommendation device 2000 outputs the candidate drug information 10 by any method. For example, the drug recommendation device 2000 stores the candidate drug information 10 in any storage device accessible from the drug recommendation device 2000. As another example, the drug recommendation device 2000 displays the candidate drug information 10 on a display device accessible from the drug recommendation device 2000. As another example, the drug recommendation device 2000 may transmit the candidate drug information 10 to any device accessible from the drug recommendation device 2000.

[0073] Although the present invention has been described above with reference to the embodiment, the present invention is not limited to the above embodiment. Various modifications that can be understood by those skilled in the art can be made to the configuration and details of the present invention within the scope of the present invention.

[0074] In the above example, the program can be stored and provided to the computer using various types of non-transitory computer readable media. The non-transitory computer readable medium includes various types of tangible storage media. Examples of the non-transitory computer readable medium include magnetic recording media (e.g., flexible disks, magnetic tapes, hard disk drives), magneto-optical recording media (e.g., magneto-optical disks), CD-ROMs, CD-Rs, CD-R / Ws, and semiconductor memories (e.g., mask ROMs, PROMs (Programmable ROMs), EPROMs (Erasable PROMs), flash ROMs, and RAMs). The program may also be provided to the computer by various types of transitory computer readable media. Examples of the transitory computer readable medium include electrical signals, optical signals, and electromagnetic waves. The transitory computer readable medium can supply the program to the computer via a wired communication path such as an electric wire or optical fiber, or via a wireless communication path.

