WO2024101840A1 - Dispositif, procédé et programme pour calculer l'efficacité médicale d'un aliment à l'aide d'une analyse de composants basée sur l'intelligence artificielle et de preuves médicales - Google Patents

Dispositif, procédé et programme pour calculer l'efficacité médicale d'un aliment à l'aide d'une analyse de composants basée sur l'intelligence artificielle et de preuves médicales Download PDF

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WO2024101840A1
WO2024101840A1 PCT/KR2023/017724 KR2023017724W WO2024101840A1 WO 2024101840 A1 WO2024101840 A1 WO 2024101840A1 KR 2023017724 W KR2023017724 W KR 2023017724W WO 2024101840 A1 WO2024101840 A1 WO 2024101840A1
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score
ingredient
food
ingredients
efficacy
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PCT/KR2023/017724
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English (en)
Korean (ko)
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이기호
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주식회사 메디푸드플랫폼
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Publication of WO2024101840A1 publication Critical patent/WO2024101840A1/fr

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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/60—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • 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
    • G16H70/00—ICT specially adapted for the handling or processing of medical references
    • 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

Definitions

  • This disclosure relates to a device for calculating the medical efficacy of food, and more specifically, to a device for calculating the medical efficacy of food using artificial intelligence-based ingredient analysis and medical evidence.
  • the purpose of the embodiments disclosed in this disclosure is to provide a device for calculating the medical efficacy of food.
  • the embodiment disclosed in the present disclosure calculates an ingredient score based on ingredient information for each of the ingredients contained in the food, and determines the symptoms of each food based on the ingredient score and the disease weight assigned to each of the ingredients for each disease.
  • the device for calculating the medical efficacy of food using artificial intelligence-based ingredient analysis and medical evidence is a method performed by the device, and is performed by the device.
  • a method of calculating the medical effect on diseases of each food comprising: calculating an ingredient score based on ingredient information stored in a storage unit for each ingredient included in the target food; and calculating an efficacy score for each disease of the target food based on the ingredient score and a disease weight assigned to each of the ingredients for each disease.
  • the ingredient score includes a ranking score that quantifies the relative ranking of the contents of the ingredients included in the target food in the storage unit, and the ingredient information includes the content and content ranking of the ingredients, and the ingredient score
  • the calculation step includes calculating the ranking score for each of the ingredients based on the content and content ranking of the ingredients.
  • the ingredient score further includes a recommended score that quantifies the relative ratio of the contents of the ingredients included in the target food to the recommended intake
  • the ingredient information further includes the recommended intake of the ingredients
  • the ingredient score calculation step It further includes calculating the recommended score for each of the ingredients based on the content and recommended intake of the ingredients.
  • the ranking score can be calculated based on Equation 1 below.
  • Ranking score ((X - content ranking of the corresponding ingredient) / X) * 100 (X: ranking standard value)
  • the recommended score can be calculated based on Equation 2 below.
  • the efficacy score calculation step includes calculating a first efficacy score for each disease of the target food based on the ranking score calculated for each of the ingredients; And it may include calculating a second efficacy score for each disease of the target food based on the recommended score calculated for each of the ingredients.
  • it may further include calculating a final efficacy score based on at least one of the first efficacy score and the second efficacy score.
  • the disease weight is set based on evidence elements regarding the influence of each of the ingredients on the disease, and the evidence elements are at least one of the following: presence or absence of a scientific mechanism, presence or absence of clinical evidence, use as a treatment, and presence or absence of alternative ingredients. It can contain one.
  • the disease weight may be set based on a first weight set based on the presence or absence of the scientific mechanism, the presence or absence of the clinical evidence, and whether or not it is used as a treatment, and a second weight given depending on the presence or absence of the substitute ingredient. there is.
  • a device for calculating the medical efficacy of food using artificial intelligence-based ingredient analysis and medical evidence is a device for calculating the medical efficacy on diseases for each food.
  • the device includes a storage unit storing ingredient information for each ingredient included in the food; And for each of the ingredients included in the target food, calculate an ingredient score based on the ingredient information stored in the storage unit, and calculate the ingredient score of the target food based on the ingredient score and a disease weight assigned to each of the ingredients for each disease. It includes a processor that calculates efficacy scores for each disease.
