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WO2025182457A1 - Dispositif d'aide à la décision clinique, procédé d'aide à la décision clinique et programme - Google Patents

Dispositif d'aide à la décision clinique, procédé d'aide à la décision clinique et programme

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Publication number
WO2025182457A1
WO2025182457A1 PCT/JP2025/003347 JP2025003347W WO2025182457A1 WO 2025182457 A1 WO2025182457 A1 WO 2025182457A1 JP 2025003347 W JP2025003347 W JP 2025003347W WO 2025182457 A1 WO2025182457 A1 WO 2025182457A1
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Prior art keywords
data
period
patient
clinical decision
decision support
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English (en)
Japanese (ja)
Inventor
吉村俊昭
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Terumo Corp
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Terumo Corp
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics

Definitions

  • This disclosure relates to a clinical decision support device, a clinical decision support method, and a program.
  • Patent Publication No. 7290624 discloses technology for estimating a patient's blood glucose level based on the amount of food consumed and the amount of insulin administered by the patient.
  • the present invention aims to solve the above-mentioned problems.
  • a first aspect of the present disclosure is a clinical decision support device comprising: an acquisition unit that acquires inferred data, which is time-series data of glucose levels obtained by setting variation factor data, which is data related to variation factors that induce variations in a patient's glucose level, in a simulation model for estimating the patient's glucose level; and a display control unit that displays information corresponding to the inferred data on a display unit; and when the variation factor data is changed, the display control unit displays information on the display unit corresponding to the inferred data obtained by setting the changed variation factor data in the simulation model.
  • a user can change the value of the variation factor data and estimate the trend of the patient's blood glucose level (glucose value) reflecting the changed variation factor data. Therefore, item (1) above simplifies the process of estimating a patient's blood glucose level.
  • the user can estimate the appropriate meal amount, meal time, insulin dose, insulin administration time, etc. for the patient.
  • the user can provide the patient with advice on the optimal meal amount, meal time, insulin dose, insulin administration time, etc.
  • the clinical decision support device described in item (1) above includes a simulation model generation unit that generates the simulation model in advance, and the simulation model generation unit includes a first acquisition step of acquiring measurement data that is time-series data of the glucose level of the patient in a first period, a second acquisition step of acquiring the fluctuation factor data related to the fluctuation factors that induced fluctuations in the glucose level of the patient during a second period that is included in the first period and is shorter than the first period, and ... assigning the fluctuation factor data acquired in the second acquisition step to contribution opportunities of each of the fluctuation factors during a period that is included in the first period but is not included in the second period, thereby generating a simulation model in advance during the first period.
  • the simulation model may be generated in advance by executing a determination step of provisionally determining the fluctuation factor data, an optimization step of setting the fluctuation factor data provisionally determined in the determination step in a mathematical model for obtaining predicted data of the time series of the patient's glucose levels, and performing an optimization process on the parameters included in the mathematical model to obtain optimal solutions for the parameters so that the predicted data obtained by the mathematical model approximates the measurement data obtained in the first acquisition step, and a generation step of generating the simulation model by setting the optimal solutions for the parameters obtained in the optimization step in the mathematical model.
  • the simulation model generation unit provisionally determines the variation factor data for the first period by assigning variation factor data for a second period, which is shorter than the first period, to the contribution opportunities of each variation factor for a period (third period) that is included in the first period but not included in the second period.
  • the patient does not need to obtain variation factor data for the third period.
  • the patient or user does not need to manually input the variation factor data for the third period into the device.
  • the configuration of item (2) above reduces the burden on the patient or user (doctor, etc.). Therefore, the configuration of item (2) above simplifies the process of estimating the patient's blood glucose level.
  • the configuration of item (2) above makes it possible to obtain an optimal solution for the parameters included in a predetermined mathematical model.
  • the configuration of item (2) above makes it possible to generate a simulation model tailored to a patient by setting an optimal solution for the parameters in a predetermined mathematical model.
  • a simulation model tailored to a patient By using a simulation model tailored to a patient, a user (doctor, etc.) can more easily estimate the patient's blood glucose level.
  • variable factor data may be changed by an input operation by a user.
  • the user can freely change the variable factor data.
  • the clinical decision support device described in item (1) or (2) above may further include an optimal solution acquisition unit that acquires an optimal solution for the variable factor data by performing optimization processing on the variable factor data based on a loss function in the inferred data, and the display control unit may display information on the display unit that corresponds to the inferred data obtained by setting the optimal solution in the simulation model.
  • the loss function may be an index reflecting a blood glucose state.
  • the loss function may be a value that maximizes the time-in range and minimizes the time-below range, or a value that maximizes the time-in range and minimizes the time-below range.
  • variable factor data acquired in the second acquisition step may include meal amount data indicating the amount of food consumed by the patient at each of multiple meal occasions during the second period, meal time data indicating the time of each of the multiple meal occasions during the second period, insulin dosage data indicating the amount of insulin administered to the patient at each of multiple insulin administration occasions during the second period, and insulin administration time data indicating the time of each of the multiple insulin administration occasions during the second period.
  • the display unit may display the estimated data of the time series of the patient's glucose levels as data distribution information for each time point, and may also display the fluctuation factor data related to the fluctuation factor that induced the fluctuation in the patient's glucose levels so that it overlaps with a graph area showing the data distribution information.
  • a second aspect of the present disclosure is a clinical decision-making support method comprising: an acquisition step of acquiring inferred data, which is time-series data of glucose levels obtained by setting fluctuation factor data, which is data related to fluctuation factors that induce fluctuations in a patient's glucose level, in a simulation model for estimating the patient's glucose level; and a display step of displaying information corresponding to the inferred data on a display unit; and when the fluctuation factor data is changed, the display step displays information corresponding to the inferred data obtained by setting the changed fluctuation factor data in the simulation model on the display unit.
