Medical information processing system, medical information processing method, and program

Abstract

To provide a medical information processing system, a medical information processing method, and a program that facilitate finding a disease other than a disease related to a chief complaint.SOLUTION: In a hospital information system constituting an in-hospital system and having an electronic medical chart system and a medical information processing system, the medical information processing system is provided with: an acquisition function 141 to acquire disease state information on the state of a disease exhibited by the patient; a conversion function 142 to map the disease state information on a disease ontology to convert the disease ontology into a modified disease ontology on which the state of the patient is reflected; a reception function 143 to receive designation related to main candidate diseases; a specification function 144 to specify sub candidate diseases different from the main candidate diseases based on the modified disease ontology; and a generation function 145 to generate first order information on the sub candidate diseases.SELECTED DRAWING: Figure 2

Description

Technical Field

[0001] The embodiments disclosed in this specification and the drawings relate to a medical information processing system, a medical information processing method, and a program.

Background Art

[0002] In recent years, a technique (disease ontology) for automatically estimating a specific disease to be examined by defining a disease as a totality of a cause and a result chain of an abnormal state and analyzing medical data such as medical image data and vital data is known. In a diagnosis using normal medical image data, medical images are taken to suspect a certain disease or for continuous observation of a certain disease, and the medical images are analyzed to discover a disease or diagnose the progression of a disease. In addition, a technique has also been proposed for presenting a diagnosis result regarding a disease other than the disease of the chief complaint by using incidental information (such as patient information) of medical data acquired for diagnosing the disease to be examined.

[0003] In a normal medical interview or the like, a doctor estimates a disease mainly based on the chief complaint. However, in this disease estimation, in order to preferentially estimate the disease related to the chief complaint, for example, it may be impossible to catch an early discovery and treatment opportunity of a disease other than the disease related to the chief complaint, for example, another serious disease in the organ targeted by the chief complaint. In addition, it may also be impossible to fully recognize information regarding complications and side effects during treatment.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] The problem that the embodiments disclosed in this specification and drawings aim to solve is to make it easier to detect diseases other than the disease related to the chief complaint. However, the problem that the embodiments disclosed in this specification and drawings aim to solve is not limited to the above problem. Problems corresponding to the effects of each configuration shown in the embodiments described later can also be positioned as other problems. [Means for solving the problem]

[0006] The medical information processing system of this embodiment includes an acquisition unit, a conversion unit, a reception unit, a specification unit, and a generation unit. The acquisition unit acquires pathological information relating to the pathological condition presented by the patient. The conversion unit converts the disease ontology into a modified disease ontology that reflects the patient's condition by mapping the pathological information onto the disease ontology. The reception unit accepts a designation regarding the primary candidate disease. The specification unit identifies secondary candidate diseases different from the primary candidate disease based on the modified disease ontology. The generation unit generates first-order information relating to the secondary candidate diseases. [Brief explanation of the drawing]

[0007] [Figure 1] A block diagram showing an example of the configuration of the hospital system 1 of the embodiment. [Figure 2] A block diagram showing an example of the configuration of the medical information processing system 100 of the embodiment. [Figure 3] A diagram illustrating an example of the contents of a disease ontology. [Figure 4] A flowchart showing an example of processing in the medical information processing system 100 of the embodiment. [Figure 5] A diagram showing an example of the content of a modified disease ontology that reflects the primary candidate disease. [Figure 6] A diagram showing an example of the contents of a modifying disease ontology in which secondary candidate diseases have been identified. [Figure 7] This diagram shows an example of the content of a modified disease ontology, in which imaging diagnostic tests are linked to each element of "disease" and "complications / side effects." [Figure 8]This diagram shows an example of the content of a modifying disease ontology, which represents the elements of "disease" and "complications / side effects" that generate imaging examination orders. [Modes for carrying out the invention]

[0008] The medical information processing system, medical information processing method, and program of the embodiment will be described below with reference to the drawings. In the embodiment, "disease" refers to specific illnesses such as diabetes, liver fibrosis, liver cirrhosis, cancer, myocardial infarction, and stroke. In addition to diseases that have already manifested, diseases may also include pre-disease states, which are not in a healthy state but have not yet manifested.

