JP2017205248A - Onset factor inference device, onset factor inference method, onset factor inference device program, and onset factor inference system - Google Patents

Abstract

CONSTITUTION: A wearable device 10 detects a patient's active mass and an environmental value showing patient's surrounding environment every one minute, and transfers minute data containing the detected active mass and environmental value to a cloud server 60. The cloud server 60 stores this transferred minute data in a database. Upon receiving a report creation request from a client PC70, the cloud server 60 defines multiple time zones on the basis of onset time of a patient-holding disease, and evaluates the minute data stored in the database every time zone. Also, the cloud server 60 infers the onset factor of the patient-holding disease based on evaluation results, and transfers the onset factor report describing the inferred onset factor to the client PC70.EFFECT: The inference accuracy of the onset factor can be increased concerning a patient-holding disease.SELECTED DRAWING: Figure 1

Description

  The present invention relates to an onset factor estimation device, an onset factor estimation method, and an onset factor estimation device program, and more particularly, to an onset factor estimation device, an onset factor estimation method, and an onset factor estimation device program for estimating an onset factor of a disease that a patient has. .

  According to Patent Literature 1, a symptom evaluation standard is created in advance by associating a symptom of a disease with a movement disorder with an analysis result of an amount of activity calculated based on a patient's consumed energy or the like. The patient's symptoms are evaluated based on the amount of activity measured by the activity meter carried by the patient and the symptom evaluation criteria. The patient's symptoms and their changes (for example, whether the symptoms improved with the presence or absence of medication or the type of treatment, or whether the activity was reduced due to sleep disorders) were evaluated by statistical analysis of the measured activity. The As a result, an objective and quantitative evaluation can be easily and accurately performed over a wide range of diseases.

Japanese Patent Laying-Open No. 2015-97612

  However, patent document 1 is not only estimating the symptom of a patient and the onset factor of the disease which a patient has, but only evaluating a patient's symptom and its change based on a patient's activity amount. Moreover, even if it is going to guess the onset factor based on the evaluation result obtained by patent document 1, there exists a limit in estimation accuracy.

  Therefore, a main object of the present invention is to provide an onset factor inference device, an onset factor inference method, an onset factor inference device program, and an onset factor inference system that can improve the accuracy of estimating the onset factor for a disease that the patient has. It is.

  The onset factor estimation device according to the present invention (60: reference numeral corresponding to the embodiment; the same applies hereinafter) is a first acquisition unit (S31) that acquires a short-period environment value that indicates a surrounding environment of a patient at a time that arrives in a short period. , S33), a definition means (S45, S47) for defining a plurality of time zones based on the onset time of the disease the patient has, and a time defined by the definition means for the short-period environment value acquired by the first acquisition means First evaluation means (S53, S55) for evaluating each band, and first estimation means (S61) for estimating the onset factor of the disease that the patient has with reference to the evaluation result of the first evaluation means.

  Preferably, a 1st evaluation means evaluates a short period environmental value in a common aspect irrespective of the length of a time slot | zone.

  Preferably, receiving means (S43) for receiving a guess request issued from the external terminal (70) is further provided, and the first guess means guesses the onset factor in response to the guess request.

  Preferably, the first acquisition unit additionally acquires the activity amount of the patient, and the first evaluation unit additionally evaluates the activity amount of the patient.

  Preferably, the apparatus further comprises second evaluation means (S39, S41, S51) for evaluating an onset time environment value indicating the surrounding environment of the patient at the onset time of the disease that the patient has, and the first inference means is the second evaluation means. The cause of the onset is estimated with further reference to the evaluation results.

  Preferably, a maximum month specifying means (S69) for specifying a month in which the number of occurrences of the disease that the patient has has a maximum value, and an evaluation corresponding to the month specified by the maximum month specifying means among the evaluation results of the first evaluation means Maximum moon evaluation result extraction means (S73, S75) for extracting the results, and second estimation means (S79) for estimating the onset factor of the disease the patient has with reference to the evaluation results extracted by the maximum moon evaluation result extraction means Is further provided.

  In a certain aspect, a 2nd estimation means estimates the onset factor for every month.

  In another aspect, the second acquisition means (S35, S37) for acquiring a long-period environment value indicating the surrounding environment of the patient at a time that arrives in a long period, and the long-period environment value acquired by the second acquisition means Long period environment value extraction means (S77) for extracting a long period environment value corresponding to the month specified by the local maximum month specification means is further provided, and the second estimation means is the long period extracted by the long period environment value extraction means. Further estimate environmental factors by referring to environmental values.

  Preferably, a minimal month identifying unit (S87) for identifying a month in which the number of occurrences of the disease the patient has has a minimum value, an evaluation result corresponding to the month identified by the minimal month identifying unit among the evaluation results of the first evaluation unit The minimal moon evaluation result extraction means (S91, S93) for extracting the disease, and the third estimation means (S95) for estimating the onset factor of the disease that the patient has with reference to the evaluation result extracted by the minimal moon evaluation result extraction means Further provided.

