JP2024073820A - Display control device and computer program - Google Patents

Abstract

To heighten usability when there exists a plurality of inference models in order to provide a specific clinical decision.SOLUTION: A display control device acquires a first inference result corresponding to a clinical decision for a subject that is obtained with regard to a prescribed item by inputting subject information concerning the subject to a first inference model, and a second inference result corresponding to a clinical decision for the subject that is obtained with regard to the prescribed item by inputting the subject information to a second inference model. The display control device causes a display device 20 to show the first inference result together with a first identifier 211 that identifies the first inference model, and causes the display device 20 to show the second inference result together with a second identifier 212 that identifies the second inference model.SELECTED DRAWING: Figure 2

Description

The present disclosure relates to an apparatus for controlling information displayed on a display device. The present disclosure also relates to a computer program executable by a processor installed in the apparatus.

Inference models that are generated using machine learning technology to replace or support the clinical judgment of medical professionals in clinical settings are known. For example, Patent Document 1 discloses a model that infers whether a subject is suffering from a specific type of arrhythmia based on whether a specific type of peak is present in electrocardiogram waveform data acquired from the subject.

Patent No. 7032747

Improve usability when multiple inference models exist to inform a particular clinical decision.

One aspect provided by the present disclosure is a display control device,
an interface for accepting subject information relating to a subject;
a processor for controlling the information displayed on the display device;
Equipped with
The processor,
inputting the subject information into a first inference model to obtain a first inference result corresponding to a clinical judgment for the subject obtained for a predetermined item;
inputting the subject information into a second inference model to obtain a second inference result corresponding to a clinical judgment for the subject obtained for the predetermined item;
displaying the first inference result together with a first identifier that identifies the first inference model on the display device;
The second inference result is displayed on the display device together with a second identifier that identifies the second inference model.

One aspect provided by the present disclosure is a computer program executable by a processor mounted on a display control device, comprising:
By being executed, the display control device
Accept information about the subject,
inputting the subject information into a first inference model to obtain a first inference result corresponding to a clinical judgment for the subject obtained for a predetermined item;
inputting the biological information into a second inference model to obtain a second inference result corresponding to a clinical judgment for the subject obtained for the specified item;
displaying the first inference result together with a first identifier that identifies the first inference model on the display device;
The second inference result is displayed on the display device together with a second identifier that identifies the second inference model.

In recent years, various inference models that output clinical judgments based on input biometric information have been put to practical use, but the inference results are not always convincing to users. In particular, when the inference model is generated through machine learning, it is not always possible to explicitly describe the basis for the inference results, and it can be difficult to be confident about the validity of the inference results.

The configurations relating to the above-mentioned exemplary embodiments can provide new methods of examination and analysis for users to make clinical judgments, such as evaluating the validity of inference results from multiple inference models by comparing different clinical judgments about the same item output from multiple inference models, or evaluating in parallel the inference results from multiple inference models that provide clinical judgments about multiple items that can be referenced for the same purpose. Therefore, usability can be improved when multiple inference models are present to provide a specific clinical judgment.

2 illustrates an example of a functional configuration of a display control device according to an embodiment. 2 shows an example of a screen displayed on the display device of FIG. 1 . 10 shows another example of the screen displayed on the display device of FIG. 10 shows another example of the screen displayed on the display device of FIG. 10 shows another example of the screen displayed on the display device of FIG. 10 shows another example of the screen displayed on the display device of FIG. 10 shows another example of the screen displayed on the display device of FIG. 10 shows another example of the screen displayed on the display device of FIG. 10 shows another example of the screen displayed on the display device of FIG. 10 shows another example of the screen displayed on the display device of FIG. 10 shows another example of the screen displayed on the display device of FIG. 10 shows another example of detailed information displayed on the display device of FIG. 1 . 10 shows another example of detailed information displayed on the display device of FIG. 1 . 10 shows another example of detailed information displayed on the display device of FIG. 1 . 1 illustrates an example of a configuration of a model evaluation system according to an embodiment.

Example embodiments are described in detail below with reference to the accompanying drawings.

The expression "at least one of A and B" used in this specification with respect to two entities, A and B, includes cases where only A is specified, where only B is specified, and where both A and B are specified. Each entity, A and B, may be singular or plural, unless otherwise specified.

