Septicemia determination program, septicemia determination device, septicemia determination method, and septicemia determination system

Abstract

To provide a septicemia determination program, a septicemia determination device, a septicemia determination method, and a septicemia determination system that enable auxiliary diagnosis of septicemia to be performed quickly.SOLUTION: An acceleration pulse wave of an object person is acquired; information on the magnitude of amplitude of the acquired acceleration pulse wave is specified; whether or not the object person is suffering from septicemia is determined on the basis of the information on the specified magnitude of amplitude; and a determination result is output on whether or not there is a possibility of the object person suffering from septicemia.SELECTED DRAWING: Figure 7

Description

[Technical field]

[0001] The present invention relates to a sepsis determination program, a sepsis determination device, a sepsis determination method, and a sepsis determination system. [Background technology]

[0002] Sepsis is a disease in which bacteria or viruses (hereinafter simply referred to as bacteria, etc.) enter the bloodstream and cause damage to organs throughout the body, and once the disease develops, symptoms may progress rapidly within a few hours to a day. Therefore, sepsis is a disease that requires, for example, early initiation of initial treatment (see Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2022-058359 A Summary of the Invention [Problem to be solved by the invention]

[0004] Here, the above-mentioned diagnosis of sepsis may be performed using an index (hereinafter, simply referred to as an index) such as the SOFA score (Sequential Organ Failure Assessment Score). However, in this case, for example, a blood test or a medical examination by a doctor is required, so that the diagnosis of sepsis cannot be performed quickly.

[0005] Furthermore, the above-mentioned diagnosis of sepsis may be performed, for example, by performing blood culture and then identifying the type of bacteria, etc., contained in the blood. However, even in this case, for example, blood culture takes time, so that sepsis cannot be diagnosed quickly.

[0006] Therefore, there is a need for a method that allows for the early initiation of initial treatment for a patient with sepsis, for example by quickly carrying out an auxiliary diagnosis of sepsis.

[0007] Therefore, an object of the present invention is to provide a sepsis determination program, a sepsis determination device, a sepsis determination method, and a sepsis determination system that enable rapid auxiliary diagnosis of sepsis. [Means for solving the problem]

[0008] In order to achieve the above-mentioned object, the sepsis determination program of the present invention causes a computer to execute processes to acquire an accelerated pulse wave of a subject, identify information relating to the magnitude of the amplitude of the acquired accelerated pulse wave, determine whether or not the subject is suffering from sepsis based on the identified information relating to the magnitude of the amplitude, and output a determination result as to whether or not the subject is likely suffering from sepsis. Effect of the Invention

[0009] According to the sepsis determination program, the sepsis determination device, the sepsis determination method, and the sepsis determination system of the present invention, it is possible to rapidly perform auxiliary diagnosis of sepsis. [Brief description of the drawings]

[0010] [Figure 1] FIG. 1 is a diagram showing an example of the configuration of a sepsis determination system 10 according to the first embodiment. [Diagram 2] FIG. 2 is a diagram showing an example of a configuration of the information processing device 1 according to the first embodiment. [Diagram 3] FIG. 3 is a diagram for explaining an outline of the sepsis determination process in the first embodiment. [Figure 4] FIG. 4 is a diagram for explaining an outline of the sepsis determination process in the first embodiment. [Diagram 5] FIG. 5 is a flowchart illustrating the sepsis determination process according to the first embodiment. [Figure 6]FIG. 6 is a flowchart illustrating the sepsis determination process according to the first embodiment. [Figure 7] FIG. 7 is a flowchart illustrating the sepsis determination process according to the first embodiment. [Figure 8] FIG. 8 is a flowchart illustrating the sepsis determination process according to the first embodiment. [Figure 9] FIG. 9 is a flowchart illustrating the sepsis determination process according to the first embodiment. [Figure 10] FIG. 10 is a flowchart illustrating the sepsis determination process according to the first embodiment. [Figure 11] FIG. 11 is a flowchart illustrating the sepsis determination process according to the first embodiment. [Figure 12] FIG. 12 is a diagram illustrating the sepsis determination process according to the first embodiment. [Figure 13] FIG. 13 is a diagram illustrating the sepsis determination process according to the first embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] [Example of configuration of sepsis determination system 10 in the first embodiment] First, a description will be given of a configuration example of a sepsis determination system 10 in the first embodiment. Fig. 1 is a diagram showing a configuration example of a sepsis determination system 10 in the first embodiment.

[0012] The sepsis determination system 10 includes, for example, an information processing device 1 (hereinafter also referred to as the sepsis determination device 1) and an acceleration plethysmograph 2. The information processing device 1 is connected to the acceleration plethysmograph 2, for example, by wire or wirelessly, and is capable of communicating with the acceleration plethysmograph 2.

[0013] The acceleration plethysmograph 2 generates time-series data (hereinafter also referred to as waveform data) regarding the acceleration plethysmograph of the subject OB, for example, by continuously measuring the acceleration plethysmograph of the subject OB.