  Preferably, the third estimating means estimates the onset factor collectively regardless of the number of months specified by the minimum moon specifying means.

  The onset factor estimation method according to the present invention is an onset factor estimation method executed by the processor (6002) of the onset factor estimation device (60), and indicates a short-period environment indicating a patient's surrounding environment at a short-arrival time Acquisition step (S31, S33) for acquiring values, definition step (S45, S47) for defining multiple time zones based on the onset time of the disease the patient has, and definition of short-period environmental values acquired by the acquisition step An evaluation step (S53, S55) for evaluating each time zone defined by the step, and an estimation step (S61) for estimating the onset factor of the disease that the patient has with reference to the evaluation result of the evaluation step are provided.

  The onset factor estimation program according to the present invention acquires, in the processor (6002) of the onset factor estimation device (60), a short period environment value indicating a surrounding environment of a patient at a time that arrives in a short period (S31, S33) ), Defining steps (S45, S47) that define multiple time zones based on the onset time of the disease the patient has, and evaluating short-period environmental values acquired by the acquiring step for each time zone defined by the defining step This is an onset factor estimation program for executing an evaluation step (S53, S55) to be performed and an estimation step (S61) for estimating an onset factor of a disease that the patient has with reference to the evaluation result of the evaluation step.

  The onset factor estimation system according to the present invention comprises a radio communication terminal (10) attached to the body of a patient having a disease and a server device (60) that communicates with the radio communication terminal. The estimation system, the wireless communication terminal is a creation means (S7) for creating a short-period environment value indicating the surrounding environment of the patient at a time that arrives in a short period, and a detection means (S19) for detecting the onset time of the disease The server device includes an acquisition unit (S31, S33) that acquires the short-period environmental value created by the creation unit, and a definition unit that defines a plurality of time zones based on the onset time detected by the detection unit ( S45, S47), evaluation means (S53, S55) for evaluating the short-period environmental value acquired by the acquisition means for each time zone defined by the definition means, and the pathogenesis of the disease with reference to the evaluation results of the evaluation means Guessing means (S61) to guess Prepare.

  The onset factor estimation method according to the present invention includes an estimation support method executed by a wireless communication terminal (10) attached to the body of a patient having a disease and a server device (60) that performs communication between the wireless communication terminal A presumption process method comprising: a presumption process method comprising: a creation step (S7) for creating a short cycle environment value indicating a surrounding environment of a patient at a time that arrives in a short cycle; A detection step (S19) for detecting the onset time of the disease, and the estimation processing method includes an acquisition step (S31, S33) for acquiring the short-period environment value created by the creation step, and the onset time detected by the detection step. Definition step (S45, S47) for defining multiple time zones as a reference, evaluation step for evaluating the short-period environmental values acquired by the acquisition step for each time zone defined by the definition step (S53, S55) and an estimation step (S61) for estimating the onset factor of the disease by referring to the evaluation result of the evaluation step.

  The onset factor estimation program according to the present invention is a server device (60) that performs communication between an estimation support program provided to a wireless communication terminal (10) attached to the body of a patient having a disease and a wireless communication terminal A presumption process program provided with a presumption process program, wherein the presumption support program creates a short cycle environment value indicating the surrounding environment of the patient at a short time arrival time (S7), and This is a program for causing the processor (1002) of the wireless communication terminal to execute the detection step (S19) for detecting the onset time of the disease, and the estimation processing program obtains the short-period environment value created by the creation step (S31, S33), a definition step (S45, S47) that defines a plurality of time zones based on the onset time detected in the detection step, and an acquisition step. Evaluation step (S53, S55) for evaluating the obtained short-period environmental value for each time zone defined by the definition step, and estimation step (S61) for inferring the onset factor of the disease by referring to the evaluation result of the evaluation step Is a program for causing the processor (6002) of the server device to execute.

  The short-period environmental value indicates the surrounding environment of the patient at the time of arrival in a short period. The short-period environmental value is evaluated for each time zone based on the onset time of the disease that the patient has, and the evaluation result obtained thereby By estimating the onset factor of the disease that the patient has based on the above, the estimation accuracy of the onset factor can be increased.

  The above object, other objects, features and advantages of the present invention will become more apparent from the following detailed description of embodiments with reference to the drawings.

