WO2024024294A1 - Dispositif d'estimation, procédé d'estimation, système d'estimation et programme d'estimation - Google Patents

Dispositif d'estimation, procédé d'estimation, système d'estimation et programme d'estimation Download PDF

Info

Publication number
WO2024024294A1
WO2024024294A1 PCT/JP2023/021364 JP2023021364W WO2024024294A1 WO 2024024294 A1 WO2024024294 A1 WO 2024024294A1 JP 2023021364 W JP2023021364 W JP 2023021364W WO 2024024294 A1 WO2024024294 A1 WO 2024024294A1
Authority
WO
WIPO (PCT)
Prior art keywords
data
subject
estimation
health
behavior
Prior art date
Application number
PCT/JP2023/021364
Other languages
English (en)
Japanese (ja)
Inventor
洋介 ▲高▼▲崎▼
Original Assignee
一般社団法人持続可能社会推進機構
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 一般社団法人持続可能社会推進機構 filed Critical 一般社団法人持続可能社会推進機構
Publication of WO2024024294A1 publication Critical patent/WO2024024294A1/fr

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance

Definitions

  • the present invention relates to an estimation device, an estimation method, an estimation system, and an estimation program, and in particular, the present invention is based on data regarding a subject's cognitive biases, cognitive characteristics, and decision-making characteristics (hereinafter referred to as "cognitive biases").
  • the present invention relates to an estimation device, an estimation method, an estimation system, and an estimation program for estimating data related to behavior.
  • the subject's cognitive bias affects the subject's behavior and outcomes regarding health, medical care, and lifestyle habits. Furthermore, the behavior of the subjects is thought to have an impact on outcomes related to health, medical care, and lifestyle habits.
  • the structure of cognitive bias is complex, as is the structure of target behavior toward health, medical care, and lifestyle habits, so it has been difficult to express it using existing models.
  • there are large individual differences in cognitive bias and behavior and existing uniform guidance and interventions are insufficient to encourage and maintain behavior in target subjects, or to improve and maintain outcomes.
  • the effectiveness of guidance and intervention by experts is extremely limited.
  • the target person's cognitive Modeling with bias in mind was unknown. Furthermore, it has not been known to consider and model the behavior of the target person in order to improve or maintain outcomes related to health, medical care, and lifestyle habits for the target person.
  • the estimation device of the present invention includes an input unit for inputting cognitive bias data regarding a target person's cognitive bias, and based on the cognitive bias data, behavioral data regarding the target person's behavior, attribute data of the target person, and by an estimation model including a neural network for estimating at least one of location data of the subject, health data of the subject, exercise data of the subject, lifestyle data of the subject, and medical data of the subject, outputting at least one of the behavior data, the attribute data, the location data, the health data, the exercise data, the lifestyle data, and the medical data estimated from the cognitive bias data input by the input unit; and an output section.
  • the estimation device of the present invention includes an input unit for inputting behavioral data regarding the behavior of the subject, and attribute data of the subject, position data of the subject, health data of the subject based on the behavioral data.
  • the behavioral data inputted by the input unit by an estimation model including a neural network for estimating at least one of the subject's exercise data, the subject's lifestyle data, and the subject's medical data.
  • an output unit that outputs at least one of the attribute data, the position data, the health data, the exercise data, the lifestyle data, and the medical data estimated from the data.
  • the present invention by estimating behavior-related data or outcomes based on data related to cognitive bias, it is possible to encourage or maintain a target's behavior regarding health, medical care, and lifestyle habits, and to improve health, medical care, and lifestyle habits. It is possible to improve or maintain a subject's outcomes regarding lifestyle habits.
  • FIG. 1 is a block diagram showing an example of the system configuration of an estimation system according to the present embodiment. It is a block diagram showing an example of the system configuration of a machine learning server of this embodiment.
  • FIG. 2 is a diagram schematically showing the configuration of various types of data associated with each other according to the present embodiment.
  • FIG. 2 is a diagram schematically showing the structure of various data of non-target person data associated with target person data of the present embodiment. It is a figure showing the generation process of the estimation model of this embodiment.
  • FIG. 2 is a diagram illustrating multimodal data fusion.
  • FIG. 1 is a block diagram showing an example of the configuration of an estimation device according to the present embodiment.
  • FIG. 3 is a diagram showing a process of transmitting learning data (teacher data) according to the present embodiment. It is a figure showing the output process of estimated data estimated by a learning model (estimation model) of this embodiment. It is a figure showing an example of cognitive bias data according to a target person, and behavioral data estimated from target person data.
  • FIG. 1 is a block diagram showing an example of the system configuration of the estimation system of this embodiment.
  • the estimation system 1 estimates behavior-related data based on cognitive bias-related data.
  • the estimation system 1 includes a machine learning server 4 and a computer (estimation device) 5 that are electrically connected via a network 2 and can communicate with each other.
  • FIG. 2 is a block diagram showing an example of the system configuration of the machine learning server of this embodiment.
  • the machine learning server 4 includes a processor (control device) 21, a storage device (for example, ROM, RAM, HDD, etc.) 22, an input section 23, and an output section 25.
  • a processor control device
  • storage device for example, ROM, RAM, HDD, etc.
  • the processor (control device) 21 is a control unit such as a CPU, MPU, or GPU, and includes a data acquisition unit 32, a machine learning unit 33, an estimation unit 35, and an identification unit 36.
  • the data acquisition section 32, machine learning section 33, estimation section 35, and identification section 36 are electrically connected by a bus (not shown) and can communicate with each other.
  • the storage device (storage unit) 22 includes cognitive bias data 38, behavioral data 39, target person data 40, non-target person data 41, a learning model storage unit 46, and an estimated data storage unit 47.
  • the cognitive bias data 38 is data (including feature data) indicating the psychological tendencies of the subject 6.
  • the cognitive bias data 38 includes at least one of a cognitive bias, a cognitive characteristic, and a decision-making characteristic of the subject 6 (including data on these feature amounts).
  • Cognitive bias data 38 includes, for example, value functions based on prospect theory, dual process theory, etc., loss aversion, endowment effect, status quo bias, ambiguity aversion, uncertainty avoidance, risk preferences (risk aversion), etc. ⁇ Risk love), reference point dependence, diminishing sensitivity, probability weighting function, mental accounting (psychological accounting), ignoring the denominator, time discounting/time preference, social preference (altruism, fairness, reciprocity, non-reciprocity)
  • the target person 6's data (including these feature data) regarding at least one of (fairness avoidance, etc.), framing, and heuristics (anchoring, availability, representativeness) is included.
  • Data regarding the value function, loss aversion, endowment effect, and status quo bias based on the value function is data that indicates the strength or weakness or asymmetry of reactions to gains and losses.
  • Data regarding ambiguity avoidance/uncertainty avoidance is data indicating the strength or weakness of avoidance toward ambiguity.
  • Data regarding risk appetite (risk aversion/risk love) shows the strength or asymmetry of the tendency to avoid risk when in a state of gain (risk aversion) and the tendency to downplay risk when in a state of loss (risk love). This is data showing.
  • the data regarding the reference point dependence is data indicating the relative rate of change from the standard (reference point) set by the subject 6 or the strength or weakness or asymmetry of the reaction to up and down.