[0009] Figure 1 is a block diagram showing an example of the configuration of the hospital system 1 of the embodiment. The hospital system 1 of the embodiment includes, for example, a Hospital Information System (HIS) 10, a Radiology Information System (RIS) 20, a medical imaging diagnostic device (modality) 30, a Picture Archiving and Communication System (PACS) 40, and a diagnostic information database (DB) 50. The HIS 10 includes an electronic medical record system 11 and a medical information processing system 100. The hospital system 1 is installed, for example, in a medical institution such as a hospital.

[0010] HIS10, RIS20, Modality30, PACS40, and Diagnostic Information DB50 are connected via a network (NW) for communication. The network (NW) refers to all information and communication networks that utilize telecommunications technology. The network (NW) includes wireless / wired LANs such as the hospital's backbone LAN (Local Area Network), the Internet network, as well as telephone communication lines, fiber optic communication networks, cable communication networks, and satellite communication networks.

[0011] HIS10 is a computer system that provides operational support within hospitals. Specifically, HIS10 has various subsystems, including an electronic medical record system 11 and a medical information processing system 100. Examples of these subsystems include a medical accounting system, an appointment scheduling system, a patient registration system, and an admission / discharge management system.

[0012] HIS10 includes, for example, computers such as server devices and client terminals equipped with a processor such as a CPU (Central Processing Unit), memory such as ROM (Read Only Memory) and RAM (Random Access Memory), a display, an input interface, and a communication interface.

[0013] Medical professionals such as doctors (hereinafter referred to as "doctors, etc.") use the electronic medical record system 11 in HIS10 to input and refer to various information about patients (hereinafter referred to as "patient information"). For example, each patient's patient information is managed by associating it with a patient ID that can identify each patient.

[0014] The electronic medical record system 11 stores electronic medical records for multiple patients. The electronic medical records contain various information about the patients, including patient information. Patient information includes information that describes the patient's characteristics. Examples of patient characteristics include the patient's age, sex, physique (height, weight, etc.), suspected illness, and medical history.

[0015] Doctors and other medical professionals input examination orders into the medical information processing system 100 in HIS10. HIS10 transfers the order information, including imaging examination orders, to other systems such as RIS20. An imaging examination order is an order that instructs image diagnostic analysis. An imaging examination order may also include an instruction to acquire medical images that will be subject to image diagnostic analysis, along with the image diagnostic analysis itself.

[0016] The medical information processing system 100 is a system that transmits instructions (orders) such as examinations and prescriptions to each department in charge. Order information includes, in addition to imaging examination orders, physical examination orders, specimen examination orders, prescription drug orders, diet maintenance orders, and the like. The medical information processing system 100 functions as an order ring system.

[0017] When a doctor or the like inputs an examination order, the HIS 10 starts generating order information by the medical information processing system 100. Prior to the generation of order information in the medical information processing system 100, the HIS 10 causes the electronic medical record system 11 to transmit the patient information of the patient to be examined, which is stored, to the medical information processing system 100.

[0018] The medical information processing system 100 generates order information based on the examination order input by a doctor or the like, the patient information transmitted by the electronic medical record system 11, the diagnostic information transmitted by the diagnostic information DB 50, and other information related to the examination target. The medical information processing system 100 transmits the generated order information to the RIS 20 together with a part or all of the patient information.

[0019] FIG. 2 is a block diagram showing an example of the configuration of the medical information processing system 100 of the embodiment. The medical information processing system 100 includes, for example, a communication interface 110, an input interface 120, a display 130, a processing circuit 140, and a memory 150. The communication interface 110, the input interface 120, and the display 130 in the medical information processing system 100 are provided separately from the communication interface, the input interface, and the display included in the HIS 10 and the electronic medical record system 11, but they may be common. The memory 150 is an example of a storage unit.