  • a user can change the value of the variation factor data and estimate the trend of the patient's blood glucose level (glucose value) reflecting the changed variation factor data. Therefore, item (9) above simplifies the process of estimating a patient's blood glucose level.
  • the user can estimate the appropriate meal amount, meal time, insulin dose, insulin administration time, etc. for the patient.
  • the user can provide the patient with advice on the optimal meal amount, meal time, insulin dose, insulin administration time, etc.
  • the simulation model may be generated in advance by executing the following steps: a first acquisition step of acquiring measurement data, which is time-series data of the glucose level of the patient in a first period; a second acquisition step of acquiring the variation factor data related to the variation factors that caused fluctuations in the glucose level of the patient during a second period that is included in the first period and is shorter than the first period; a determination step of provisionally determining the variation factor data for the first period by assigning the variation factor data acquired in the second acquisition step to contribution opportunities of each of the variation factors for a period that is included in the first period but not included in the second period; an optimization step of setting the variation factor data provisionally determined in the determination step in a mathematical model for obtaining predicted data of the time series of the glucose level of the patient, and performing optimization processing on parameters included in the mathematical model so that the predicted data obtained by the mathematical model and the measurement data acquired in the first acquisition step are similar to each other, thereby obtaining optimal solutions of the parameters; and a generation
  • the variation factor data for the first period is provisionally determined by assigning variation factor data for a second period, which is shorter than the first period, to the contribution opportunities of each variation factor for a period (third period) that is included in the first period but not included in the second period.
  • the patient does not need to obtain variation factor data for the third period.
  • the patient or user (doctor, etc.) does not need to manually input variation factor data for the third period into the device.
  • the configuration of item (10) above reduces the burden on the patient or user (doctor, etc.). Therefore, the configuration of item (10) above simplifies the process of estimating the patient's blood glucose level.
  • the configuration of item (10) above makes it possible to obtain an optimal solution for the parameters included in a predetermined mathematical model.
  • the configuration of item (10) above makes it possible to generate a simulation model tailored to a patient by setting an optimal solution for the parameters in a predetermined mathematical model.
  • a simulation model tailored to a patient By using a simulation model tailored to a patient, a user (doctor, etc.) can more easily estimate the patient's blood glucose level.
  • variable factor data may be changed by an input operation by a user.
  • the user can freely change the variable factor data.
  • the clinical decision-making support method described in item (9) or (10) above may further include an optimal solution acquisition step of performing optimization processing on the variable factor data based on a loss function in the inferred data to acquire an optimal solution for the variable factor data, and in the display step, information corresponding to the inferred data obtained by setting the optimal solution in the simulation model may be displayed on the display unit.
  • the loss function may be an index reflecting a blood glucose state.
  • the loss function may be a value that maximizes the time-in range and minimizes the time-below range, or a value that maximizes the time-in range or minimizes the time-below range.
  • variable factor data acquired in the second acquisition step may include meal amount data indicating the amount of food consumed by the patient at each of multiple meal occasions during the second period, meal time data indicating the time of each of the multiple meal occasions during the second period, insulin dosage data indicating the amount of insulin administered to the patient at each of multiple insulin administration occasions during the second period, and insulin administration time data indicating the time of each of the multiple insulin administration occasions during the second period.
  • the display step may display the estimated data of the time series of the patient's glucose levels as data distribution information for each time point, and may also display the fluctuation factor data related to the fluctuation factor that induced the fluctuation in the patient's glucose levels so as to overlap with a graph area showing the data distribution information.
  • a third aspect of the present disclosure is a program for causing a computer to execute the clinical decision support method described in any one of items (9) to (16) above.
  • the present invention allows for better estimation of a patient's blood glucose level.
  • FIG. 1 is a schematic diagram of a clinical decision support system.
  • FIG. 2 is a flowchart of the simulation model generation process.
  • FIG. 3 is a graph showing the patient's measurement data over seven days, as well as the amount of food eaten and the amount of insulin administered on the first day.
  • FIG. 4 is a graph showing the patient's measurement data for 7 days, and the amount of food eaten and the amount of insulin administered on each of the first to seventh days.
  • FIG. 5 is a graph displayed on the display unit.
  • FIG. 6 is a graph of the modified insulin data.
  • FIG. 7 is a flowchart of the clinical decision support process in manual mode.
  • FIG. 8 is a flow chart of the clinical decision support process in automatic mode.
  • glucose level simply refers to the glucose level in the interstitial fluid under the patient's skin.
  • the glucose level in interstitial fluid correlates with the blood glucose level. Therefore, in this specification, the glucose level may be treated as synonymous with the blood glucose level.
  • FIG. 1 is a schematic diagram of a clinical decision support system (CDSS) 10.
  • the clinical decision support system 10 includes a CGM (Continuous Glucose Monitoring) device 12, a clinical decision support device 14, and a server 16.
  • the CGM device 12 and the server 16 are communicatively connected to each other via a communication line 18 such as the Internet.
  • the clinical decision support device 14 and the server 16 are communicatively connected to each other via the communication line 18.
  • the server 16 is not an essential component of the clinical decision support system 10.
  • the CGM device 12 and the clinical decision support device 14 are connected to each other wirelessly or via a wired connection so that they can communicate with each other.
  • the CGM device 12 and the clinical decision support device 14 may be connected to each other so that they can communicate with each other via a communication line 18, or they may be connected to each other so that they can communicate with each other via short-range wireless communication such as Bluetooth (registered trademark).
  • the measurement data acquired by the CGM device 12 may be compiled into a compatible file format, and the clinical decision support device 14 may read it.
  • the CGM device 12 measures the glucose level in the interstitial fluid under the patient's skin.
  • the CGM device 12 includes a sensor device 20, which is an on-body device, and a data acquisition device 22.
  • the sensor device 20 and the data acquisition device 22 are connected to each other so as to be able to communicate with each other via wire or wirelessly.
  • the sensor device 20 is worn on the patient's body.
  • the sensor device 20 includes a sensor and a transmitter (neither shown).