  • the data regarding decreasing sensitivity is data indicating the strength of sensitivity with respect to time or frequency, based on the phenomenon that when the same event occurs repeatedly, the subject 6 gradually gets used to the event and the sensitivity decreases.
  • the data regarding the probability weighting function is data that shows the strength or weakness or asymmetry of the tendency to overestimate when the objective probability is low and underestimate when the objective probability is high.
  • Data related to mental accounting is data that shows the strength or weakness, or asymmetry, of the tendency for decisions made when using money to change depending on the source of the money and what it is used for.
  • Data regarding neglect of the denominator is data that shows the strength or weakness or asymmetry of a tendency to evaluate probability highly when the numerator is large, even when the numerator/denominator probabilities are the same.
  • Time discount/time preference data is data that shows the discount rate for time, which is how much lower the future value of rewards and gains (delayed rewards) is estimated than the present value (immediate rewards).
  • Subject 6 with a low time discount rate (time preference rate) is able to hold back until future rewards (strong self-control), and subject 6 with a high time discount rate (time preference rate) is able to hold back until future rewards. cannot (strong impulsiveness).
  • Data on social preferences are important for humans who care not only about their own material rewards but also about the rewards and/or intentions that lead to rewards for reference groups. This is data that shows trends. Data regarding framing changes the impression given depending on what is emphasized, and shows the degree to which it influences decision-making. Data related to heuristics is data that shows an intuitive decision-making process that does not necessarily lead to the correct answer, but can obtain an answer close to the correct answer to a certain level.
  • Cognitive bias data 38 includes quantitative data, text data, audio data, etc. (including these feature data) obtained by conducting questionnaires, psychological tests, sensitivity tests, games, etc. on the subject 6 regarding cognitive bias ). Further, the cognitive bias data 38 is quantitative data, text data, voice data, etc. (including feature amount data thereof) regarding cognitive bias in SNS, e-mail, blogs, etc.
  • the behavior data 39 includes data related to at least one of text, images, video, sounds (audio data, etc.), light, smell, and force for urging or maintaining behavior in the subject 6 (characteristic data of these) (including).
  • the behavior data 39 is quantitative data, text data, audio data, etc. (including feature data) regarding behaviors corresponding to each cognitive bias.
  • the behavioral data 39 is quantitative data, text data, audio data, etc. (including these feature data) obtained by administering behavioral questionnaires, psychological tests, sensitivity tests, games, etc. to the subject 6. be.
  • the behavior data 39 is quantitative data, text data, voice data, etc. (including feature amount data of these) related to behavior in SNS, e-mail, blogs, etc.
  • the behavior data 39 includes quantitative data, text data, voice data, etc.
  • the behavioral data 39 may be quantitative data such as messages that encourage or maintain the behavior of the target person 6 regarding health, medical care, or lifestyle habits, text data, voice data, etc., or may be obtained by natural language processing of these data. It is a feature quantity.
  • the behavior data 39 becomes label data (correct data) of the cognitive bias data 38 in machine learning.
  • the subject data 40 includes at least one of the subject 6's attribute data, location data, health data, exercise data, lifestyle data, and medical data (including feature amount data thereof).
  • the health data includes at least one of vital data and biomarker data (including their feature data).
  • the subject data 40 becomes label data (correct data) of the cognitive bias data 38 in machine learning.
  • the subject data 40 includes (1) the subject 6's weight, age, gender, address, diet, drinking, smoking, exercise, stress, medication, socio-economic factors (e.g. income, occupation, etc.), medical history, and family composition data, (2) location information of the subject 6 by GPS etc. (e.g. time, place, surrounding environment, etc.) and map information data, (3) physical activity of the subject 6 (e.g. number of steps, (4) Genetic information, test result information, prescription information, medical treatment of subject 6 at medical institutions
  • This data includes information (for example, surgery, procedure, etc.), medical examination results, and diagnostic information (disease name).
  • these data may be statistical values based on a plurality of data.
  • these data may be statistical values based on subject data at a plurality of times.
  • these data may be data (for example, food type, amount, calories, etc.) estimated by machine learning based on image data (for example, images of meals, etc.).
  • the subject data 40 includes time-series data 42 and non-time-series data 42 of at least one of attribute data, location data, health data, exercise data, lifestyle data, and medical data of the subject 6.
  • the identification unit 36 identifies time series data and non-time series data from at least one of attribute data, location data, health data, exercise data, lifestyle data, and medical data, which are the subject data 40.
  • the time-series data 42 is data that changes over time and can be obtained at multiple times.
  • Time data is associated with the time series data 42.
  • the non-time series data 43 is data that hardly changes with the passage of time (unchangeable data), or data that is substantially impossible to obtain or does not need to be obtained at a plurality of times.
  • the time series data 42 includes heart rate, electrocardiogram, oxygen saturation, sleep, blood pressure, blood sugar level, and the like.
  • the non-time series data 43 includes genetic information and the like.
  • the non-target person data 41 includes at least one of attribute data, location data, health data, exercise data, lifestyle data, and medical data (including these feature data) of non-target people other than the target person 6.
  • the health data includes at least one of vital data and biomarker data (including their feature data).
  • the non-target person data 41 includes (1) weight, age, gender, address, diet, drinking, smoking, exercise, stress, medication, and socio-economic factors (e.g., educational background, income, occupation, position, etc.) of the non-target person; ), past medical history, and family composition data; (2) location information (e.g., time, place, surrounding environment, etc.) of non-target individuals using GPS, and map information; (3) physical activity of non-target individuals. (e.g., number of steps, exercise, muscle strength, muscle mass, etc.), data on heart rate, electrocardiogram, oxygen saturation, blood pressure, blood sugar level, sleep, etc.
  • socio-economic factors e.g., educational background, income, occupation, position, etc.
  • This data includes prescription information, medical practice information (for example, surgery, procedure, etc.), medical examination results, and diagnostic information (disease name). Moreover, these data may be statistical values based on data of a plurality of non-target persons. Moreover, these data may be statistical values based on non-target person data at a plurality of times. Further, these data may be data (for example, food type, amount, calories, etc.) estimated by machine learning based on image data (for example, images of meals, etc.).
  • the non-target person data includes models that parametrically approximate input and output (for example, a value function or a probability weighting function expressed as a mathematical formula) that has been clarified by previous research.
  • the cognitive bias data 38, behavioral data 39, and target person data 40 of the target person 6 can be determined or estimated.
  • the non-target person data 41 includes time series data 44 and non-time series data of at least one of attribute data, location data, health data, exercise data, lifestyle data, and medical data.
  • the identification unit 36 identifies time series data and non-time series data from at least one of attribute data, location data, health data, exercise data, lifestyle data, and medical data, which are non-target person data 41.
  • the time-series data 44 is data that changes over time and can be obtained at multiple times.
  • Time data is associated with the time series data 44.
  • the non-time series data 45 is data that hardly changes over time (unchangeable data), or data that is substantially impossible to obtain or does not need to be obtained at a plurality of times.
