Medical support device

Abstract

To provide a medical examination support device that presents a plurality of pieces of document information that may correspond to a patient, while reducing time and effort for referring to document information related to a medical examination.SOLUTION: A medical examination support device comprises a guideline search function 15b as a first extraction unit, a second extraction unit, and a third extraction unit, and an output unit. The first extraction unit extracts, from a plurality of pieces of document information related to a medical examination, first document information related to a medical word group included in information on a medical examination for a patient. The second extraction unit extracts, from first word groups included in itemization description parts in the first document information, a first word group based on the degree of association with the medical word group. The third extraction unit extracts, from second word groups included in itemization description parts in second document information, a second word group based on the degree of association with the extracted first word group and the medical word group. The output unit outputs the first document information and the second document information based on the extracted first word group and the extracted second word group.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] An embodiment of the present invention relates to a medical support device. [Background technology]

[0002] Before and after examining a patient, doctors refer to literature related to their medical treatment to (a) update treatment methods, (b) expand treatment options and engage in shared decision-making (SDM) with the patient, and (c) confirm the evidence for and precautions regarding treatment methods.

[0003] Literature information related to medical treatment includes guidelines, papers, case studies, etc., each of which contains information according to medical specialty or disease. Below, guidelines are described as an example of literature information. Each guideline has different granularity and scope of information depending on the specialty, and using only one guideline may provide insufficient or biased information.

[0004] On the other hand, it is time-consuming for doctors to refer to all guidelines that may be applicable to a patient, regardless of their own specialty. As a result, doctors may refer to only one guideline that corresponds to their specialty, without referring to several potentially applicable guidelines, in providing medical care. In this case, there is a possibility that optimal medical care will not be provided due to a lack of information or bias in the guideline referred to. Specifically, for example, there is a possibility that the optimal treatment method for the patient will not be selected, or that treatment will not be administered at the optimal time. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Special Publication No. 2011-520195 Summary of the Invention [Problem to be solved by the invention]

[0006] One of the problems to be solved by the embodiments disclosed in this specification and drawings is to present multiple pieces of literature information that may be relevant to a patient while reducing the effort required to refer to literature information related to medical treatment. However, the problems to be solved by the embodiments disclosed in this specification and drawings are not limited to the above problem. Problems corresponding to the effects of each configuration shown in the embodiments described below can also be positioned as other problems. [Means for solving the problem]

[0007] A medical support device according to an embodiment includes a first extraction unit, a second extraction unit, a third extraction unit, and an output unit. The first extraction unit extracts first literature information related to a medical term group included in a patient's medical information from a plurality of literature information related to medical treatment. The second extraction unit extracts any first term group from each first term group corresponding to each itemized description portion in the first literature information based on a first degree of association with the medical term group. The third extraction unit extracts any second term group from each second term group corresponding to each itemized description portion in second literature information different from the first literature information based on a second degree of association between the extracted first term group and the medical term group. The output unit outputs the first literature information and the second literature information based on the extracted first term group and the extracted second term group. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 1 is a block diagram showing a medical assistance device and its peripheral configuration according to the first embodiment. [Figure 2] FIG. 2 is a block diagram showing the configuration of the medical assistance device according to the first embodiment. [Figure 3] FIG. 3 is a flowchart for explaining the operation in the first embodiment. [Figure 4] FIG. 4 is a flowchart for explaining the operation shown in step ST10 of FIG. [Figure 5]FIG. 5 is a schematic diagram illustrating a category query in the first embodiment. [Figure 6] FIG. 6 is a schematic diagram for explaining a matrix representation of the category query in FIG. [Figure 7] FIG. 7 is a schematic diagram showing a tree diagram included in the guideline A in the first embodiment. [Figure 8] FIG. 8 is a schematic diagram for explaining guideline categories for each CQ included in guideline A in FIG. [Figure 9] FIG. 9 is a schematic diagram for explaining the similarity between the category query in FIG. 5 and the guideline category in FIG. [Figure 10] FIG. 10 is a schematic diagram for explaining a guideline extended category query in the first embodiment. [Figure 11] FIG. 11 is a schematic diagram showing an importance table for explaining a guideline extended category query in the first embodiment. [Figure 12] FIG. 12 is a schematic diagram for explaining calculation of the similarity in the first embodiment. [Figure 13] FIG. 13 is a schematic diagram for explaining the importance of the guideline extended category query of FIG. [Figure 14] FIG. 14 is a schematic diagram for explaining expansion using a guideline expansion category query. [Figure 15] FIG. 15 is a schematic diagram showing an example of a display screen in the first embodiment. [Figure 16] FIG. 16 is a schematic diagram showing another example of the display screen in the first embodiment. [Figure 17] FIG. 17 is a schematic diagram showing yet another example of the display screen in the first embodiment. [Figure 18] FIG. 18 is a schematic diagram showing yet another example of the display screen in the first embodiment. [Figure 19] FIG. 19 is a schematic diagram for explaining a modified example of the first embodiment. [Figure 20]FIG. 20 is a schematic diagram for explaining another modified example of the first embodiment. [Figure 21] FIG. 21 is a schematic diagram for explaining yet another modified example of the first embodiment. [Figure 22] FIG. 22 is a block diagram showing the configuration of a medical assistance device according to the second embodiment. [Figure 23] FIG. 23 is a flowchart for explaining the operation in the second embodiment. [Figure 24] FIG. 24 is a schematic diagram for explaining the search results of each guideline in the second embodiment. [Figure 25] FIG. 25 is a block diagram showing the configuration of a medical assistance device according to the third embodiment. [Figure 26] FIG. 26 is a flowchart for explaining the operation in the third embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0009] Each embodiment will be described below with reference to the drawings. In the following description, guidelines will be used as an example of literature information related to medical treatment, among which guidelines, papers, case studies, etc. Accordingly, as an example of each itemized description section in a guideline (literature information), a section described by clinical question (CQ) items will be used as an example of a section described by clinical question (CQ) items, among which a section described by smallest items or clinical question (CQ) items will be used.

[0010] First Embodiment FIG. 1 is a block diagram showing a medical support device and its peripheral configuration according to a first embodiment. The medical support device 1 shown in FIG. 1 is, for example, a device capable of comprehensively observing medical information. The medical support device 1 is equipped with, for example, an integrated viewer. The integrated viewer is an application that comprehensively presents medical information to a user. The integrated viewer may be implemented in any form, such as a web application, a fat client application, or a thin client application. The medical support device 1 is communicably connected to a hospital information system (HIS) 2, a radiology information system (RIS) 3, a medical image diagnostic device 4, a medical image management system (PACS: Picture Archiving and Communication Systems) 5, and a data warehouse (DWH: Data Ware House) 6 via an intra-hospital network such as a local area network (LAN).