  • an apparatus for providing customized food information based on the medical efficacy of food includes ingredient information for each of the ingredients included in each of a plurality of foods, the plurality of foods, An ingredient score calculated based on the ingredient information for each of the ingredients included in each of the foods, and a disease weight assigned to each of the ingredient scores and the ingredients for each disease.
  • a storage unit where efficacy scores for each disease are stored; And when analysis target information including information on the disease of the analysis target is received, at least one food for the analysis target based on the disease-specific efficacy score of each of the plurality of foods stored in the storage unit and the analysis target information. It includes a processor that derives recommended food information.
  • a computer program stored in a computer-readable recording medium for execution to implement the present disclosure may be further provided.
  • a computer-readable recording medium recording a computer program for executing a method for implementing the present disclosure may be further provided.
  • the effect of calculating the medical efficacy of food is provided.
  • an ingredient score is calculated based on ingredient information for each of the ingredients contained in the food, and each ingredient is calculated based on the ingredient score and the disease weight assigned to each ingredient for each disease. It has the effect of calculating efficacy scores for food diseases.
  • FIG. 1 is a diagram schematically illustrating calculating the medical efficacy of food according to an embodiment of the present disclosure.
  • Figure 2 is a block diagram of a device for calculating the medical efficacy of food according to an embodiment of the present disclosure.
  • 3 and 4 are flowcharts of a method for calculating the medical efficacy of food according to an embodiment of the present disclosure.
  • Figure 5 is a diagram illustrating the types of ingredients included in each food, the content of the ingredients, and the ranking of the ingredient contents.
  • Figure 6 is a diagram illustrating weights set according to the influence of each component on each group.
  • Figure 7 is a diagram illustrating the basis for calculating the weight of Figure 6.
  • Figure 8 is a diagram illustrating the calculation of the first efficacy score and the second efficacy score for each group of scallops and groupfish.
  • Figure 9 is a diagram illustrating providing user-customized food by expanding the method of calculating the medical efficacy of food according to an embodiment of the present disclosure.
  • first and second are used to distinguish one component from another component, and the components are not limited by the above-mentioned terms.
  • the identification code for each step is used for convenience of explanation.
  • the identification code does not explain the order of each step, and each step may be performed differently from the specified order unless a specific order is clearly stated in the context. there is.
  • 'device for calculating the medical efficacy of food according to the present disclosure includes all various devices that can perform computational processing and provide results to the user.
  • the device for calculating the medical efficacy of food according to the present disclosure may include all of a computer, a server device, and a portable terminal, or may take the form of any one.
  • the computer may include, for example, a laptop, desktop, laptop, tablet PC, slate PC, etc. equipped with a web browser.
  • the server device is a server that processes information by communicating with external devices and may include an application server, computing server, database server, file server, game server, mail server, proxy server, and web server.
  • the portable terminal is, for example, a wireless communication device that guarantees portability and mobility, such as PCS (Personal Communication System), GSM (Global System for Mobile communications), PDC (Personal Digital Cellular), PHS (Personal Handyphone System), and PDA. (Personal Digital Assistant), IMT (International Mobile Telecommunication)-2000, CDMA (Code Division Multiple Access)-2000, W-CDMA (W-Code Division Multiple Access), WiBro (Wireless Broadband Internet) terminal, smart phone ), all types of handheld wireless communication devices, and wearable devices such as watches, rings, bracelets, anklets, necklaces, glasses, contact lenses, or head-mounted-device (HMD). may include.
  • PCS Personal Communication System
  • GSM Global System for Mobile communications
  • PDC Personal Digital Cellular
  • PHS Personal Handyphone System
  • PDA Personal Digital Assistant
  • IMT International Mobile Telecommunication
  • CDMA Code Division Multiple Access
  • W-CDMA Wideband Code Division Multiple Access
  • WiBro Wireless Broadband Internet
  • smart phone smart phone
  • the processor may consist of one or multiple processors.