  • the time series data 44 includes heart rate, electrocardiogram, oxygen saturation, sleep, blood pressure, blood sugar level, and the like.
  • the non-time series data 45 includes genetic information and the like.
  • FIG. 3 is a diagram schematically showing the structure of various associated data.
  • subject data such as subject ID 51, attribute data 52, location data 53, health data 54, exercise data 55, lifestyle data 56, and medical Data 57 and non-target person data 60 (including these feature amount data) are associated.
  • non-target person data 60 includes attribute data, location data, health data, exercise data, lifestyle data, and medical data of the non-target person.
  • FIG. 4 is a diagram schematically showing the structure of various data of non-target person data 60 associated with target person data 51 to 57. As shown in FIG. 4, non-target person data 60 includes a non-target person ID 61, attribute data 62, location data 63, health data 64, exercise data 65, lifestyle data 66, and medical data 67 (these feature amount data ) are associated.
  • the target person data 52 to 57 and the non-target person data 62 to 67 are classified into time series data and non-time series data.
  • FIG. 5 is a diagram showing the estimation model generation process of this embodiment.
  • step S1 the input unit 23 inputs various data (cognitive bias data 38, behavioral data 39, target person data 40, and non-target person data 41).
  • the input unit 23 executes a storage instruction for various data, associates the various data, and stores the data in the storage device 22 (step S2, step S3).
  • the data acquisition unit 32 acquires various data stored in the storage device 22 by executing the acquisition command (step S4).
  • the machine learning unit 33 generates a learning model (estimated model) using a neural network based on the acquired various data (including these feature amount data) by executing the estimated model generation command (step S5). .
  • the machine learning unit 33 inputs the cognitive bias data 58 (for example, B-1 to B-5 in FIG. 3) to the input layer, and inputs the behavioral data 59 associated with the cognitive bias data 58 (for example, C- in FIG. 3).
  • a learning model (estimated model) is generated by a neural network that inputs 1 to C-5) as correct values of the output layer.
  • the machine learning unit 33 inputs the cognitive bias data 58 (for example, B-1 to B-5 in FIG. 3) to the input layer, and the attribute data 52 associated with the cognitive bias data 58 (for example, B-1 to B-5 in FIG. 3).
  • E-11 to E-15 location data 53 (for example, E-21 to E-25 in FIG. 3), health data 54 (for example, E-31 to E-35 in FIG. 3), exercise data 55 (for example, , E-41 to E-45 in FIG. 3), lifestyle data 56 (for example, E-51 to E-55 in FIG. 3), and medical data 57 (for example, E-61 to E-65 in FIG. 3)
  • a learning model (estimated model) is generated by a neural network that inputs at least one of the above as the correct value of the output layer.
  • the intermediate layer of the neural network is provided with, for example, an Affine layer or a Convolution layer. Downsampling processing or the like may be performed as appropriate. Further, the number of layers, the number of neurons, and the activation function of the intermediate layer are optimally selected so that the estimation result is highly accurate.
  • the configuration of the neural network includes Feed Forward Neural Network (FFNN), Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), attention mechanism, etc. .
  • the machine learning unit 33 uses parameters (weights) initialized with predetermined values such as random numbers to calculate the value output to the output layer when the cognitive bias data 58 is input to the input layer and the correct answer of the output layer.
  • a loss function representing the deviation from the value (behavior data 59 or/and subject data 52 to 57) is calculated, and the differential value of the loss function is used as the slope to calculate the difference between the value output to the output layer and the correct value of the output layer.
  • a learning model (estimated model) is generated by changing parameters (weights) so that the deviation becomes smaller.
  • the machine learning unit 33 creates a learning model (estimated model ) is generated.
  • the learning model (estimated model) includes a neural network including at least one of a convolution layer, a recursive calculation layer, and an attention mechanism.
  • the learning model includes attribute data 52, location data 53, health data 54, exercise data 55, lifestyle data 56, and It may also include a neural network for estimating cognitive bias data and/or subject data (outcome data) of the subject 6 based on at least one subject data of the medical data 57 (including these feature data). .
  • the learning model (estimation model) includes attribute data 62, location data 63, health data 64, exercise data 65, lifestyle data 66, and medical data 67 (these feature amount data) of non-target persons other than the target person 6. It may also include a neural network for estimating cognitive bias data and/or subject data (outcome data) of the subject 6 based on at least one non-target subject data 60 (including the above).
  • the learning model (estimation model) includes attribute data 52, location data 53, health data 54, exercise data 55, and lifestyle data 56 of the subject 6. , and medical data 57 (including these feature data), cognitive bias data, attribute data, location data, health data, exercise data, lifestyle data, and It may also include a neural network for estimating at least one subject data (outcome data) of the medical data.
  • the estimated data becomes the label data (correct data), and the data associated with this data (cognitive bias data of subject 6 and subject data) becomes the input layer of the neural network.
  • a learning model (estimated model) is constructed based on input and learning. For example, by using a learning model (estimation model) learned from Subject 6's past sleep time, other subject data, and cognitive bias data, Subject 6's future sleep time (outcome data) can be estimated. Ru.
  • the learning process is similar to above.
  • the learning model (estimation model) includes attribute data 62, location data 63, health data 64, exercise data 65, lifestyle data 66, and medical data 67 (these feature amount data) of non-target persons other than the target person 6.
  • the estimated data becomes label data (correct data), and the data associated with this data (cognitive bias data of non-target people and data of non-target people) is input to the input layer of the neural network and the learning is performed.
  • a model (estimated model) may be constructed.
  • the cognitive bias data of the target person 6 is estimated by using the cognitive bias data of the non-target person or a learning model (estimated model) learned from the non-target person data.
  • the learning process is similar to above.
  • the learning model includes at least one of a convolution layer, a recursive calculation layer, and an attention mechanism, and connects target person data (or target person data and non-target person data).
  • This is an estimation model that is machine-learned by inputting the acquired data) into the input layer of a neural network.
  • Non-subject data includes statistical values based on data from multiple non-subjects and data on evidence obtained from previous research.
  • (1) a method of extracting feature amounts after concatenating the raw data of target person data and non-target person data;
  • methods such as (3) a method in which feature amounts are individually extracted from raw data of non-target person data and then the feature amounts are connected, and (3) a method in which these are combined.
  • image data it is also possible to extract feature amounts in the Convolution layer and then connect them with other data.
  • the learning model is based on the cognitive bias of the target person 6 or non-target person output from the intermediate layer or output layer of the neural network.
  • At least one of data, attribute data, location data, health data, exercise data, lifestyle data, medical data, and behavioral data may be input to the input layer of the neural network.
  • the data input to the input layer of the neural network is data m concat that is a concatenation of target person data it and non-target person data z 1 and z 2 .
  • the subject data it , time-series data output from at least one of Cell State (plays the role of long-term memory) and Hidden State (plays the role of short-term memory) of the neural network is used.
  • the non-target person data z 1 and z 2 data obtained by dimensionally compressing a plurality of non-target person data Modalities 1 and 2 using Autoencoders 1 and 2, respectively, is used.