[0011] 1, the HIS2 includes, for example, an electronic medical record system that manages information related to electronic medical records. The information related to electronic medical records includes, for example, patient information and multiple pieces of medical information. The patient information is information specific to a patient, and includes, for example, a patient ID, a patient name, a patient gender, an age, and the like.

[0012] The multiple pieces of medical information are associated with a patient ID and are information that medical professionals have learned about a patient's physical condition, medical condition, treatment, etc. during the course of medical care. Each piece of medical information individually includes various types of information, such as image information, examination history information, electrocardiogram information, vital sign information, medication history information, report information, medical chart information, and nursing record information. The various pieces of information within the medical information are distinguishable by data type. Similarly, the various pieces of information included in each of the image information, examination history information, electrocardiogram information, vital sign information, medication history information, report information, medical chart information, and nursing record information are distinguishable by data type. The image information is, for example, information indicating the location of medical images acquired by photographing a patient. The image information includes, for example, information indicating the location of medical image files (described below) generated by the medical image diagnostic device 4 as a result of an examination. The examination history information is, for example, information indicating the history of test results acquired as a result of specimen tests, bacteriological tests, etc., performed on a patient. Electrocardiogram information is, for example, information regarding an electrocardiogram waveform measured from a patient. Vital sign information is, for example, basic information related to the patient's life. Vital sign information includes, for example, pulse rate, respiratory rate, oxygen concentration, body temperature, blood pressure, and level of consciousness. Medication history information is, for example, information indicating the history of the amount of medication administered to a patient. Report information is, for example, information summarized by a radiologist in the radiology department after interpreting medical images such as X-ray images, CT images, MRI images, and ultrasound images in response to an examination request from a medical doctor in a clinical department, regarding the patient's condition and disease. Report information includes, for example, interpretation report information representing an interpretation report created by the radiologist with reference to medical image files stored in a PACS5. Note that, since report information is generally stored in a PACS5, the electronic medical record system can display the report information by reading the report information from the PACS5.

[0013] The medical record information is, for example, information entered into an electronic medical record by a doctor, etc. The medical record information includes, for example, a medical record at the time of hospitalization, a patient's medical history, a drug prescription history, etc.

[0014] The nursing record information is, for example, information entered into an electronic medical record by a nurse, etc. The nursing record information includes nursing records at the time of hospitalization, etc.

[0015] The information related to the electronic medical record also includes, for example, examination implementation information. The examination implementation information is generated by the medical imaging diagnostic device 4 that performed the examination in accordance with the examination order information. The examination implementation information is information representing the examination performed by the medical imaging diagnostic device 4. The examination implementation information includes the order number, examination UID (Unique ID), patient ID, modality type, imaging region, and imaging conditions. The examination UID is an identifier that can uniquely identify the examination. The modality type indicates the modality used for imaging. Examples of modality types include "X-ray computed tomography device," "X-ray diagnostic device," "magnetic resonance imaging device," and "ultrasound diagnostic device." The imaging region corresponds to the examination region included in the examination order information. Examples of imaging regions include the abdomen, brain, and chest. Imaging conditions include the body position, imaging direction, and whether or not a contrast agent was used.

[0016] The HIS 2 also includes, for example, an ordering system that manages reservation information, order information, etc. The HIS 2 may also be configured such that the electronic medical record system includes an ordering system.

[0017] The appointment information includes, for example, information about consultation appointments and examination appointments. The information about consultation appointments includes, for example, the consultation date, consultation time, reception number, requesting physician, and requesting department. The information about examination appointments includes, for example, the examination date, examination time, and reception number. The order information is, for example, information about orders requested by medical doctors, etc., such as order information about imaging tests, specimen tests, physiological tests, prescriptions, and medications. If the order information is examination order information requesting an imaging test, the examination order information includes, for example, an order number that can identify the test, a patient ID, an examination type, an examination site, and requester information. The order number is a number issued when the examination order information is entered and is an identifier that uniquely identifies the examination order information within, for example, a single hospital. Examination types include X-ray examinations, computed tomography (CT) examinations, magnetic resonance (MR) examinations, and radio isotope (RI) examinations. Examination sites include, for example, the abdomen, brain, and chest. The requester information includes the name of the medical department, the name of the doctor in charge, etc. Information about examination reservations is linked to order information.

[0018] RIS3 is a system that manages examination reservation information related to radiological examination work. RIS3 adds various setting information to examination order information input by a medical doctor in, for example, an order system included in HIS2, accumulates the information, and manages the accumulated information as examination reservation information. RIS3 may also add various setting information to examination order information using an irradiation record that records various setting information set in the medical image diagnostic device 4 during past examinations. RIS3 transmits an examination order to the medical image diagnostic device 4 in accordance with the examination reservation information. RIS3 also transmits examination implementation information generated by the medical image diagnostic device 4 as a result of the examination to an electronic medical record system included in HIS2.

[0019] The medical image diagnostic device 4 is a device that performs an examination by taking images of a patient, etc. The medical image diagnostic device 4 includes, for example, an X-ray computed tomography device, an X-ray diagnostic device, a magnetic resonance imaging device, a nuclear medicine diagnostic device, an ultrasound diagnostic device, etc. The medical image diagnostic device 4 performs an examination based on examination reservation information transmitted from, for example, the RIS 3. The medical image diagnostic device 4 generates examination implementation information and transmits it to the RIS 3.

[0020] Furthermore, the medical image diagnostic device 4 generates medical image data by performing an examination. The medical image data is, for example, X-ray CT image data, X-ray image data, MRI image data, nuclear medicine image data, and ultrasound image data. The medical image diagnostic device 4 generates a medical image file by converting the generated medical image data into a format that complies with, for example, the DICOM (Digital Imaging and Communication in Medicine) standard. The medical image file is, for example, a file in a format that complies with the DICOM standard. The medical image diagnostic device 4 transmits the generated medical image file to the PACS 5.

[0021] The PACS 5 is a system for managing various medical image files. The PACS 5 stores, for example, medical image files transmitted from the medical image diagnostic device 4. The PACS 5 may also store report information attached to the medical image files or report information for examinations related to multiple medical image files.

[0022] The DWH 6 is a database system that collectively stores medical information (medical big data) generated by medical and nursing care institutions, and also stores multiple literature information related to medical treatments provided by the medical and nursing care institutions. The multiple literature information includes at least one guideline. The DWH 6 is realized, for example, by a general server device. As shown in FIG. 1, the DWH 6 includes, for example, a processing circuit 61, a memory 62, and a communication interface 63. The processing circuit 61, the memory 62, and the communication interface 63 are connected to each other so as to be able to communicate with each other, for example, via a bus.

