Anesthetic effect prediction system and program

Abstract

To provide an anesthetic effect prediction system and a program for providing information which can be easily utilized even by an anesthesiologist with little knowledge and experience.SOLUTION: The anesthetic effect predicting system 1 includes an information acquiring section 11 which acquires a measured value of biological information of a target patient to whom an anesthetic is administered, and an arithmetic processing section 12 which predicts a future variation of the biological information of the target patient based on the measured value.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to an anesthesia effect prediction system and program for predicting the effect of anesthesia. [Background technology]

[0002] Anesthesiologists not only administer anesthetic drugs to patients to induce a coma before surgery, but also assess the patient's condition from measurements taken during surgery, such as the patient's heart rate, blood pressure, and blood flow, and then administer additional anesthetic drugs, painkillers, and hypertensives to stabilize the patient's condition.

[0003] For example, Patent Document 1 proposes a system that calculates an index value of a patient's nociceptive stimulus response level from the patient's measurements. By monitoring the index value calculated by such a system in addition to the above-mentioned measurements, anesthesiologists can grasp the patient's condition more accurately. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Laid-Open No. 2018-130542 Summary of the Invention [Problem to be solved by the invention]

[0005] However, in order for anesthesiologists to refer to a patient's measurements and index values ​​and provide appropriate treatment to the patient, they need the knowledge and experience to properly utilize this information, so there were only a limited number of anesthesiologists who could fully utilize a system such as the one proposed in Patent Document 1.

[0006] The present invention has been made to solve the above-mentioned problems, and provides an anesthetic effect prediction system and program that provide information that can be easily used even by anesthesiologists with little knowledge or experience. [Means for solving the problem]

[0007] In order to achieve the above-mentioned objectives, the anesthetic effect prediction system disclosed below comprises an information acquisition unit that acquires measurements of the biometric information of a target patient who has been administered an anesthetic, and a calculation processing unit that predicts future changes in the biometric information of the target patient based on the measurements. [Effects of the Invention]

[0008] The above-described system for predicting the effect of anesthesia can obtain future biological information about a target patient. Therefore, even an anesthesiologist with so little knowledge and experience that he or she is unable to predict future biological information from the current biological information of a target patient can obtain the future biological information about the target patient and easily determine the treatment that should be performed now. Therefore, this system for predicting the effect of anesthesia can provide information that can be easily utilized even by an anesthesiologist with little knowledge and experience. [Brief explanation of the drawings]

[0009] [Figure 1] FIG. 1 is a block diagram showing an example of the configuration of an anesthetic effect prediction system 1 according to an embodiment of the present invention. [Figure 2] FIG. 2 is a graph showing an example of a method for outputting prediction results by the anesthetic effect prediction system 1 according to an embodiment of the present invention. [Figure 3] FIG. 3 is a graph showing another example of a method for outputting a prediction result by the anesthetic effect prediction system 1 according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0010] An embodiment of the present invention will be described below with reference to the drawings. The present invention is not limited to the following embodiment, and appropriate design modifications can be made within the scope of the configuration of the present invention. In the following description, the same reference numerals are used in different drawings for identical parts or parts having similar functions, and repeated description thereof will be omitted. The configurations described in the embodiment and modified examples may be combined or modified as appropriate. To facilitate understanding of the description, the drawings referred to below show simplified or schematic configurations, or some of the configurations may be omitted.

[0011] First, an example of the configuration of an anesthetic effect prediction system 1 according to an embodiment of the present invention will be described with reference to the drawings. Fig. 1 is a block diagram showing an example of the configuration of an anesthetic effect prediction system 1 according to an embodiment of the present invention. In the following, a patient who undergoes surgery in a coma state after being administered an anesthetic and whose anesthesia effect is predicted by the anesthetic effect prediction system 1 will be referred to as a "target patient."

[0012] As shown in FIG. 1, the anesthetic effect prediction system 1 includes an information acquisition unit 11, a calculation processing unit 12, an output unit 13, and a storage unit .

[0013] The information acquisition unit 11 is composed of an interface into which measurement values ​​are input from various measuring devices (sphygmomanometer, electrocardiograph, thermometer, pulse oximeter, etc.) attached to the target patient, and communication devices that receive these measurement values ​​via wireless communication.

[0014] The arithmetic processing unit 12 is a processing device such as a CPU (Central Processing Unit). It is composed.

[0015] The output unit 13 is composed of a device that outputs visually, such as a display device that displays images or a segment display that displays alphanumeric characters, or a device that outputs auditorily, such as a speaker that emits voice or warning sounds.

[0016] The storage unit 14 is configured by, for example, a non-volatile storage device such as a hard disk drive (HDD) or a solid state drive (SSD), or a volatile storage device such as a random access memory (RAM).