  • cognitive bias data of the target person 6 and non-target person data may be connected and input to the input layer of the neural network.
  • the generated learning model (estimated model) is stored in the learning model storage unit 46 (step S6).
  • FIG. 7 is a block diagram showing an example of the configuration of the estimation device of this embodiment.
  • the computer (estimation device) 5 inputs cognitive bias data and outputs behavioral data and/or outcome data acquired by the learning model (estimation model).
  • the computer (estimation device) 5 includes a processor (control device) 210, a storage device (for example, ROM, RAM, HDD, etc.) 220, an input section 230, and an output section 250.
  • a processor control device
  • storage device for example, ROM, RAM, HDD, etc.
  • the processor (control device) 210 is a control unit such as a CPU, MPU, or GPU, and includes a data acquisition unit 320, an estimation unit 350, and an identification unit 360.
  • the data acquisition unit 320, the estimation unit 350, and the identification unit 360 are electrically connected by a bus (not shown) and can communicate with each other.
  • the storage device (storage unit) 220 includes cognitive bias data 380, behavioral data 390, subject data 400, and estimated data storage unit 470.
  • the input unit 230 inputs cognitive bias data 380 regarding the cognitive bias of the subject 6.
  • the input unit 230 inputs various data (cognitive bias data 380, behavior data 390, subject data 400).
  • the input unit 230 associates various types of data and stores them in the storage device 220 by executing a command to store various types of data.
  • the cognitive bias data 380, behavioral data 390, subject data 400 (attribute data, location data, health data, exercise data, lifestyle data, and medical data) and their feature data are the above cognitive bias data. 38, behavioral data 39, and subject data 40.
  • the identification unit 360 identifies time series data and non-time series data from at least one of cognitive bias data, attribute data, location data, health data, exercise data, lifestyle data, and medical data, which are the subject data 400. do.
  • the computer (estimation device) 5 transmits learning data (teacher data) to the machine learning server 4 via the network 2
  • the computer (estimation device) 5 outputs various data (cognitive bias data 380, etc.) to the machine learning server 4 in order to generate a learning model (estimation model).
  • FIG. 8 is a diagram showing the process of transmitting learning data (teacher data) in this embodiment.
  • the input unit 230 inputs various data (cognitive bias data 380, behavior data 390, subject data 400).
  • the input unit 230 associates various types of data and stores them in the storage device 220 by executing a command to store various types of data (step S20, step S30).
  • the data acquisition unit 320 acquires various data stored in the storage device 220 by executing the acquisition command.
  • the output unit 250 transmits various data to the machine learning server 4 via the network 2 by executing the transmission command (step S40).
  • the input unit 23 of the machine learning server 4 inputs various data (cognitive bias data 380, behavior data 390, and subject data 400) received from the estimation device 5.
  • the various input data (cognitive bias data 380, behavioral data 390, and subject data 400) are stored in the storage device 22 as cognitive bias data 38, behavioral data 39, and subject data 40, respectively.
  • the time series data and non-time series data are identified by the identification unit 36.
  • the data acquisition unit 32 of the machine learning server 4 acquires various data stored in the storage device 22, and the machine learning unit 33 of the machine learning server 4 executes the estimation model generation command to obtain the various acquired data.
  • a learning model (estimated model) is generated by a neural network based on the data (including these feature data).
  • the computer (estimation device) 5 has a function of outputting learning data (teacher data) to the machine learning server 4.
  • the cognitive bias data 380, the behavior data 390, and the subject data 400 are associated with each other and used as learning data by the machine learning unit 33 of the machine learning server 4. Further, the cognitive bias data 380, the behavior data 390, and the target person data 400 may be used as non-target person data 41 of the target person other than the target person 6.
  • the learning model (estimated model) stored in the learning model storage unit 46 of the machine learning server 4 is used as the learning model (estimated model);
  • the learning model (estimated model) By storing the learning model (estimation model) in the storage device 220 of the computer (estimation device) 5 via the network 2, even if the learning model (estimation model) stored in the computer (estimation device) 5 is used. good.
  • the cognitive bias data 380 of the subject 6 and the non-time series data of the subject data are sent from the computer (estimation device) 5 to the machine learning server 4 in advance, the computer (estimation device) 5
  • the time series data of the subject data of the subject 6 is transmitted to the machine learning server 4.
  • the computer (estimation device) 5 inputs the new cognitive bias data 380 and the new non-time series data of the subject data.
  • the non-time-series data is sent to the machine learning server 4.
  • the machine learning server 4 that has received the various data updates the learning model (estimated model) using the machine learning unit 33 based on the new data.
  • FIG. 9 is a diagram showing the output process of estimated data estimated by the learning model (estimation model) of this embodiment.
  • the input unit 230 inputs various data (subject data 400).
  • the input unit 230 associates various types of data and stores them in the storage device 220 by executing a command to store various types of data (step S20, step S30).
  • target person data 400 (attribute data, location data, health data, exercise data, lifestyle data, and medical data) and their feature data are similar to the above-mentioned target person data 40, but the machine learning server This data is different from the learning data (teacher data) by the machine learning unit 33 of No. 4.
  • the data acquisition unit 320 acquires various data stored in the storage device 220 by executing the acquisition command.
  • the estimation unit 350 transmits various data to the machine learning server 4 via the network 2 by executing the estimation command (step S400).
  • the input unit 23 of the machine learning server 4 inputs various data (target person data 400) received from the estimation device 5.
  • the estimation unit 35 of the machine learning server 4 estimates behavioral data using a learning model (estimation model) based on the various acquired data (including these feature data) by executing the estimation command.
  • the estimation unit 35 also includes at least one of attribute data, location data, health data, exercise data, lifestyle data, and medical data of the subject 6.
  • the behavioral data of the subject 6 may be estimated based on the subject data 40.
  • the estimation unit 35 also includes at least one of attribute data, location data, health data, exercise data, lifestyle data, and medical data of the subject 6. Based on the subject data 40, at least one of cognitive bias data, attribute data, location data, health data, exercise data, lifestyle data, and medical data of the subject 6 may be estimated as outcome data.
  • the estimation unit 35 also collects attribute data, location data, health data, exercise data, lifestyle data, and medical information of non-target persons other than the target person 6.
  • the behavior data of the target person 6 may be estimated based on at least one non-target person data 41 of the data.
  • the estimation unit 35 also collects attribute data, location data, health data, exercise data, lifestyle data, and medical information of non-target persons other than the target person 6. Based on at least one non-target person data 41 of the data, at least one of the target person 6's cognitive bias data, attribute data, location data, health data, exercise data, lifestyle data, and medical data is used as outcome data. It may be estimated.
  • the estimation unit 35 reads the learning model (estimated model) from the learning model storage unit 46, inputs the cognitive bias data 380 into the input layer of the neural network of the learning model (estimated model), and inputs the estimated estimation data (behavior data, At least one of cognitive bias data, attribute data, location data, health data, exercise data, lifestyle data, and medical data is obtained from the output layer.