  • the sensor continuously measures the glucose level in the patient's subcutaneous interstitial fluid.
  • the transmitter transmits a signal indicative of the glucose level measured by the sensor to the data acquisition device 22.
  • the data acquisition device 22 is carried by the patient.
  • the data acquisition device 22 may be, for example, a portable terminal such as a smartphone.
  • the data acquisition device 22 includes a receiver, a controller (processor, etc.), a monitor screen, a transmitter, memory, and an input unit (none of which are shown).
  • the receiver receives a signal indicating the glucose value transmitted from the sensor device 20.
  • the controller displays the patient's glucose value on the monitor screen.
  • the transmitter transmits a signal indicating the glucose value to the server 16 via the communication line 18.
  • the memory stores the time-series glucose values.
  • the patient may input variable factor data into the data acquisition device 22 via the input unit.
  • the clinical decision support device 14 is an information processing device that has a function of simulating a patient's blood glucose trend.
  • the clinical decision support device 14 also has a function of generating a simulation model for simulating a patient's blood glucose trend. That is, the clinical decision support device 14 also functions as a simulation model generation device. After generating a simulation model corresponding to the patient, the clinical decision support device 14 simulates the patient's blood glucose trend.
  • the clinical decision support device (simulation model generation device) 14 generates a simulation model corresponding to a patient based on "measurement data” and "variation factor data.” The clinical decision support device 14 also obtains "estimated data” by setting the patient's "variation factor data” in the simulation model. Before explaining the configuration of the clinical decision support device 14, we will define the names of each piece of data.
  • the measurement data is time series data of the patient's glucose levels over a first period (e.g., three days, one week, one month, etc.).
  • the measurement data is the glucose concentration in the patient's interstitial fluid measured continuously over a predetermined period.
  • the measurement data is typically obtained once every one to five minutes.
  • the measurement data is time series data of glucose levels measured by the CGM device 12 and is stored on the server 16.
  • the fluctuation factor data is data related to fluctuation factors during a second period (e.g., 24 hours) that is included in the first period but is shorter than the first period.
  • the fluctuation factor is the patient's behavior (disturbance) that triggers fluctuations in the patient's glucose levels, such as food intake and insulin administration.
  • the fluctuation factor data includes dietary data related to food intake and insulin data related to insulin administration.
  • the dietary data includes food amount data and meal time data corresponding to the food amount data.
  • the insulin data includes insulin dosage data and insulin administration time data corresponding to the insulin dosage data.
  • Food amount data is data indicating the amount of carbohydrates or sugars ingested by the patient at each of multiple meal occasions (breakfast, lunch, dinner, snacks, etc.) during the second period (24 hours).
  • Meal time data is data indicating the time at which each of multiple meal occasions occurred during the second period.
  • Insulin dosage data is data indicating the amount of insulin administered to the patient at each of multiple insulin administration occasions (before or during meals, before bedtime, etc.) during the second period.
  • Insulin administration time data is data indicating the time at which each of multiple insulin administration occasions occurred during the second period.
  • the patient can save the variable factor data on the server 16, for example, via a communication terminal (such as the CGM device 12 or a personal computer (not shown)) owned by the patient.
  • a communication terminal such as the CGM device 12 or a personal computer (not shown) owned by the patient.
  • Inferred data is time-series data of a patient's glucose levels obtained using a simulation model.
  • the estimated data is obtained by setting the patient's variable factor data for a given period in the simulation model corresponding to the patient.
  • the clinical decision support device 14 acquires time-series data and variable factor data from the server 16 via the communication line 18. While this specification describes an embodiment in which variable factor data is acquired from the server 16, the variable factor data may also be input by a user (such as a doctor) operating the input unit 30. For example, a patient may declare variable factor data to the user. In this case, the user operates the input unit 30 to input the variable factor data declared by the patient into the control unit 34 (memory unit 38).
  • the clinical decision support device 14 may be, for example, a terminal such as a personal computer or tablet.
  • the clinical decision support device 14 includes an input unit 30, a display unit 32, and a control unit 34.
  • the control unit 34 further includes a calculation unit 36 and a memory unit 38.
  • the input unit 30 is a user-machine interface that can be operated by the user.
  • the input unit 30 may include a mouse, touch panel, keyboard, voice input device, etc.
  • the input unit 30 transmits signals corresponding to input operations by the user to the control unit 34.
  • the display unit 32 displays images corresponding to video signals transmitted from the control unit 34.
  • the calculation unit 36 of the control unit 34 may be configured, for example, by a processor such as a CPU (Central Processing Unit) or a GPU (Graphics Processing Unit). In other words, the calculation unit 36 may be configured by a processing circuit. At least a portion of the calculation unit 36 may be realized by an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field-Programmable Gate Array). At least a portion of the calculation unit 36 may be realized by an electronic circuit including discrete devices.
  • a processor such as a CPU (Central Processing Unit) or a GPU (Graphics Processing Unit).
  • the calculation unit 36 may be configured by a processing circuit.
  • At least a portion of the calculation unit 36 may be realized by an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field-Programmable Gate Array). At least a portion of the calculation unit 36 may be realized by an electronic circuit including discrete devices.
  • ASIC Application Specific Integrated Circuit
  • FPGA Field-Programm
  • the calculation unit 36 includes a simulation model generation unit 40, an acquisition unit 42 (optimal solution acquisition unit), and a display control unit 44.
  • the simulation model generation unit 40, the acquisition unit 42, and the display control unit 44 can be realized by the calculation unit 36 executing a program stored in the memory unit 38.
  • the simulation model generation unit 40 identifies parameters corresponding to the patient by performing an optimization process on the parameters included in a predetermined mathematical model for estimating glucose levels. As a result, the simulation model generation unit 40 generates a mathematical model including parameters corresponding to the patient.
  • a predetermined mathematical model including parameters corresponding to the patient is referred to as a simulation model corresponding to the patient. Specific examples of predetermined mathematical models will be described later.