[0020] The communication interface 110 communicates with external devices such as the RIS 20, modality 30, PACS 40, etc. via a network NW such as a LAN. The communication interface 110 includes a communication interface such as a NIC (Network Interface Card). The network NW may include the Internet, a cellular network, a Wi-Fi network, a WAN (Wide Area Network), etc. instead of or in addition to the LAN.

[0021] The input interface 120 receives various input operations from a medical doctor or the like, converts the received input operations into electrical signals, and transmits them to the processing circuit 140. For example, when an input operation is performed by a medical doctor or the like, the input interface 120 generates information corresponding to the input operation. The input interface 120 transmits the generated information corresponding to the input operation to the processing circuit 140.

[0022] The input interface 120 includes, for example, a mouse, a keyboard, a trackball, a switch, a button, a joystick, a touch panel, etc. The input interface 120 may be a user interface that receives voice input such as a microphone. When the input interface 120 is a touch panel, the input interface 120 may also have the display function of the display 130.

[0023] Note that in this specification, the input interface is not limited to those having physical operation components such as a mouse and a keyboard. For example, an electrical signal processing circuit that receives an electrical signal corresponding to an input operation from an external input device provided separately from the device and outputs this electrical signal to the control circuit is also included in the examples of the input interface.

[0024] The physician (attending physician) diagnoses the patient and obtains findings regarding the patient's disease based on the results of interviews, biochemical tests, basic tests, etc. The physician inputs the disease indicated by the obtained findings into the input interface 120. The input interface 120 transmits the disease information related to the attending physician's findings to the processing circuit 140. The attending physician's findings include diseases related to the chief complaint (hereinafter referred to as primary candidate diseases), etc.

[0025] The display 130 displays various types of information. For example, the display 130 displays images generated by the processing circuit 140, or a GUI (Graphical User Interface) for receiving various input operations from the operator. For example, the display 130 may be an LCD (Liquid Crystal Display), a CRT (Cathode Ray Tube) display, or an organic EL (Electro Luminescence) display.

[0026] The processing circuit 140 includes, for example, an acquisition function 141, a conversion function 142, a reception function 143, a specific function 144, and a generation function 145. The processing circuit 140 realizes these functions, for example, by a hardware processor (computer) executing a program stored in the memory (storage circuit) 150.

[0027] Hardware processors refer to circuits such as CPUs, GPUs (Graphics Processing Units), Application Specific Integrated Circuits (ASICs), programmable logic devices (e.g., Simple Programmable Logic Devices (SPLDs) or Complex Programmable Logic Devices (CPLDs)), and Field Programmable Gate Arrays (FPGAs).

[0028] Instead of storing the program in memory 150, the system may be configured to directly integrate the program into the hardware processor's circuitry. In this case, the hardware processor performs its functions by reading and executing the program integrated into the circuitry. The program may be stored in memory 150 beforehand, or it may be stored on a non-temporary storage medium such as a DVD or CD-ROM, and installed from the non-temporary storage medium to memory 150 when the non-temporary storage medium is mounted on a drive device (not shown) of the medical information processing system 100. Alternatively, the program may be stored online (e.g., in the cloud), and the online program may be executed via a communication interface.

[0029] A hardware processor is not limited to being configured as a single circuit; it may be configured as a single hardware processor by combining multiple independent circuits to realize each function. Alternatively, multiple components may be integrated into a single hardware processor to realize each function. The hardware processor and memory in the medical information processing system 100 are provided separately from the hardware processor and memory in the HIS 10, but they may be common to each other.

[0030] Memory 150 stores multiple disease ontologs 151 in which disease information is graph-structured. The disease ontologs may be stored on the same medium as the program, or on a different medium, including online. The disease ontologs are read and used, for example, when the program is executed. Figure 3 shows an example of the contents of a disease ontolog. The disease ontolog 151 is represented, for example, as a disease concept network. The disease concept network presents a hierarchy, for example, "pathology," "disease," and "complications / side effects." Elements are defined for each hierarchy.