  • the estimated data is stored in the estimated data storage section 47.
  • the estimation data is transmitted by the output unit 25 of the machine learning server 4 to the computer (estimation device) 5 via the network 2.
  • the input unit 230 of the computer (estimation device) 5 receives the estimation data by executing the reception command (step S50).
  • the input unit 230 stores the estimated data in the estimated data storage unit 470.
  • the output unit 250 By executing the estimated data output command, the output unit 250 generates a learning model (estimation model) including a neural network for estimating behavioral data that urges or maintains behavior in the subject 6 based on the cognitive bias data. Accordingly, behavioral data estimated from the cognitive bias data input by the input unit 230 is output (step S60). Furthermore, the output unit 250 outputs other estimated data by executing the estimated data output command (step S60).
  • a learning model estimation model including a neural network for estimating behavioral data that urges or maintains behavior in the subject 6 based on the cognitive bias data. Accordingly, behavioral data estimated from the cognitive bias data input by the input unit 230 is output (step S60). Furthermore, the output unit 250 outputs other estimated data by executing the estimated data output command (step S60).
  • the output estimation data is displayed on the display of a communication terminal (tablet, wearable terminal, etc.) electrically connected to the computer (estimation device) 5 or on the display of the computer (estimation device) 5.
  • a communication terminal tablet, wearable terminal, etc.
  • FIG. 10 is a diagram illustrating an example of cognitive bias data according to a subject and behavioral data and outcome data estimated from the subject data.
  • the input unit 230 inputs cognitive bias data regarding the cognitive bias of subject 7. is input, and the output unit 250 uses an estimation model including a neural network for estimating behavioral data that urges or maintains behavior in the target person 7 based on the cognitive bias data and the target person data. Outputs behavioral data (such as messages emphasizing numerical targets for improving hypertension) and outcome data (estimated blood pressure, etc.) estimated from the input cognitive bias data and subject data.
  • the output unit 250 outputs data regarding sounds, lights, odors, and forces that encourage or maintain medication or exercise at a predetermined time to a speaker, a light emitting device, an odor mixture generator, and a driver. It may also be output to a device (for example, a massage device or a motor).
  • a device for example, a massage device or a motor.
  • the blood pressure data is acquired from the wearable terminal as time-series data, and new behavioral data and future blood pressure values (outcome data) are estimated according to the acquisition time of the blood pressure data.
  • the output unit 250 Output data (such as audio that emphasizes the benefits of restricting calorie intake) and outcome data (such as estimated body weight).
  • outcome data such as estimated body weight
  • the output unit 250 outputs data regarding sounds, lights, odors, and forces for suppressing meals and snacks to speakers, light emitting devices, odor mixture generators, and driving devices (e.g., It may also be output to a massage device, motor, etc.
  • the output unit 250 outputs behavioral data. (e.g., a message emphasizing the risk of high stress) and outcome data (e.g., an estimated depression scale).
  • behavioral data e.g., a message emphasizing the risk of high stress
  • outcome data e.g., an estimated depression scale
  • the output unit 250 outputs data related to stress-reducing music, light, smell, and force at a predetermined time to a speaker, a light emitting device, an odor mixture generator, and a drive device (for example, a massage device). or a motor), etc.
  • the output The unit 250 outputs behavioral data (such as a message proposing an immediately executable method for resolving sleep deprivation) and outcome data (such as estimated sleep time and quality).
  • behavioral data such as a message proposing an immediately executable method for resolving sleep deprivation
  • outcome data such as estimated sleep time and quality
  • the output unit 250 outputs data regarding sleep-inducing music, light, smell, and force at a predetermined time to a speaker, a light emitting device, an odor mixture generator, and (for example, a massage device or a motor). You can also output it to
  • the computer (estimation device) 5 outputs behavior data and/or outcome data estimated by the learning model (estimation model).
  • the behavior of the target person regarding health, medical care, and lifestyle habits that are affected by cognitive bias is modeled, and the behavior of the target person regarding health, medical care, and lifestyle habits is encouraged and maintained. It is possible to construct a learning model (estimation model) for
  • the target person's outcomes regarding health, medical care, and lifestyle habits that are affected by cognitive bias are modeled, and the target person's outcomes regarding health, medical care, and lifestyle habits are improved or maintained.
  • a learning model estimation model
  • estimated data (behavioral data, cognitive bias data, attribute data, location data, health data, exercise data) of the subject based on the subject's own subject data may be used. , lifestyle data, and medical data), highly accurate estimation data that matches the characteristics of the subject can be output.
  • the accuracy is improved by taking into account the general characteristics of the person other than the target person. It is possible to output highly estimated data.
  • step S1 the input unit 23 inputs various data (behavior data 39, target person data 40, and non-target person data 41).
  • the input unit 23 executes a storage instruction for various data, associates the various data, and stores the data in the storage device 22 (step S2, step S3).
  • the data acquisition unit 32 acquires various data stored in the storage device 22 by executing the acquisition command (step S4).
  • the machine learning unit 33 generates a learning model (estimated model) using a neural network based on the acquired various data (including these feature amount data) by executing the estimated model generation command (step S5). .
  • the machine learning unit 33 inputs the behavioral data 59 (for example, C-1 to C-5 in FIG. 3) to the input layer, and inputs the subject data (for example, C-1 to C-5 in FIG. 3) associated with the behavioral data 59 to the input layer. C-5) to the input layer, attribute data 52 (for example, E-11 to E-15 in FIG. 3) associated with behavior data 59, and position data 53 (for example, E-21 to E in FIG. -25), health data 54 (for example, E-31 to E-35 in Figure 3), exercise data 55 (for example, E-41 to E-45 in Figure 3), lifestyle data 56 (for example, E-51 to E-55) and medical data 57 (for example, E-61 to E-65 in FIG. 3) are inputted as correct values of the output layer. generate.
  • attribute data 52 for example, E-11 to E-15 in FIG. 3 associated with behavior data 59
  • position data 53 for example, E-21 to E in FIG. -25
  • health data 54 for example, E-31 to E-35 in
  • the machine learning unit 33 uses parameters (weights) initialized with predetermined values such as random numbers to determine the value output to the output layer when the behavioral data 59 is input to the input layer and the correct value of the output layer. (Target person data 52 to 57) A loss function representing the deviation from the output layer is calculated, and the differential value of the loss function is used as the slope, so that the deviation between the value output to the output layer and the correct value of the output layer becomes small. A learning model (estimated model) is generated by changing parameters (weights).
  • the machine learning unit 33 generates a learning model (estimated model) using a neural network that inputs the behavior data 59 into the input layer and inputs the subject data 52 to 57 as correct values in the output layer.
  • the learning model (estimated model) includes a neural network including at least one of a convolution layer, a recursive calculation layer, and an attention mechanism.
  • the learning model includes attribute data 52, location data 53, health data 54, exercise data 55, lifestyle data 56, and medical data of the subject 6 in addition to or separately from the behavioral data 59.
  • the neural network may include a neural network for estimating the subject data (outcome data) of the subject 6 based on at least one subject data of 57 (including these feature data).