[0014] The information processing device 1 is, for example, a physical machine or a virtual machine, and performs a process (hereinafter also referred to as sepsis determination process) to determine (auxiliary diagnosis) whether or not the subject OB is suffering from sepsis by using waveform data generated by the acceleration pulse wave meter 2.

[0015] Specifically, the information processing device 1 in this embodiment acquires, for example, waveform data generated from the accelerated pulse wave of the subject OB from the acceleration pulse wave meter 2. Then, the information processing device 1 identifies, for example, information related to the magnitude of the amplitude in the acquired waveform data. The information related to the magnitude of the amplitude is, for example, the magnitude (value) of the amplitude. Furthermore, the information related to the magnitude of the amplitude is, for example, information indicating the shape characteristics of the acquired waveform data. Next, the information processing device 1 determines whether or not the subject OB is suffering from sepsis, for example, based on the identified information related to the magnitude of the amplitude. Thereafter, the information processing device 1 outputs, for example, a determination result regarding whether or not the subject OB is likely to be suffering from sepsis.

[0016] That is, the information processing device 1 in this embodiment determines whether or not the subject OB is likely to be suffering from sepsis, based on, for example, a measurement result of the accelerated pulse wave of the subject OB.

[0017] Thereby, the sepsis determination system 10 in the present embodiment can, for example, quickly perform auxiliary diagnosis of sepsis. Specifically, the sepsis determination system 10 can quickly perform auxiliary diagnosis of sepsis even in a place where, for example, a doctor is not present. Therefore, the sepsis determination system 10 can, for example, start initial treatment for a subject OB suffering from sepsis early.

[0018] In the following, a case will be described where the sepsis determination system 10 has one information processing device 1, but the sepsis determination system 10 may have, for example, a plurality of information processing devices 1. In addition, the sepsis determination process may be performed in a distributed manner among, for example, the plurality of information processing devices 1.

[0019] In addition, the following description will be given assuming that the information processing device 1 is located adjacent to the acceleration pulse wave meter 2, but the information processing device 1 may be located, for example, in a different location from the acceleration pulse wave meter 2 (for example, a data center not shown).

[0020] [Configuration Example of Information Processing Device 1 in First Embodiment] Next, a description will be given of a configuration example of the information processing device 1. Fig. 2 is a diagram showing a configuration example of the information processing device 1 in the first embodiment.

[0021] 2, the information processing device 1 includes, for example, a CPU 101 which is a processor, a memory 102, a communication device 103, a storage medium 104, and an output device 105. Each unit is connected to each other via a bus 106.

[0022] The storage medium 104 has, for example, a program storage area (not shown) for storing a program 110 for performing a sepsis determination process.

[0023] The storage medium 104 also includes a storage unit 130 (hereinafter also referred to as an information storage area 130) that stores information used when performing the sepsis determination process. The storage medium 104 may be, for example, a hard disk drive (HDD) or a solid state drive (SSD).

[0024] The CPU 101 executes, for example, a program 110 loaded from the storage medium 104 to the memory 102 to perform sepsis determination processing.

[0025] The communication device 103 is connected, for example, by wire or wirelessly to the acceleration plethysmograph 2 and communicates with the acceleration plethysmograph 2.

[0026] The output device 105 is, for example, a device such as a display, and is a device that outputs the determination result of the sepsis determination process.

[0027] [Outline of Sepsis Determination Processing in the First Embodiment] Next, an outline of the sepsis determination process in the first embodiment will be described below. Figures 3 and 4 are diagrams for explaining the outline of the sepsis determination process in the first embodiment.

[0028] As shown in FIG. 3, the information processing device 1 realizes various functions including, for example, an input receiving unit 111, a pulse wave acquiring unit 112, a feature identifying unit 113, a sepsis determining unit 114, an information generating unit 115, and an information output unit 116.

[0029] 4, for example, waveform data 131, morbidity data 132, and output data 133 are stored in the information storage area 130. The waveform data 131 is, for example, time series data on the accelerated pulse wave of the subject OB. The morbidity data 132 is, for example, data related to the subject OB (for example, so-called clinical data) including data indicating whether the subject OB has been diagnosed with sepsis (whether the subject OB is currently suffering from sepsis). The output data 133 is, for example, data indicating the determination result of the sepsis determination process.

[0030] The input receiving unit 111 receives, for example, an input of an instruction to start a sepsis assessment process for a subject OB (hereinafter, also simply referred to as a start instruction).

[0031] For example, when the input receiving unit 111 receives an input of a start instruction, the pulse wave acquiring unit 112 acquires waveform data 131 for the subject OB from the acceleration plethysmograph 2. Then, the pulse wave acquiring unit 112 stores the acquired waveform data 131 in the information storage area 130, for example.