  • one or more processors may be a general-purpose processor such as a CPU, AP, or DSP (Digital Signal Processor), a graphics-specific processor such as a GPU or VPU (Vision Processing Unit), or an artificial intelligence-specific processor such as an NPU.
  • One or more processors control input data to be processed according to predefined operation rules or artificial intelligence models stored in memory.
  • the artificial intelligence dedicated processors may be designed with a hardware structure specialized for processing a specific artificial intelligence model.
  • Predefined operation rules or artificial intelligence models are characterized by being created through learning.
  • being created through learning means that the basic artificial intelligence model is learned using a large number of learning data by a learning algorithm, thereby creating a predefined operation rule or artificial intelligence model set to perform the desired characteristics (or purpose). It means burden.
  • This learning may be accomplished in the device itself that performs the artificial intelligence according to the present disclosure, or may be accomplished through a separate server and/or system. Examples of learning algorithms include, but are not limited to, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning.
  • An artificial intelligence model may be composed of multiple neural network layers.
  • Each of the plurality of neural network layers has a plurality of weight values, and neural network calculation is performed through calculation between the calculation result of the previous layer and the plurality of weights.
  • Multiple weights of multiple neural network layers can be optimized by the learning results of the artificial intelligence model. For example, a plurality of weights may be updated so that loss or cost values obtained from the artificial intelligence model are reduced or minimized during the learning process.
  • DNN deep neural networks
  • CNN Convolutional Neural Network
  • DNN Deep Neural Network
  • RNN Recurrent Neural Network
  • RBM Restricted Boltzmann Machine
  • DBN Deep Belief Network
  • BNN Bidirectional Recurrent Deep Neural Network
  • DNN Deep Q-Networks
  • a processor may implement artificial intelligence.
  • Artificial intelligence refers to a machine learning method based on an artificial neural network that allows machines to learn by imitating human biological neurons.
  • Methodology of artificial intelligence includes supervised learning, in which the answer (output data) to the problem (input data) is determined by providing input data and output data together as training data according to the learning method, and only input data is provided without output data.
  • unsupervised learning in which the solution (output data) to the problem (input data) is not determined, and a reward is given from the external environment whenever an action is taken in the current state, , It can be divided into reinforcement learning, which conducts learning in the direction of maximizing these rewards.
  • artificial intelligence methodologies can be divided according to the architecture, which is the structure of the learning model.
  • the architecture of widely used deep learning technology is Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), It can be divided into transformers, generative adversarial networks (GAN), etc.
  • CNN Convolutional Neural Network
  • RNN Recurrent Neural Network
  • GAN generative adversarial networks
  • the device 100 for calculating the medical efficacy of food may include an artificial intelligence model.
  • An artificial intelligence model may be a single artificial intelligence model or may be implemented as multiple artificial intelligence models.
  • Artificial intelligence models may be composed of neural networks (or artificial neural networks) and may include statistical learning algorithms that mimic biological neurons in machine learning and cognitive science.
  • a neural network can refer to an overall model in which artificial neurons (nodes), which form a network through the combination of synapses, change the strength of the synapse connection through learning and have problem-solving capabilities. Neurons in a neural network can contain combinations of weights or biases.
  • a neural network may include one or more layers consisting of one or more neurons or nodes.
  • a device may include an input layer, a hidden layer, and an output layer. The neural network that makes up the device can infer the result (output) to be predicted from arbitrary input (input) by changing the weight of neurons through learning.
  • the processor creates a neural network, trains or learns a neural network, performs calculations based on received input data, generates an information signal based on the results, or generates a neural network.
  • the network can be retrained.
  • Neural network models include CNN (Convolution Neural Network), R-CNN (Region with Convolution Neural Network), RPN (Region Proposal Network), and RNN, such as GoogleNet, AlexNet, and VGG Network.
  • a neural network may include a deep neural network.
  • Neural networks include CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), perceptron, multilayer perceptron, FF (Feed Forward), RBF (Radial Basis Network), DFF (Deep Feed Forward), and LSTM.
  • the processor may support a Convolution Neural Network (CNN), a Region with Convolution Neural Network (R-CNN), a Region Proposal Network (RPN), a Recurrent Neural Network (RNN), such as GoogleNet, AlexNet, VGG Network, etc.