The expression "at least one of A, B, and C" used in this specification with respect to three subjects, A, B, and C, means cases where only A is specified, only B is specified, only C is specified, A and B are specified, B and C are specified, A and C are specified, and all of A, B, and C are specified. Each of the subjects A, B, and C may be singular or plural, unless otherwise specified. The same applies to cases where the subject of the description is four or more subjects.

FIG. 1 illustrates a functional configuration of a display control device 10 according to one embodiment. The display control device 10 is a device for controlling information displayed on a display device 20.

The display control device 10 includes an input interface 11. The input interface 11 is configured as a hardware interface that receives subject information SB of the subject 30. The subject information SB may be in the form of digital data or analog data depending on the specifications of the sensor or measuring device.

When the subject information SB is in the form of analog data, the input interface 11 is equipped with an appropriate conversion circuit including an A/D converter. This description also applies to other signals and data that can be accepted by the input interface 11, which will be described later.

The term "subject information" used in this specification includes not only information related to the biological parameters of the subject, but also information related to the attributes of the subject, information related to the health condition of the subject, and information related to the settings of devices connected to the subject. Examples of information related to biological parameters include general vital sign parameters (respiratory rate, blood pressure, heart rate, pulse rate, etc.) and test results (white blood cell count, serum creatinine value, urine specific gravity, etc.). Examples of information related to attributes include height, weight, age, and sex. Examples of information related to health conditions include medical history, underlying diseases, complications, surgical history, medication history, and length of hospitalization. Examples of information related to settings include the flow rate of the artificial respirator and whether or not a pacemaker is implanted. "Subject information" may be obtained from the subject through various sensors and measuring devices, or may be input by medical professionals or the subject himself through various input devices.

The display control device 10 includes a processor 12. The processor 12 is configured to input subject information SB to a first inference model 41 and a second inference model 42.

The first inference model 41 is an inference algorithm configured to output a first inference result corresponding to a clinical judgment obtained for a specified item when subject information SB acquired from the subject 30 is input. The first inference result may indicate the clinical judgment deterministically or probabilistically. In particular, in the latter case, the inference algorithm may be generated through various types of machine learning.

The second inference model 42 is an inference algorithm configured to output a second inference result corresponding to a clinical judgment obtained for the above-mentioned specified item based on a method different from that of the first inference model 41 when subject information SB obtained from the subject 30 is input. The second inference result may deterministically indicate the clinical judgment, or may indicate probabilistically. In particular, in the latter case, the inference algorithm may be generated through various types of machine learning.

As used herein, the expression "clinical judgments derived based on different techniques" is intended to include the following cases:

-Different clinical judgments obtained for the same item by using algorithms provided by different vendors. -Different clinical judgments obtained for the same item by using algorithms of different versions provided by the same vendor. -Different clinical judgments obtained for the same item by using algorithms generated through different machine learning techniques. -Different clinical judgments obtained for the same item by using algorithms generated through machine learning using different teacher data (including cases where the teacher labels are assigned by different people). -Different clinical judgments obtained for the same item by using algorithms that require different inputs. -Different clinical judgments obtained for different items.

The display control device 10 includes an output interface 13. The processor 12 is configured as a hardware interface that outputs a display control signal DC to the display device 20. The display control signal DC is configured to cause the display device 20 to display the first inference result together with a first identifier that identifies the first inference model 41, and to display the second inference result together with a second identifier that identifies the second inference model 42.

The display control signal DC of the output interface 13 may be a digital signal or an analog signal depending on the specifications of the display device 20. If the display control signal DC is an analog signal, the output interface 13 is equipped with an appropriate conversion circuit including a D/A converter. This description also applies to other signals and data that can be output by the output interface 13 described below.

Figure 2 shows an example of a model selection screen 21 displayed on the display device 20 based on the display control signal DC.

The model selection screen 21 includes a first identifier 211. The first identifier 211 identifies "Infusion Model 1." "Infusion Model 1" is an example of a first inference model 41. "Infusion Model 1" is an inference algorithm that outputs the probability that the subject 30 is dehydrated when the heart rate and blood pressure of the subject 30 are input as the subject information SB of the subject 30. "Dehydration" is an example of a predetermined item, and "probability of being dehydrated" is an example of a clinical judgment. In the model selection screen 21, the probability is displayed together with the first identifier 211.