[0023] The processing circuitry 61 is a processor that functions as the core of the DWH 6. The processing circuitry 61 executes programs stored in the memory 62 or the like to realize functions corresponding to the programs. For example, the processing circuitry 61 can appropriately use functions such as collecting desired information from the HIS 2, the RIS 3, the medical image diagnostic device 4, and the PACS 5, and storing the collected information in the memory 62. This allows, for example, electronic medical record information and literature information related to medical treatment to be collected from the HIS 2, examination reservation information to be collected from the RIS 3, and medical image files to be collected from the medical image diagnostic device 4 or the medical image management system 5. Literature information related to medical treatment includes guidelines, papers, case descriptions, etc., each of which describes information according to medical specialty, disease, etc. Below, as described above, guidelines are used as an example of literature information. Furthermore, for example, medical information collected from the HIS 2 is stored in the memory 62 according to preset rules. The predetermined rule is, for example, an order using event dates and times associated with medical events such as outpatient visits, surgeries, imaging tests, specimen tests, bacteriological tests, electrocardiogram measurements, vital sign measurements, drug administration, report creation, and medical record entries for each patient. The event dates and times are, for example, the dates and times when medical events occurred or the dates and times when medical events were scheduled. The event dates and times include, for example, the dates and times when imaging tests were performed, the dates and times when specimen tests and the like were performed, the dates and times when electrocardiogram waveforms were measured, the dates and times when vital signs were measured, the dates and times when drugs were administered, the dates and times when reports were created, and the dates and times when medical records were entered. As a result, the collected medical information is stored in memory 62 in, for example, the order of the event dates and times.

[0024] The memory 62 is a storage device such as a hard disk drive (HDD), a solid state drive (SSD), or an integrated circuit storage device that stores various information. The memory 62 may also be a drive or the like that reads and writes various information from and to a portable storage medium such as a CD-ROM drive, a DVD drive, or a flash memory. The memory 62 stores, for example, a control program or the like that causes the processing circuit 61 to realize various functions, such as a function to collect desired information and a function to store the collected information in the memory 62. The program may be stored in a non-transitory storage medium, distributed, read from the non-transitory storage medium, and installed in the memory 62.

[0025] The communication interface 63 performs data communication with the medical assistance device 1, HIS 2, RIS 3, medical image diagnostic device 4, and PACS 5, which are connected via the hospital network. Any standard may be used for communication with the medical assistance device 1, HIS 2, RIS 3, medical image diagnostic device 4, and PACS 5, and examples include HL7 (Hearth Level 7), DICOM, or both.

[0026] Next, details of the medical assistance device 1 according to this embodiment will be described with reference to Fig. 2. Fig. 2 is a block diagram showing the functional configuration of the medical assistance device 1 shown in Fig. 1.

[0027] 2 includes a memory 11, an input interface 12, a display 13, a communication interface 14, and a processing circuit 15. The memory 11, the input interface 12, the display 13, the communication interface 14, and the processing circuit 15 are connected to each other so as to be able to communicate with each other, for example, via a bus.

[0028] The memory 11 is composed of memories for recording electrical information, such as a read-only memory (ROM), a random access memory (RAM), a hardware disk drive (HDD), and an image memory, as well as peripheral circuits associated with these memories, such as a memory controller and a memory interface. The memory 11 stores various programs, such as the medical information processing program of the medical support device, as well as various data, such as information about the electronic medical record (EMR) acquired from the DWH 6, patient medical information, guidelines related to medical treatment, guideline categories related to each term group corresponding to each clinical question (CQ) in the guidelines, various tables, data in the middle of processing, and data after processing. The information about the EMR includes, for example, patient information including a patient ID and medical information associated with the patient ID. Details of the patient information and medical information are as described above.

[0029] The input interface 12 may be implemented by a trackball, switch buttons, mouse, keyboard, touchpad (or trackpad) for inputting various instructions, commands, information, selections, and settings from the operator (user) into the medical assistance device main body, a touch panel display (or touch screen) that integrates a display screen and a touchpad, or the like. The input interface 12 is connected to the processing circuitry 15 and converts input operations received from the user into electrical signals and outputs them to the processing circuitry 15. In this case, the input interface 12 may display a user interface (GUI: Graphical User Interface) on the display 13, allowing the user to input various instructions using physical operation components such as a mouse and keyboard. Note that, in this specification, the input interface 12 is not limited to those having physical operation components. For example, an electrical signal processing circuit that receives electrical signals corresponding to input operations from an external input device provided separately from the device and outputs these electrical signals to the processing circuitry 15 is also included as an example of the input interface 12. In the following description, "operation of the input interface 12 by the user" is also referred to as "user operation."

[0030] The display 13 is composed of a display main body that displays literature information, etc., an internal circuit that supplies display signals to the display main body, and peripheral circuits such as connectors and cables that connect the display to the internal circuit. The display 13 can appropriately display any data, for example, a category query indicating a group of medical terms contained in a patient's medical information, a guideline category, or a literature information search result. The display 13 is an example of a display unit.

[0031] The communication interface 14 is a circuit for connecting the medical assistance device 1 to a network and communicating with other devices. For example, a network interface card (NIC) can be used as the communication interface 14. In the following description, the fact that the communication interface 14 is involved in communication between the medical assistance device 1 and other devices will be omitted.

[0032] The processing circuitry 15 reads the medical assistance program stored in the memory 11 based on instructions input by the user via the input interface 12 and controls the medical assistance device 1 in accordance with the program. For example, the processing circuitry 15 is a processor that implements each function of the medical assistance device 1 in accordance with the medical assistance program read from the memory 11. Examples of the functions include a category query generation function 15a, a guideline search function 15b, a query expansion function 15c, and a display control function 15d. Each function may be distributed among multiple processors as appropriate. Alternatively, each function or part of each function may be executed by another device as appropriate. For example, among the functions, the category query generation function 15a may be executed by another device (not shown). In other words, the category query generation function 15a is an optional additional function that is not necessarily required for the medical assistance device 1 and may be omitted from the medical assistance device 1.

[0033] Next, the category query creation function 15a, guideline search function 15b, query expansion function 15c, and display control function 15d will be described in order. However, the allocation of functions described below is for convenience and can be changed as appropriate. This is because even if a process that is assigned to one function is assigned to another function, the processing circuitry 15 still executes that process. Note that the ability to change the allocation of functions also applies to the following embodiments and modifications.