  • the learning model (estimation model) includes attribute data 62, location data 63, health data 64, exercise data 65, lifestyle data 66, and medical data 67 (these feature amount data) of non-target persons other than the target person 6.
  • the neural network may include a neural network for estimating target person data (outcome data) of the target person 6 based on at least one non-target person data 60 (including the above).
  • the learning model also includes attribute data 52, location data 53, health data 54, exercise data 55, lifestyle data 56, and Based on at least one subject data of the medical data 57 (including these feature data), behavioral data, attribute data, location data, health data, exercise data, lifestyle data, and medical data of the subject 6 are determined. It may also include a neural network for estimating at least one subject data (outcome data).
  • the estimated data (especially time series data) becomes the label data (correct data), and the data associated with this data (behavior data of subject 6 and subject data) is input to the input layer of the neural network.
  • a learning model (estimation model) that has been trained using the following methods is constructed. For example, by using a learning model (estimation model) learned from Subject 6's past sleep time, other subject data, and cognitive bias data, Subject 6's future sleep time (outcome data) can be estimated. Ru.
  • the learning process is similar to above.
  • the learning model (estimation model) includes attribute data 62, location data 63, health data 64, exercise data 65, lifestyle data 66, and medical data 67 (these feature amount data) of non-target persons other than the target person 6.
  • At least one subject data (outcome data) of the subject 6's attribute data, location data, health data, exercise data, lifestyle data, and medical data is based on at least one non-target subject data 60 of may include a neural network for estimating.
  • the estimated data becomes label data (correct data), and the data associated with this data (behavior data of non-target people and data of non-target people) is input to the input layer of the neural network to learn the learning model.
  • (estimation model) may be constructed.
  • the outcome data of the target person 6 is estimated by using a learning model (estimation model) learned from the non-target person's behavior data and the non-target person data.
  • the learning process is similar to above.
  • non-subject data is a model that parametrically approximates the quantitative data and inputs and outputs clarified by previous research for each variable, and is based on the data quantified by the evidence of previous research.
  • Behavioral data and subject data (outcome data) of the subject 6 may be determined or estimated from the subject data.
  • the learning model includes at least one of a convolution layer, a recursive calculation layer, and an attention mechanism, and connects target person data (or target person data and non-target person data).
  • This is an estimation model that is machine-learned by inputting the acquired data) into the input layer of a neural network.
  • Non-subject data includes statistical values based on data from multiple non-subjects and data on evidence obtained from previous research.
  • the computer (estimation device) 5 inputs behavioral data and outputs outcome data acquired by a learning model (estimation model).
  • the computer (estimation device) 5 transmits learning data (teacher data) to the machine learning server 4 via the network 2
  • the computer (estimation device) 5 outputs various data (cognitive bias data 380, etc.) to the machine learning server 4 in order to generate a learning model (estimation model).
  • step S10 the input unit 230 inputs various data (behavior data 390 and subject data 400).
  • the input unit 230 associates various types of data and stores them in the storage device 220 by executing a command to store various types of data (step S20, step S30).
  • the data acquisition unit 320 acquires various data stored in the storage device 220 by executing the acquisition command.
  • the output unit 250 transmits various data to the machine learning server 4 via the network 2 by executing the transmission command (step S40).
  • the input unit 23 of the machine learning server 4 inputs various data (behavior data 390 and subject data 400) received from the estimation device 5.
  • the input various data (behavior data 390 and subject data 400) are stored in the storage device 22 as behavior data 39 and subject data 40, respectively.
  • the time series data and non-time series data are identified by the identification unit 36.
  • the data acquisition unit 32 of the machine learning server 4 acquires various data stored in the storage device 22, and the machine learning unit 33 of the machine learning server 4 executes the estimation model generation command to obtain the various acquired data.
  • a learning model (estimated model) is generated by a neural network based on the data (including these feature data).
  • the computer (estimation device) 5 has a function of outputting learning data (teacher data) to the machine learning server 4.
  • the behavior data 390 and the subject data 400 (correct data) are associated with each other and used as learning data by the machine learning unit 33 of the machine learning server 4. Further, the behavior data 390 and the target person data 400 may be used as non-target person data 41 of the target person other than the target person 6.
  • the computer (estimation device) 5 since the behavioral data 390 of the target person 6 and the non-time series data of the target person data are sent from the computer (estimation device) 5 to the machine learning server 4 in advance, the computer (estimation device) 5 The time series data of the subject data of the person 6 is transmitted to the machine learning server 4. Note that when new behavior data 390 and new non-time series data of the subject data are input from the input unit 230, the computer (estimation device) 5 inputs the new behavior data 390 and new non-time series data of the subject data. Send the time series data to the machine learning server 4. The machine learning server 4 that has received the various data updates the learning model (estimated model) using the machine learning unit 33 based on the new data.
  • FIG. 9 is a diagram showing the output process of estimated data estimated by the learning model (estimation model) of this embodiment.
  • the input unit 230 inputs various data (subject data 400).
  • the input unit 230 associates various types of data and stores them in the storage device 220 by executing a command to store various types of data (step S20, step S30).
  • the data acquisition unit 320 acquires various data stored in the storage device 220 by executing the acquisition command.
  • the estimation unit 350 transmits various data to the machine learning server 4 via the network 2 by executing the estimation command (step S400).
  • the input unit 23 of the machine learning server 4 inputs various data (target person data 400) received from the estimation device 5.
  • the estimation unit 35 of the machine learning server 4 calculates subject data (outcome data) using a learning model (estimation model) based on the various acquired data (including these feature data). Estimate.
  • the estimating unit 35 also collects at least one of the target person's attribute data, location data, health data, exercise data, lifestyle data, and medical data of the target person 6.
  • the subject data (outcome data) of the subject 6 may be estimated based on the data 40.
  • the estimating unit 35 also collects at least one of the target person's attribute data, location data, health data, exercise data, lifestyle data, and medical data of the target person 6. Based on the data 40, at least one of attribute data, location data, health data, exercise data, lifestyle data, and medical data of the subject 6 may be estimated as outcome data.
  • the estimation unit 35 may collect attribute data, location data, health data, exercise data, lifestyle data, and medical data of non-target persons other than the target person 6.
  • the outcome data of the subject 6 may be estimated based on at least one non-target subject data 41.
  • the estimation unit 35 may collect attribute data, location data, health data, exercise data, lifestyle data, and medical data of non-target persons other than the target person 6. Based on at least one subject data 41, at least one of cognitive bias data, attribute data, location data, health data, exercise data, lifestyle data, and medical data of the subject 6 is estimated as outcome data. Good too.
  • the estimation unit 35 reads the learning model (estimated model) from the learning model storage unit 46, inputs the behavior data 390 to the input layer of the neural network of the learning model (estimated model), and inputs the estimated estimation data (attribute data, position data, health data, exercise data, lifestyle data, and medical data) from the output layer.