  • the simulation model generation unit 40 includes a first acquisition unit 48, a second acquisition unit 50, a determination unit 52, an optimization unit 54, and a generation unit 56.
  • the first acquisition unit 48 acquires measurement data measured by the CGM device 12 during a first period (e.g., one week, one month, etc.) from the server 16 in response to user operation of the input unit 30.
  • the second acquisition unit 50 acquires variable factor data for the above-mentioned second period (e.g., 24 hours, etc.) from the server 16 in response to user operation of the input unit 30 or the data acquisition device 22 of the CGM device 12. Alternatively, the second acquisition unit 50 reads variable factor data directly input to the input unit 30.
  • the determination unit 52 assigns the variation factor data acquired by the second acquisition unit 50 to the contribution opportunities (meal opportunities, insulin administration opportunities) of each variation factor in a third period that is included in the first period but not included in the second period. In this way, the determination unit 52 provisionally determines the variation factor data for the third period.
  • the optimization unit 54 performs optimization processing to obtain optimal solutions for the variation factors in the first period (second period + third period) and the parameters included in a predetermined mathematical model.
  • the optimization unit 54 optimizes the variation factor data for the first period provisionally determined by the determination unit 52 with respect to the measurement data for the first period. Furthermore, the optimization unit 54 optimizes the variation factor data and parameters so that the measurement data approximates the predetermined mathematical model. By performing these processes, the optimization unit 54 obtains optimal solutions for the parameters included in the predetermined mathematical model.
  • the predetermined mathematical model will be described later.
  • the generation unit 56 generates a simulation model that estimates the patient's glucose level by setting the optimal solution for the parameters obtained by the optimization unit 54 into a predetermined mathematical model.
  • the simulation model generated here is a simulation model that corresponds to the patient.
  • the acquisition unit 42 acquires inferred data by setting the variable factor data input via the input unit 30 or the variable factor data stored in the memory unit 38 in the simulation model corresponding to the patient generated by the simulation model generation unit 40.
  • the display control unit 44 performs display control to display various types of information on the display unit 32.
  • the display control unit 44 performs display control to display the estimated data acquired by the acquisition unit 42 on the display unit 32.
  • the display control unit 44 transmits video signals to the display unit 32 to display various types of information.
  • the memory unit 38 is a computer-readable storage medium.
  • the memory unit 38 is composed of a volatile memory (not shown) and a non-volatile memory (not shown).
  • the volatile memory is, for example, a RAM (Random Access Memory).
  • the non-volatile memory is, for example, a ROM (Read Only Memory), a flash memory, etc. Data, etc. are stored in the volatile memory, for example. Programs, tables, maps, etc. are stored in the non-volatile memory, for example.
  • At least a portion of the memory unit 38 may be provided in the processor, integrated circuit, etc. described above.
  • the storage unit 38 is a storage medium that stores programs for implementing each component of the calculation unit 36.
  • the storage unit 38 is also a storage medium that stores programs for executing the simulation model generation process and clinical decision support process described below.
  • the server 16 may be provided by a physical server or a cloud server, and includes a processor and a memory (neither of which are shown).
  • the server 16 acquires the patient's measurement data from the data acquisition device 22 of the CGM device 12 via the communication line 18.
  • the server 16 also acquires the patient's variable factor data from a communication terminal owned by the patient (such as the data acquisition device 22 or a personal computer (not shown)) via the communication line 18.
  • the measurement data and variable factor data are stored in memory.
  • the server 16 transmits a signal indicating the measurement data and variable factor data to the clinical decision support device 14 via the communication line 18.
  • the optimization unit 54 of the simulation model generation unit 40 performs optimization processing to obtain an optimal solution for parameters included in a predetermined mathematical model.
  • the mathematical model for example, the Hovorka equation disclosed in Japanese Patent No. 7290624 or the equation disclosed in the specification of U.S. Patent No. 6,923,763 can be used.
  • the Hovorka equation is the following simultaneous ordinary differential equation with time t as a variable.
  • the function i(t) is the insulin dose.
  • the function cho(t) is the amount of food consumed (amount of carbohydrates).
  • the function G(t) is the glucose level.
  • F 01 C is expressed by the following formula.
  • F R is 0.003(G-9) ⁇ VG when G>9, and is 0 when G>9 or less.
  • the simultaneous ordinary differential equations include 15 parameters V G , F 01 , k 12 , F R , EGP 0 , k b1 , k a1 , k b2 , k a2 , k b3 , k a3 , k a , V I , k e , and t max .
  • V G Volume of distribution of glucose (unit: liters)
  • F 01 Non-insulin-dependent glucose transfer rate (unit: mmol/min)
  • k 12 Rate constant of glucose transfer from tissue to blood (unit: min ⁇ 1 ) k a1 , k a2 , k a3 : insulin inactivation rate constants (unit: min ⁇ 1 )
  • F R urinary excretion constant of glucose (unit: mmol/min)
  • EGP 0 endogenous glucose production rate per hour (unit: min ⁇ 1 ) k b1 , k b2 , k b3 : insulin activation rate constants (unit: min ⁇ 1 )
  • k a Absorption rate of subcutaneously injected insulin (unit: min ⁇ 1 )
  • V I Volume of distribution of insulin (unit: liters)
  • k e plasma elimination rate of insulin (unit: min ⁇ 1 )
  • t max time to peak glucose absorption ingested by
  • the memory unit 38 stores a predetermined mathematical model.
  • the memory unit 38 stores the simultaneous ordinary differential equations described above.
  • the CGM device 12 acquires measurement data for the first period.
  • the server 16 acquires the measurement data for the first period from the CGM device 12.
  • the patient inputs variable factor data for a second period, which is included in the first period but is shorter than the first period, to a communication terminal (e.g., the CGM device 12) owned by the patient.
  • the server 16 acquires the variable factor data for the second period from the communication terminal owned by the patient.