  • CNN Convolution Neural Network
  • R-CNN Region with Convolution Neural Network
  • RPN Region Proposal Network
  • RNN Recurrent Neural Network
  • GoogleNet GoogleNet
  • AlexNet AlexNet
  • VGG Network a Recurrent Neural Network
  • FIG. 1 is a schematic diagram illustrating a method for calculating the medical efficacy of food according to an embodiment of the present disclosure.
  • the first storage unit 131 stores ingredient information for each of the ingredients included in the food
  • the second storage unit 132 stores the disease weight assigned for each disease to each of the ingredients. information is stored.
  • the device 100 for calculating the medical efficacy of food calculates the efficacy of each food on various diseases by utilizing the information constructed in this way.
  • the device 100 for calculating the medical efficacy of food calculates an ingredient score for each ingredient included in each food based on the data of the first storage unit 131, and stores the calculated ingredient score and the second storage unit. By reflecting the weight assigned to each disease to each of the ingredients stored in the unit 132, an efficacy score for each food disease is calculated.
  • ingredients may include nutrients, active ingredients, etc.
  • a device for calculating the medical efficacy of a food calculates an efficacy score according to the type of disease of the first food.
  • the apparatus 100 for calculating the medical efficacy of food is illustrated as calculating the efficacy score of each food for each disease, but it is not limited to diseases and applies to all diseases, symptoms, symptoms, and signs. This is possible.
  • the device 100 for calculating the medical efficacy of food can calculate the efficacy score of each food for each group.
  • the group may refer to a group of people/animals with at least one of a specific disease, symptom, symptom, or sign, or a group of people classified by a specific criterion.
  • it may be a person/animal suffering from a specific disease, a person/animal with a specific symptom, a person/animal with a specific symptom, or a group of people/animals with a specific symptom, including the elderly, newborns, infants and young children.
  • a group of people/animals classified by specific criteria such as adolescents, pregnant women, men, women, etc.
  • the device 100 for calculating the medical efficacy of food has the effect of numerically informing how effective each food is on various diseases.
  • the device 100 for calculating the medical efficacy of food is configured to further include a server device and may be used as a server.
  • users can receive information about recommended foods by accessing the server through the web or app and entering user data.
  • Figure 2 is a block diagram of an apparatus 100 for calculating the medical efficacy of food according to an embodiment of the present disclosure.
  • the device 100 for calculating the medical efficacy of food includes a processor 110, a communication unit 120, a storage unit 130, and a calculation unit 140.
  • the device may include fewer or more components than those shown in FIG. 2 .
  • the processor 110 can calculate the medical efficacy of food based on various commands and algorithms stored in the storage unit 130, and can use a pre-trained artificial intelligence model.
  • the processor 110 has a memory that stores data for an algorithm for controlling the operation of components in the device 100 for calculating the medical efficacy of food or a program that reproduces the algorithm, and uses the data stored in the memory as described above. It may be implemented with at least one processor 110 that performs an operation. At this time, the memory and processor 110 may each be implemented as separate chips. Alternatively, the memory and processor 110 may be implemented as a single chip.
  • the processor 110 combines any one or a plurality of the above-described components to implement various embodiments according to the present disclosure described in the drawings below on the device 100 for calculating the medical efficacy of food. You can control it.
  • the processor 110 may control the overall operation of the device 100, which typically calculates the medical efficacy of food.
  • the processor 110 can provide or process appropriate information or functions to the user by processing signals, data, information, etc. input or output through the components discussed above, or by running an application program stored in memory.
  • the processor 110 may control at least some of the components of the device 100 that calculates the medical efficacy of food in order to run an application program stored in the memory. Furthermore, in order to run the application, the processor 110 may operate at least two of the components included in the device 100 for calculating the medical efficacy of food in combination with each other.
  • the communication unit 120 may include one or more modules that connect the device 100 for calculating the medical efficacy of food to one or more networks.
  • the communication unit 120 may include one or more components that enable communication with an external device, for example, at least one of a broadcast reception module, a wired communication module, a wireless communication module, a short-range communication module, and a location information module. It can be included.