The model selection screen 21 includes a second identifier 212. The second identifier 212 identifies "Infusion Model 2." "Infusion Model 2" is an example of a second inference model 42. "Infusion Model 2" is an inference algorithm that outputs the probability that the subject 30 is dehydrated when the heart rate, blood pressure, and infusion volume are input as subject information SB of the subject 30. In the model selection screen 21, the probability is displayed together with the second identifier 212.

In other words, the parameters input as subject information SB may differ between the first inference model 41 and the second inference model 42.

As another example, a "lactate value model 1" may be used as the first inference model 41, and a "lactate value model 2" may be used as the second inference model 42. In both "lactate value model 1" and "lactate value model 2", subject information SB of the subject 30, such as height, weight, percutaneous arterial oxygen saturation (SpO2), end-tidal carbon dioxide concentration (EtCO2), hemoglobin content (Hb), heart rate, cardiac output (CO), respiratory rate, and peripheral body temperature, are input.

"Lactate value model 1" is an inference algorithm that outputs the blood lactate value [mmol/L] of the subject 30 in response to the input. "Blood lactate value" is an example of a predetermined item and an example of clinical judgment. "Lactate value model 2" is an inference algorithm that infers whether the blood lactate value of the subject 30 exceeds 2 [mmol/L] in response to the input, and outputs either "high" or "low." "Whether the blood lactate value is high or low" is an example of clinical judgment. In other words, the manner in which clinical judgment is shown for a predetermined item may differ between the first inference model 41 and the second inference model 42.

The number of inference models to which the processor 12 inputs the subject information SB may be three or more. In this case, three or more identifiers each identifying the three or more inference models are displayed on the model selection screen 21 of the display device 20.

Figure 3 shows another example of a model selection screen 21 displayed on the display device 20 based on the display control signal DC. In this example, multiple inference models that provide different clinical judgments are displayed to comprehensively judge the circulatory dynamics of the subject 30. In other words, multiple inference models that provide clinical judgments about multiple items that can be referenced for a common purpose can be displayed on the display device 20.

As shown in the figure, the form in which the clinical judgment is indicated may be not only numerical values or letters, but also figures, symbols, colors, etc.

In recent years, various inference models that output clinical judgments based on input biometric information have been put to practical use, but the inference results are not always convincing to users. In particular, when the inference model is generated through machine learning, it is not always possible to explicitly describe the basis for the inference results, and it can be difficult to be confident about the validity of the inference results.

The above configuration provides a new method of examination and analysis for users to make clinical judgments, such as evaluating the validity of the inference results from each inference model by comparing different clinical judgments about the same item output from multiple inference models, or evaluating in parallel the inference results from multiple inference models that provide clinical judgments about multiple items that can be referenced for the same purpose. Therefore, usability can be improved when multiple inference models are available to provide a specific clinical judgment.

As illustrated in FIG. 2, the model selection screen 21 includes a first evaluation GUI 213 and a second evaluation GUI 214. The first evaluation GUI 213 is a GUI for inputting an evaluation of the inference result of "Infusion Model 1". The second evaluation GUI 214 is a GUI for inputting an evaluation of the inference result of "Infusion Model 2". The smiling image is configured to be clicked or tapped when a high evaluation is given to the inference result. The worried face image is configured to be clicked or tapped when a low evaluation is given to the inference result.

The first evaluation GUI 213 and the second evaluation GUI 214 can be an example of the user interface 50 illustrated in FIG. 1. That is, the input interface 11 of the display control device 10 can be configured to receive evaluation information EV corresponding to an evaluation of at least one of the inference result by the first inference model 41 and the inference result by the second inference model 42.

The user interface 50 for inputting the evaluation information EV may take any suitable form, such as voice input, gaze input, or gesture input, in addition to or instead of the above-described forms involving cursor operation or touch operation.

The processor 12 is configured to store the evaluation information EV in the storage 60. The storage 60 is a storage device that can be realized by a semiconductor memory, a hard disk drive, a magnetic tape drive, etc. The evaluation information EV may be sent directly from the processor 12 to the storage 60, or may be sent via the output interface 13.

With this configuration, it is possible to accumulate information on the evaluation of the validity of the inference results output by each inference model. Furthermore, since the evaluation can be made by referring to multiple inference models displayed on the display device 20, it is possible to add a new measure of relative evaluation between multiple inference models in addition to the absolute evaluation of each inference model.