[0031] The "pathological condition" hierarchy defines elements such as "loss of appetite," "cough," "shortness of breath," "palpitations," "excessive sweating," "convulsions," "chest pain," "shallow breathing," "syncope," "headache," "dizziness," "jaundice," "nausea," "high fever," "edema," and "diarrhea." Other elements may be included in the pathological condition hierarchy, or some of these may be omitted. The "pathological condition" hierarchy is just one example of the layers related to pathological conditions.

[0032] A "disease" can be, for example, an element classified based on the International Classification of Diseases (ICD-11). A "disease" can also be an element structured in application software such as Disease Compass. The hierarchy of "diseases" defines elements such as "pleural effusion," "ascites," "hepatitis," "heart failure," "lung cancer," "liver cancer," "cardiomyopathy," "pneumonia," "pancreatitis," "COPD," and "pancreatic cancer." Other elements may be included in a disease, or some of these may not be included. The hierarchy of "diseases" is just one example of a hierarchy related to diseases.

[0033] The "complications and side effects" hierarchy defines symptoms that appear as complications or side effects when treating a specific element of a "disease" (hereinafter referred to as the "target disease") as a group of diseases that are attributes of that target disease. Elements such as "pleural effusion," "ascites," "myocarditis," "bone loss," and "pulmonary fibrosis" are defined in the "complications and side effects" hierarchy. The "complications and side effects" hierarchy is defined for each type of "disease." In the example in Figure 3, the elements of "complications and side effects" when the "disease" is "lung cancer" are shown.

[0034] Each element of "disease" and "complications / side effects" is linked to a diagnostic imaging test. The relationship between each element of "disease" and "complications / side effects" and the diagnostic imaging test will be explained later.

[0035] The acquisition function 141 acquires patient information transmitted by the electronic medical record system 11. The acquisition function 141 causes the patient's diagnostic information indicated by the transmitted patient information to be sent to the diagnostic information DB 50. The acquisition function 141 also acquires the diagnostic information transmitted by the diagnostic information DB 50. Both patient information and diagnostic information are information relating to the patient's condition (hereinafter referred to as condition information). Condition information is information that includes patient information and diagnostic information.

[0036] The conversion function 142 reads the disease ontology 151 stored in memory 150 and maps the pathological information to the disease ontology 151. By mapping the pathological information, the conversion function 142 converts the disease ontology into a modified disease ontology, thereby generating a modified disease ontology. The modified disease ontology reflects the patient's condition. The procedure for converting a disease ontology into a modified disease ontology will be explained further later.

[0037] The reception function 143 accepts the disease information transmitted via the input interface 120 as a designation for the primary candidate disease by performing natural language analysis or the like. The disease information may also be transmitted via a network NW from a device other than the input interface 120, such as a user terminal used exclusively by a doctor or the like.

[0038] The identification function 144 identifies secondary candidate diseases, which are diseases other than those related to the chief complaint, and which differ from the primary candidate disease accepted by the reception function 143, based on the modified disease ontology generated by the conversion function 142 when the disease ontology 151 is converted. The identification of secondary candidate diseases will be explained further later.

[0039] The generation function 145 generates order information for secondary candidate diseases. The generation function 145 further generates order information for complications and side effects of the primary candidate disease. The generation function 145 transmits the generated order information for secondary candidate diseases, as well as the order information for complications and side effects of the primary candidate disease, to the RIS 20.

[0040] For each disease shown as an element of a disease, a test is associated with it to identify the disease or to assess its severity, and the associated disease and test information is stored in memory 150 as necessary test information. The generation function 145 reads the necessary test information corresponding to the secondary candidate disease identified by the identification function 144 from memory 150 and generates order information for the secondary candidate disease. The order information for the secondary candidate disease is an example of first order information. The order information for complications and side effects of the primary candidate disease is an example of second order information.

[0041] RIS20 is a computer system that provides operational support in the diagnostic imaging department. RIS20 manages the reservation of imaging examination orders in conjunction with HIS10, as well as integrating reservation information with examination equipment and managing examination information. RIS20 includes, for example, a server device and client terminals equipped with a processor such as a CPU, memory such as ROM and RAM, a display, input interfaces, and communication interfaces.