[0034] The category query creation function 15a acquires the patient's medical information from the DWH 6 and creates a category query from a group of medical terms contained in the medical information. Here, a category query is a query that expresses the relationships between words grouped by medical meaning, stratified according to upper and lower levels. Specifically, for example, the category query creation function 15a extracts each medical term contained in the patient's medical information by classification category related to medical treatment, and creates a category query from the group of medical terms containing medical terms for each classification category. The classification category is each of multiple classification categories having a hierarchical relationship. Examples of classification categories that can be used as appropriate include disease name, treatment phase, disease type, treatment method, and symptoms. For example, in the case of cancer treatment, guidelines are categorized from top to bottom in the order of treatment phase (diagnosis or treatment), subtype, disease stage (or metastatic site), treatment method, and clinical question (CQ). Therefore, the same medical term may belong to different classification categories, and a simple term search may not link to the page the doctor wants to refer to. Taking this into consideration, as described above, a group of medical terms for each classification category is created as a category query. The category query creating function 15a is an example of a medical term group creating unit.

[0035] The guideline search function 15b extracts a first guideline related to a group of medical terms (category query) included in the patient's medical information from among multiple guidelines related to medical treatment. The guideline search function 15b also extracts one of the first guideline categories from each of the first term groups (first guideline categories) corresponding to each clinical question (CQ) in the first guideline based on a first relevance with the category query. The first guideline categories are created in advance as first term groups including first medical terms for each classification category by extracting each of the first medical terms included in each clinical question (CQ) in the first guideline by classification category related to medical treatment. The classification categories are the same as the classification categories of the category query. For example, if the first term group includes a first treatment method term representing a first treatment method, the first treatment method term is extracted so as to be included in the classification category indicating the treatment method. The first relevance is, for example, a value based on a first similarity between each of the first guideline categories and the category query.

[0036] Similarly, the guideline search function 15b extracts any second guideline category from each second term group (second guideline category) corresponding to each clinical question (CQ) in a second guideline different from the first guideline, based on a second relevance between the extracted first guideline category and the guideline extension category obtained from the category query. The second guideline category is created in advance as a second term group including second medical terms for each classification category by extracting each second medical term included in each clinical question (CQ) in the second guideline by classification category related to medical treatment. The classification category is the same as the classification category of the category query. Therefore, for example, if the second term group includes a second treatment method term representing a second treatment method different from the first treatment method, the second treatment method term is extracted so as to be included in the classification category indicating the treatment method. The second relevance is, for example, a value based on a second similarity between each second guideline category and the extracted first guideline category and category query. The guideline search function 15b is an example of a first extraction unit, a second extraction unit, a third extraction unit, a first creation unit, and a second creation unit.

[0037] The query expansion function 15c creates a guideline extension category from the extracted first guideline category and category query. The guideline extension category is used by the guideline search function 15b to search for (extract) other guideline categories.

[0038] The display control function 15d outputs a first guideline and a second guideline based on the extracted first guideline category and the extracted second guideline category. For example, the display control function 15d displays and outputs a clinical question (CQ) in the first guideline corresponding to the extracted first guideline category on the display 13. The display control function 15d also displays and outputs a clinical question (CQ) in the second guideline corresponding to the extracted second guideline category on the display 13.

[0039] Furthermore, the display control function 15d displays and outputs, on the display 13, a clinical question (CQ) in the first guideline that includes the first treatment method term and corresponds to the extracted first guideline category. When a first treatment method indicated by the first treatment method term being displayed and output causes a symptom in the medical information, the display control function 15d displays and outputs, on the display 13, a clinical question (CQ) in the second guideline that includes a second treatment method term indicating a second treatment method that improves the symptom and corresponds to the extracted second guideline category. The display control function 15d is an example of an output unit.

[0040] Next, the operation of the medical information processing system equipped with the medical assistance device configured as above will be described with reference to the flowcharts of FIGS. 3 and 4 and the schematic diagrams of FIGS.

[0041] Now, assume that the DWH 6 stores patient information, patient medical information, each guideline, and each guideline category in the memory 62. In this state, step ST10 is started to create a category query from the patient medical information. Step ST10 includes steps ST11 to ST17.

[0042] (Step ST10) The processing circuitry 15 of the medical assistance device 1 acquires the medical information of the patient from the DWH 6 based on the patient ID of the patient (step ST11), and stores the acquired medical information in the memory 11.

[0043] The processing circuit 15 determines whether or not a diagnosis is included in the patient's medical information (step ST12), and if a diagnosis is not included, the patient has not yet been diagnosed, so the processing circuit 15 extracts symptoms from the medical information (step ST13) and proceeds to step ST17.

[0044] Furthermore, if the result of the determination in step ST12 indicates that a diagnosis has been made, the processing circuitry 15 extracts a subtype and a stage of the disease from the medical information, since the diagnosis has been made (step ST14). Furthermore, the processing circuitry 15 extracts a medical history from the medical information, which is a medical history (step ST15). Furthermore, the processing circuitry 15 extracts symptoms from the medical information (step ST16).

[0045] In step ST13 or steps ST14 to ST16, the processing circuit 15 extracts each medical term included in the medical information by classification category related to medical treatment. The definition of the classification category is determined by the chapter division of the guideline related to the disease or the definition of the medical professional.

[0046] Thereafter, the processing circuit 15 creates a group of medical terms including medical terms for each classification category having a hierarchical structure as shown in FIG. 5, as a category query q1 (step ST17).

[0047] This category query q1 includes the disease name L1 "lung cancer," the treatment phase L2 "post-diagnosis," the disease type L3 "non-small cell lung cancer," "stage III," and "suspected bone metastasis," the treatment method L4 "radiotherapy performed," and the symptom L5 "numbness in the hands and feet." For this category query q1, the processing circuit 15 converts a group of medical terms included in the classification categories into a matrix representation Mq1 using a dictionary-based one-hot representation or the like, as shown in FIG. 6. This completes step ST10, which consists of steps ST11 to ST17.

[0048] (Step ST20) In step ST20, the processing circuit 15 searches for a guideline category based on the category query. In the following description, the first guideline is referred to as guideline A, the second guideline is referred to as guideline B, and the third guideline is referred to as guideline C.

[0049] First, the processing circuit 15 extracts guideline A relating to a category query included in the patient's medical information from among a plurality of guidelines relating to medical treatment.

[0050] As shown in Figures 7 and 8, the guideline categories for Guideline A were created in advance by extracting the primary medical terms included in each clinical question (CQ) into classification categories related to medical treatment using the chapter structure and tree diagram F1 of Guideline A7. In Figure 8, guideline category CA1 was created corresponding to clinical question CQ19 in Guideline A7, and guideline category CA2 was created corresponding to clinical question CQ21 in Guideline A7. Guideline categories CA1 and CA2 have a structure similar to the category query q1 described above, but unlike category query q1, related medical terms are connected by edges. Examples of related medical terms include medical terms included in chapters related to higher-level medical terms in the chapter structure. Related items include medical terms that appear in the same paragraph. Medical terms may also be extracted using a dictionary that lists target terms for extraction.