  • Wired communication modules include various wired communication modules such as Local Area Network (LAN) modules, Wide Area Network (WAN) modules, or Value Added Network (VAN) modules, as well as USB (Universal Serial Bus) modules. ), HDMI (High Definition Multimedia Interface), DVI (Digital Visual Interface), RS-232 (recommended standard 232), power line communication, or POTS (plain old telephone service).
  • LAN Local Area Network
  • WAN Wide Area Network
  • VAN Value Added Network
  • USB Universal Serial Bus
  • HDMI High Definition Multimedia Interface
  • DVI Digital Visual Interface
  • RS-232 Recommended standard 232
  • power line communication or POTS (plain old telephone service).
  • wireless communication modules include GSM (global System for Mobile Communication), CDMA (Code Division Multiple Access), WCDMA (Wideband Code Division Multiple Access), and UMTS (universal mobile telecommunications system). ), TDMA (Time Division Multiple Access), LTE (Long Term Evolution), 4G, 5G, 6G, etc. may include a wireless communication module that supports various wireless communication methods.
  • GSM Global System for Mobile Communication
  • CDMA Code Division Multiple Access
  • WCDMA Wideband Code Division Multiple Access
  • UMTS universal mobile telecommunications system
  • TDMA Time Division Multiple Access
  • LTE Long Term Evolution
  • 4G, 5G, 6G, etc. may include a wireless communication module that supports various wireless communication methods.
  • the wireless communication module may include a wireless communication interface including an antenna and a transmitter that transmits signals.
  • the wireless communication module may further include a signal conversion module that modulates a digital control signal output from the processor 110 through a wireless communication interface into an analog wireless signal under the control of the processor 110.
  • the short-range communication module is for short-range communication and includes Bluetooth (Bluetooth), RFID (Radio Frequency Identification), Infrared Data Association (IrDA), UWB (Ultra Wideband), ZigBee, and NFC (Near Field). Communication), Wi-Fi (Wireless-Fidelity), Wi-Fi Direct, and Wireless USB (Wireless Universal Serial Bus) technology can be used to support short-distance communication.
  • the storage unit 130 may store data supporting various functions of the device 100 that calculates the medical efficacy of food.
  • the storage unit 130 includes a plurality of application programs (application programs or applications) running on the device 100 for calculating the medical efficacy of food, and data for the operation of the device 100 for calculating the medical efficacy of food. , you can save commands. At least some of these applications may exist for the basic function of the device 100, which is to calculate the medical efficacy of food. Meanwhile, the application program may be stored in the storage unit 130, installed in the device 100, and driven to perform an operation (or function) by the processor 110.
  • the storage unit 130 can store data supporting various functions of the device 100 for calculating the medical efficacy of food and a program for the operation of the processor 110, and can store input/output data (e.g. , music files, still images, videos, etc.) and a plurality of applications (application programs or applications) running on the device 100 that calculates the medical efficacy of food, a device that calculates the medical efficacy of food Data and commands for the operation of (100) can be stored. At least some of these applications may be downloaded from an external server via wireless communication.
  • input/output data e.g. , music files, still images, videos, etc.
  • applications application programs or applications
  • the storage unit 130 includes a flash memory type, a hard disk type, a solid state disk type, an SDD type (Silicon Disk Drive type), and a multimedia card micro type ( multimedia card micro type), card type storage (e.g. SD or XD storage, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), It may include at least one type of storage medium among electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic storage, magnetic disk, and optical disk.
  • the storage unit 130 is separate from the device 100 that calculates the medical efficacy of food, but may be a database connected by wire or wirelessly.
  • the storage unit 130 may be provided with a plurality of processes for an apparatus 100 and a method for calculating the medical efficacy of food.
  • the calculation unit 140 can perform various calculations to calculate the medical efficacy of food, and calculates various data stored in the storage unit 130 based on preset mathematical equations under the control of the processor 110. Calculation results can be printed.
  • the device 100 for calculating the medical efficacy of food may further include an input unit, a display unit, and an interface unit.