[0042] Modality 30 performs imaging (taking images) according to imaging conditions (imaging protocol) determined, for example, based on an imaging examination order. Examples of modality 30 include X-ray computed tomography scanners, X-ray diagnostic equipment, magnetic resonance imaging equipment, ultrasound diagnostic equipment, and nuclear medicine diagnostic equipment. Modality 30 is operated by operators such as physicians (radiologists) or radiological technologists. Modality 30 transmits the medical images (image data) generated by imaging to PACS 40.

[0043] PACS40 is a computer system that receives medical images transmitted by modality 30 and stores them in a database. PACS40 transmits (transfers) the medical images stored in the database in response to requests from clients. PACS40 includes a server computer with a processor such as a CPU, memory such as ROM and RAM, a display, input interfaces, and communication interfaces.

[0044] Medical images stored in PACS40 are accompanied by supplementary information, including details about the patient being photographed and the imaging process. This supplementary information includes, for example, patient ID, examination ID, and imaging conditions (imaging protocol) in a format compliant with the DICOM (Digital Imaging and Communication in Medicine) standard. PACS40 stores medical images of multiple patients that have been imaged in the past.

[0045] The diagnostic information DB 50 stores information obtained through the diagnosis of a patient (hereinafter referred to as diagnostic information). The diagnostic information stored in the diagnostic information DB 50 includes, for example, medical history information 51, biochemical information 52, and basic test information 53. Medical history information 51 includes, for example, information obtained by a doctor or other medical professional through a medical interview with the patient. Biochemical information 52 includes, for example, information obtained through biochemical tests. Basic test information 53 includes, for example, information on basic tests performed before a medical history interview, such as electrocardiogram information.

[0046] The diagnostic information DB 50 may be included in the electronic medical record system 11. In this case, when the electronic medical record system 11 transmits patient information to the medical information processing system 100, it may read the diagnostic information indicated by the patient information from the diagnostic information DB 50 and transmit it to the medical information processing system 100 together with the patient information. The diagnostic information may include the results of imaging diagnostic tests.

[0047] The configuration of hospital system 1 is not limited to the above. Hospital system 1 may integrate several elements of hospital system 1. For example, HIS10 and RIS20 may be integrated into a single system.

[0048] Next, the processing in the medical information processing system 100 will be described. Figure 4 is a flowchart showing an example of the processing in the medical information processing system 100 of the embodiment. First, the medical information processing system 100 acquires patient information transmitted by the electronic medical record system 11 and diagnostic information transmitted by the diagnostic information DB 50 using the acquisition function 141 to acquire disease state information (step S101).

[0049] Next, the conversion function 142 reads the disease ontology 151 stored in memory 150 (step S103). Subsequently, the conversion function 142 maps pathological information to the read disease ontology 151 and converts the disease ontology into a modified disease ontology (step S105).

[0050] Next, the reception function 143 determines whether or not it has received disease information (primary candidate disease) transmitted via the input interface 120 (step S107). If it determines that it has not received disease information, the reception function 143 repeats the process in step S107. If it determines that it has received disease information, the reception function 143 accepts the disease based on the received disease information as the primary candidate disease (step S109). Subsequently, the conversion function 142 reflects the primary candidate disease accepted by the reception function 143 into the modified disease ontology.

[0051] Figure 5 shows an example of the content of a modified disease ontology that reflects the primary candidate disease. When converting a disease ontology to a modified disease ontology, the conversion function 142 graphs each element of "pathophysiology" based on the medical history information, biochemical test information, and basic test information included in the diagnostic information. For example, the conversion function 142 performs natural language analysis on the text information included in the medical history information and graphs the number of each element of "pathophysiology" included in the medical history information as an indicator. For example, the more elements included in the medical history information, the larger each element in the "pathophysiology" of the disease ontology will be graphed.