[0032] The feature identifying section 113 identifies, for example, information relating to the magnitude of the amplitude in the waveform data 131 acquired by the pulse wave acquiring section 112 .

[0033] Specifically, feature identification section 113 identifies, as information relating to the amplitude magnitude, for example, the magnitude (value) of the amplitude of each of a plurality of consecutive peaks (hereinafter also simply referred to as a plurality of peaks) located consecutively in waveform data 131 acquired by pulse wave acquisition section 112. The plurality of peaks are, for example, a plurality of consecutive peaks located consecutively in waveform data 131 corresponding to a predetermined time period among waveform data 131 acquired by pulse wave acquisition section 112. The predetermined time period may be, for example, a time period of about the most recent several tens of seconds to several minutes.

[0034] Furthermore, the feature identifying unit 113 identifies, for example, information related to the amplitude magnitude, such as the ratio of the amplitude magnitude of each of the multiple peaks (i.e., information indicating the shape characteristics of the waveform data 131 acquired by the pulse wave acquiring unit 112) instead of or together with the amplitude magnitude (value) of each of the multiple peaks. Note that in this case, the feature identifying unit 113 may identify, for example, the ratio of the amplitude magnitude of each peak to a predetermined reference magnitude as the amplitude magnitude ratio of each of the multiple peaks.

[0035] The sepsis determining section 114 determines whether or not the subject OB is suffering from sepsis, based on information relating to the magnitude of the amplitude identified by the feature identifying section 113, for example.

[0036] Specifically, the sepsis determination unit 114 determines whether or not the subject OB is suffering from sepsis based on, for example, the maximum value of the amplitude of each of multiple consecutive peaks in the waveform data 131 acquired by the pulse wave acquisition unit 112.

[0037] In addition, the sepsis determination unit 114 determines whether or not the subject OB is suffering from sepsis based on, for example, the ratio of the amplitudes of multiple consecutive peaks in the waveform data 131 acquired by the pulse wave acquisition unit 112.

[0038] The information generating unit 115 generates output data 133 based on, for example, the determination result by the sepsis determining unit 114. Then, the information generating unit 115 stores the generated output data 133 in the information storage area , for example.

[0039] The information output unit 116 outputs, for example, the output data 133 generated by the information generation unit 115 to the output device 105 .

[0040] The input receiving unit 111 may receive, for example, input of morbidity data 132 for the subject OB. The input receiving unit 111 may store, for example, the input of the morbidity data 132 in the information storage area 130. Thereafter, the information generating unit 115 may generate output data 133 based on, for example, the determination result by the sepsis determining unit 114 and the morbidity data 132 received by the input receiving unit 111.

[0041] In addition, the information output unit 116 may, for example, transmit (store) the output data 133 generated by the information generation unit 115 to another information processing device (not shown) that can communicate with the information processing device 1 via a network (not shown) such as the Internet.

[0042] [Flowchart of sepsis determination process in the first embodiment] Next, the sepsis determination process in the first embodiment will be described. Figures 5 to 11 are flow charts for explaining the sepsis determination process in the first embodiment. Figures 12 and 13 are diagrams for explaining the sepsis determination process in the first embodiment.

[0043] [Data reception process] First, the process of accepting input of the morbidity data 132 (hereinafter, also referred to as data acceptance process) in the sepsis diagnosis process will be described. Fig. 5 is a flow chart for explaining the data acceptance process.

[0044] As shown in FIG. 5, the input receiving unit 111 waits until it receives input of the morbidity data 132 of the subject OB (NO in S11), for example.

[0045] Then, when input of the morbidity data 132 of the subject OB is accepted (YES in S11), the input accepting unit 111 stores, for example, the accepted input of the morbidity data 132 in the information storage area 130 (S12).

[0046] [Data acquisition process] Next, the timing of acquiring the waveform data 131 (hereinafter, also referred to as data acquisition processing) in the sepsis determination processing will be described below. Fig. 6 is a flow chart for explaining the data acquisition processing.

[0047] 6, the pulse wave acquiring unit 112 waits until the data acquiring timing (NO in S21), for example. The data acquiring timing may be the timing when information is input to acquire the waveform data 131 of the subject OB, for example. The data acquiring timing may be the timing immediately after the data receiving process is performed, for example.

[0048] Then, when the data acquisition timing arrives (YES in S21), the pulse wave acquisition unit 112 acquires waveform data 131 for the subject OB, for example, from the acceleration plethysmograph 2 (S22).

[0049] Specifically, the pulse wave acquiring unit 112 acquires, for example, from the acceleration plethysmograph 2, waveform data 131 for the subject OB corresponding to a predetermined time period (for example, a time period of about the most recent several tens of seconds to several minutes).

[0050] Thereafter, the pulse wave acquirer 112 stores, for example, the waveform data 131 received in the process of S22 in the information storage area 130 (S23). A specific example of the waveform data 131 will be described below.