  • the input unit is for inputting image information (or signal), audio information (or signal), data, or information input from a user, and may include at least one of at least one camera, at least one microphone, and a user input unit. . Voice data or image data collected from the input unit can be analyzed and processed as a user's control command.
  • the display unit displays (outputs) information processed by the device 100 for calculating the medical efficacy of food.
  • the display unit displays execution screen information of an application program (eg, an application) running on the device 100 for calculating the medical efficacy of food, or a UI (User Interface), GUI (Graphic User Interface) according to this execution screen information. Interface) information can be displayed.
  • an application program eg, an application
  • GUI Graphic User Interface
  • the interface unit serves as a passageway for various types of external devices connected to the device 100 for calculating the medical efficacy of food.
  • These interface units include a wired/wireless headset port, an external charger port, a wired/wireless data port, a memory card port, and a port for connecting a device equipped with an identification module (SIM) ( port), an audio input/output (I/O) port, a video input/output (I/O) port, and an earphone port.
  • SIM identification module
  • I/O audio input/output
  • I/O video input/output
  • earphone port an earphone port
  • Figure 3 is a flowchart of a method for calculating the medical efficacy of food according to an embodiment of the present disclosure.
  • the method for calculating the medical efficacy of food may consist of the following steps.
  • the processor 110 calculates an ingredient score for each ingredient included in the target food based on ingredient information stored in the storage unit. (S100)
  • the processor 110 calculates an efficacy score for each disease of the food based on the ingredient score calculated in S100 and the disease weight assigned to each ingredient for each disease. (S200)
  • the processor 110 calculates the final efficacy score for each disease of the target food based on the efficacy score calculated in S200. (S300)
  • the device for calculating the medical efficacy of food can calculate the medical efficacy of the target food on various diseases through S100 to S300, and by expanding this, the medical efficacy of all foods on various diseases. It is possible to calculate up to
  • the component score may include a ranking score and a recommended score.
  • the ranking score refers to a score that quantifies the relative ranking of the contents of ingredients contained in the target food within the storage unit.
  • Ingredient information may include the content and content ranking of the ingredients.
  • the ingredient score calculation step (S100) may further include a step (S150) in which the processor calculates a ranking score for each of the ingredients included in the target food based on the content and content ranking of the ingredients.
  • the processor 110 may calculate a ranking score for each component based on Equation 1 below.
  • Ranking score ((X - content ranking of the corresponding ingredient) / X) * 100 (X: ranking standard value)
  • the recommended score refers to a score that quantifies the relative ratio of the content of ingredients contained in the target food to the recommended intake.
  • the ingredient score may include the recommended intake score for Yangyang cows.
  • the ingredient score calculation step (S100) may further include a step (S170) in which the processor calculates a recommended score for each of the ingredients included in the target food based on the content and recommended intake of the ingredients.
  • the processor 110 may calculate a recommended score for each ingredient based on Equation 2 below.
  • the processor 110 calculates the first efficacy score for each disease of the target food based on the ranking score calculated for each of the ingredients. (S250)
  • the processor 110 calculates a second efficacy score for each disease of the target food based on the recommended score calculated for each of the ingredients. (S270)
  • Figure 5 is a diagram illustrating the types of ingredients included in each food, the content of the ingredients, and the ranking of the ingredient contents.
  • the storage unit 130 stores ingredient information for each ingredient included in the target food, and the ingredient information includes the content and content ranking of the ingredients.
  • the content ranking of each ingredient means the ranking of ingredient content among foods containing the ingredient.
  • the content ranking may apply to all foods or to a large number of foods used in the device for calculating the medical efficacy of foods according to an embodiment of the present disclosure, so the number of foods included in the ranking calculation is It may be applied in various ways depending on the embodiment.
  • the ranking value of the B component content of food A is 1.
  • the processor 110 may calculate the ranking score of the component based on Equation 1, and for example, 1000 may be applied to X.
  • the ranking standard value refers to the lower limit of the ranking that is subject to ranking-based score calculation.
  • content ranking for a specific ingredient it may refer to a standard ranking that enables discrimination based on the ranking.
  • the content ranking of the first ingredient is meaningful from 1st to 500th place, but has no significant meaning from 501st place, it means that there is no significant difference between 501st place, 600th place, and 700th place, and therefore the ranking of the first ingredient
  • the reference value can be set to 500.