The evaluation information EV input through the user interface 50 may include attribute information of the user who performed the evaluation. The attribute information may include at least one of the following: name, sex, age, name of medical department, name of affiliated organization, job function, position, hospital room in charge, patient in charge, length of work history, length of hospital stay, specialized skills, and acquired qualifications.

The evaluation information EV can be output from the output interface 13 so as to be output from an output device in the form of a report. Examples of output devices include a printer, a speaker, a display, a data transmission device, etc.

The attribute information may be entered through text input, through biometric authentication, or by optically or magnetically reading attribute information stored in a card or tag that the user can carry, or by reading the attribute information through near-field wireless communication.

For example, even people with the same job title may evaluate the same inference result differently depending on their level of experience. By adding attribute information about the evaluator to the evaluation information, measures such as reliability and objectivity can be introduced into the evaluation. In other words, it may become possible to evaluate the evaluation itself.

The processor 12 may read out the evaluation information EV stored in the storage 60 as necessary and display it on the display device 20. The evaluation information EV may be read out directly from the storage 60 or may be received through the input interface 11. In other words, the processor 12 may output, from the output interface 13, a display control signal DC that causes the display device 20 to display the evaluation information EV.

Figure 4 shows another example of the model selection screen 21 displayed on the display device 20. In this example, "urine volume model 1" is identified by the first identifier 211, and "urine volume model 2" is identified by the second identifier 212.

Figure 5 illustrates an example of a state in which evaluation information EV previously input for "urine volume model 1" is displayed. In this example, when the user taps the first identifier 211, the evaluation information EV is displayed. Note that the evaluation information EV may be constantly displayed on the model selection screen 21 according to the layout specifications of the model selection screen 21.

Specifically, as illustrated in FIG. 1, an instruction signal IS is generated in response to an instruction to display evaluation information EV input to user interface 50. When the instruction signal IS is received by input interface 11, processor 12 reads evaluation information EV corresponding to the instruction signal IS from storage 60, and outputs a display control signal DC from output interface 13 to display evaluation information EV on display device 20.

As illustrated in FIG. 5, the evaluation information EV may include more detailed comments about the inference model. The comments may be entered into the user interface 50 through text input or voice input by the user.

With this configuration, users can make clinical decisions by referring not only to the inference results of each inference model, but also to the accumulated evaluations for that inference model.

Figure 6 shows another example of the model selection screen 21 displayed on the display device 20. In this example, the first identifier 211 and the second identifier 212 are different colors. The color of the second identifier 212 means that the evaluation of "urine volume model 2" is low. In other words, the processor 12 can be configured to change the display mode of at least one of the first identifier 211 and the second identifier 212 depending on the high or low evaluation of the inference model. In this example, a display control signal DC that changes the color of the second identifier 212 to indicate that the evaluation of "urine volume model 2" is low is output from the output interface 13.

The display manner can be changed by changing at least one of the shape of the identifier, the display position of the identifier, and the text displayed with the identifier, in addition to or instead of the color of the identifier.

This configuration makes it possible to improve the visibility of the evaluations accumulated for each inference system. It also makes it easier to guide users to more specific evaluation information for each inference system. Figure 7 shows an example of a state in which a user who wishes to confirm the reason why "Urine Volume Model 2" is rated low has displayed evaluation information EV in a manner similar to the example shown in Figure 5.

In addition to or instead of the examples given so far, the evaluation information EV may include the number of times a particular evaluation was made, the number of times an inference model was used, the number of evaluations relative to the number of times it was used, etc.

In addition to or instead of the above-mentioned evaluation information EV, more detailed information regarding the inference results of each inference model may be displayed on the display device 20. One example of the detailed information is the change in the inference results over time.

Figure 8 illustrates an example of a state in which detailed information DT about "Infusion Model 1" is displayed by tapping the first identifier 211 on the model selection screen 21 illustrated in Figure 2. As described above, in the example illustrated in Figure 2, the probability that the subject 30 is currently in a dehydrated state, as inferred by "Infusion Model 1," is displayed together with the first identifier 211. The detailed information DT illustrated in Figure 8 illustrates the change over time from the past to the present in the probability inferred by "Infusion Model 1."