  • the estimated data is stored in the estimated data storage section 47.
  • the estimation data is transmitted by the output unit 25 of the machine learning server 4 to the computer (estimation device) 5 via the network 2.
  • the input unit 230 of the computer (estimation device) 5 receives the estimation data by executing the reception command (step S50).
  • the input unit 230 stores the estimated data in the estimated data storage unit 470.
  • the output unit 250 By executing the estimated data output command, the output unit 250 outputs information input from the input unit 230 using a learning model (estimation model) including a neural network for estimating outcome data for the subject 6 based on behavioral data. Outcome data estimated from the behavior data is output (step S60). Furthermore, the output unit 250 outputs other estimated data by executing the estimated data output command (step S60).
  • a learning model estimation model
  • the output unit 250 outputs other estimated data by executing the estimated data output command (step S60).
  • the outcomes for health, medical care, and lifestyle habits that are affected by the behavior of the target person are modeled, and the outcomes for health, medical care, and lifestyle habits are modeled to improve or maintain the outcomes for health, medical care, and lifestyle habits.
  • a learning model estimation model
  • estimated data In addition to or apart from behavioral data, estimated data (behavioral data, cognitive bias data, attribute data, location data, health data, exercise data, lifestyle data, (at least one of habit data and medical data), highly accurate estimation data that matches the characteristics of the subject can be output.
  • the estimated data of the target person is estimated based on non-target person data other than the target person in addition to or separately from the behavioral data, highly accurate estimation takes into account the general characteristics of the other person. Data can be output.
  • the computer (estimation device) 5 may be a laptop computer, a desktop computer, a tablet terminal, a smartphone, or a wearable terminal.
  • the computer (estimation device) 5 is electrically connected to other communication terminals (laptop computer, desktop computer, tablet terminal, smartphone, wearable terminal, etc.), and the input unit 230 is connected to other communication terminals (laptop computer, desktop computer, tablet terminal, smartphone, wearable terminal, etc.).
  • the output unit 250 may output various data to other communication terminals.
  • the machine learning server 4 can also serve as the estimation device of the present invention.
  • the functions of the input unit 23, output unit 25, data acquisition unit 32, estimation unit 35, identification unit 36, and estimated data storage unit 47 of the machine learning server 4 are the functions of the input unit 230 of the computer (estimation device) 5, They correspond to the functions of the output section 250, the data acquisition section 320, the estimation section 350, the identification section 360, and the estimated data storage section 470, respectively.
  • the machine learning server 4 generates the learning model (estimated model), but the computer (estimation device) 5 may generate the learning model (estimated model).
  • the behavioral data may include data regarding incentives for encouraging the target person to take action.
  • the behavioral data may be data regarding coupons, points, money, and special rights that can be used at a predetermined location.
  • the present embodiment also includes a step of inputting cognitive bias data regarding the target person's cognitive bias, and based on the cognitive bias data, behavioral data regarding the target person's behavior, attribute data of the target person, by an estimation model including a neural network for estimating at least one of location data of the subject, health data of the subject, exercise data of the subject, lifestyle data of the subject, and medical data of the subject, outputting at least one of the behavior data, the attribute data, the location data, the health data, the exercise data, the lifestyle data, and the medical data estimated from the cognitive bias data input by the input unit;
  • the method includes the steps of:
  • the present embodiment also includes a step of inputting behavioral data regarding the behavior of the subject, and based on the behavioral data, attribute data of the subject, location data of the subject, health data of the subject, Estimated from the behavioral data input by the input unit by an estimation model including a neural network for estimating at least one of the subject's exercise data, the subject's lifestyle data, and the subject's medical data. and outputting at least one of the attribute data, the location data, the health data, the exercise data, the lifestyle data, and the medical data.
  • the present embodiment also includes cognitive bias data regarding the target person's cognitive bias, behavioral data regarding the target person's behavior labeled with the cognitive bias data, attribute data of the target person, and location data of the target person.
  • Machine learning for causing a neural network to perform machine learning using at least one of the subject's health data, the subject's exercise data, the subject's lifestyle data, and the subject's medical data as training data.
  • an input unit for inputting cognitive bias data regarding the cognitive bias of the subject; and the behavioral data and the attributes estimated from the cognitive bias data input by the input unit by an estimation model including the neural network.
  • an output unit that outputs at least one of data, the location data, the health data, the exercise data, the lifestyle data, and the medical data.
  • the present embodiment also includes behavioral data regarding the behavior of the subject, attribute data of the subject labeled with the behavioral data, location data of the subject, health data of the subject, and a machine learning unit for causing a neural network to perform machine learning using at least one of exercise data, lifestyle data of the subject, and medical data of the subject as training data; the attribute data, the position data, the health data, the exercise data, the lifestyle data estimated from the behavioral data input by the input unit, using an input unit to input, and an estimation model including the neural network; and an output unit that outputs at least one of the medical data.
  • the present embodiment also includes an estimation program executed by a computer in which a processor estimates the behavior of a target person, wherein the computer labels cognitive bias data regarding the cognitive bias of the target person and the cognitive bias data.
  • cognitive bias data regarding the behavior of the subject, attribute data of the subject, location data of the subject, health data of the subject, exercise data of the subject, lifestyle data of the subject, and an acquisition function for acquiring at least one of medical data of a person; the cognitive bias data; the behavior data; the attribute data; the location data; the health data; the exercise data; the lifestyle data;
  • the estimation program includes a machine learning function for causing a neural network to perform machine learning using at least one of the data as training data.
  • the present embodiment also includes an estimation program executed by a computer in which a processor estimates the behavior of a target person, wherein the computer generates behavior data regarding the behavior of the target person, and the behavior data labeled with the behavior data.
  • a processor estimates the behavior of a target person, wherein the computer generates behavior data regarding the behavior of the target person, and the behavior data labeled with the behavior data.
  • Acquire at least one of attribute data of the subject, location data of the subject, health data of the subject, exercise data of the subject, lifestyle data of the subject, and medical data of the subject.
  • the acquisition function, the behavior data, the attribute data, the location data, the health data, the exercise data, the lifestyle data, and at least one of the medical data are used as teacher data to cause a neural network to perform machine learning. It includes a machine learning function for, and an estimation program that realizes.
  • the present invention by estimating behavioral data and/or outcome data regarding a subject's behavior, it is useful as an estimation device, etc. that promotes and maintains improvements in the subject's behavior and outcomes regarding health, medical care, and lifestyle habits. It is.