It is a block diagram which shows an example of the network structure of this embodiment. It is a block diagram which shows an example of a structure of the wearable device shown in FIG. It is a block diagram which shows an example of a structure of the cloud server shown in FIG. It is a block diagram which shows an example of a structure of the client PC shown in FIG. It is an illustration figure which shows an example of a structure of the database provided in the cloud server shown in FIG. It is an illustration figure which shows an example of a structure of the other database provided in the cloud server shown in FIG. It is an illustration figure which shows an example of a structure of the other database provided in the cloud server shown in FIG. It is an illustration figure which shows an example of a structure of the table provided in the cloud server shown in FIG. It is an illustration figure which shows an example of a structure of the other table provided in the cloud server shown in FIG. It is an illustration figure which shows an example of a structure of the other table provided in the cloud server shown in FIG. It is an illustration figure which shows an example of a structure of the other table provided in the cloud server shown in FIG. It is an illustration figure which shows an example of a structure of the onset factor report produced by the cloud server shown in FIG. It is a flowchart which shows a part of operation | movement of CPU provided in the wearable device. It is a flowchart which shows a part of operation | movement of CPU provided in the cloud server. It is a flowchart which shows a part of other operation | movement of CPU provided in the cloud server. It is a flowchart which shows a part of other operation | movement of CPU provided in the cloud server. It is a flowchart which shows another part of operation | movement of CPU provided in the cloud server. It is a flowchart which shows a part of operation | movement of CPU provided in client PC.

  Referring to FIG. 1, wearable device 10 is a wristwatch-type wireless communication terminal worn on the wrist of a patient having a disease such as migraine, and wireless communication base station 20 connected to public line network 30 by wire. Wireless communication with The public line network 30 is connected to a location information API server 40, a weather API server 50, and a cloud server 60, and further connected to a client PC 70 operated by the attending physician.

  The onset factor estimation system according to this embodiment includes the wearable device 10, the cloud server 60, and the client PC 70. Further, the cloud server 60 corresponds to an onset factor estimation device that estimates an onset factor of a disease that a patient has.

  The wearable device 10 accesses the location information API server 40, the weather API server 50, or the cloud server 60 via the radio communication base station 20 and the public line network 30. The client PC 70 also accesses the cloud server 60 via the public line network 30.

  The wearable device 10 is configured as shown in FIG. According to FIG. 2, the bus BS1 has a wireless communication circuit 1001, a gyro sensor 1002, an acceleration sensor 1003, a heart rate sensor 1004, a blood pressure sensor 1005, a body temperature sensor 1006, a clock circuit 1007, a GPS device 1008, a CPU 1009, a DRAM 1010, a nonvolatile memory. A memory 1011 is connected.

  The radio communication circuit 1001 performs radio communication with the radio communication base station 20 using the antenna ANT1. The gyro sensor 1002 and the acceleration sensor 1003 are used for measuring the amount of exercise of the patient, and the heart rate sensor 1004 is used for measuring the heart rate of the patient. The blood pressure sensor 1005 is used for measuring the blood pressure of the patient, and the body temperature sensor 1006 is used for measuring the body temperature of the patient. The current time (year / month / day and hour / minute) is measured by the clock circuit 1007.

  The GPS device 1008 receives a radio wave transmitted from a GPS satellite and detects the latitude and longitude of the current position of the patient. The detected latitude and longitude are converted into place names (facility names) such as “home”, “company”, “xx station” using the position information API server 40. The latitude and longitude detected by the GPS device 1008 are also used for measuring the amount of movement of the patient. Further, weather information (atmospheric pressure, temperature, humidity, weather, pollen scattering amount, etc.) of the surrounding area of the patient is acquired from the weather API server 50.

  The cloud server 60 is configured as shown in FIG. According to FIG. 3, communication I / F 6001, CPU 6002, DRAM 6003 and HDD 6004 are connected to bus BS2. The HDD 6004 is provided with databases DB1 to DB3 and tables TBL1 to TBL4.

  The client PC 70 is configured as shown in FIG. According to FIG. 4, a communication I / F 7001, a CPU 7002, a keyboard / mouse 7003, a monitor 7004, a DRAM 7005, and an HDD 7006 are connected to the bus BS3.

  The database DB1 is configured as shown in FIG. 5, the database DB2 is configured as shown in FIG. 6, and the database DB3 is configured as shown in FIG. Further, the table TBL1 is configured as shown in FIG. 8, the table TBL2 is configured as shown in FIG. 9, the table TBL3 is configured as shown in FIG. 10, and the table TBL4 is configured as shown in FIG. .

  Referring to FIG. 5, the database DB1 is a database for registering minute unit data created every minute. Minute data includes the data creation time (specifically, date, hour and minute), the amount of activity of the patient (specifically, the amount of exercise of the patient from the time one minute before, the current heart rate, blood pressure, body temperature) and The environmental value indicating the surrounding environment of the patient (specifically, the current patient position, the amount of movement of the patient from the time one minute before, the current pressure, temperature, and humidity). Therefore, in each of the plurality of columns constituting the database DB1, the data creation time, the activity amount, and the environmental value are described every minute.

  The above-mentioned “1 minute” is an example of a short cycle. Further, since minute unit data is created in a short cycle, the activity amount and environment value constituting the minute unit data can be defined as “short cycle activity amount” and “short cycle environment value”, respectively.