In this example, when the first identifier 211 is tapped by the user, the detailed information DT is displayed. Note that the detailed information DT may be constantly displayed on the model selection screen 21 according to the layout specifications of the model selection screen 21.

Specifically, the processor 12 can be configured to store data corresponding to the result in a storage area such as the storage 60 each time an inference result is obtained for the "infusion model 1". As illustrated in FIG. 1, an instruction signal IS is generated in response to an instruction to display detailed information DT input to the user interface 50. When the instruction signal IS is received by the input interface 11, the processor 12 reads out the detailed information DT corresponding to the instruction signal IS from the storage 60 or the like, and outputs a display control signal DC from the output interface 13 to display the detailed information DT on the display device 20.

Figure 9 illustrates an example of a state in which detailed information DT about "Infusion Model 2" is similarly displayed by tapping the second identifier 212 on the model selection screen 21 illustrated in Figure 2.

For example, even if two inference models output inference results corresponding to the same clinical judgment at a certain point in time, if the inference algorithms are different, the history of the inference results may differ. With the above configuration, the validity of the inference results, including the history, can be examined.

In addition, if the inference algorithm is capable of outputting future predictions for a particular clinical judgment, the time axis used to show changes in the inference results over time in the detailed information DT may include future points in time.

As illustrated in FIG. 10, the displayed detailed information DT may include an event identifier 215 that indicates the occurrence of an event related to the subject information SB that occurred at a particular time point. In this example, the event identifier 215 indicates that an "increase in the amount of infusion" was performed.

For example, the user can specify the type of event and the time when the event occurred through the user interface 50. The user interface 50 generates an instruction signal IS for adding information related to the specified event to the detailed information DT. When the instruction signal IS is received by the input interface 11, the processor 12 outputs a display control signal DC from the output interface 13 to cause the display device 20 to additionally display the event identifier 215 corresponding to the instruction signal IS.

It is also possible to adopt a configuration in which the occurrence of a specific event related to a particular clinical judgment output by the inference model is automatically detected and reflected in the detailed information DT.

When there are multiple inference models that infer clinical judgments for the same item, the behavior for a specific event may differ due to differences in the inference algorithms. With the above configuration, it may be easy to evaluate the validity of the inference results of the inference model in terms of the behavior for a specific event. In addition, it may be easy to understand what events affect the behavior of the inference model.

As illustrated in FIG. 11, the displayed detailed information DT may include a rating identifier that indicates the rating given to the inference result at a particular point in time. In this example, rating identifier 216a and rating identifier 216b indicate that a high rating was given to the inference result. Rating identifier 216c indicates that a low rating was given to the inference result.

For example, the user can specify a specific point in time in the change of the inference result over time and an evaluation of the inference result at that point in time through the user interface 50. The user interface 50 generates an instruction signal IS for adding information related to the specified evaluation to the detailed information DT. When the instruction signal IS is received by the input interface 11, the processor 12 outputs a display control signal DC from the output interface 13, which causes the display device 20 to additionally display an evaluation identifier corresponding to the instruction signal IS.

The above configuration visualizes which behaviors of an inference model are being evaluated in what way, making it easier to understand the basis for the evaluation of the inference model.

The processor 12 may change the display mode of the evaluation identifier depending on the amount of time that has elapsed since the evaluation information EV was accepted. In the example shown in FIG. 11, the evaluation identifier 216b is displayed lighter than the other evaluation identifiers. The processor 12 is configured to display evaluation identifiers that have been evaluated for a longer period of time lighter.

This configuration allows relatively new ratings to be distinguished from relatively old ratings on the same screen, so that the ratings themselves can be evaluated in terms of the time that has elapsed since the rating was made.

Figure 12 shows another example of detailed information DT displayed on the display device 20. In this example, when the subject information SB input to the inference model includes multiple biological parameters, the contribution of each biological parameter to the inference result output from the inference model is displayed in the form of a bar graph. Note that in addition to or instead of the contribution, the measured value of each biological parameter may be displayed.

Figure 13 shows another example of the contribution display included in the detailed information DT. In this example, each of the multiple biological parameters included in the subject information SB is displayed in the form of a bar graph, along with the degree to which it acts to increase or decrease the value output from the inference model. A bar graph extending to the right indicates that the corresponding biological parameter is acting in the direction to increase the value output from the inference model. A bar graph extending to the left indicates that the corresponding biological parameter is acting in the direction to decrease the value output from the inference model. Note that the measured value of each biological parameter may be displayed in addition to or instead of the contribution degree.