[0075] A part or all of the above-described embodiments can be described as, but is not limited to, the following supplementary notes. (Appendix 1) An acquisition unit that acquires disease information on a disease suffered by a subject, gene mutation information on a gene mutation of the subject, and previously used drug information on a drug used by the subject; A drug recommendation device having a generation unit that uses the disease information, the genetic mutation information, and the previously used drug information to generate candidate drug information, which is information regarding candidate drugs to be recommended or prescribed for use by the subject. (Appendix 2) The generation unit is Identifying one or more candidate drugs that may be recommended or prescribed for the subject using first knowledge information indicating a correspondence between a disease and a drug that may be used for the disease and the disease information; identifying the resistance or susceptibility of the subject to each of the candidate drugs by using second knowledge information indicating information on the resistance or susceptibility of the person having the gene mutation to the drug in correspondence with a combination of the gene mutation and the drug, and the gene mutation information; Using third knowledge information on side effects that may occur when multiple drugs are used in combination and the previously used drug information, for each of the candidate drugs, side effects that may occur in the subject when the candidate drug is used are identified; A drug recommendation device as described in Appendix 1, which generates the candidate drug information indicating one or more of the candidate drugs based on the subject's resistance or sensitivity to each of the candidate drugs and side effects that may occur in the subject when each of the candidate drugs is used. (Appendix 3) The generation unit is For each of the candidate drugs, a recommendation score is calculated based on the subject's resistance or sensitivity to the candidate drug and side effects that may occur in the subject when the candidate drug is used, which indicates the degree to which the candidate drug is recommended; 3. The drug recommendation device of claim 2, which generates the candidate drug information indicating the candidate drugs whose recommendation score is equal to or greater than a threshold, or the candidate drug information indicating each of the candidate drugs together with its recommendation score. (Appendix 4) The acquisition unit acquires attribute information indicating attributes of the subject, the second knowledge information indicates information on resistance or susceptibility of a person having a gene mutation and an attribute to the drug in association with the combination of the gene mutation, the drug, and the attribute; The generation unit is The drug recommendation device according to claim 2 or 3, wherein the genetic mutation information and the attribute information are used to identify, for each of the candidate drugs, the subject's resistance or sensitivity to the candidate drug. (Appendix 5) The acquisition unit acquires attribute information indicating attributes of the subject, the third knowledge information indicates, in association with a combination of a plurality of drugs and an attribute, a side effect that may occur when a person having the attribute takes the plurality of drugs in combination; The drug recommendation device according to any one of claims 2 to 4, wherein the generation unit uses the gene mutation information and the attribute information to identify, for each of the candidate drugs, side effects that may occur in the subject when the candidate drug is used. (Appendix 6) 1. A computer-implemented control method comprising: An acquisition step of acquiring disease information on a disease suffered by the subject, gene mutation information on gene mutations of the subject, and previously used drug information on drugs used by the subject; A control method comprising: a generation step of generating candidate drug information, which is information regarding candidate drugs to be recommended or prescribed for use by the subject, using the disease information, the genetic mutation information, and the previously used drug information. (Appendix 7) In the generating step, Identifying one or more candidate drugs that may be recommended or prescribed for the subject using first knowledge information indicating a correspondence between a disease and a drug that may be used for the disease and the disease information; identifying the resistance or susceptibility of the subject to each of the candidate drugs by using second knowledge information indicating information on the resistance or susceptibility of the person having the gene mutation to the drug in correspondence with a combination of the gene mutation and the drug, and the gene mutation information; Using third knowledge information on side effects that may occur when multiple drugs are used in combination and the previously used drug information, for each of the candidate drugs, side effects that may occur in the subject when the candidate drug is used are identified; The control method described in Appendix 6, wherein the candidate drug information indicating one or more of the candidate drugs is generated based on the subject's resistance or sensitivity to each of the candidate drugs and side effects that may occur in the subject when each of the candidate drugs is used. (Appendix 8) In the generating step, For each of the candidate drugs, a recommendation score is calculated based on the subject's resistance or sensitivity to the candidate drug and side effects that may occur in the subject when the candidate drug is used, which indicates the degree to which the candidate drug is recommended; A control method as described in Appendix 7, which generates candidate drug information indicating the candidate drugs whose recommendation score is above a threshold, or candidate drug information indicating each of the candidate drugs together with its recommendation score. (Appendix 9) In the acquiring step, attribute information indicating attributes of the subject is acquired; the second knowledge information indicates information on resistance or susceptibility of a person having a gene mutation and an attribute to the drug in association with the combination of the gene mutation, the drug, and the attribute; In the generating step, The control method according to claim 7 or 8, further comprising using the gene mutation information and the attribute information to identify, for each of the candidate drugs, the subject's resistance or sensitivity to the candidate drug. (Appendix 10) In the acquiring step, attribute information indicating attributes of the subject is acquired; the third knowledge information indicates, in association with a combination of a plurality of drugs and an attribute, a side effect that may occur when a person having the attribute takes the plurality of drugs in combination; The control method according to any one of appendix 7 to 9, wherein in the generation step, the genetic mutation information and the attribute information are used to identify, for each of the candidate drugs, side effects that may occur in the subject when the candidate drug is used. (Appendix 11) On the computer, An acquisition step of acquiring disease information on a disease suffered by the subject, gene mutation information on gene mutations of the subject, and previously used drug information on drugs used by the subject; A computer-readable medium storing a program for executing a generation step of generating candidate drug information, which is information regarding candidate drugs to be recommended or prescribed for the subject, using the disease information, the genetic mutation information, and the previously used drug information. (Appendix 12) In the generating step, Identifying one or more candidate drugs that may be recommended or prescribed for the subject using first knowledge information indicating a correspondence between a disease and a drug that may be used for the disease and the disease information; identifying the resistance or susceptibility of the subject to each of the candidate drugs by using second knowledge information indicating information on the resistance or susceptibility of the person having the gene mutation to the drug in correspondence with a combination of the gene mutation and the drug, and the gene mutation information; Using third knowledge information on side effects that may occur when multiple drugs are used in combination and the previously used drug information, for each of the candidate drugs, side effects that may occur in the subject when the candidate drug is used are identified; The computer-readable medium of claim 11, further comprising: generating the candidate drug information indicating one or more of the candidate drugs based on the subject's resistance or sensitivity to each of the candidate drugs and side effects that may occur in the subject when each of the candidate drugs is used. (Appendix 13) In the generating step, For each of the candidate drugs, a recommendation score is calculated based on the subject's resistance or sensitivity to the candidate drug and side effects that may occur in the subject when the candidate drug is used, which indicates the degree to which the candidate drug is recommended; The computer-readable medium of claim 12, further comprising: generating candidate drug information indicating the candidate drugs whose recommendation score is equal to or greater than a threshold value; or generating candidate drug information indicating each of the candidate drugs together with its recommendation score. (Appendix 14) In the acquiring step, attribute information indicating attributes of the subject is acquired; the second knowledge information indicates information on resistance or susceptibility of a person having a gene mutation and an attribute to the drug in association with the combination of the gene mutation, the drug, and the attribute; In the generating step, The computer-readable medium of claim 12 or 13, wherein the genetic mutation information and the attribute information are used to identify, for each of the candidate drugs, the subject's resistance or sensitivity to the candidate drug. (Appendix 15) In the acquiring step, attribute information indicating attributes of the subject is acquired; the third knowledge information indicates, in association with a combination of a plurality of drugs and an attribute, a side effect that may occur when a person having the attribute takes the plurality of drugs in combination; The computer-readable medium of any one of appendix 12 to 14, wherein in the generating step, the genetic mutation information and the attribute information are used to identify, for each of the candidate drugs, side effects that may occur in the subject when the candidate drug is used. [Explanation of symbols]

[0076] 10. Candidate Drug Information 20 Disease Information 30 Genetic Mutation Information 40 Drug Information 50 1st knowledge information 52 Disease 53 attributes 54 Drugs 60 2nd knowledge information 62 Genetic Mutations 64 Drugs 65 attributes 66 Properties 70 Third knowledge information 72 First drug 74 Second Drug 75 attributes 76 Side effects 500 Computers 502 Bus 504 Processor 506 Memory 508 Storage Devices 510 Input / Output Interface 512 Network Interface 2000 Drug Recommendation Device 2020 Acquisition Department 2040 Generation part