  Referring to FIG. 6, the database DB2 is a database for registering daily data created at a reference time (specifically, every morning at 6:00). The daily data is composed of a data creation date (specifically, year / month / day) and an environmental value (specifically, atmospheric pressure, pollen scattering amount) indicating the surrounding environment of the patient. Therefore, the data creation date and the environmental value are described for each day in each of the plurality of columns constituting the database DB2.

  Note that “one day”, which is the cycle in which the reference time arrives, is an example of a long cycle. Also, since the daily data is created with a long cycle, the environmental value constituting the daily data can be defined as a “long cycle environmental value”.

  Referring to FIG. 7, database DB3 is a database for registering onset data that is created each time an onset button (not shown) provided on wearable device 10 is pressed. Onset data include the time of onset (specifically, date, hour and minute), the amount of activity of the patient (specifically, current heart rate, blood pressure, and body temperature), and environmental values that indicate the patient's surrounding environment (specifically, , Current patient position, current barometric pressure, temperature, humidity, weather).

  Therefore, in each of the plurality of columns constituting the database DB3, the onset time, the activity amount, and the environmental value are described for each operation of the onset button. A column identification number is assigned to each column on the database DB3.

  Since the onset data is created when the onset button is pressed (that is, onset), the environmental value constituting the onset data can be defined as “onset time environmental value”.

  Referring to FIG. 8, table TBL <b> 1 is a table for describing the evaluation results of the activity amount and the environmental value constituting the onset data. The activity amount evaluation result has heart rate, blood pressure, and body temperature as evaluation items, and the environmental value evaluation result has patient position, temperature, and weather as evaluation items. In addition, the evaluation results described in the Kth (K: 1, 2,...) Column of the table TBL1 are created based on the onset data described in the Kth column of the database DB3.

  Regarding the activity evaluation results, each item of heart rate and blood pressure is expressed as “high”, “medium”, or “low”, and the body temperature item is “high fever”, “low fever”, “normal heat” Expressed in either. As for the evaluation result of the environmental value, the item of temperature is expressed by any of “high”, “medium”, and “low”, while the items of position and weather are expressed similarly to the database DB3.

  Referring to FIG. 9, table TBL <b> 2 is a table for describing activity amounts and environmental value evaluation results for M minutes (M: adjusted in the range of 30 to 90) before the onset time. The activity amount evaluation result includes exercise amount, heart rate, blood pressure, and body temperature as evaluation items, and the environmental value evaluation result includes patient movement amount, atmospheric pressure, temperature, and humidity as evaluation items.

  Each evaluation item is expressed by how much the numerical value fluctuates in the increasing direction and how much the fluctuating direction decreases in the M minutes before the onset time. The evaluation result described in the Kth column of the table TBL2 is created based on minute unit data for M minutes before the onset time described in the Kth column of the database DB3.

  Referring to FIG. 10, table TBL3 is a table for describing the evaluation results of the activity amount and the environmental value for H time (H: adjusted in the range of 3 to 9) before the onset time. The activity amount evaluation result includes exercise amount as an evaluation item, and the environmental value evaluation result includes patient movement amount, atmospheric pressure, temperature, and humidity as evaluation items.

  Each evaluation item is expressed by how much the numerical value fluctuates in the increasing direction and how much the fluctuating direction decreases in the H time before the onset time. Moreover, the evaluation result described in the Kth column of the table TBL3 is created based on minute unit data of H hours before the onset time described in the Kth column of the database DB3.

  Thus, the table TBL2 targets the minute unit data for M minutes before the onset time, while the table TBL3 targets the minute unit data for H hours before the onset time. However, the evaluation mode or the evaluation method is common regardless of whether the length of the time zone is M minutes or H hours.

  Referring to FIG. 11, table TBL4 is a table for describing a tabulated result obtained by summing up the number of onsets of diseases that a patient has every month, and is created based on onset data registered in database DB3. . From the change over time in the number of onsets described in the table TBL4, a month in which the number of onsets shows a maximum value (= maximum month) and a month in which the number of onsets shows a minimum value (= minimum month) are found.

  CPU1009 provided in the wearable device 10 performs the process according to the flowchart shown in FIG. 13 in order to support estimation of the onset factor of the disease which a patient has. Moreover, CPU6002 provided in the cloud server 60 performs the process according to the flowchart shown in FIGS. 14-17 in order to guess the onset factor of the disease which a patient has. Further, the CPU 7002 provided in the client PC 70 requests the cloud server 60 to estimate the onset factor of the disease that the patient has, and obtains the onset factor report from the cloud server 60 by performing processing according to the flowchart shown in FIG. Run.

  A control program (estimation support program) corresponding to the flowchart shown in FIG. 13 is stored in the nonvolatile memory 1011 shown in FIG. 2, and a control program (estimation processing program) corresponding to the flowcharts shown in FIGS. Is stored in the HDD 6004 shown in FIG. 3, and a control program (report acquisition program) corresponding to the flowchart shown in FIG. 18 is stored in the HDD 7006 shown in FIG.