[0052] In Figure 5, the elements represented in the graph are indicated, for example, by the size of the markers. In the example shown in Figure 5, elements such as "chest pain," "shortness of breath," and "shallow breathing" are included in large quantities in the text information, and these elements are represented prominently in the graph. Therefore, the markers representing elements such as "chest pain," "shortness of breath," and "shallow breathing" are displayed in a large size.

[0053] Here, the number of each element included in the medical questionnaire information is used as an indicator for graphing, but graphs may also be created based on indicators other than numbers. For example, graphs may be created based on indicators other than numbers, such as the magnitude of influence or the degree of emphasis, or these indicators may be evaluated in a multi-dimensional manner and graphed. In this case, for example, the representation of the indicators may be changed for each indicator being graphed. For example, the magnitude of influence may be shown by color (intensity), and the degree of emphasis may be shown by shape (circle, square, triangle, etc.).

[0054] The conversion function 142 further graphs the "pathological" elements in the disease ontology based on biochemical test information and basic test information. For example, if the biochemical test or basic test results show that symptoms related to each element are likely to be observed, that element will be graphed larger. The indicators for each element included in the biochemical test or basic test may be indicators other than numbers, similar to the information from the medical interview.

[0055] Furthermore, the conversion function 142 graphs the "disease" element in the disease ontology based on the disease information received by the reception function 143. In the example shown in Figure 5, the reception function 143 receives "lung cancer" as disease information. In this case, the "lung cancer" element in the disease is graphed prominently. Also, because "lung cancer" is graphed most prominently as the "disease," the "complications and side effects" element is selected as the element for "complications and side effects" when the "disease" is "lung cancer."

[0056] Next, the identification function 144 identifies secondary candidate diseases (step S111). In identifying secondary candidate diseases, the identification function 144 uses the primary candidate disease received by the reception function 143 and the graphed elements of the pathology in the modifying disease ontology. The identification function 144 may identify secondary candidate diseases in any way. For example, the identification function 144 may predict diseases with a high probability of occurrence and identify them as secondary candidate diseases based on the primary candidate disease and the graphed elements of the pathology in the modifying disease ontology. When predicting diseases with a high probability of occurrence, for example, a trained model generated by machine learning or a rule-based model may be used.

[0057] Figure 6 shows an example of the contents of a modifying disease ontology in which secondary candidate diseases have been identified. In the example shown in Figure 6, the primary candidate disease is "lung cancer," and pathological symptoms such as "chest pain," "palpitations," "shortness of breath," and "shallow breathing" are prominently graphed. From these results, "heart failure" is identified as a secondary candidate disease.

[0058] Furthermore, each element of "disease" and "complications / side effects" is associated with a diagnostic imaging test. Figure 7 shows an example of the contents of a modified disease ontology, where diagnostic imaging tests are associated with each element of "disease" and "complications / side effects." For example, "heart failure" in the "disease" category is associated with "UL (ultrasonography)" and "CT (Computed Tomography)." Similarly, "myocarditis" in the "complications / side effects" category is associated with "UL" and "CT." The diagnostic imaging test protocols are coded to facilitate identification.

[0059] Next, the generation function 145 generates imaging test orders for each element of "complications and side effects" corresponding to the secondary candidate disease identified by the identification function 144 and the primary candidate disease received by the reception function 143 (step S113). For example, the generation function 145 includes imaging tests required for the examination of the secondary candidate disease identified by the identification function 144 in the imaging test order. The generation function 145 also includes imaging tests required for the examination of elements of "complications and side effects" corresponding to the primary candidate disease that have a high need for examination in the imaging test order.

[0060] The generation function 145 selects the elements of "complications and side effects" that have a high need for testing by calculating the recommendation level for each element and weighting them according to the recommendation level. In calculating the recommendation level, the generation function 145 uses text information included in the medical history, basic test information, clinical practice guidelines for the primary candidate disease, and the patient's past test results.