  • the server 16 stores the measurement data and variable factor data acquired from the communication terminal owned by the patient.
  • variable factor data for the second period directly into the input unit 30 of the clinical decision support device 14. In this case, it is sufficient that one day's worth of variable factor data is input as the variable factor data for the second period.
  • the variable factor data may be input by the patient or by another user, such as a medical professional.
  • the patient may input information indicating specific meal contents (e.g., the name of the meal menu, actual intake amount) into the communications terminal (e.g., CGM device 12).
  • the communications terminal's memory or the server 16's memory may pre-store a conversion table that converts information indicating meal contents into food intake data.
  • the communications terminal may obtain information for converting information indicating meal contents into food intake data from a source other than the server 16. This allows the communications terminal or server 16 to obtain food intake data based on the conversion table when information indicating meal contents is input.
  • FIG. 2 is a flowchart of the simulation model generation process.
  • the simulation model generation process is executed by the clinical decision support device (simulation model generation device) 14.
  • a user performs a predetermined operation on the input unit 30.
  • the input unit 30 instructs the calculation unit 36 of the control unit 34 to execute the simulation model generation process.
  • the calculation unit 36 executes the simulation model generation process shown in Figure 2 in response to the instruction signal output from the input unit 30.
  • step S1 the first acquisition unit 48 acquires the patient's measurement data for a first period from the server 16 via the communication line 18.
  • the first acquisition unit 48 may acquire the patient's measurement data directly from the CGM device 12 (or via the data acquisition device 22).
  • curve 60 in Figure 3 shows seven days' worth of measurement data for a patient.
  • the variation factor data may be acquired from information stored on the server 16.
  • the variation factor data may be a representative value for the first day of the first period, which may be directly input by the user via the input unit 30. For example, if the variation factor data is dietary data, the patient's perceived carbohydrate intake and representative values for meal time data are input based on the patient's self-report. Alternatively, if the variation factor data is insulin data related to insulin administration, the insulin dose data and insulin administration time data are directly input based on a prescription plan.
  • the variable factor data is not limited to dietary data and insulin data, and may also include information such as the type of exercise, amount of exercise, and fever.
  • the determination unit 52 provisionally determines the variation factor data for the first period.
  • a period that is included in the first period but not the second period is referred to as the third period.
  • the determination unit 52 provisionally inputs the meal data (meal amount data and meal time data) for each meal occasion on the first day (second period) acquired in step S2 as the initial values for the meal data for each meal occasion on each of the second to seventh days (third period). That is, the determination unit 52 assigns the meal data for the breakfast occasion on the first day to the breakfast occasion on each of the second to seventh days. The determination unit 52 also assigns the meal data for the lunch occasion on the first day to the lunch occasion on each of the second to seventh days.
  • the determination unit 52 also assigns the meal data for the dinner occasion on the first day to the dinner occasion on each of the second to seventh days. For snacks other than breakfast, lunch, and dinner, the determination unit 52 also assigns the meal data for the snack occasion on the first day to the snack occasions on each of the second to seventh days.
  • the decision unit 52 also uses the insulin data (insulin dosage data and insulin administration time data) for the insulin administration occasion on day 1 (second period) acquired in step S2 as the insulin data for each insulin administration occasion on days 2 to 7 (third period). That is, the decision unit 52 also assigns the insulin data for the insulin administration occasion at breakfast on day 1 to the insulin administration occasion at breakfast on each of days 2 to 7. The decision unit 52 also assigns the insulin data for the insulin administration occasion at lunch on day 1 to the insulin administration occasion at lunch on each of days 2 to 7. The decision unit 52 also assigns the insulin data for the insulin administration occasion at dinner on day 1 to the insulin administration occasion at dinner on each of days 2 to 7. The decision unit 52 also assigns the insulin data for the insulin administration occasion at a predetermined time on day 1 (before going to bed in this embodiment) to the insulin administration occasion at a predetermined time on days 2 to 7.
  • step S3 the fluctuation factor data for each day of the seven days (first period) is provisionally determined, as shown in Figure 4.
  • step S4 the optimization unit 54 executes a process (optimization process) to optimize the parameters and variable factor data included in a predetermined mathematical model (e.g., the simultaneous ordinary differential equations described above).
  • a predetermined mathematical model e.g., the simultaneous ordinary differential equations described above.
  • the optimization unit 54 acquires parameters corresponding to the patient's measurement data acquired by the first acquisition unit 48 in step S1. These parameters can be treated as parameters corresponding to that patient.
  • the parameters included in the predetermined mathematical model are unknown. If parameters corresponding to the patient are set in the predetermined mathematical model, the time series inferred data obtained by setting the variable factor data for the first period in the predetermined mathematical model should approximate the measurement data acquired in step S1.
  • the optimization unit 54 sets the variable factor data for the first period acquired in step S3 in a predetermined mathematical model including unknown parameters. Then, the optimization unit 54 performs an optimization process for the parameters of the mathematical model so that the time series inferred data obtained by the predetermined mathematical model approximates the measurement data acquired in step S1.
  • the optimization unit 54 performs optimization processing using at least one of predetermined optimization methods (e.g., steepest gradient method, quasi-Newton method, Newton method, Markov chain-Monte Carlo method, Bayesian optimization, etc.). As a result, the optimization unit 54 obtains an optimal solution for the parameters included in the predetermined mathematical model.
  • predetermined optimization methods e.g., steepest gradient method, quasi-Newton method, Newton method, Markov chain-Monte Carlo method, Bayesian optimization, etc.
  • the optimization unit 54 performs optimization processing using at least one of the predetermined optimization methods for each data included in the variation factor data to obtain an optimal solution for the variation factor data provisionally determined in step S3. Steps S3 and S4 may be performed multiple times. As a result, the optimization unit 54 fine-tunes the provisionally determined variation factor data.