  Referring to FIG. 13, in step S1, it is determined whether or not a preset detection cycle (= 1 minute) has come. If the determination result is NO, the process proceeds directly to step S11, while if the determination result is YES, the following processing is executed in steps S5 to S9, and then the process proceeds to step S11.

  In step S5, the amount of activity of the patient is detected. The activity amount of the patient includes the patient's exercise amount from the time one minute ago, the current heart rate, blood pressure, and body temperature as items. The amount of exercise is detected using the gyro sensor 1002 and the acceleration sensor 1003, and the heart rate is detected using the heart rate sensor 1004. The blood pressure is detected using a blood pressure sensor 1005, and the body temperature is detected using a body temperature sensor 1006.

  In step S7, an environmental value indicating the surrounding environment of the patient is detected. The environmental values are the current patient position (strictly, “home”, “company”, “XX station”, etc.), the amount of movement of the patient from the time one minute ago, the current pressure, temperature , Humidity is an item. The current position and amount of movement are detected using the GPS device 1008, and the atmospheric pressure, temperature, and humidity are detected using the weather API server 50.

  The activity amount and the environmental value detected in steps S5 and S7 are held in the DRAM 1010. In step S <b> 9, minute unit data including the activity amount and environmental value on the DRAM 1010 and the current time (year / month / day, hour / minute) is created, and the created minute unit data is transferred to the cloud server 60. The minute unit data is output from the wireless communication circuit 1001 and given to the cloud server 60 via the wireless communication base station 20 and the public network 30.

  In step S11, it is determined whether or not a reference time (= 6: 00 every morning) has arrived. If the determination result is NO, the process proceeds directly to step S17. If the determination result is YES, steps S13 and S15 are followed. The process proceeds to step S17.

  In step S13, the weather API server 50 is accessed to detect today's environmental values (atmospheric pressure, pollen scattering amount). The detected environmental value is held in the DRAM 1010. In step S15, the detected environmental value is transferred to the cloud server 60 as daily data. The daily data is also given to the cloud server 60 via the wireless communication circuit 1001, the wireless communication base station 20, and the public line network 30.

  In step S17, it is determined whether or not a disease that the patient has has occurred (= whether or not an unillustrated onset button is pressed). If a determination result is NO, it will return to step S1 as it is, and if a determination result is YES, it will return to step S1 after performing the following processes by step S19-S25.

  In step S19, the current time is detected using the clock circuit 1007. In step S21, the amount of activity of the patient is detected, and in step S23, an environmental value indicating the surrounding environment of the patient is detected. The amount of activity detected in step S21 includes the heart rate, blood pressure, and body temperature of the patient at the time of onset as items. Moreover, the environmental value detected in step S23 has the patient's position at the time of onset, atmospheric pressure, temperature, humidity, and weather as items.

  The detected time, activity amount, and environmental value are held in the DRAM 1010. In step S <b> 25, onset data including the time, activity, and environmental value stored in the DRAM 1010 is created, and the created onset data is transferred to the cloud server 60. The onset data is also given to the cloud server 60 via the wireless communication circuit 1001, the wireless communication base station 20, and the public line network 30.

  Referring to FIG. 14, in step S <b> 31, it is determined whether or not minute unit data transmitted from wearable device 10 has been received by communication I / F 6001. If the determination result is NO, the process proceeds to step S35, while if the determination result is YES, the process proceeds to step S33.

  In step S33, the received minute unit data is fetched into the DRAM 6003, and the fetched minute unit data is registered in the database DB1 shown in FIG. When the process of step S33 is completed, the process proceeds to step S35.

  In step S35, it is determined whether or not the daily data transmitted from the wearable device 10 has been received by the communication I / F 6001. If the determination result is NO, the process proceeds to step S39, while if the determination result is YES, the process proceeds to step S37.

  In step S37, the received daily data is taken into the DRAM 6003 and the taken daily data is registered in the database DB2 shown in FIG. When the process of step S37 is completed, the process proceeds to step S39.

  In step S39, it is determined whether or not the onset data transmitted from the wearable device 10 has been received by the communication I / F 6001. If the determination result is NO, the process proceeds to step S43 while if the determination result is YES, the process proceeds to step S41.

  In step S41, the received onset data is taken into the DRAM 6003, and the taken onset data is registered in the database DB3 shown in FIG. When the process of step S41 is completed, the process proceeds to step S43.

  In step S43, it is determined whether a report creation request (estimation request) has been received from the client PC 70 or not. If a determination result is NO, it will return to Step S31, and if a determination result is YES, it will progress to Step S45. In step S45, a numerical value indicating the evaluation range of minute unit data (= eg “60”) is set in the variable M, and in step S47, a numerical value indicating the evaluation range of time unit data (= eg “6”) is set in the variable H. Set to.