[0051] [Example of waveform data 131] Figures 12 and 13 are specific examples of waveform data 131. In the waveform data 131 shown in Figures 12 and 13, the horizontal axis indicates time, and the vertical axis indicates the magnitude (amplitude) of the accelerated pulse wave.

[0052] Waveform data 131 (hereinafter also referred to as waveform data 131a) shown in Fig. 12 is, for example, waveform data in which peak values ​​of a substantially constant value occur regularly (periodically). Specifically, each peak value in waveform data 131a shown in Fig. 12 is about 10,000. In other words, waveform data 131a shown in Fig. 12 is waveform data having a shape similar to that of, for example, electrocardiogram waveform data in a normal case.

[0053] Moreover, the waveform data 131 shown in Fig. 13 (hereinafter also referred to as waveform data 131b) is, for example, waveform data in which non-constant peak values ​​occur irregularly (so-called tooth-shaped waveform data). Specifically, each peak value in the waveform data 131b shown in Fig. 13 is, for example, about 500 to 1000. That is, the waveform data 131b shown in Fig. 13 is waveform data having a shape similar to that of electrocardiogram waveform data in the case of ventricular fibrillation, for example.

[0054] [Main process of sepsis determination] Next, the main process of the sepsis determination process (hereinafter, simply referred to as the main process) will be described. Figures 7 and 8 are flow charts for explaining the main process.

[0055] 7, the feature specification unit 113 waits until it is time to make a judgment (NO in S31). The judgment timing may be, for example, the timing when information is input to judge whether the subject OB is suffering from sepsis. The data acquisition timing may be, for example, the timing immediately after the data acquisition process is performed.

[0056] Then, when the judgment timing arrives (YES in S31), the feature identification unit 113 identifies, for example, the amplitude magnitude (value) of each of multiple consecutive peaks in the waveform data 131 (waveform data 131 acquired in the data acquisition process) stored in the information storage area 130 (S32).

[0057] Furthermore, the sepsis determining unit 114 determines whether or not the subject OB is suffering from sepsis based on, for example, the maximum value of the amplitude identified in the process of S32 (S33).

[0058] Thereafter, the information generating unit 115 generates output data 133 based on, for example, the determination result in the process of S33 and the morbidity data 132 stored in the information storage area 130 (morbidity data 132 inputted and accepted in the data accepting process) (S34). The processes of S33 and S34 will be described in detail below.

[0059] [Details of processing in S33 and S34] 8, the sepsis determining unit 114 determines whether or not the maximum value of the amplitude determined in the process of S32 is less than a threshold value (hereinafter, also referred to as a first threshold value) (S41). The first threshold value may be, for example, 1000.

[0060] As a result, if it is determined that the maximum value of the amplitude magnitude identified in the processing of S32 is less than the first threshold value (YES in S41), the sepsis determination unit 114 identifies, for example, a specific degree of certainty (hereinafter also referred to as the first degree of certainty) as the degree of certainty that the subject OB is suffering from sepsis (S42).

[0061] On the other hand, if it is determined that the maximum value of the amplitude magnitude identified in the processing of S32 is greater than or equal to the first threshold value (NO in S41), the sepsis determination unit 114 identifies, for example, another degree of certainty (hereinafter also referred to as the second degree of certainty) that is lower than the first degree of certainty that the subject OB is suffering from sepsis (S44).

[0062] That is, the sepsis determining unit 114 determines whether or not the subject OB is suffering from sepsis by using the maximum value of the amplitude identified in the process of S32, for example.

[0063] Specifically, for example, when the maximum value of the amplitude magnitude identified in the processing of S32 is less than a first threshold value, the sepsis determination unit 114 identifies a first certainty that the subject OB is suffering from sepsis, which is higher than the second certainty.

[0064] On the other hand, if the maximum value of the amplitude magnitude identified in the processing of S32 is greater than or equal to the first threshold, the sepsis determination unit 114 identifies a second probability that the subject OB is suffering from sepsis, which is lower than the first probability.

[0065] Then, for example, after the process of S42, the information generating section 115 generates information indicating the first certainty identified in the process of S42 as the output data 133 (S43).

[0066] Specifically, in this case, the information generating unit 115 generates, as the output data 133, information such as "high possibility of suffering from sepsis."

[0067] More specifically, for example, when the morbidity data 132 corresponding to the subject OB is not stored in the information storage area 130, or when the morbidity data 132 indicating that the subject OB is not currently suffering from sepsis is stored in the information storage area 130, the information generating unit 115 generates information such as "highly likely to have sepsis" as the output data 133. Also, for example, when the morbidity data 132 indicating that the subject OB is currently suffering from sepsis is stored in the information storage area 130, the information generating unit 115 generates information such as "still suffering from sepsis" as the output data 133.

[0068] Furthermore, for example, after the process of S44, the information generating section 115 generates information indicating the second certainty identified in the process of S44 as the output data 133 (S45).