[0061] The generation function 145 calculates the recommendation level Ei using, for example, the following equation (1) which uses the probability of occurrence Ri, the recommendation rank Si, and the coefficient of change Ci. Ei = Ri × Si × Ci ... (1)

[0062] The probability of occurrence Ri is the probability of a condition occurring that is prone to complications or side effects of the primary candidate disease. The generation function 145 calculates the probability of occurrence Ri based on the patient's symptoms and basic examination information extracted by natural language analysis of text information included in the medical history. The recommendation rank Si is determined based on the recommendation rank specified in the clinical practice guidelines. For example, the recommendation rank Si is a 5-level weighting system in the clinical practice guidelines, where "5" indicates a high recommendation for the test and "1" indicates a low recommendation. The generation function 145 may also utilize the level of attention and severity at the time of onset in the clinical practice guidelines.

[0063] The coefficient of change Ci is calculated, for example, when a patient has undergone some kind of examination in the past. If a patient has undergone an examination in the past, the past examination results are included as information within the modifying disease ontology. Therefore, the coefficient of change Ci is calculated using the change in the patient's statement (PCi) and the change in basic examination information (BCi), for example, by formula (2) below. The change in the patient's statement (PCi) and the change in basic examination information (BCi) are set to four levels, for example, "1" if there is no change or if the change is improving, and "5" if the change is worsening, with the larger value depending on the magnitude of the change. Ci = PCI × BCi ···(2)

[0064] The generation function 145 calculates the recommendation level for implementation using formula (1), and generates imaging examination orders for each element of "complications and side effects" corresponding to the primary candidate disease using weighting according to the calculated recommendation level for implementation. For example, the generation function 145 may include imaging examinations of elements whose recommendation level for implementation exceeds a predetermined threshold in the imaging examination order, or it may include imaging examinations of a predetermined number of elements with high recommendation levels in the imaging examination order.

[0065] Figure 8 shows an example of the contents of a modified disease ontology, illustrating the elements of "disease" and "complications / side effects" for which imaging examination orders were generated. In the example shown in Figure 8, imaging examination orders were generated for "liver cancer" among the "diseases" and "ascites," "myocarditis," "pulmonary fibrosis," and "bone loss" among the "complications / side effects."

[0066] The generation function 145 may select imaging tests to include in an imaging order based on whether the imaging diagnostic tests and analyses of the disease can be performed using the imaging diagnostic test protocol instructed by the physician. For example, the generation function 145 may include imaging tests that can be performed using the imaging diagnostic test protocol instructed by the physician, and exclude imaging tests that cannot be performed using the imaging diagnostic test protocol instructed by the physician. In this case, the generation function 145 may suggest to the physician that imaging tests that cannot be performed using the imaging diagnostic test protocol instructed by the physician be performed as additional tests.

[0067] Next, the generation function 145 sends the generated image inspection order to the RIS 20 (step S115). In this way, the medical information processing system 100 completes the processing of the flow shown in Figure 4.

[0068] The medical information processing system 100 of this embodiment uses a disease ontology to infer secondary candidate diseases other than the primary candidate disease and generates order information for the secondary candidate diseases. The disease ontology used here is a modified disease ontology that reflects the patient's condition, obtained by mapping pathological information onto the disease ontology. Therefore, it is possible to easily discover diseases other than those related to the chief complaint.

[0069] Furthermore, the medical information processing system 100 of this embodiment generates test orders for complications and side effects of the primary candidate disease. This makes it easier to detect complications and side effects of the primary candidate disease. The complications and side effects for which test orders are generated are determined based on the recommendation level for implementation. This helps to suppress excessive testing for complications and side effects.

[0070] In the above embodiment, the medical information processing system 100 is provided within the HIS 10, but the medical information processing system 100 may be provided in other locations. For example, the medical information processing system 100 may be provided independently of the HIS 10, or it may be provided in a user terminal operated by a physician or in the electronic medical record system 11.

[0071] In the above embodiment, the medical information processing system 100 generates order information for image examination orders, but it may also generate order information other than image examination orders. For example, the medical information processing system 100 may generate order information for physiological examination orders or specimen examination orders.