  In step S49, the variable K is set to “1”. In step S51, based on the onset data described in the Kth column of the database DB3, the activity amount of the patient at the onset and the environmental value at the onset are evaluated. In step S51, the evaluation result is also written in the Kth column of the table TBL1 shown in FIG.

  In step S53, the activity amount and the environmental value of the patient in M minutes before the onset time described in the K-th column of the database DB3 are stored in the minute unit data registered in the database DB1 (strictly, the M before the onset time). Evaluation based on minute unit data created in minutes). In step S53, the evaluation result is also written in the Kth column of the table TBL2 shown in FIG.

  In step S55, the activity amount and the environmental value of the patient at the H time before the onset time described in the Kth column of the database DB3 are stored in the minute unit data (strictly speaking, the H before the onset time is registered in the database DB1). Based on minute-by-minute data created in time). In step S55, the evaluation result is also written in the Kth column of the table TBL3 shown in FIG.

  The evaluation mode or the evaluation method in step S55 is common to the evaluation mode or the evaluation method in step S53.

  In step S57, the variable K is incremented, and in step S59, it is determined whether or not the variable K exceeds the variable Kmax (= the column identification number of the latest column in which onset data is registered). If a determination result is NO, it will return to Step S51, and if a determination result is YES, it will progress to Step S61.

  In step S61, the onset factor for M minutes is estimated based on the descriptions in the tables TBL1 and TBL2, and the onset factor for H time is estimated based on the descriptions in the tables TBL1 and TBL3. In step S63, the onset factor thus estimated is described in the upper part of the onset factor report.

  The onset factor report has the appearance shown in FIG. 12 and is preliminarily developed in the DRAM 6003 with the entry field blank. According to FIG. 12, three items, “estimation based on time-series data”, “estimation for each month with a high incidence”, and “estimation of a month with a low incidence” are provided as major items. Under “Estimation with time series data”, “1. Estimation by minute” and “2. Estimation by hour” are provided. 1. Months with many onsets and “2. Onset factors” are provided.

  In step S63, the onset factor for M minutes is described in the column “1. Estimation in minutes”, and the onset factor for H time is described in the column “2. Estimation in hours”. To do. When the gradient of the movement amount described in each of the tables TBL2 and TBL3 is small, the column “1. Estimation in minutes” and the column “2. "Not" is described as the onset factor.

  In step S65, the number of onsets is tabulated monthly with reference to the database DB3, and the tabulated results are listed in the table TBL4 shown in FIG. In step S67, the variable L is set to “1”, and in step S69, whether or not the number of onsets described in the Lth column of the table TBL4 has a maximum value (= the month in the Lth column is the maximum month). Whether or not). If the determination result is NO, the process proceeds directly to step S83, and if the determination result is YES, the following process is executed in steps S71 to S81, and then the process proceeds to step S83.

  In steps S71, S73, and S75, the evaluation results of the onset data created in the month of the Lth column of the table TBL4 are extracted from the tables TBL1, TBL2, and TBL3, respectively. In step S77, the daily data corresponding to the onset data created in the month of the Lth column of the table TBL4 is extracted from the database DB2.

  In step S79, the onset factor in time units and the onset factor of seasonality are estimated based on the evaluation result and the daily data thus extracted. Specifically, the onset factor in time units is estimated based on the evaluation results extracted from the tables TBL1, TBL2, and TBL3, and the seasonal onset factor is estimated based on the daily data extracted from the database DB2. .

  In step S81, the onset factor and the seasonal onset factor estimated in this way are listed in the middle of the onset factor report together with the target maximum moon. The target maximum month is described in the column “1. Months with frequent onset”, and the onset factor and seasonal onset factor are described in the “2. Onset factor” column.

  In step S83, the variable L is incremented. In step S85, it is determined whether or not the variable L exceeds the variable Lmax (= total number of columns in which the number of onsets is described). If a determination result is NO, it will return to Step S69.

  Accordingly, if March and July are maximum months, “March” and “July” are described in the column “1. As the onset factor and the onset factor of seasonality for March, for example, “when you go out, you are not on a moving object” and “the amount of pollen scattering is large” are described. Furthermore, for example, “high temperature, rainy weather, not riding a moving object” and “low atmospheric pressure” are described as the onset factor and the seasonal onset factor for July. .

  If the determination result of step S85 is YES, it will progress to step S87. In step S87, one or two or more columns (= minimal month column) indicating the minimum value of the onset frequency are specified from the table TBL4.

  In steps S89, S91, and S93, the evaluation results of the onset data created in the minimal moon are extracted from the tables TBL1, TBL2, and TBL3, respectively. In step S95, onset factors are estimated based on the evaluation results thus extracted. The onset factor is presumed collectively regardless of the number of minimal months.