[0069] Specifically, in this case, the information generating unit 115 generates, as the output data 133, information such as "there is a low possibility that the patient is suffering from sepsis."

[0070] More specifically, for example, when the morbidity data 132 corresponding to the subject OB is not stored in the information storage area 130, or when the morbidity data 132 indicating that the subject OB is not currently suffering from sepsis is stored in the information storage area 130, the information generating unit 115 generates information such as "It is unlikely that the subject OB has sepsis" as the output data 133. Also, for example, when the morbidity data 132 indicating that the subject OB is currently suffering from sepsis is stored in the information storage area 130, the information generating unit 115 generates information such as "It is highly likely that the sepsis has been cured" as the output data 133.

[0071] Returning to FIG. 7, the information output unit 116 outputs, for example, the output data 133 generated in the process of S34 (the process of S43 or the process of S45) to the output device 105 (S35).

[0072] This enables the information processing device 1 to quickly output a determination result as to whether or not the subject OB is suffering from sepsis, for example.

[0073] Specifically, the maximum value of the amplitude in the waveform data 131a described in Fig. 12 is, for example, about 10000. Therefore, for example, when the first threshold value is 1000, the information processing device 1 can output a determination result indicating that the subject OB corresponding to the waveform data 131a described in Fig. 12 is unlikely to suffer from sepsis.

[0074] On the other hand, the maximum value of the amplitude in the waveform data 131b described in Fig. 13 is, for example, about 500 to 1000. Therefore, for example, when the first threshold is 1000, the information processing device 1 can output a determination result indicating that the subject OB corresponding to the waveform data 131b described in Fig. 13 is highly likely to be suffering from sepsis.

[0075] If it is determined that the maximum value of the amplitude determined in the process of S32 is equal to or greater than the first threshold value (NO in S41), the sepsis determination unit 114 may determine whether or not the maximum value of the amplitude determined in the process of S32 is less than another threshold value (hereinafter also referred to as a second threshold value) that is greater than the first threshold value. The second threshold value may be, for example, 2000.

[0076] Then, when it is determined that the maximum value of the amplitude magnitude specified in the process of S32 is equal to or greater than the first threshold value and less than the second threshold value, the sepsis determination unit 114 may, for example, specify another degree of certainty that the subject OB is suffering from sepsis (hereinafter also referred to as a third degree of certainty) that is lower than the first degree of certainty and higher than the second degree of certainty. After that, in this case, the sepsis determination unit 114 may, for example, generate information indicating the specified third degree of certainty as the output data 133.

[0077] That is, the sepsis determining section 114 may determine whether or not the subject OB has a symptom of sepsis, in addition to determining whether or not the subject OB has sepsis.

[0078] Specifically, for example, if it is determined that the maximum value of the amplitude magnitude identified in the processing of S32 is greater than or equal to the first threshold value and less than the second threshold value, and if the morbidity data 132 corresponding to the subject OB is not stored in the information storage area 130, or if the morbidity data 132 indicating that the subject OB is not currently suffering from sepsis is stored in the information storage area 130, the information generation unit 115 may generate information such as “there are signs of sepsis” as output data 133.

[0079] Furthermore, for example, if it is determined that the maximum amplitude magnitude identified in the processing of S32 is greater than or equal to the first threshold value and less than the second threshold value, and disease data 132 indicating that the subject OB is currently suffering from sepsis is stored in the information storage area 130, the information generating unit 115 may generate information such as “the symptoms of sepsis are alleviating” or “sepsis is being cured” as output data 133.

[0080] That is, the sepsis determining unit 114 may determine, for example, whether or not the subject OB is suffering from sepsis, as well as determine the healing status of the sepsis in the subject OB.

[0081] This enables the information processing device 1 to output, for example, a more detailed determination result as to whether or not the subject OB is suffering from sepsis.

[0082] [Main processes other than sepsis determination process] Next, another main process of the sepsis determination process (hereinafter, simply referred to as another main process) will be described. Figures 9 to 11 are flow charts explaining the other main process. The other main process is a process that can be performed, for example, instead of the main process described in Figures 7 and 8, or together with the main process described in Figures 7 and 8.

[0083] As shown in FIG. 9, the feature identification unit 113 waits until the determination timing, for example (NO in S51).

[0084] Then, when the judgment timing arrives (YES in S51), the feature identification unit 113 identifies, for example, the ratio of the amplitude magnitudes of each of multiple consecutive peaks in the waveform data 131 (waveform data 131 acquired in the data acquisition process) stored in the information storage area 130 (S52).

[0085] Furthermore, the sepsis determining section 114 determines whether or not the subject OB is suffering from sepsis based on, for example, the ratio of the amplitudes identified in the process of S52 (S53).

[0086] Thereafter, the information generating unit 115 generates output data 133 based on, for example, the determination result in the process of S53 and the morbidity data 132 stored in the information storage area 130 (morbidity data 132 inputted and accepted in the data accepting process) (S54). The processes of S53 and S54 will be described in detail below.