Claims

1. An acquisition unit that acquires disease information on a disease suffered by a subject, gene mutation information on a gene mutation of the subject, and previously used drug information on a drug used by the subject; A generating unit that generates candidate drug information, which is information on candidates of drugs to be recommended or prescribed for the subject, using the disease information, the gene mutation information, and the previously used drug information, The generation unit is Identifying one or more candidate drugs that may be recommended or prescribed for use by the subject using first knowledge information indicating a correspondence between a disease and a drug that may be used for the disease and the disease information; identifying the resistance or susceptibility of the subject to each of the candidate drugs by using second knowledge information indicating information on the resistance or susceptibility of the person having the genetic mutation to the drug in correspondence with a combination of the genetic mutation and the drug, and the genetic mutation information; Using third knowledge information on side effects that may occur when multiple drugs are used in combination and the previously used drug information, for each of the candidate drugs, side effects that may occur in the subject when the candidate drug is used are identified; A drug recommendation device that generates the candidate drug information indicating one or more of the candidate drugs based on the subject's resistance or sensitivity to each of the candidate drugs and side effects that may occur in the subject when each of the candidate drugs is used.

2. The generation unit is For each of the candidate drugs, a recommendation score is calculated based on the subject's resistance or sensitivity to the candidate drug and side effects that may occur in the subject when the candidate drug is used, which indicates the degree to which the candidate drug is recommended; The drug recommendation device according to claim 1 , further comprising: generating the candidate drug information indicating the candidate drugs whose recommendation scores are equal to or greater than a threshold value; or generating the candidate drug information indicating each of the candidate drugs together with its recommendation score.

3. The acquisition unit acquires attribute information indicating attributes of the subject, the second knowledge information indicates information on resistance or susceptibility of a person having a gene mutation and an attribute to the drug in association with the combination of the gene mutation, the drug, and the attribute; The generation unit is The drug recommendation device according to claim 1 or 2, wherein the gene mutation information and the attribute information are used to identify, for each of the candidate drugs, the subject's resistance or sensitivity to the candidate drug.

4. The acquisition unit acquires attribute information indicating attributes of the subject, the third knowledge information indicates, in association with a combination of a plurality of drugs and an attribute, a side effect that may occur when a person having the attribute takes the plurality of drugs in combination; The drug recommendation device according to claim 1 , wherein the generation unit uses the previously used drug information and the attribute information to identify, for each of the candidate drugs, side effects that may occur in the subject when the candidate drug is used.

5. 1. A computer-implemented control method comprising: An acquisition step of acquiring disease information on a disease suffered by the subject, gene mutation information on gene mutations of the subject, and previously used drug information on drugs used by the subject; and generating candidate drug information, which is information on candidates of drugs to be recommended or prescribed for the subject, using the disease information, the gene mutation information, and the previously used drug information, In the generating step, Identifying one or more candidate drugs that may be recommended or prescribed for use by the subject using first knowledge information indicating a correspondence between a disease and a drug that may be used for the disease and the disease information; identifying the resistance or susceptibility of the subject to each of the candidate drugs by using second knowledge information indicating information on the resistance or susceptibility of the person having the genetic mutation to the drug in correspondence with a combination of the genetic mutation and the drug, and the genetic mutation information; Using third knowledge information on side effects that may occur when multiple drugs are used in combination and the previously used drug information, for each of the candidate drugs, side effects that may occur in the subject when the candidate drug is used are identified; A control method that generates candidate drug information indicating one or more of the candidate drugs based on the subject's resistance or sensitivity to each of the candidate drugs and side effects that may occur in the subject when each of the candidate drugs is used.

6. In the generating step, For each of the candidate drugs, a recommendation score is calculated based on the subject's resistance or sensitivity to the candidate drug and side effects that may occur in the subject when the candidate drug is used, which indicates the degree to which the candidate drug is recommended; The control method according to claim 5 , further comprising generating the candidate drug information indicating the candidate drugs whose recommendation scores are equal to or greater than a threshold value, or generating the candidate drug information indicating each of the candidate drugs together with its recommendation score.

7. On the computer, An acquisition step of acquiring disease information on a disease suffered by the subject, gene mutation information on gene mutations of the subject, and previously used drug information on drugs used by the subject; and generating candidate drug information, which is information on candidates of drugs to be recommended or prescribed for the subject, using the disease information, the gene mutation information, and the previously used drug information; In the generating step, Identifying one or more candidate drugs that may be recommended or prescribed for use by the subject using first knowledge information indicating a correspondence between a disease and a drug that may be used for the disease and the disease information; identifying the resistance or susceptibility of the subject to each of the candidate drugs by using second knowledge information indicating information on the resistance or susceptibility of the person having the genetic mutation to the drug in correspondence with a combination of the genetic mutation and the drug, and the genetic mutation information; Using third knowledge information on side effects that may occur when multiple drugs are used in combination and the previously used drug information, for each of the candidate drugs, side effects that may occur in the subject when the candidate drug is used are identified; A program that generates candidate drug information indicating one or more of the candidate drugs based on the subject's resistance or sensitivity to each of the candidate drugs and side effects that may occur in the subject when each of the candidate drugs is used.

Images