  In step S97, the estimated onset factor is described in the lower part of the onset factor report shown in FIG. In the column of “estimation of month with less onset” in the onset factor report, for example, “the amount of exercise is high, the heart rate is high, the temperature is low” is described.

  In step S99, the onset factor report thus created is transferred to the request source. The onset factor report on the DRAM 6003 is transferred to the client PC 70 via the communication I / F 6001 shown in FIG. 3 and the public line network 30 shown in FIG. When the process of step S99 is completed, the process returns to step S31.

  Referring to FIG. 18, in step S101, the minute evaluation range and the time evaluation range referenced in steps S45 and S47 described above are initialized. In step S103, it is determined whether a report request operation has been performed using the keyboard / mouse 7003. In step S105, it is determined whether or not an evaluation range adjustment operation has been performed using the keyboard / mouse 7003.

  If both the determination result in step S103 and the determination result in step S105 are NO, the process returns to step S103. If the determination result in step S103 is YES, the process proceeds to step S107. If the determination result in step S105 is YES, the process proceeds to step S103. The process proceeds to S113.

  In step S107, a report creation request is issued to the cloud server 60. The report creation request is output from the communication I / F 7001 and given to the cloud server 60 via the public line network 30. In step 109, it is repeatedly determined whether or not the onset factor report transmitted from the cloud server 60 has been received by the communication I / F 7001. When the determination result is updated from NO to YES, the process proceeds to step S111.

  In step S111, the received onset factor report is expanded in the DRAM 7005, and a report display command is given to the monitor 7004. As a result, the onset factor report developed in the DRAM 7005 is output from the monitor 7004. When the process of step S111 is completed, the process returns to step S103.

  In step S113, the evaluation range in minutes is adjusted, and in step S115, the evaluation range in hours is adjusted. The evaluation range in minutes is adjusted in the range of 30 minutes to 90 minutes, and the evaluation range in hours is adjusted in the range of 3 hours to 9 hours. When the process of step S115 is completed, the process returns to step S103.

  As can be seen from the above description, the CPU 1009 provided in the wearable device 10 detects the activity amount of the patient and the environmental value indicating the surrounding environment of the patient every minute (S5, S7), and the detected activity amount and The minute unit data including the environmental value is transferred to the cloud server 60 (S9).

  The CPU 6002 provided in the cloud server 60 stores the unit data thus transferred in the database DB1 (S31, S33). When receiving the report creation request from the client PC 70, the CPU 6002 defines a plurality of time zones (M minutes and H hours) based on the onset time of the disease that the patient has (S45, S47), and is stored in the database DB1. The minute unit data is evaluated for each time zone (S53, S55). The CPU 6002 also estimates the onset factor of the disease the patient has based on the evaluation result (S61), and transfers the onset factor report describing the estimated onset factor to the client PC 70 (S99).

  The activity amount and the environmental value constituting the minute unit data indicate the activity amount and the surrounding environment of the patient at the time of arrival every minute. The activity amount and the environmental value indicate the onset time of the disease that the patient has. It is evaluated for each reference time zone. By estimating the onset factor of the disease that the patient has based on the evaluation result thus obtained, the accuracy of estimating the onset factor can be increased.

DESCRIPTION OF SYMBOLS 10 ... Wearable device 20 ... Wireless communication base station 30 ... Public network 40 ... Location information API server 50 ... Weather API server 60 ... Cloud server 70 ... Client PC
1002, 6002, 7002 ... CPU
DESCRIPTION OF SYMBOLS 1004 ... GPS apparatus 1005 ... Clock circuit 1006 ... Gyro sensor 1008 ... Heart rate sensor 1009 ... Blood pressure sensor 1010 ... Body temperature sensor

Claims (15)