[0087] [Details of processing in S53 and S54] As shown in FIG. 10, for example, the sepsis determination unit 114 identifies a number of peaks (hereinafter also referred to as the first number) that are one or more and have a small amplitude magnitude ratio identified in the processing of S52 from multiple peaks that are consecutively located in the waveform data 131 stored in the information storage area 130 (S61).

[0088] Specifically, the sepsis determining unit 114 identifies a first number of peaks in ascending order of amplitude magnitude ratio identified in the process of S52, for example.

[0089] In addition, the sepsis determination unit 114, for example, identifies a number of peaks (hereinafter also referred to as the second number) that are greater than or equal to one and have a large ratio of amplitude magnitudes identified in the processing of S52 from multiple peaks that are consecutively located in the waveform data 131 stored in the information storage area 130 (S62).

[0090] Specifically, the sepsis determining unit 114 identifies a second number of peaks in order from the largest amplitude magnitude ratio identified in the process of S52, for example.

[0091] Then, the sepsis determining unit 114 calculates, for example, a value indicating a ratio of the amplitudes of the first number of peaks identified in the process of S61 (hereinafter also referred to as a first value) (S63).

[0092] Specifically, the sepsis determining unit 114 calculates, as the first value, for example, the average value or the median value of the ratio of the amplitude magnitudes in each of the first number of peaks identified in the process of S61.

[0093] In addition, if only one peak is identified in the processing of S61, that is, if only the peak with the smallest amplitude magnitude is identified in the processing of S61, the sepsis determination unit 114 may, for example, use the ratio of the amplitude magnitudes of the peaks identified in the processing of S61 as the first value.

[0094] Furthermore, the sepsis determining unit 114 calculates, for example, a value indicating a ratio of the magnitude of the amplitudes of the second number of peaks identified in the process of S63 (hereinafter also referred to as a second value) (S64).

[0095] Specifically, the sepsis determining unit 114 calculates, as the second value, for example, the average value or the median value of the ratio of the amplitude magnitudes in each of the second number of peaks identified in the process of S63.

[0096] In addition, if only one peak is identified in the processing of S63, i.e., if only the peak with the largest amplitude is identified in the processing of S63, the sepsis determination unit 114 may, for example, use the ratio of the amplitudes of the peaks identified in the processing of S63 as the second value.

[0097] Next, the sepsis determination unit 114 calculates the ratio (hereinafter also simply referred to as the ratio) of the second value calculated in the processing of S64 to the first value calculated in the processing of S63, for example, by dividing the second value calculated in the processing of S64 by the first value calculated in the processing of S63 (S65).

[0098] 11, the sepsis determination unit 114 determines whether or not the ratio calculated in the process of S65 is less than a threshold value (hereinafter, also referred to as a third threshold value) (S71). The third threshold value may be, for example, 100.

[0099] As a result, if it is determined that the ratio calculated in the processing of S65 is less than the third threshold value (YES in S71), the sepsis determination unit 114 identifies, for example, a specific degree of certainty (hereinafter also referred to as the fourth degree of certainty) as the degree of certainty that the subject OB is suffering from sepsis (S72).

[0100] On the other hand, if it is determined that the ratio calculated in the processing of S65 is greater than or equal to the third threshold value (NO in S71), the sepsis determination unit 114 identifies, for example, another degree of certainty (hereinafter also referred to as the fifth degree of certainty) that is lower than the fourth degree of certainty that the subject OB is suffering from sepsis (S74).

[0101] That is, the sepsis determining unit 114 determines whether or not the subject OB is suffering from sepsis by using the ratio calculated in the process of S65, for example.

[0102] Specifically, for example, when the ratio calculated in the process of S65 is less than the third threshold, the sepsis determination unit 114 specifies a fourth probability, which is higher than the fifth probability, as the probability that the subject OB is suffering from sepsis.

[0103] On the other hand, for example, when the ratio calculated in the process of S65 is equal to or greater than the third threshold, the sepsis determination unit 114 specifies a fifth probability, which is lower than the fourth probability, as the probability that the subject OB is suffering from sepsis.

[0104] Then, for example, after the process of S72, the information generating section 115 generates information indicating the fourth certainty identified in the process of S72 as the output data 133 (S73).

[0105] Specifically, the information generating unit 115 generates, as the output data 133, information such as "high possibility of suffering from sepsis."

[0106] Furthermore, for example, after the process of S74, the information generating unit 115 generates information indicating the fifth degree of certainty identified in the process of S74 as the output data 133 (S75).

[0107] Specifically, the information generating unit 115 generates, as the output data 133, information such as "there is a low possibility that the patient is suffering from sepsis."

[0108] Returning to FIG. 9, the information output unit 116 outputs, for example, the output data 133 generated in the process of S54 (the process of S73 or the process of S75) to the output device 105 (S55).