  • CIR Carbohydrate insulin ratio: CIR
  • ISF Insulin sensitivity factor: ISF
  • step S5 the generation unit 56 sets the optimal solution for the parameters obtained by the optimization unit 54 in a predetermined mathematical model. As a result, the generation unit 56 generates a simulation model that estimates the patient's glucose level. The generation unit 56 stores the generated simulation model and the fine-tuned fluctuation factor data obtained by the optimization unit 54 in the memory unit 38.
  • step S6 the acquisition unit 42 estimates time-series data for glucose levels by setting the fluctuation factor data in a simulation model. Specifically, the acquisition unit 42 acquires estimated data by setting the fluctuation factor data determined in step S4 (the fluctuation factor data stored in the memory unit 38) in the simulation model generated in step S5. The estimated data may be generated for the same period as the first period.
  • the acquisition unit 42 may analyze the estimated data using statistical methods and acquire the data obtained by the analysis in any display format. In this case, for example, the variation or bias in blood glucose level fluctuations over 24 hours can be expressed using known distribution expression methods (e.g., box-and-whisker plots, percentile curves, etc. at any time). When expressed as a box-and-whisker plot, outliers may also be displayed.
  • each analysis result displayed in a format showing the 24-hour blood glucose level distribution obtained by analyzing the estimated data is referred to as a data distribution chart.
  • the acquisition unit 42 may convert the estimated data into blood glucose level data.
  • step S7 the display control unit 44 causes the display unit 32 to display the estimation results from step S6. Specifically, the display control unit 44 transmits to the display unit 32 a video signal for displaying the data distribution chart acquired in step S6 and the fluctuation factor data determined in step S4. As a result, the display unit 32 displays the data distribution chart and the fluctuation factor data. For example, the display unit 32 displays the graph shown in Figure 5. This graph contains information based on the estimation data and the fluctuation factor data. This graph represents blood glucose fluctuations over 24 hours.
  • Figure 5 shows a graph displayed by the display unit 32.
  • the data distribution chart shown in Figure 5 shows the data distribution for each time period.
  • Each of the three points 72 shown in Figure 5 represents meal data (meal amount data and meal time data) stored in the memory unit 38.
  • Each of the four points 74 in Figure 5 represents insulin data (insulin dose data and insulin administration time data) stored in the memory unit 38.
  • the initial values of the meal data and insulin data may be the variable factor data reported by the patient or values after optimization processing.
  • displaying insulin data and meal data, including time information, within the display area of a data distribution chart showing 24-hour blood glucose fluctuations makes it easier to visually grasp the relationship between daily treatment and blood glucose trends.
  • points 72 and 74 do not need to be displayed within the graph frame.
  • display fields showing food amount data, meal time data, insulin dose data, and insulin administration time data may be set separately near the graph.
  • the number of meal data may be equivalent to the total number of meals and snacks per day.
  • the number of insulin administrations may also be any number.
  • steps S6 and S7 are processes that allow the user to check the estimated data obtained when the optimized variable factor data is set in the simulation model.
  • the determination unit 52 provisionally determines the variation factor data for the first period by assigning the variation factor data for the second period, which is shorter than the first period, to the contribution opportunities of each variation factor for the third period, which is included in the first period but not included in the second period. Therefore, the patient does not need to separately obtain variation factor data for the third period. Furthermore, the patient or user (doctor, etc.) does not need to input the variation factor data for the third period into the device.
  • the clinical decision support device 14 can reduce the effort required for the user to record meal amounts and meal times over multiple days, and the effort required for the user to input meal amounts and meal times into a recording device such as the data acquisition device 22. Therefore, the clinical decision support device 14 simplifies the process of estimating the patient's blood glucose level.
  • the clinical decision-making support device 14 can obtain optimal solutions for parameters included in a predetermined mathematical model.
  • the clinical decision-making support device 14 can generate a simulation model tailored to the patient by setting optimal parameter solutions in the predetermined mathematical model.
  • the user can more accurately estimate the patient's blood glucose level. More specifically, for the variable factor data in step S2, if the user arbitrarily inputs variable factor data for the first day of the first period via the input unit 30, time-series estimated data based on the input values can be obtained with high accuracy each time.
  • the clinical decision-making support device 14 can display a data distribution chart based on the estimated data.
  • the clinical decision support device 14 executes a process for predicting, based on a simulation model corresponding to the patient, how a patient's blood glucose level will change when the patient's variable factor data is changed. This process is referred to as a clinical decision support process.
  • a user of the clinical decision support device 14 can make clinical decisions based on the results of the clinical decision support process.
  • the clinical decision support device 14 can execute the clinical decision support process in a manual mode and an automatic mode.
  • the user can use the input unit 30 to change the value of the fluctuation factor data. For example, the user selects one of the three points 74 representing the insulin data (e.g., point 74A) by, for example, pointing the mouse pointer at one of the points 74. The user then moves the selected point 74 up or down. To increase the insulin dose, the user moves the point 74 up. To decrease the insulin dose, the user moves the point 74 down. As shown by arrow 80 in FIG. 6 , when the user drags the point 74 downward, the selected point 74 moves downward.
  • the user can use this scale as a guide to increase or decrease the insulin dose.
  • the user moves the point 74 downward to the same position as the time axis of the data distribution chart.
  • the insulin administration time can be arbitrarily changed by moving the selected point 74 left or right (along the time axis of the graph).
  • the distance between the selected point 74 (e.g., point 74A) and the non-selected points 74 (e.g., points 74B, 74C, and 74D) and the range within which the selected point 74 can be moved can be set to any insulin dose depending on the type of medication. In this way, the user can change the value of the patient's insulin data.
  • the user can change the value of the meal data by moving point 72.
  • the data distribution chart has a scale for meal volume (carbohydrate value) on the vertical axis
  • the user can use this scale as a guide to increase or decrease the meal volume.
  • point 72 for example, point 72A to change the breakfast meal volume to zero
  • the distance between the selected point 72 (point 72A) and the unselected points 72 (points 72B and 72C) and the range within which the selected point 72 can be moved can be set arbitrarily.