First acquisition means for acquiring a short-period environment value indicating a surrounding environment of the patient at a time that arrives in a short period;
Definition means for defining a plurality of time zones based on the onset time of the disease that the patient has,
A first evaluation unit that evaluates the short-period environmental value acquired by the first acquisition unit for each time period defined by the definition unit; and a disease that the patient has with reference to an evaluation result of the first evaluation unit An onset factor inference device comprising first inference means for inferring the onset factor.   The onset factor estimation device according to claim 1, wherein the first evaluation unit evaluates the short-period environment value in a common manner regardless of the length of the time zone. It further comprises accepting means for accepting a guess request issued from an external terminal,
The onset factor estimation device according to claim 1 or 2, wherein the first estimation means estimates the onset factor in response to the estimation request. The first acquisition means additionally acquires the amount of activity of the patient;
The onset factor estimation device according to any one of claims 1 to 3, wherein the first evaluation unit additionally evaluates the amount of activity of the patient. Further comprising second evaluation means for evaluating an onset time environment value indicating the surrounding environment of the patient at the onset time of the disease that the patient has,
The onset factor estimation device according to any one of claims 1 to 4, wherein the first estimation unit estimates the onset factor by further referring to an evaluation result of the second evaluation unit. A maximum month specifying means for specifying a month in which the number of occurrences of the disease that the patient has has a maximum value; and an evaluation result corresponding to the month specified by the maximum month specifying means among the evaluation results of the first evaluation means 6. A maximum moon evaluation result extracting means for performing the estimation, and a second estimation means for estimating a disease onset factor of the patient with reference to the evaluation result extracted by the maximum moon evaluation result extracting means. The onset factor estimation apparatus in any one of.   The onset factor estimation device according to claim 6, wherein the second estimation unit estimates the onset factor every month. A second acquisition means for acquiring a long-period environment value indicating a surrounding environment of the patient at a time that arrives in a long period; and the maximum-month specifying means among the long-period environment values acquired by the second acquisition means A long-period environment value extracting means for extracting a long-period environment value corresponding to the month,
The onset factor inference device according to claim 6 or 7, wherein the second inference unit further estimates the onset factor with reference to the long cycle environment value extracted by the long cycle environment value extraction unit. A minimum moon specifying means for specifying a month in which the number of times of occurrence of the disease the patient has has a minimum value;
Of the evaluation results of the first evaluation means, the minimum moon evaluation result extraction means for extracting the evaluation result corresponding to the month specified by the minimum moon specifying means, and the evaluation result extracted by the minimum moon evaluation result extraction means The onset factor inference device according to any one of claims 1 to 8, further comprising third inference means for inferring an onset factor of a disease that the patient has with reference.   The onset factor inference device according to claim 9, wherein the third inference means collectively estimates onset factors regardless of the number of months specified by the minimum moon specifying means. An onset factor estimation method executed by a processor of an onset factor estimation device,
An acquisition step of acquiring a short-period environment value indicating the surrounding environment of the patient at a time that arrives in a short period;
A defining step for defining a plurality of time zones based on the onset time of the disease the patient has;
An evaluation step for evaluating the short-period environmental value acquired by the acquisition step for each time zone defined by the definition step, and estimating the onset factor of the disease that the patient has with reference to the evaluation result of the evaluation step An onset factor estimation method comprising an estimation step. To the processor of the onset factor estimation device,
An acquisition step of acquiring a short-period environment value indicating the surrounding environment of the patient at a time that arrives in a short period;
A defining step for defining a plurality of time zones based on the onset time of the disease the patient has;
An evaluation step for evaluating the short-period environmental value acquired by the acquisition step for each time zone defined by the definition step, and estimating the onset factor of the disease that the patient has with reference to the evaluation result of the evaluation step An onset factor inference program for executing an inference step. An onset factor estimation system configured by a wireless communication terminal attached to the body of a patient having a disease and a server device that communicates with the wireless communication terminal,
The wireless communication terminal is
A creation means for creating a short-period environment value indicating the surrounding environment of the patient at a time that arrives in a short period; and a detection means for detecting the onset time of the disease,
The server device
Obtaining means for obtaining the short-period environmental value created by the creating means;
Defining means for defining a plurality of time zones based on the onset time detected by the detecting means;
Evaluation means for evaluating the short-period environmental value acquired by the acquisition means for each time zone defined by the definition means, and estimation means for estimating the onset factor of the disease by referring to the evaluation result of the evaluation means Equipped with onset factor estimation system. An onset factor estimation method comprising a guess support method executed by a wireless communication terminal attached to the body of a patient having a disease and a guess processing method executed by a server device that communicates with the wireless communication terminal There,
The guess support method is:
A creation step of creating a short cycle environment value indicating the surrounding environment of the patient at a time that arrives in a short cycle, and a detection step of detecting the onset time of the disease,
The guess processing method includes:
An acquisition step of acquiring the short-period environment value created by the creation step;
A definition step for defining a plurality of time zones based on the onset time detected by the detection step;
An evaluation step for evaluating the short-period environmental value acquired by the acquisition step for each time zone defined by the definition step, and an estimation step for estimating the onset factor of the disease by referring to the evaluation result of the evaluation step A method for estimating onset factors. An onset factor estimation program comprising an estimation support program provided to a wireless communication terminal attached to the body of a patient having a disease and an estimation processing program provided to a server device that communicates with the wireless communication terminal There,
The guess support program includes:
A program for causing a processor of the wireless communication terminal to execute a creation step of creating a short cycle environment value indicating a surrounding environment of the patient at a time that arrives in a short cycle, and a detection step of detecting the onset time of the disease ,
The guess processing program is:
An acquisition step of acquiring the short-period environment value created by the creation step;
A definition step for defining a plurality of time zones based on the onset time detected by the detection step;
An evaluation step for evaluating the short-period environmental value acquired by the acquisition step for each time zone defined by the definition step, and an estimation step for estimating the onset factor of the disease by referring to the evaluation result of the evaluation step An onset factor estimation program, which is a program for causing a processor of the server device to execute.

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