[0109] This enables the information processing device 1 to quickly output the determination result as to whether or not the subject OB is suffering from sepsis, for example, in the same manner as in the cases described with reference to FIGS.

[0110] Specifically, for example, when the first value in the waveform data 131a described in Fig. 12 is 10000 and the second value is 20, the information processing device 1 calculates 500 as the ratio corresponding to the waveform data 131a described in Fig. 12. Therefore, for example, when the third threshold is 100, the information processing device 1 can output a determination result indicating that the subject OB corresponding to the waveform data 131a described in Fig. 12 is unlikely to suffer from sepsis.

[0111] On the other hand, when the first value in the waveform data 131b described in Fig. 13 is 500 and the second value is 20, the information processing device 1 calculates 25 as the ratio corresponding to the waveform data 131b described in Fig. 13. Therefore, for example, when the third threshold is 100, the information processing device 1 can output a determination result indicating that the subject OB corresponding to the waveform data 131b described in Fig. 13 is highly likely to be suffering from sepsis.

[0112] In addition, if it is determined that the ratio calculated in the processing of S65 is equal to or greater than the third threshold (NO in S71), the sepsis determination unit 114 may, for example, determine whether the ratio calculated in the processing of S65 is less than another threshold (hereinafter also referred to as the fourth threshold) that is greater than the third threshold.

[0113] Then, when it is determined that the ratio calculated in the process of S65 is equal to or greater than the third threshold and less than the fourth threshold, the sepsis determination unit 114 may, for example, specify another degree of certainty that the subject OB is suffering from sepsis (hereinafter also referred to as a sixth degree of certainty) that is lower than the fourth degree of certainty and higher than the fifth degree of certainty. After that, in this case, the sepsis determination unit 114 may, for example, generate information indicating the specified sixth degree of certainty as the output data 133.

[0114] This enables the information processing device 1 to output a more detailed determination result as to whether or not the subject OB is suffering from sepsis, for example, in the same manner as in the cases described with reference to FIGS.

[0115] In this manner, the information processing device 1 in this embodiment acquires, for example, waveform data generated from the accelerated pulse wave of the subject OB from the acceleration pulse wave meter 2. Then, the information processing device 1 identifies, for example, information related to the magnitude of the amplitude in the acquired waveform data. Next, the information processing device 1 determines whether or not the subject OB is suffering from sepsis, for example, based on the information related to the magnitude of the amplitude that has been identified. After that, the information processing device 1 outputs, for example, a determination result regarding whether or not the subject OB is likely suffering from sepsis.

[0116] That is, the information processing device 1 in this embodiment determines whether or not the subject OB is likely to be suffering from sepsis, based on, for example, a measurement result of the accelerated pulse wave of the subject OB.

[0117] Thereby, the sepsis determination system 10 in the present embodiment can, for example, quickly perform auxiliary diagnosis of sepsis. Specifically, the sepsis determination system 10 can quickly perform auxiliary diagnosis of sepsis even in a place where, for example, a doctor is not present. Therefore, the sepsis determination system 10 can, for example, start initial treatment for a subject OB suffering from sepsis early.

[0118] In addition, the sepsis determination system 10 in this embodiment can also determine the healing status of the subject OB suffering from sepsis, for example, from the measurement result of the accelerated pulse wave of the subject OB. Therefore, the sepsis determination system 10 can be used, for example, to monitor the healing status of the subject OB suffering from sepsis. In other words, the measurement result by the sepsis determination system 10 can be used, for example, as a surrogate marker for the healing of the subject OB suffering from sepsis.

[0119] In addition, the sepsis determination system 10 in this embodiment may determine whether or not the subject OB is suffering from sepsis by using a learning model generated by machine learning, for example. [Explanation of symbols]

[0120] 1: Information processing device 2: Acceleroplethysmometer 10:Sepsis assessment system 101:CPU 102: Memory 103: Communication equipment 104:Storage medium 105: Output device 106: Bus 110: Program 111: Input reception section 112: Pulse wave acquisition unit 113: Feature identification section 114: Sepsis assessment department 115: Information generation section 116: Information output section 130: Information storage area 131: Waveform data 132: Morbidity data 133: Output data OB: Target

Claims

1. Acquire the subject's accelerated pulse wave, Identifying information regarding the magnitude of the amplitude of the acquired accelerated pulse wave; determining whether the subject is suffering from sepsis based on information relating to the magnitude of the identified amplitude; outputting a determination result as to whether or not the subject is likely to suffer from sepsis; A sepsis determination program that causes a computer to execute a process.

2. In the process of determining, whether or not the subject is suffering from sepsis is determined based on a maximum value of amplitude of each of a plurality of consecutive peaks in the waveform of the accelerated pulse wave. The sepsis determination program according to claim 1 .