[0017] The information acquisition unit 11, the arithmetic processing unit 12, and the storage unit 14 may be configured as part of a personal computer or a server computer. The anesthesia effect prediction system 1 may also be configured as part of an anesthesia management system that records and manages biological information of a patient undergoing surgery. The arithmetic processing unit 12 and the storage unit 14 may also be configured as part of a server or the like installed in a location remote from the information acquisition unit 11 and the output unit 13, and may exchange information with the information acquisition unit 11 and the output unit 13 via communication devices.

[0018] The memory unit 14 stores a trained model 141 and simulation information 142. In addition to this information, the memory unit 14 also stores measurement values ​​acquired by the information acquisition unit 11 and programs necessary for the operation of the anesthesia effect prediction system 1 (and further the anesthesia management system).

[0019] The trained model 141 can be obtained by, for example, using a neural network model or other model for machine learning of time-series data of biological information of patients who have been administered anesthetics as training data. Note that the training data may include data of multiple patients recorded by an anesthesia management system or the like in past surgeries (for example, administration history of drugs such as anesthetics, vasopressors, and analgesics). and time-series data of the patient's biological information). Any model, such as a deep neural network (DNN), a convolutional neural network (CNN), or a recurrent neural network (RNN), can be used.

[0020] The simulation information 142 is data for performing a simulation using a pharmacokinetic model such as a multi-compartment model, as introduced in "Simulation Using a Pharmacokinetic Multi-Compartment Model and Clinical Anesthesia" (authors: Masui Kenichi and Kazama Tomie), Vol. 26, No. 3, pp. 145-151 of "Simulation" published by the Japan Society for Simulation Technology (to calculate the concentration of an anesthetic agent in the body of a target patient and its changes).

[0021] Next, the operation of the anesthesia effect prediction system 1 according to the embodiment of the present invention will be described. First, in the anesthesia effect prediction system 1, measurement values ​​from a measuring device attached to a target patient are acquired via the information acquisition unit 11. Note that if the anesthesia effect prediction system 1 is configured as part of an anesthesia management system that records and manages the biological information of a target patient during surgery, the measurement values ​​input to the anesthesia management system 1 will also be used in the anesthesia effect prediction system 1. Furthermore, the information acquisition unit 11 periodically acquires measurement values, for example, at a predetermined cycle when the measuring device operates.

[0022] The calculation processing unit 12 inputs the measurement values ​​input via the information acquisition unit 11 to the trained model 141, and predicts future fluctuations in the biological information of the target patient based on the obtained output results. At this time, the calculation processing unit 12 may input, for example, measurement values ​​within a certain period of time in the past (for example, the past few minutes) to the trained model 141.

[0023] In addition, the calculation processing unit 12 calculates the simulation results of the anesthetic concentration in the target patient based on the measurement value simulation information 142 input via the information acquisition unit 11, and predicts future fluctuations in the biological information based on this simulation result.

[0024] For example, the calculation processing unit 12 may calculate a prediction result of future changes in the biological information of the target patient by using the results of both the output of the trained model 141 and the simulation results in parallel, such as by combining and comparing them.

[0025] Furthermore, for example, the calculation processing unit 12 may serially use the results of both the measured values ​​and the simulation results, for example, by inputting the simulation results into the trained model 141 and obtaining output results. In this case, simulation results may be calculated for each of multiple patient data recorded by an anesthesia management system or the like in past surgeries, and these may be used as training data to perform machine learning on the model. The effect of anesthesia is more strongly correlated with the concentration of the anesthetic in the body than with the amount or timing of administration. Therefore, by focusing on the simulation results of the concentration and using them as training data to perform machine learning to obtain the trained model 141, and inputting the simulation results of the anesthetic concentration in the target patient into this trained model 141, future changes in the target patient's biological information can be more accurately determined.

[0026] The output unit 13 outputs the output results obtained by the calculations performed by the calculation processing unit 12 in a manner that can be recognized by humans, such as a visual method such as an image or an auditory method such as a sound. An example of this output method will be described with reference to Fig. 2. Fig. 2 is a graph showing an example of a method for outputting prediction results by the anesthetic effect prediction system 1 according to an embodiment of the present invention.

[0027] In FIG. 2, the calculation processing unit 12 predicts future changes in the biological information of the subject patient and outputs the prediction result as time-series information, and the output unit 13 displays the time-series information as an image. 2, the measured values ​​of the target patient are displayed as a time-series graph 131 for the time portion before the present, and the prediction results by the calculation processing unit 12 are displayed as a time-series graph 132 for the time portion after the present. In this way, when the prediction results are output as time-series information, even an anesthesiologist with little knowledge or experience can easily determine not only the necessity of treatment for the target patient but also the urgency (for example, how much time remains until the patient becomes dangerous).