  • the user can change the meal intake time by moving the selected point 74 left or right (along the time axis on the graph).
  • the clinical decision support device 14 displays estimated data corresponding to the change in variable factor data.
  • the insulin data corresponding to the other time periods may also be changed by the same amount.
  • at least one piece of insulin data corresponding to the other time periods may also be changed by the same amount.
  • each piece of insulin data within a 24-hour period may be independently changeable.
  • the fluctuation factor data corresponding to each time period may be independently movable, or may be movable simultaneously. The same applies to meal data.
  • FIG. 7 is a flowchart of the clinical decision support process in manual mode.
  • the clinical decision support process uses the simulation model generated by the simulation model generation process shown in FIG. 2.
  • the user performs a predetermined operation on the input unit 30. This causes the input unit 30 to instruct the calculation unit 36 of the control unit 34 to execute the clinical decision support process.
  • the calculation unit 36 executes the clinical decision support process shown in FIG. 7 in response to the instruction signal output from the input unit 30.
  • step S11 the acquisition unit 42 estimates time-series data for glucose levels by setting the fluctuation factor data stored in the memory unit 38 in a simulation model corresponding to the patient. Note that, similar to the processing in step S6 of the simulation model generation processing, the acquisition unit 42 can acquire a data distribution chart.
  • step S12 the display control unit 44 causes the display unit 32 to display the estimation results from step S11. Specifically, the display control unit 44 transmits to the display unit 32 a video signal for displaying the data distribution chart (or set of estimated data) acquired in step S11 and the variation factor data stored in the memory unit 38. As a result, the display unit 32 displays the data distribution chart (or set of estimated data) and the variation factor data. For example, the display unit 32 displays the graph shown in FIG. 5.
  • the user can change the value of the variation factor data by performing an input operation on the input unit 30.
  • the processing from step S13 onwards is performed when the user changes the variation factor data.
  • step S13 the acquisition unit 42 acquires the changed fluctuation factor data. For example, as shown in FIG. 6, if the user moves point 74 (e.g., point 74A) indicating insulin data a predetermined amount in the direction of arrow 80, the acquisition unit 42 calculates the fluctuation value of the insulin dosage data corresponding to the amount of movement of point 74A. Furthermore, the acquisition unit 42 acquires the value of the changed insulin dosage data by adding the fluctuation value of the insulin dosage data to the value of the insulin dosage data before the movement.
  • the changed fluctuation factor data is stored in the memory unit 38 together with the fluctuation factor data before the change.
  • step S13 will be explained in more detail.
  • the insulin dosage data for a specific time period is changed by moving point 74 (e.g., 74A) representing the insulin dosage data corresponding to the morning time slot on the graph shown in Figure 5.
  • the acquisition unit 42 adds a specific amount to the insulin dosage data for days 1 to 7 (first period) shown in Figure 4 that is assigned to the same time slot as the moved point 74 (e.g., point 74A). At this time, the acquisition unit 42 does not add a change amount to the insulin dosage data corresponding to other time periods.
  • the acquisition unit 42 may multiply all insulin dosage data (point 64A) assigned to the morning time slot in the first time period in the patient's measurement data ( Figure 4) by the ratio of the insulin dosage after adding 2 units of insulin to the insulin dosage at point 74A.
  • the insulin dosage data for other time slots can also be increased or decreased independently.
  • the acquisition unit 42 performs a process of allocating the amount of change in the insulin data for an insulin administration occasion corresponding to a specified time slot in the second period to an insulin administration occasion corresponding to the specified time slot in the third period. Similar to changing the insulin data, by increasing or decreasing the meal data (point 72) shown on the data distribution chart in Figure 5, it is possible to increase or decrease the meal amount data in Figure 4 corresponding to that meal data (for example, point 62A for breakfast).
  • the acquisition unit 42 sets the insulin dosage data at the time corresponding to the selected point 74 to 0.
  • the acquisition unit 42 can also perform similar processing on food intake data. Note that when insulin data or food data is moved along the time axis, the acquisition unit 42 calculates a time variation value.
  • step S14 the acquisition unit 42 estimates time-series data for glucose levels by setting the variation factor data calculated in step S13 in a simulation model corresponding to the patient.
  • This process is the same as the process in step S6 in Figure 2.
  • the acquisition unit 42 acquires estimated data by setting at least one of the variation factor data changed via the input unit 30 and the variation factor data stored in the memory unit 38 in a simulation model corresponding to the patient generated by the simulation model generation unit 40.
  • the acquisition unit 42 can acquire a data distribution chart based on the estimated data.
  • step S15 the display control unit 44 displays the estimation results from step S14 on the display unit 32. Specifically, the display control unit 44 transmits to the display unit 32 a signal to display the data distribution chart (or estimated data) acquired in step S14 and at least one of the post-change and pre-change variation factor data stored in the memory unit 38. As a result, the display unit 32 displays the data distribution chart (or estimated data) and the variation factor data.
  • step S16 the acquisition unit 42 determines whether the user's operation to change the variable factor data has ended. For example, if the user stops operating the mouse, the acquisition unit 42 determines that the change operation has ended. Alternatively, if the user selects to save a data distribution chart generated based on at least one of the variable factor data before the change and the variable factor data after the change, the acquisition unit 42 determines that the change operation has ended. If the change operation has ended (step S16: YES), the clinical decision-making support processing ends. On the other hand, if the change operation has not ended (step S16: NO), the processing returns to step S13. In this case, the clinical decision-making support processing continues.
  • the user can change the variation factor data to any numerical value.
  • This allows a simulation model based on patient-specific blood glucose prediction parameters to sequentially generate predicted data distribution charts when meal amounts and meal times are changed.
  • the variation factor data (points 72, 74) are displayed in a distinguishable color and shape in the data distribution chart area. This allows for a simpler and more compact display on the clinical decision support device 14, and makes it easier to grasp the fluctuations in the data distribution chart in response to changes in the variation factor data.