3. In the process of determining, it is determined whether the maximum value is less than a first threshold value; When it is determined that the maximum value is less than the first threshold value, it is determined that the certainty that the subject is suffering from sepsis is a first certainty, and when it is determined that the maximum value is equal to or greater than the first threshold value, it is determined that the certainty that the subject is suffering from sepsis is a second certainty that is lower than the first certainty; In the output process, when it is determined that the degree of certainty that the subject is suffering from sepsis is the first degree of certainty, information indicating the first degree of certainty is output as the determination result, and when it is determined that the degree of certainty that the subject is suffering from sepsis is the second degree of certainty, information indicating the second degree of certainty is output as the determination result. The sepsis determination program according to claim 2 .

4. In the determination process, If the maximum value is equal to or greater than the first threshold, determining whether the maximum value is less than a second threshold; When it is determined that the maximum value is less than the second threshold value, it is determined that the certainty that the subject is suffering from sepsis is a third certainty that is lower than the first certainty and higher than the second certainty, and when it is determined that the maximum value is equal to or greater than the second threshold value, it is determined that the certainty that the subject is suffering from sepsis is the second certainty; In the output process, when it is determined that the probability that the subject is suffering from sepsis is the third probability, information indicating the third probability is output as the determination result. The sepsis determination program according to claim 3 .

5. and receiving input of morbidity data indicating whether the subject is afflicted with sepsis. The process is executed by a computer, In the output process, when it is determined that the degree of certainty that the subject is suffering from sepsis is the first degree of certainty and the morbidity data indicates that the subject is suffering from sepsis, information indicating that the subject may not have recovered is output as the determination result. The sepsis determination program according to claim 3 .

6. and receiving input of morbidity data indicating whether the subject is afflicted with sepsis. The process is executed by a computer, In the output process, when it is determined that the degree of certainty that the subject is suffering from sepsis is the second degree of certainty and the morbidity data indicates that the subject is suffering from sepsis, information indicating that the subject may have been cured is output as the determination result. The sepsis determination program according to claim 3 .

7. and receiving input of morbidity data indicating whether the subject is afflicted with sepsis. The process is executed by a computer, In the output process, when it is determined that the degree of certainty that the subject is suffering from sepsis is the third degree of certainty and the morbidity data indicates that the subject is suffering from sepsis, information indicating that the subject may not have recovered or information indicating that the subject may be recovering is output as the determination result. The sepsis determination program according to claim 4 .

8. In the process of determining, whether or not the subject is suffering from sepsis is determined based on a ratio of amplitudes of a plurality of peaks that are consecutively located in a waveform of the accelerated pulse wave. The sepsis determination program according to claim 1 .

9. In the determination process, identifying a first number of peaks from the plurality of peaks that have a small amplitude magnitude ratio; identifying a second number of peaks from the plurality of peaks that have a large ratio of the amplitude magnitudes; calculating a first value indicating a ratio of the magnitude of the amplitudes at the identified first number of peaks; calculating a second value indicating a ratio of the magnitude of the amplitudes at the identified second number of peaks; determining whether a ratio of the second value to the first value is less than a third threshold; If the ratio is less than the third threshold, the probability that the subject is suffering from sepsis is determined to be a fourth probability, and if the ratio is equal to or greater than the third threshold, the probability that the subject is suffering from sepsis is determined to be a fifth probability lower than the fourth probability; In the output process, when it is determined that the degree of certainty that the subject is suffering from sepsis is the fourth degree of certainty, information indicating the fourth degree of certainty is output as the determination result, and when it is determined that the degree of certainty that the subject is suffering from sepsis is the fifth degree of certainty, information indicating the fifth degree of certainty is output as the determination result. The sepsis determination program according to claim 8 .

10. A pulse wave acquiring unit that acquires an accelerated pulse wave of a subject; a feature identifying unit that identifies information regarding the magnitude of the amplitude of the acquired accelerated pulse wave; a sepsis determination unit that determines whether or not the subject is suffering from sepsis based on information regarding the magnitude of the identified amplitude; and an information output unit that outputs a determination result as to whether or not the subject may be suffering from sepsis. A sepsis determination device comprising:

11. Acquire the subject's accelerated pulse wave, Identifying information regarding the magnitude of the amplitude of the acquired accelerated pulse wave; determining whether the subject is suffering from sepsis based on information relating to the magnitude of the identified amplitude; outputting a determination result as to whether or not the subject is likely to suffer from sepsis; A method for determining sepsis, characterized in that processing is executed by a computer.

12. A sepsis determination system comprising an acceleration plethysmograph that measures an acceleration pulse wave of a subject, and a sepsis determination device that can communicate with the acceleration plethysmograph, The sepsis assessment device comprises: acquiring an acceleration pulse wave of the subject from the acceleration pulse wave meter; Identifying information regarding the magnitude of the amplitude of the acquired accelerated pulse wave; determining whether the subject is suffering from sepsis based on information relating to the magnitude of the identified amplitude; outputting a determination result as to whether or not the subject is likely to suffer from sepsis; A sepsis determination system comprising:

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