[0028] As described above, the anesthetic effect prediction system 1 according to an embodiment of the present invention can obtain future biological information of a target patient. Therefore, even an anesthesiologist with so little knowledge and experience that he or she cannot predict future biological information from the current biological information of a target patient can obtain the future biological information of the target patient and easily determine the treatment that should be performed now. Therefore, the anesthetic effect prediction system 1 can provide information that can be easily used even by an anesthesiologist with little knowledge and experience.

[0029] <Deformation, etc.> The above-described embodiments are merely examples for carrying out the present invention. The present invention is not limited to the above-described embodiments, and can be practiced by appropriately modifying the above-described embodiments without departing from the spirit of the present invention.

[0030] For example, in the above-described embodiment, the memory unit 14 has been described as storing both the trained model 141 and the simulation information 142, but it may store only one of them, or it may store data related to a prediction method other than these.

[0031] Furthermore, for example, the trained model 141 may be a model that has been machine-learned based on not only the patient's measurements but also attribute information (e.g., gender, age, height, weight, etc.). In this case, the calculation processing unit 12 may input the attribute information of the target patient into the trained model 141 to obtain a prediction result. The effects of anesthesia vary depending on gender, age, etc., and also on physique, so by learning and inputting these separately, it is possible to accurately predict the target patient's biological information.

[0032] Furthermore, for example, in the above-described embodiment, the biological information to be measured and predicted may include blood pressure. In this case, the anesthetic effect prediction system 1 can predict blood pressure, which is an important index in the management of anesthesia for a target patient.

[0033] 2, the case where the arithmetic processing unit 12 outputs the predicted results of the biological information of the target patient as time-series information has been described, but the arithmetic processing unit 12 may output information notifying the target patient that treatment is necessary in addition to (or instead of) the time-series data. In this case, even an anesthesiologist with so little knowledge and experience that it is difficult for him or her to determine the necessary treatment can recognize that treatment is necessary.

[0034] 2 illustrates an output image that combines graph 131 of the measurement values ​​of the target patient and graph 132 of the prediction result, but the output unit 13 may output an output image other than this. For example, as shown in FIG. 3, a graph 131 of the measurement values ​​of the target patient's biological information may be displayed superimposed with a past prediction result 133 of the biological information. For example, prediction result 133 at a certain point in the past is the biological information of the target patient at the certain point in time that was predicted a predetermined time before (for example, several minutes before) from the certain point in time.

[0035] The calculation processing unit 12 outputs time-series information in which the past prediction results of the biological information of the target patient are superimposed on the measurement values ​​of the biological information of the target patient so as to obtain the output result as exemplified in FIG. Therefore, it is possible to clearly show whether the measured values ​​of the target patient and the predicted results generally match (whether the prediction accuracy is sufficiently high). Therefore, the anesthesiologist who is the user can easily determine whether the output results of the anesthetic effect prediction system 1 are reliable.

[0036] The above-described anesthetic effect prediction system can be explained as follows.

[0037] The anesthetic effect prediction system comprises an information acquisition unit that acquires measurements of biological information of a target patient who has been administered an anesthetic, and a calculation processing unit that predicts future changes in the biological information of the target patient based on the measurements (first configuration). With this configuration, future biological information of the target patient can be obtained. Therefore, even an anesthesiologist with so little knowledge and experience that he or she is unable to predict future biological information from the target patient's current biological information can obtain the target patient's future biological information, and can easily determine the treatment that should be performed now. Therefore, this anesthetic effect prediction system can provide information that even an anesthesiologist with little knowledge and experience can easily use.

[0038] In the first configuration, a storage unit may be further provided for storing a trained model obtained by machine learning using time-series data of the biological information of a patient administered an anesthetic as training data, and the calculation processing unit may predict future fluctuations in the biological information of the target patient based on an output result obtained by inputting the measurement values ​​of the target patient into the trained model (second configuration). With this configuration, the biological information of the target patient can be predicted with high accuracy.

[0039] In addition, in the second configuration, the trained model may be machine-learned using time-series data of the patient's biological information and attribute information of the patient as training data, the information acquisition unit acquires the attribute information of the target patient, and the calculation processing unit may predict future fluctuations in the biological information of the target patient based on an output result obtained by inputting the measurement values ​​and the attribute information of the target patient into the trained model (third configuration). With this configuration, predictions can be made taking the attributes of the target patient into consideration, thereby making it possible to predict the biological information of the target patient with even greater accuracy.

[0040] In any one of the first to third configurations, the arithmetic processing unit may predict future fluctuations in the blood pressure of the subject patient (fourth configuration). With this configuration, it is possible to predict blood pressure, which is an important index in the anesthesia management of the subject patient.