  • the ranking of the content of the second ingredient is meaningful from 1st to 100th, but is not significant from 101st, this means that there is no significant difference between 101st, 300th, and 1000th, and therefore the standard ranking value of the second ingredient can be set to 100.
  • the processor 110 may give the ranking score a preset value.
  • a device that calculates the medical efficacy of food can prevent the ranking score from becoming negative.
  • the device for calculating the medical efficacy of food prevents such errors from occurring through the above-mentioned features. It works.
  • the processor 110 may assign a ranking score of 0 or 1 to component B of food A.
  • the content ranking may apply to all foods or to a large number of foods used in the device for calculating the medical efficacy of foods according to an embodiment of the present disclosure, so the number of foods included in the ranking calculation is It may be applied in various ways depending on the embodiment.
  • X is a ranking standard for content ranking, and may be set differently for each type of ingredient.
  • the storage unit 130 stores ingredient information for each ingredient contained in the food, and the ingredient information includes the recommended intake amount of the ingredients.
  • the recommended score quantifies the relative ratio of the content of ingredients contained in the target food to the recommended intake amount.
  • the ingredients included in each food, the content of each ingredient, and the recommended intake of each ingredient are stored.
  • Equation 2 the recommended score calculated by the processor based on Equation 2 is shown as an example.
  • the processor 110 may calculate a final efficacy score based on at least one of the first efficacy score and the second efficacy score in S300.
  • the device 100 for calculating the medical efficacy of food using artificial intelligence-based ingredient analysis and medical evidence may utilize one of the first efficacy score or the second efficacy score, or two. You can also use them in combination.
  • the device 100 for calculating the medical efficacy of food using artificial intelligence-based ingredient analysis and medical evidence uses two efficacy scores in combination with the first efficacy score and the second efficacy score. can be used in combination, or the two average values can be used, and a preset weight can be assigned to each to calculate the final efficacy score.
  • the first efficacy score can be recommended to people who maintain a normal diet by emphasizing specific ingredients, and can be useful when manufacturing higher doses than the recommended amount.
  • the second efficacy score can be useful in cases where it is necessary to replace a meal or compose a diet.
  • a meal or compose a diet can be useful in cases where it is necessary to replace a meal or compose a diet.
  • Figure 6 is a diagram illustrating weights set according to the influence of each component on each group.
  • Figure 7 is a diagram illustrating the basis for calculating the weight of Figure 6.
  • Figure 6 illustrates weights set according to the influence of each component on each of the first to seventeenth groups.
  • Group 1 Group for people/animals with arteriosclerosis
  • Group 2 Group for people/animals with osteoporosis
  • Group 3 Group of people/animals with hierarchies
  • Group 4 Group for people/animals with premenstrual syndrome
  • Group 5 Group for people/animals with stomatitis
  • Group 6 Group for people/animals with diabetes
  • Group 7 Group for people with motor needs
  • Group 8 Group for people with attention deficit disorder
  • Group 9 Group for people/animals with chronic fatigue syndrome
  • Group 10 Group for people/animals with constipation
  • Group 11 Group for people/animals with dementia
  • Group 12 Group for people/animals with high blood pressure
  • Group 13 Group for people/animals with insomnia
  • Group 14 Group for people/animals with urolithiasis
  • Group 15 Group for people/animals with gout
  • Group 16 Group for people/animals with diarrhea
  • Group 17 Group for people/animals with hypoglycemia
  • the weight illustrated in FIG. 6 is a weight for each group (group weight), and since the embodiment uses a disease as an example, the disease weight is explained as a representative example.
  • phosphorus is set to a negative weight in the 15th group (gout)
  • zinc is set to a negative weight in the 17th group (hypoglycemia).
  • the efficacy score calculated in the embodiment of the present disclosure can be calculated as both positive and negative.
  • the efficacy score for a specific group of a specific food is calculated as positive, it means that the food has a good effect on the people/animals of that group, and a higher score means a better effect.
  • the efficacy score for a specific group of a specific food is calculated as negative, it means that the food has a bad effect on the people/animals of that group, and a lower score can mean that it has a worse effect.