[0051] Next, the processing circuit 15 extracts one of the guideline categories CA1, CA2, ... corresponding to each clinical question (CQ) in the guideline A7 based on a first relevance with the category query q1. Here, the first relevance is the similarity between each of the guideline categories CA1, CA2, ... and the category query q1. For example, as shown in FIG. 9, the processing circuit 15 calculates the similarity between each of the guideline categories CA1, CA2, ... and the category query q1, and extracts the guideline category CA2 with the highest similarity. Note that the similarity is calculated, for example, as an inner product of the matrix representation Mq1 of the category query q1 and the matrix representation of each of the guideline categories CA1, CA2, .... This completes step ST20.

[0052] (Step ST30) In step ST30, the processing circuit 15 expands the found guideline category CA2 based on the category query q1. For example, as shown in Fig. 10, the processing circuit 15 combines the category query q1 with the found guideline category CA2 to create a new guideline expansion category query ECA2. Note that in Fig. 10, the guideline expansion category query ECA2 changes the attribute of medical terms in the guideline category CA2 that are supported by facts (category query q1) from hypotheses (dotted boxes) to facts (solid boxes).

[0053] Specifically, such a guideline expanded category query ECA2 is created as follows: Specifically, the processing circuit 15 performs a logical OR operation on medical terms included in the category query q1 and the guideline category CA2 of the search results. Note that information such as edges is retained. Furthermore, the processing circuit 15 increases the weight of medical terms with high overall importance based on an importance table T1, as shown in FIG. 11 . The importance table T1 is used to calculate an index of overall importance from four indices related to the importance of medical terms. The four indices are “fact / hypothesis,” “presence or absence of an edge with fact,” “term importance,” and “category query-specific importance.” The “fact / hypothesis” indicator indicates the importance of terms recognized as facts from patient data (terms in the category query q1). In other words, since terms described in the guideline include possibilities and options, this indicator distinguishes them as hypotheses. Furthermore, the “presence or absence of an edge with fact” indicator indicates the importance of information that is strongly related to terms recognized as facts (i.e., information that has an edge connected to a term recognized as fact). The indicator "term importance" is an importance predefined based on the prior knowledge of doctors and guidelines. The indicator "category query specific importance" is an importance determined for each patient for the extracted words or layers using data mining or other methods based on the patient's medical information. For example, the category query specific importance may be determined based on the frequency of occurrence of the term. Alternatively, the category query specific importance may be determined based on a rule base according to the patient's condition. For example, the rule base may indicate that administering drug X results in a high importance of "drug rash."

[0054] In FIG. 11, the value of the index "fact / hypothesis" is "fact" if it matches a term in the category query q1, and "hypothesis" otherwise. The value of the index "presence or absence of edge with fact" is "1" if there is an edge to the term that is "fact," and "0" otherwise. The value of the index "overall importance" is calculated by weighting the values ​​of the other indexes. For the index "fact / hypothesis," the weighting value is 0.3 for "fact" and 0 for "hypothesis." For the index "presence or absence of edge with fact," the weighting value is 0.1 for "edge (1)" and 0 for "no edge (0)." For the index "term importance" and the index "category query-specific importance," the weighting value is 1 (the importance is used as is). Therefore, for example, for the term "non-small cell lung cancer," the value of the index "overall importance" is calculated as 0.3 (fact) + 0.1 (edge) + 0.4 + 0.4 = 1.2.

[0055] Such a guideline extended category query ECA2 is converted into a matrix representation using the overall importance of the importance table T1 by the processing circuit 15. This completes step ST30.

[0056] (Step ST40) In step ST40, the processing circuit 15 searches for other guideline categories based on the guideline extension category.

[0057] First, the processing circuit 15 selects another guideline. As a selection method, for example, the following methods (1) to (4) can be used as appropriate.

[0058] (1) A method predefined by the physician.

[0059] (2) A method for grouping highly related guidelines.

[0060] (3) A method for automatically selecting highly relevant guidelines based on the patient's condition.

[0061] (4) A method for calculating the similarity between the patient's medical information and the entire guideline using medical terms that appear in the guideline.

[0062] Furthermore, the same guideline may be repeatedly searched for, because by repeatedly searching for the same guideline based on a guideline extension category using other guidelines, a search with higher relevance can be performed.

[0063] Here, it is assumed that the processing circuitry 15 selects a guideline B that is different from guideline A. As shown in Fig. 12, the processing circuitry 15 extracts one of the guideline categories CB1,... corresponding to each clinical question (CQ) in guideline B8 based on a second relevance with the guideline extension category query ECA2. The second relevance is the similarity between each guideline category CB1,... and the guideline extension category query ECA2.

[0064] For example, the processing circuit 15 calculates the similarity between each guideline category CB1,... and the guideline extension category query ECA2, as described above, and extracts the guideline category CB1 with the highest similarity. Note that the similarity is calculated as the inner product of the matrix expression M_ECA2 of the guideline extension category query ECA2 and the matrix expression of each guideline category CB1,..., as shown in Fig. 13, for example. This completes step ST40.

[0065] (Step ST50) In step ST50, the processing circuit 15 expands the other guideline category CB1 based on the guideline expansion category query ECA2, as shown in FIG.

[0066] (Step ST60) In step ST60, the processing circuit 15 determines whether or not the process has ended, and if not, returns to step ST40 to continue the process. On the other hand, if the result of the determination is that the process has ended, the process proceeds to step ST70.

[0067] (Step ST70) In step ST70, the processing circuitry 15 displays guidelines A7, B8, C, etc. on the display 13 based on the searched guideline categories CA2, CB1, etc. For example, the processing circuitry 15 displays a clinical question (CQ) in guideline A corresponding to the searched guideline category CA2 on the display 13. Furthermore, for example, the processing circuitry 15 displays a clinical question (CQ) in guideline B corresponding to the searched guideline category CB1 on the display 13. Specifically, for example, the processing circuitry 15 displays a clinical question (CQ) in guideline A7 corresponding to the extracted guideline category CA2, which includes a first treatment method term, on the display 13. When a first treatment method indicated by the first treatment method term being displayed causes a symptom in the medical information, the processing circuitry 15 displays a clinical question (CQ) in guideline B corresponding to the extracted guideline category CB1, which includes a second treatment method term indicating a second treatment method that improves the symptom, on the display 13.

[0068] 15 to 18 are schematic diagrams showing examples of display screens displayed on the display 13. In one display screen, as shown in Fig. 15, a chart entry corresponding to the patient's medical information is displayed in tab Tb1, guideline A7 is displayed in tab Tb2 of guideline candidates, and guideline B8 is displayed in tab Tb3 of guideline candidates.