[0041] In any one of the first to fourth configurations, the arithmetic processing unit may predict future fluctuations in the biological information of the target patient and output the prediction results as time-series information (fifth configuration). With this configuration, even an anesthesiologist with little knowledge or experience can easily determine the necessity and urgency of treatment for the target patient.

[0042] Furthermore, in any one of the first to fifth configurations, the arithmetic processing unit may output time-series information in which past prediction results for the biological information of the target patient are superimposed on the measurement values ​​of the biological information (sixth configuration). This configuration makes it possible to clearly indicate whether the measurement values ​​of the target patient and the prediction results generally match (whether the prediction accuracy is sufficiently high). Therefore, the anesthesiologist, who is the user, can easily determine whether the output results of the anesthetic effect prediction system are reliable.

[0043] In addition, in any one of the first to sixth configurations, the arithmetic processing unit may output information for notifying a user when the predicted biological information of the target patient indicates that treatment is required for the target patient now or in the future (seventh configuration). According to this configuration, even an anesthesiologist with so little knowledge and experience that it is difficult to determine the necessary treatment can recognize that treatment is required.

[0044] In addition, in any one of the first to seventh configurations, the information acquisition unit may acquire information on the anesthetic administered to the target patient, and the arithmetic processing unit may calculate a simulation result of the concentration of the anesthetic in the target patient based on the information of the anesthetic administered to the target patient, and predict future fluctuations in the biological information of the target patient based on the simulation result (eighth configuration). With this configuration, the biological information of the target patient can be predicted with high accuracy.

[0045] Furthermore, the first configuration may further include a memory unit that stores a trained model that has been machine-learned using time-series data of the body information of a patient administered an anesthetic and a simulation result of the anesthetic concentration in the patient as training data, wherein the information acquisition unit acquires information about the anesthetic administered to the target patient, and the arithmetic processing unit calculates the simulation result of the anesthetic concentration in the target patient based on the information about the anesthetic administered to the target patient, and the arithmetic processing unit predicts future fluctuations in the biological information of the target patient based on an output result obtained by inputting the measurement values ​​of the target patient and the simulation result of the anesthetic concentration in the target patient into the trained model (ninth configuration). With this configuration, focusing on the simulation result of the anesthetic concentration in the body, which has a strong correlation with the effect of anesthesia, machine learning is performed using the simulation result as training data to obtain a trained model, and the simulation result of the anesthetic concentration in the target patient is input into the trained model, thereby making it possible to more accurately determine future fluctuations in the biological information of the target patient.

[0046] In addition, the program used in the above-mentioned anesthetic effect prediction system causes a computer to perform the following processes: acquiring measurements of the biometric information of a target patient who has been administered an anesthetic; and predicting future changes in the biometric information of the target patient based on the measurements (tenth configuration). [Explanation of symbols]

[0047] 1...anesthetic effect prediction system, 11...information acquisition unit, 12...arithmetic processing unit, 13...output unit, 14...storage unit, 141...trained model, 142...simulation information

Claims

1. an information acquisition unit that acquires measurements of biological information of a subject who has been administered an anesthetic; a calculation processing unit that predicts future fluctuations in the biological information of the subject patient based on the measurement values, The anesthetic effect prediction system, wherein the calculation processing unit predicts future changes in the biological information of the target patient and outputs the prediction results as information for displaying in chronological order.

2. The system further includes a storage unit that stores a trained model obtained by machine learning using time-series data of the biological information of a patient who has been administered an anesthetic as training data, The anesthetic effect prediction system of claim 1, wherein the calculation processing unit predicts future changes in the biological information of the target patient based on the output results obtained by inputting the measurement values ​​of the target patient into the trained model.

3. the trained model is trained by machine learning using time-series data of the patient's biological information and attribute information of the patient as training data, the information acquisition unit acquires the attribute information of the target patient; The anesthetic effect prediction system of claim 2, wherein the calculation processing unit predicts future changes in the biological information of the target patient based on the output results obtained by inputting the measurement values ​​and attribute information of the target patient into the trained model.

4. The anesthetic effect prediction system according to claim 1 , wherein the calculation processing unit predicts future fluctuations in the blood pressure of the subject patient.

5. The anesthetic effect prediction system of claim 1, wherein the calculation processing unit outputs information to notify a user if the predicted biological information of the target patient indicates that treatment for the target patient is required currently or in the future.

6. The anesthetic effect prediction system of any one of claims 1 to 5, wherein the calculation processing unit outputs information in which past prediction results of the biological information of the target patient are superimposed on the measured values ​​of the biological information, and information for displaying the prediction results in chronological order.

7. obtaining biometric measurements of a subject patient who has been administered an anaesthetic; A process of predicting future fluctuations in the biological information of the subject patient based on the measurement values; a process of predicting future changes in the biological information of the subject patient and outputting the prediction results as information for displaying in chronological order; A program that causes a computer to execute the following.

Images