Figure 14 shows another example of the contribution display included in the detailed information DT. In this example, the extent to which each part of the measurement waveform of the biological parameter included in the subject information SB contributes to the inference result output from the inference model is displayed in the form of a heat map.

According to the configurations of the examples described with reference to Figures 12 to 14, how the subject information SB affects the inference result is visualized, so that it is easy to evaluate the validity of the inference result by the inference model from the viewpoint of the behavior in response to the input of a specific biological parameter. In addition, it is easy to understand which biological parameter affects the behavior of the inference model.

The display control device 10, first inference model 41, second inference model 42, display device 20, user interface 50, and storage 60 described above may constitute a model evaluation system 70 for evaluating multiple inference models. The model evaluation system 70 may take various forms.

Figure 15 shows an example of the configuration of a model evaluation system 70. Each of the display device 20, the user interface 50, and the storage 60 is communicatively connected to the display control device 10 via a communication network N.

The model evaluation system 70 may include a server device 71. The server device 71 is communicatively connected to the display control device 10 via a communication network N. At least one of the first inference model 41 and the second inference model 42 may be mounted on the display control device 10 or on the server device 71. In the example shown in FIG. 15, the first inference model 41 is mounted on the display control device 10, and the second inference model 42 is mounted on the server device 71. When multiple server devices are connected to the communication network N to configure the model evaluation system 70, the first inference model 41 and the second inference model 42 may be mounted on different server devices.

The display device 20 may be mounted on the display control device 10. The user interface 50 may be mounted on the display device 20 or on the display control device 10. The storage 60 may be mounted on the display control device 10 or on the server device 71.

As illustrated in FIG. 1, N display control devices 10 (101 to 10N; N is an integer equal to or greater than 2) can be communicatively connected to the storage 60. In other words, the evaluation information EV output from each display control device 10 can be collectively stored in the storage 60.

This configuration not only makes it easier to accumulate more evaluations of the inference model, but also makes it easier for multiple medical professionals to share the evaluations. In addition, ensemble learning of new inference models can be made possible using the inference results collected from each display control device and information related to the evaluations of those results.

The processor 12 of the display control device 10 having the various functions described above can be realized by a general-purpose microprocessor that operates in cooperation with a general-purpose memory. Examples of the general-purpose microprocessor include a CPU, an MPU, and a GPU. Examples of the general-purpose memory include a ROM and a RAM. In this case, the ROM can store a computer program that realizes the function. The ROM is an example of a non-transient computer-readable medium that stores a computer program. The general-purpose microprocessor specifies at least a part of the program stored in the ROM, expands it on the RAM, and works with the RAM to execute the above-mentioned processing. The computer program may be pre-installed in the general-purpose memory, or may be downloaded from an external server device via a communication network and then installed in the general-purpose memory. In this case, the external server device is an example of a non-transient computer-readable medium that stores a computer program.

The processor 12 may be realized by a dedicated integrated circuit such as a microcontroller, ASIC, or FPGA capable of executing the above computer program. In this case, the above computer program is pre-installed in a memory element included in the dedicated integrated circuit. The memory element is an example of a computer-readable medium that stores a computer program. The processor 12 may also be realized by a combination of a general-purpose microprocessor and a dedicated integrated circuit.

The various configurations described thus far are merely examples intended to facilitate understanding of the present disclosure. Each configuration example may be modified as appropriate or combined with other configuration examples.

The configurations listed below also form part of this disclosure.

Item 1:
an interface for accepting subject information relating to a subject;
a processor for controlling the information displayed on the display device;
Equipped with
The processor,
inputting the subject information into a first inference model to obtain a first inference result corresponding to a clinical judgment for the subject obtained for a predetermined item;
inputting the subject information into a second inference model to obtain a second inference result corresponding to a clinical judgment for the subject obtained for the predetermined item;
displaying the first inference result together with a first identifier that identifies the first inference model on the display device;
displaying the second inference result on the display device together with a second identifier that identifies the second inference model;
Display control device.

Item 2:
the interface receives evaluation information corresponding to an evaluation of at least one of the first inference result and the second inference result;
The processor stores the evaluation information in a storage device.
Item 2. A display control device according to item 1.