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Health & Medical Sciences (AREA)
  • Theoretical Computer Science (AREA)
  • General Health & Medical Sciences (AREA)
  • Biomedical Technology (AREA)
  • General Physics & Mathematics (AREA)
  • Computational Linguistics (AREA)
  • Data Mining & Analysis (AREA)
  • Evolutionary Computation (AREA)
  • Artificial Intelligence (AREA)
  • Molecular Biology (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Biophysics (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Epidemiology (AREA)
  • Medical Informatics (AREA)
  • Primary Health Care (AREA)
  • Public Health (AREA)
  • Medical Treatment And Welfare Office Work (AREA)

Abstract

L'invention concerne un dispositif d'estimation qui estime des données d'action et/ou des données de résultat concernant les actions d'un sujet pour permettre de favoriser ou de maintenir une amélioration de l'action ou du résultat du sujet par rapport à la santé, aux soins médicaux et aux habitudes de style de vie. Le dispositif d'estimation de la présente invention comprend : une unité d'entrée destinée à entrer des données de biais cognitif concernant un biais cognitif du sujet ; et une unité de sortie qui utilise un modèle d'estimation comprenant un réseau neuronal destiné à estimer, sur la base des données de biais cognitif, des données d'action concernant une action du sujet, des données d'attribut du sujet, des données de position du sujet, des données de santé du sujet, des données d'exercice du sujet, des données d'habitudes de style de vie du sujet, et des données de soins médicaux du sujet, pour délivrer au moins l'une des données d'action, des données d'attribut, des données de position, des données de santé, des données d'exercice, des données d'habitudes de style de vie et des données de soins médicaux qui ont été estimées à partir de l'entrée de données de biais cognitif au moyen de l'unité d'entrée.
PCT/JP2023/021364 2022-07-26 2023-06-08 Dispositif d'estimation, procédé d'estimation, système d'estimation et programme d'estimation WO2024024294A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
JP2022118904 2022-07-26
JP2022-118904 2022-07-26

Publications (1)

Publication Number Publication Date
WO2024024294A1 true WO2024024294A1 (fr) 2024-02-01

Family

ID=89706145

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/JP2023/021364 WO2024024294A1 (fr) 2022-07-26 2023-06-08 Dispositif d'estimation, procédé d'estimation, système d'estimation et programme d'estimation

Country Status (1)

Country Link
WO (1) WO2024024294A1 (fr)

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2009122829A (ja) * 2007-11-13 2009-06-04 Sony Corp 情報処理装置、情報処理方法、およびプログラム
JP2020201898A (ja) * 2019-06-13 2020-12-17 株式会社Ｍｅａｌｔｈｙ 生活習慣病予防改善支援システム
JP2021531098A (ja) * 2018-07-27 2021-11-18 ユニバーシティー オブ マイアミUniversity Of Miami Ａｉを利用した眼の状態判定のためのシステムおよび方法
JP2021189707A (ja) * 2020-05-29 2021-12-13 コニカミノルタ株式会社 医療診断支援システム、医療診断支援プログラム、および、医療診断支援方法

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2009122829A (ja) * 2007-11-13 2009-06-04 Sony Corp 情報処理装置、情報処理方法、およびプログラム
JP2021531098A (ja) * 2018-07-27 2021-11-18 ユニバーシティー オブ マイアミUniversity Of Miami Ａｉを利用した眼の状態判定のためのシステムおよび方法
JP2020201898A (ja) * 2019-06-13 2020-12-17 株式会社Ｍｅａｌｔｈｙ 生活習慣病予防改善支援システム
JP2021189707A (ja) * 2020-05-29 2021-12-13 コニカミノルタ株式会社 医療診断支援システム、医療診断支援プログラム、および、医療診断支援方法

Similar Documents

Publication Publication Date Title
Spiegelhalter The art of statistics: Learning from data
Valentine et al. Judging a man by the width of his face: The role of facial ratios and dominance in mate choice at speed-dating events
Burnette et al. Forgiveness results from integrating information about relationship value and exploitation risk
JP6151441B2 (ja) 目標最適化システム
US20210256542A1 (en) Methods of predicting emotional response to sensory stimuli based on individual traits
US11620489B2 (en) Prospective media content generation using neural network modeling
Talukder et al. Continued use intention of wearable health technologies among the elderly: an enablers and inhibitors perspective
JP2010170534A (ja) 健康サポートシステム及び健康サポート方法
AlSlaity et al. Mobile applications for health and wellness: a systematic review
JP2023547875A (ja) 個人化された認知介入システム及び方法
Cherubini et al. The unexpected downside of paying or sending messages to people to make them walk: Comparing tangible rewards and motivational messages to improve physical activity
Foxall Brain, emotion, and contingency in the explanation of consumer behaviour
Lindner Molecular politics, wearables, and the aretaic shift in biopolitical governance
Bajaj et al. Prediction of Mental Health Treatment Adherence using Machine Learning Algorithms
JP2011128922A (ja) 美容サポートシステム及び美容サポート方法
Chatzimina et al. Employing conversational agents in palliative care: a feasibility study and preliminary assessment
WO2024024294A1 (fr) Dispositif d'estimation, procédé d'estimation, système d'estimation et programme d'estimation
JP7133207B2 (ja) 情報処理装置及びプログラム
JP6450986B1 (ja) ヘルスケア支援サーバ、ヘルスケア支援方法、及びコンピュータで読み取り可能なプログラム
JP6988034B1 (ja) 妊産婦うつ症状の推定システムおよび推定方法、推定モデル生成装置
Miller et al. Dating and sexualities across the life course: The interactive effects of aging and gender
KR20230114847A (ko) 인공지능 챗봇을 이용한 우울증 진단 시스템 및 그 방법
Annuar et al. Celebrity endorsements in organ donation advertisements: Altruism as a moderator
EP3674915A1 (fr) Procédé et système pour l'optimisation automatique des changements de comportement d'un utilisateur
Wilhelms et al. Fuzzy-trace theory explains paradoxical dissociations in affective forecasting

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 23846030

Country of ref document: EP

Kind code of ref document: A1