[0069] In tab Tb1, medical terms with high importance are marked with highlight HL1. Furthermore, by operating guideline expansion button b1, a guideline A7 related to the highlighted medical term is expanded.

[0070] Similarly, in tab Tb2, medical terms with high importance are highlighted with HL1 and HL2. Operating the guideline expansion button b2 expands a guideline B8 related to the highlighted medical term. The expansion buttons b1 to b3 may display a list of highly relevant guidelines using a pull-down menu or the like, allowing selection. As shown in tabs Tb1 and Tb2, clinical questions (CQs) from search results may be displayed with highlights HL1 and HL2 using a tree diagram or the like. The highlights HL1, HL2, HL3, etc. represent different colors, and highlights of the same color (same symbol) indicate information in the same category.

[0071] Next, another display screen displays clinical questions (CQs) of the guidelines related to the treatment options, as shown in FIG.

[0072] For example, suppose a clinical question (CQ) containing the treatment options "surgery" and "radiotherapy" is displayed in guideline A7. Here, when the option "radiotherapy" in guideline A7 is selected, a clinical question (CQ) containing the treatment option "external beam radiation therapy" in guideline B8, which is related to "radiotherapy," is displayed. Similarly, when the option "external beam radiation therapy" in guideline B8 is selected, a clinical question (CQ) containing the treatment option "radiotherapy" in guideline C9, which is related to "external beam radiation therapy," is displayed.

[0073] Next, another display screen, as shown in FIG. 17, displays clinical questions (CQs) of guidelines related to the possible symptoms.

[0074] For example, from "Platinum therapy" and "Anticancer drug A" in the patient information, "Neutropenia," "Thrombocytopenia," and "Interstitial pneumonia" are displayed as possible symptoms in the chemotherapy CQ·· in Guideline A7. Here, when the displayed symptom "Neutropenia" is selected, other Clinical Questions (CQs) in Guideline A7 related to that symptom are displayed.

[0075] Next, as shown in FIG. 18, another display screen displays related guidelines based on the guideline content, patient condition, and treatment method.

[0076] For example, if the patient's condition is "BMA administration" and "Ca decrease," and guideline A lists the treatment method as "bone-modifying drugs" and the adverse event as "hypocalcemia," then the clinical question (CQ) in the related guideline B, including "Radiation therapy prevents pathological fractures and bone marrow compression," will be displayed based on "BMA" and "hypocalcemia" in the guideline B.

[0077] Specifically, for example, the processing circuitry 15 displays and outputs on the display 13 a clinical question (CQ) in guideline A7 corresponding to the extracted guideline category CA2, which includes the first treatment method term "bone-modifying drug." When a first treatment method indicated by the first treatment method term being displayed and output causes the symptom "Ca decrease" in the medical information, the processing circuitry 15 displays and outputs on the display 13 a clinical question (CQ) in guideline B8 corresponding to the extracted guideline category CB1, which includes the second treatment method term "radiation therapy," which indicates a second treatment method that improves the symptom.

[0078] As described above, according to the first embodiment, a first guideline related to a group of medical terms (category query) included in the patient's medical information is extracted from among multiple guidelines related to medical care. Furthermore, from among each first group of terms (first guideline categories) corresponding to each clinical question (CQ) in the first guideline, any first guideline category is extracted based on a first relevance with the category query. Furthermore, from each second group of terms (second guideline categories) corresponding to each clinical question (CQ) in a second guideline different from the first guideline, any second guideline category is extracted based on a second relevance with the extracted first guideline category and a guideline extension category obtained from the category query. Furthermore, the first guideline and the second guideline are output based on the extracted first guideline category and the extracted second guideline category. Therefore, it is possible to present multiple literature information that may be relevant to the patient while reducing the effort required to refer to literature information related to medical care.

[0079] Additionally, conventional systems are unable to refer to knowledge in other related fields of expertise via guidelines. In contrast, according to the first embodiment, by starting from the patient's condition and expanding multiple guidelines in a chain, it is possible to integrate and display information from multiple guidelines related to the patient. This also broadens treatment options. It also allows for early consultation with doctors in other fields.

[0080] Furthermore, according to the first embodiment, a clinical question (CQ) in the first guideline corresponding to the extracted first guideline category is displayed on the display 13. The display control function 15d also displays a clinical question (CQ) in the second guideline corresponding to the extracted second guideline category on the display 13. In this way, the clinical questions (CQs) in the guidelines are displayed, so you can display the minimum items you want to refer to.

[0081] Furthermore, according to the first embodiment, the first guideline category includes a first treatment method term representing a first treatment method, and the second guideline category includes a second treatment method term representing a second treatment method different from the first treatment method. Furthermore, a clinical question (CQ) in the first guideline corresponding to the extracted first guideline category, which includes the first treatment method term, is displayed and output on the display 13. Furthermore, if a first treatment method indicated by the first treatment method term being displayed and output causes a symptom in the medical information, a clinical question (CQ) in the second guideline corresponding to the extracted second guideline category, which includes a second treatment method term representing a second treatment method that improves the symptom, is displayed and output on the display 13. Therefore, if a first treatment method that causes a symptom is displayed, a second treatment method that improves the symptom can be displayed.

[0082] Furthermore, according to the first embodiment, by extracting each medical term included in the patient's medical information by classification category related to medical treatment, a group of medical terms including medical terms by classification category is created as a category query, which makes it possible to create a category query suitable for searching for guidelines from the patient's medical information.

[0083] Furthermore, according to the first embodiment, first medical terms included in each clinical question (CQ) in the first guideline are extracted by classification category related to medical treatment, and a first guideline category containing first medical terms by classification category is created. This makes it possible to create a first guideline category suitable for searching each clinical question (CQ) from the first guideline. This also applies to a second guideline that is different from the first guideline.

[0084] Furthermore, according to the first embodiment, the classification category is each of a plurality of classification categories having a hierarchical relationship. Therefore, for example, even if the same medical term belongs to different classification categories and a simple term search alone does not yield the desired results, a term search that takes classification categories into consideration can be expected to yield the desired results.

[0085] Furthermore, according to the first embodiment, the first relevance is a value based on the first similarity between each first guideline category and the category query. The second relevance is a value based on the second similarity between each second guideline category and the extracted first guideline category and category query. In this case, the similarity can be easily obtained by calculating the inner product of the matrix expression of the guideline category and the matrix expression of the category query.

[0086] Furthermore, according to the first embodiment, the plurality of literature information includes at least one guideline, and the itemized description portion in the guideline is a portion described by dividing it into the smallest items or clinical questions (CQs). Therefore, depending on the search results, the clinical questions (CQs) or the smallest items can be displayed.