[0072] According to at least one embodiment described above, by having an acquisition unit that acquires pathological information relating to the pathological condition presented by the patient, a conversion unit that maps the pathological information onto a disease ontology to convert the disease ontology into a modified disease ontology that reflects the patient's condition, a reception unit that accepts a designation regarding a primary candidate disease, a identification unit that identifies secondary candidate diseases different from the primary candidate disease based on the modified disease ontology, and a generation unit that generates first-order information relating to the secondary candidate disease, secondary diseases other than the primary disease can also be easily discovered.

[0073] The embodiments described above can be expressed as follows. Equipped with processing circuitry, The aforementioned processing circuit is Obtain pathological information regarding the patient's condition, By mapping the aforementioned pathological information onto the disease ontology, the disease ontology is transformed into a modified disease ontology that reflects the patient's condition. We accept designations for the primary candidate disease. Based on the modified disease ontology, secondary candidate diseases different from the primary candidate disease are identified. To generate first-order information regarding the aforementioned secondary candidate diseases, Medical information processing device.

[0074] While several embodiments have been described, these embodiments are presented as examples only and are not intended to limit the scope of the invention. These embodiments can be carried out in a variety of other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their variations are included in the scope and spirit of the invention, as well as in the claims and their equivalents. [Explanation of Symbols]

[0075] 1…Hospital system 10…HIS 11…Electronic medical record system 20…Hereafter, RIS 30…Modality 40…PACS 50…Diagnostic Information Database 51…Medical interview information 52…Biochemical information 53…Basic Examination Information 100…Medical Information Processing System 110...Communication Interface 120... Input Interface 130…Display 140… Processing circuit 141... Acquisition function 142...Conversion function 143... Reception function 144...Specific function 145…Generation function 150...memory 151… Disease Ontology NW...Network

Claims

1. An acquisition unit that acquires pathological information regarding the patient's condition, A conversion unit that maps the aforementioned pathological information onto a disease ontology, thereby converting the disease ontology into a modified disease ontology that reflects the patient's condition, The reception desk that accepts designations for the primary candidate disease, An identification unit that identifies secondary candidate diseases different from the primary candidate disease based on the modified disease ontology, The system comprises a generation unit that generates first-order information relating to the aforementioned secondary candidate diseases, Medical information processing system.

2. The aforementioned disease ontology includes at least a layer relating to pathophysiology and a layer relating to disease. The medical information processing system according to claim 1.

3. The system further comprises a memory unit for storing the aforementioned disease ontology. A medical information processing system according to claim 1 or 2.

4. The generation unit further generates second-order information relating to at least one of the complications or side effects corresponding to the primary candidate disease. The medical information processing system according to claim 1.

5. The generation unit generates the second order information based on the recommendation level for at least one of the complications or side effects. The medical information processing system according to claim 4.

6. The generating unit calculates the recommendation level based on at least one of the probability of occurrence of at least one of the complications or side effects, the recommendation rank, or the coefficient of change. The medical information processing system according to claim 5.

7. The aforementioned recommendation rank is determined based on the clinical practice guidelines for the primary candidate disease. The medical information processing system according to claim 6.

8. The coefficient of change is determined based on the patient's past test results. The medical information processing system according to claim 6.

9. Computers Obtain pathological information regarding the patient's condition, By mapping the aforementioned pathological information onto the disease ontology, the disease ontology is transformed into a modified disease ontology that reflects the patient's condition. We accept designations for the primary candidate disease. Based on the modified disease ontology, secondary candidate diseases different from the primary candidate disease are identified. To generate first-order information regarding the aforementioned secondary candidate diseases, Medical information processing method.

10. On the computer, Obtain pathological information regarding the patient's condition, By mapping the aforementioned pathological information onto the disease ontology, the disease ontology is transformed into a modified disease ontology that reflects the patient's condition. We accept designations for the primary candidate disease. Based on the modified disease ontology, secondary candidate diseases different from the primary candidate disease are identified. The system generates first-order information regarding the aforementioned secondary candidate diseases. program.

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