Item 3:
The evaluation information includes attribute information of the person who performed the evaluation.
3. A display control device according to item 2.

Item 4:
The processor causes the display device to display the evaluation information.
4. The display control device according to item 2 or 3.

Item 5:
The processor changes a display mode of at least one of the first identifier and the second identifier depending on the level of the evaluation.
5. A display control device according to any one of items 2 to 4.

Item 6:
The processor causes the display device to display at least one of the change over time of the first inference result and the change over time of the second inference result.
6. A display control device according to any one of items 1 to 5.

Item 7:
The interface receives event information indicating an occurrence of an event related to the subject information at a specific time point;
The processor causes an identifier corresponding to the event to be displayed at a position corresponding to the time point in the time-lapse image.
Item 7. A display control device according to item 6.

Item 8:
the interface accepts evaluation information corresponding to an evaluation of at least one of the first inference result and the second inference result at a particular time;
the processor causes an identifier corresponding to the evaluation to be displayed at a location corresponding to the time point in the time course.
8. A display control device according to item 6 or 7.

Item 9:
The processor changes a display mode of the identifier in accordance with an elapsed time since the evaluation information was received.
Item 9. A display control device according to item 8.

Item 10:
the processor causes the display device to display, for at least one of the first inference result and the second inference result, at least one of the measurement values of a plurality of biological parameters included in the subject information and the contributions of the measurement values to the inference result;
10. A display control device according to any one of items 1 to 9.

10: Display control device, 11: Input interface, 12: Processor, 20: Display device, 211: First identifier, 212: Second identifier, 215: Event identifier, 216a-216c: Evaluation identifier, 30: Subject, 41: First inference model, 42: Second inference model, 60: Storage, DT: Detailed information, EV: Evaluation information, SB: Subject information

Claims (11)

an interface for accepting subject information relating to a subject;
a processor for controlling the information displayed on the display device;
Equipped with
The processor,
inputting the subject information into a first inference model to obtain a first inference result corresponding to a clinical judgment for the subject obtained for a predetermined item;
inputting the subject information into a second inference model to obtain a second inference result corresponding to a clinical judgment for the subject obtained for the predetermined item;
displaying the first inference result together with a first identifier that identifies the first inference model on the display device;
displaying the second inference result on the display device together with a second identifier that identifies the second inference model;
Display control device. the interface receives evaluation information corresponding to an evaluation of at least one of the first inference result and the second inference result;
The processor stores the evaluation information in a storage device.
The display control device according to claim 1 . The evaluation information includes attribute information of the person who performed the evaluation.
The display control device according to claim 2 . The processor causes the display device to display the evaluation information.
The display control device according to claim 2 . The processor changes a display mode of at least one of the first identifier and the second identifier depending on the level of the evaluation.
The display control device according to claim 2 . The processor causes the display device to display at least one of the change over time of the first inference result and the change over time of the second inference result.
The display control device according to claim 1 . The interface receives event information indicating an occurrence of an event related to the subject information at a specific time point;
The processor causes an identifier corresponding to the event to be displayed at a position corresponding to the time point in the time-lapse image.
The display control device according to claim 6. the interface accepts evaluation information corresponding to an evaluation of at least one of the first inference result and the second inference result at a particular time;
the processor causes an identifier corresponding to the evaluation to be displayed at a location corresponding to the time point in the time course.
The display control device according to claim 6. The processor changes a display manner of the identifier according to an elapsed time since the evaluation information was accepted.
The display control device according to claim 8. the processor causes the display device to display, for at least one of the first inference result and the second inference result, at least one of the measurement values of a plurality of biological parameters included in the subject information and the contributions of the measurement values to the inference result;
The display control device according to claim 1 . A computer program executable by a processor mounted on a display control device,
By being executed, the display control device
Accept information about the subject,
inputting the subject information into a first inference model to obtain a first inference result corresponding to a clinical judgment for the subject obtained for a predetermined item;
inputting the subject information into a second inference model to obtain a second inference result corresponding to a clinical judgment for the subject obtained for the predetermined item;
displaying the first inference result together with a first identifier that identifies the first inference model on a display device;
displaying the second inference result on the display device together with a second identifier that identifies the second inference model;
Computer program.

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