[0087] [Modification of the first embodiment] The first embodiment as described above may be implemented as shown in the following modified examples.

[0088] [1] The category query creation function 15a may predefine the medical term group and classification category to be extracted. Here, the medical term group to be extracted (a dictionary of target words) may be predefined using prior knowledge of doctors and guidelines. Alternatively, a medically important word group may be extracted as a medical term group by data mining using natural language processing. Alternatively, TF-IDF (a method for calculating importance based on word distribution) may be used, or important words may be extracted using machine learning or the like.

[0089] Furthermore, the classification categories (word classifications) to be extracted may be defined as classification categories by using machine learning or other methods to classify medical terms into topics. Furthermore, classification categories may be defined by using topic analysis and clustering techniques.

[0090] [2] The category query creation function 15a may define in advance the importance of the extracted medical terms and classification category hierarchies and use the weights for query search. For example, as shown in FIG. 19, a weight matrix indicating the importance for each medical term and classification category L1 to L5 may be defined and integrated into the matrix representation Mq1 of the category query q1, and used for query search. Here, the importance may be defined in advance based on prior knowledge of doctors and guidelines, or the importance of medical terms and hierarchies may be defined based on intermediate data of the machine learning method used in [1] above. The intermediate data of the machine learning method may be, for example, parameters (weights, biases) of a neural network. Furthermore, the importance may be adjusted individually, or the overall importance may be increased.

[0091] [3] The category query creation function 15a may use a level of importance specific to the category query. Specifically, the importance of medical terms may be determined for each patient by data mining or other methods based on the patient's medical information for the extracted medical term group or classification category hierarchy. For example, the importance may be determined based on the frequency of appearance of the medical term. Alternatively, the importance may be determined based on a rule such as "administering drug X → the importance of 'drug rash' is high" depending on the patient's condition.

[0092] "4" The category query creation function 15a may create multiple category queries with different structures. For example, multiple category queries may be created depending on the type of search target. Specifically, a category query may be created for each disease (when a patient suffers from multiple diseases). For example, in the case of cancer, a category query for specimen test results such as tumor markers may be created. Similarly, in the case of heart disease, a category query for vital sign information may be created. Also, a category query may be created for each treatment method (in the case of combination therapy). For example, in the case of chemotherapy, a category query with an additional layer of drug information may be created. Also, in the case of radiation therapy, a category query with an additional layer of radiation therapy plan may be created.

[0093] This also allows the processing circuitry 15 to perform a search using multiple category queries. In this case, any search method such as AND search, OR search, or ranking search can be used as appropriate.

[0094] [5] The guideline search function 15b may rank search results taking into consideration the recommendation level of the clinical questions (CQs). Specifically, as shown in Fig. 20, the related guideline category CA2 may be searched for by the product of the similarity and the recommendation level. In other words, the first relevance level may be a value obtained by multiplying the first similarity and the recommendation level described for the clinical questions (CQs) corresponding to each first guideline category.

[0095] [6] The category query creation function 15a may create a category query by specifying a search query by the user, as shown in FIG. 21. For example, a guideline search tab Tb0 is displayed on the display 13. The tab Tb0 includes a main guideline list DL1, a search guideline list DL2, a search axis DL3, a search term input field IA1, and a guideline search button b0. Therefore, when the main guideline, search guideline, search axis, search term, etc. are specified in response to a user operation, the processing circuit 15 creates a category query based on this specification.

[0096] <Second embodiment> Next, a medical assistance device according to a second embodiment will be described with reference to Fig. 22. In the following description, elements that are substantially the same as those in the above-mentioned drawings will be assigned the same reference numerals and detailed description thereof will be omitted, and different elements will be mainly described.

[0097] The second embodiment takes into consideration situations where you want to search not only for highly relevant items but also for items that you want to avoid overlooking, and searches through guidelines to find descriptions that may lead to serious situations such as aggravation of the condition.

[0098] Specifically, as shown in Fig. 22, the processing circuit 15 further includes an important category search function 15e. Also, at least one of the first guideline categories CA1, CA2, ... and the second guideline categories CB1, ... includes important terms related to serious situations.

[0099] Here, the important category search function 15e searches for a guideline category that includes a key term from among the extracted first guideline category CA1 and second guideline category CB1. Note that the important category search function 15e is an example of a search unit.

[0100] Accordingly, the processing circuitry 15 preferentially outputs the clinical questions (CQs) from the first guideline A and the second guideline B that correspond to the searched term group.

[0101] The other configurations are the same as those in the first embodiment.

[0102] According to the above configuration, the processing circuit 15 prepares guideline categories including key terms in response to a user operation (step ST20_prep) at least before step ST20 of searching for guideline categories, as shown in the flowchart of Fig. 23. Note that step ST20_prep may be executed before step ST10.

[0103] In any case, after step ST20_prep, steps ST20 to ST60 are executed in the same manner as described above.

[0104] After step ST60, in step ST70a, the processing circuit 15 searches for a guideline category that includes a key term from the extracted first guideline category CA1 and second guideline category CB1. As a result of the search, for example, as shown in Figure 24, the second guideline category CB1 that includes the key term Tm3 is obtained. In addition, the medical term Tm2 in the first guideline CA1 is obtained as a term related to the key term Tm3, and the medical term Tm1 in the category query q1 is obtained as a term related to the medical term Tm2.

[0105] Accordingly, the processing circuitry 15 preferentially outputs clinical questions (CQs) corresponding to the searched term groups from the first guideline A and the second guideline B. In this case, medical terms Tm1, Tm2, ​​and Tm3 linked to key terms are preferentially displayed.

[0106] As described above, according to the second embodiment, at least one of the first guideline categories CA1, CA2, ... and the second guideline categories CB1, ... includes key terms related to serious situations. Furthermore, among the extracted first guideline category CA1 and second guideline category CB1, a guideline category including key terms is searched for. Furthermore, among the first guideline A and the second guideline B, clinical questions (CQs) corresponding to the searched term group are preferentially output. Therefore, in addition to the effects of the first embodiment, it is possible to prevent overlooking key terms related to serious situations.

[0107] <Third embodiment> Next, a medical assistance device according to a third embodiment will be described with reference to FIG.

[0108] The third embodiment takes into consideration cases where a doctor wants to search for possible symptoms or treatments that are not included in the patient's medical information, and by analyzing the doctor's behavior, the category query is modified to relate to the information the doctor wants to refer to.

[0109] Specifically, as shown in FIG. 25, the processing circuit 15 further includes a behavior analysis processing function 15f.