  • [4-2 Automatic Mode] 8 is a flowchart of the clinical decision support process in the automatic mode. The following description will focus on the process in the automatic mode that is different from the clinical decision support process in the manual mode (steps S23 and S24).
  • step S21 the acquisition unit 42 estimates time-series data for glucose levels by setting the fluctuation factor data stored in the memory unit 38 into a simulation model corresponding to the patient.
  • step S22 the display control unit 44 causes the display unit 32 to display the inference result from step S21.
  • the user may be able to select automatic mode by performing an input operation on the input unit 30.
  • the processing from step S23 onwards is performed in automatic mode.
  • the acquisition unit 42 acquires optimal estimated data for the patient based on a simulation model corresponding to the patient.
  • the acquisition unit 42 defines a loss function (E) for performing the optimization process.
  • the loss function (E) is the objective function in the optimization process.
  • the loss function (E) is an index that reflects the blood glucose state.
  • any one of the following equations which include TIR (time in range) and TBR (time below range), can be used.
  • TIR is a numerical value that indicates the percentage of time during a day that the glucose value is within the target range.
  • TBR is a numerical value that indicates the percentage of time during a day that the glucose value is within the low glucose range ( ⁇ target range).
  • a new term can also be added to the following equation.
  • an index that reflects the blood glucose state may be set as the loss function for performing the optimization process.
  • the loss function (E), weight (a), and threshold (b) are each pre-stored in the memory unit 38.
  • step S24 the acquisition unit 42 acquires variation factor data that minimizes the loss function (E).
  • the acquisition unit 42 performs optimization processing on the variation factor data so that the loss function (E) is minimized in the time-series estimated data obtained by the simulation model corresponding to the patient.
  • the acquisition unit 42 performs optimization processing on the variation factor data with the aim of maximizing TIR and minimizing TBR.
  • the acquisition unit 42 performs optimization processing using at least one of predetermined optimization methods (e.g., steepest gradient method, quasi-Newton method, Newton method, Markov chain-Monte Carlo method, Bayesian optimization, etc.). In this way, the acquisition unit 42 obtains an optimal solution for the variation factor data.
  • the acquisition unit 42 may also perform optimization processing on the variation factor data with the aim of maximizing TIR or minimizing TBR.
  • step S25 the acquisition unit 42 estimates time-series data for glucose levels by setting the fluctuation factor data calculated in step S24 in a simulation model corresponding to the patient.
  • step S26 the display control unit 44 causes the display unit 32 to display the inference result from step S25.
  • users In automatic mode, users (doctors, etc.) can automatically obtain optimal estimated data.
  • the clinical decision-making support device 14 allows users (doctors, etc.) to change the values of variation factor data and estimate trends in the patient's blood glucose levels (glucose levels) that reflect the changed variation factor data.
  • insulin dosages and meal amounts have been set based on doctors' many years of experience, and depending on the doctor and patient, it can take months to determine the optimal insulin dosage and meal amount for an individual patient.
  • patients face the challenge of not knowing how their blood glucose trends will change when they change the amount of food they eat or the time of their meals.
  • the clinical decision-making support device 14 simplifies the process of estimating fluctuations in a patient's blood glucose levels when the insulin dosage, insulin administration time, meal amount, meal time, etc. are changed.
  • the clinical decision support device 14 allows the user to estimate the appropriate meal amount, meal times, insulin dose, insulin administration time, etc. for the patient.
  • the user can provide the patient with advice on the optimal meal amount, meal times, insulin dose, insulin administration time, etc. For example, by checking how the data distribution chart has changed, the user can easily estimate which insulin should be adjusted to most effectively improve blood sugar trends for the patient's current eating pattern, which can help formulate a more specific treatment plan.

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Abstract

Un dispositif d'aide à la décision clinique (14) comprend : une unité d'acquisition (42) qui acquiert des données d'estimation qui sont des données chronologiques de niveaux de glucose obtenues par application, à un modèle de simulation pour estimer des niveaux de glucose d'un patient, de données de facteur de fluctuation qui sont des données se rapportant à un facteur de fluctuation qui induit une fluctuation du niveau de glucose d'un patient ; et une unité de commande d'affichage (44) qui affiche, sur une unité d'affichage (32), des informations correspondant aux données d'estimation. Une fois les données de facteur de fluctuation modifiées, l'unité de commande d'affichage (44) affiche, sur l'unité d'affichage (32), des informations correspondant à des données d'estimation obtenues par application des données de facteur de fluctuation modifiées au modèle de simulation.
PCT/JP2025/003347 2024-02-28 2025-02-03 Dispositif d'aide à la décision clinique, procédé d'aide à la décision clinique et programme Pending WO2025182457A1 (fr)

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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2005328924A (ja) * 2004-05-18 2005-12-02 Toyama Univ 血糖値予測装置、血糖値予測モデル作成装置、およびプログラム
JP2008545489A (ja) * 2005-06-03 2008-12-18 メドトロニック・ミニメッド・インコーポレーテッド 糖尿病患者を教育し、治療するためのバーチャルペイシェント（仮想患者）ソフトウェアシステム
JP2023124716A (ja) * 2022-02-25 2023-09-06 富士通株式会社 情報処理プログラム、情報処理方法、および情報処理装置

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2005328924A (ja) * 2004-05-18 2005-12-02 Toyama Univ 血糖値予測装置、血糖値予測モデル作成装置、およびプログラム
JP2008545489A (ja) * 2005-06-03 2008-12-18 メドトロニック・ミニメッド・インコーポレーテッド 糖尿病患者を教育し、治療するためのバーチャルペイシェント（仮想患者）ソフトウェアシステム
JP2023124716A (ja) * 2022-02-25 2023-09-06 富士通株式会社 情報処理プログラム、情報処理方法、および情報処理装置

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