[0110] Here, the behavior analysis processing function 15f analyzes the behavior of the operator while the guideline is being output, modifies the category query q1 so as to add medical terms corresponding to the analysis results, and re-executes the guideline search function 15b, the query expansion function 15c, and the display control function 15d in accordance with the modified category query q1. The behavior analysis processing function 15f is an example of a behavior analysis unit.

[0111] Other configurations are the same as those of the second embodiment, but the third embodiment may be applied to the first embodiment.

[0112] According to the above configuration, as shown in the flowchart of FIG. 23, after step ST70a in which the guideline is displayed, the processing circuit 15 determines whether or not the operation has ended depending on whether or not an end operation has been performed (step ST80), and if the operation has not ended, analyzes the behavior of the operator (step ST80).

[0113] The processing circuitry 15 also modifies the category query q1 to add medical terms corresponding to the analyzed results. Alternatively, the processing circuitry 15 may enhance the importance of terms or edges based on the search results selected by the doctor. In either case, the processing circuitry 15 re-executes the guideline search function 15b, the query expansion function 15c, and the display control function 15d in response to the modified category query q1.

[0114] Thereafter, based on the corrected category query q1, the processes from step ST20_prep onwards are executed in the same manner as described above.

[0115] As described above, according to the third embodiment, the operator's behavior is analyzed while the guideline is being output, the category query q1 is modified so as to add medical terms corresponding to the analysis results, and the guideline search function 15b, the query expansion function 15c, and the display control function 15d are re-executed in accordance with the modified category query q1. Therefore, in addition to the effects of the second embodiment, the category query can be modified in relation to the information that the doctor wants to refer to, taking into account cases in which the doctor wants to search based on some assumed symptoms or possible treatment methods.

[0116] According to at least one of the embodiments described above, it is possible to present a plurality of literature information that may be relevant to a patient while reducing the effort required to refer to literature information related to medical treatment.

[0117] The term "processor" used in the above description refers to a circuit such as a CPU (central processing unit), a GPU (graphics processing unit), an application specific integrated circuit (ASIC), a programmable logic device (e.g., a simple programmable logic device (SPLD), a complex programmable logic device (CPLD), and a field programmable gate array (FPGA)). If the processor is a CPU, for example, the processor realizes its function by reading and executing a program stored in a memory circuit. On the other hand, if the processor is an ASIC, for example, instead of storing a program in a memory circuit, the function is directly incorporated into the processor circuit as a logic circuit. Note that each processor in the present embodiment is not limited to being configured as a single circuit for each processor, but may be configured as a single processor by combining multiple independent circuits to realize its function. Furthermore, multiple components in FIG. 1, FIG. 2, FIG. 22, or FIG. 25 may be integrated into a single processor to realize its function.

[0118] Although several embodiments of the present invention have been described, these embodiments are presented as examples and are not intended to limit the scope of the invention. These embodiments can be embodied in various other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their modifications are included in the scope of the invention and its equivalents as defined in the claims, as well as in the scope and spirit of the invention. [Explanation of symbols]

[0119] 1 Medical support equipment 11,62 memory 12 Input Interface 13. Display 14,63 Communication Interface 15,61 Processing circuit 15a Category query creation function 15b Guideline Search Function 15c Query Extensions 15d Display control function 15e Important Category Search Function 15f Behavioral analysis processing function 2 HIS 3 RIS 4 Medical imaging diagnostic equipment 5. PACS 6 DWH

Claims

1. a first extraction unit that extracts first literature information related to a group of medical terms included in the patient's medical information from a plurality of literature information related to medical treatment; a second extraction unit that extracts one of the first term groups corresponding to each of the itemized description portions in the first literature information based on a first degree of association with the medical term group; a third extraction unit that extracts any one of the second term groups corresponding to each of the itemized description portions in second literature information different from the first literature information based on a second degree of association between the extracted first term group and the medical term group; an output unit that outputs the first document information and the second document information based on the extracted first term group and the extracted second term group; A medical support device equipped with the above.

2. the output unit causes a display unit to display and output an itemized description portion in the first literature information corresponding to the extracted first term group, and causes a display unit to display and output an itemized description portion in the second literature information corresponding to the extracted second term group. The medical support device according to claim 1.

3. the first term group includes a first treatment method term representing a first treatment method; the second term group includes a second treatment method term representing a second treatment method different from the first treatment method; the output unit causes a display unit to display and output an itemized description portion in the first literature information that includes the first treatment method term and corresponds to the extracted first term group, and when a first treatment method indicated by the first treatment method term being displayed and output causes a symptom in the medical information, causes the display unit to display and output an itemized description portion in the second literature information that includes a second treatment method term indicating the second treatment method that improves the symptom and corresponds to the extracted second term group.

3. The medical support device according to claim 1 or 2.

4. a medical term group creation unit that creates the medical term group including medical terms for each classification category by extracting each medical term included in the medical information for each classification category related to medical treatment; The medical support device according to claim 1 , further comprising:

5. a first creation unit that creates the first term group including the first medical terms by classification category related to medical treatment by extracting each first medical term included in each itemized description portion in the first literature information by classification category related to medical treatment; The medical support device according to claim 1 , further comprising:

6. a second creation unit that creates the second term group including the second medical terms by classification category related to medical treatment by extracting each second medical term included in each itemized description portion in the second literature information by classification category related to medical treatment; The medical support device according to claim 1 , further comprising:

7. The medical support device according to claim 4 , wherein the classification category is each of a plurality of classification categories having a hierarchical relationship.

8. the first relevance is a value based on a first similarity between each of the first term groups and the medical term group; 8. The medical support device according to claim 1, wherein the second relevance is a value based on a second similarity between each of the second term groups and the extracted first term group and the medical term group.

9. 9. The medical support device according to claim 8, wherein the first relevance is a value obtained by multiplying the first similarity by a recommendation level described for the itemized description portion corresponding to each of the first term groups.

10. Further comprising a search unit, At least one of the first term group and the second term group includes key terms related to serious situations; the search unit searches for a term group including the key term from among the extracted first term group and the extracted second term group; The medical support device according to claim 1 , wherein the output unit preferentially outputs, from the first literature information and the second literature information, an itemized description portion corresponding to the searched term group.

11. A medical support device as described in any one of claims 1 to 10, further comprising a behavioral analysis unit that analyzes the behavior of an operator during output by the output unit, modifies the group of medical terms to add medical terms corresponding to the results of the analysis, and re-executes the first extraction unit, the second extraction unit, the third extraction unit, and the output unit in accordance with the modified group of medical terms.

12. the plurality of literature information includes at least one guideline; The itemized description portion in the guideline is a portion described by dividing it into the smallest items or clinical questions (CQs), The medical support device according to any one of claims 1 to 11.

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