  • the disease weight is set based on evidence elements regarding the influence of each component on the disease.
  • Evidence elements may include at least one of the following: presence or absence of scientific mechanism, presence or absence of clinical evidence, use as a treatment, presence or absence of substitute ingredients, and presence or absence of substitute ingredients.
  • the evidence elements include a first evidence element and a second evidence element.
  • the first element of evidence includes at least one of the following: presence or absence of scientific mechanism, presence or absence of clinical evidence, use as a treatment, and presence or absence of alternative ingredients.
  • the processor may calculate a disease weight for the influence of each component on the disease based on at least one of the first evidence element and the second evidence element.
  • the disease weight may be set based on at least one of a first weight set based on the first evidence element and a second weight set based on the second evidence element.
  • Figure 7 illustrates the first and second evidence elements in detail.
  • a weight of 50 is set, and if there are more than 3 RCTs or meta-analysis proves efficacy, a weight of 70 is set, and whether or not it is used as a treatment An example is setting a weight of 90 if the drug is already being used as a treatment or has been approved.
  • FIG. 7 The matters shown in FIG. 7 are examples to explain the disease weight, first weight, and second weight of the present disclosure, and the values of each item and weight are not limited as shown in FIG. 7 .
  • Figure 8 is a diagram illustrating the calculation of the first efficacy score and the second efficacy score for each group of scallops and groupfish.
  • the first efficacy score and the second efficacy score are calculated as shown in FIG. 8.
  • the apparatus 100 for calculating the medical efficacy of food may calculate the final efficacy score using at least one of the first efficacy score and the second efficacy score calculated as shown in FIG. 8 .
  • Figure 9 is a diagram illustrating providing user-customized food by expanding the method of calculating the medical efficacy of food according to an embodiment of the present disclosure.
  • the device 100 for calculating the medical efficacy of food using artificial intelligence-based ingredient analysis and medical evidence receives or receives user data through the communication unit 120. and information about at least one customized food can be provided to the user (analysis target) based on user data.
  • the processor 110 may provide customized food information optimized to the user based on information about the user's (analysis target) disease included in the user data.
  • the processor 110 may derive at least one user-customized food based on the final efficacy score calculated for each food according to user data.
  • An apparatus for providing customized food information based on the medical efficacy of food includes ingredient information for each of the ingredients included in each of a plurality of foods, and each of the ingredients included in each of the plurality of foods.
  • a storage unit and analysis that stores the ingredient score calculated based on the ingredient information, and the disease-specific efficacy score calculated for each of the plurality of foods based on the ingredient score and the disease weight assigned to each of the ingredients for each disease.
  • analysis object information including information on the object's disease is received
  • the efficacy score for each disease of each of the plurality of foods stored in the storage unit and the analysis object information include at least one food for the analysis object. It may include a processor that derives recommended food information.
  • the ingredient score, disease weight, and efficacy score for each disease for each food are calculated and stored in advance, and the analysis target (e.g., user)
  • the analysis target e.g., user
  • the analysis target information can be entered into the artificial intelligence model to derive and provide recommended food information including at least one food for the analysis target.
  • the device 10 for calculating the medical efficacy of food is not limited to diseases and can be applied to a variety of diseases, symptoms, signs, etc., so it provides services to a variety of users other than patients suffering from diseases. There is an effect that can be provided.
  • the method of calculating the medical efficacy of food using artificial intelligence-based ingredient analysis and medical evidence is a method of calculating the medical efficacy on each group of foods performed by a device, where the target food is Calculating an ingredient score based on ingredient information stored in a storage unit for each of the ingredients included; and calculating an efficacy score for each group of the target food based on the ingredient score and a group weight assigned to each of the ingredients for each group.
  • the ingredient score includes a ranking score that quantifies the relative ranking of the contents of the ingredients included in the target food in the storage unit
  • the ingredient information includes the content and content ranking of the ingredients
  • the ingredient score The calculation step may include calculating the ranking score for each of the ingredients based on the content and content ranking of the ingredients.
  • the ingredient score further includes a recommended score that quantifies the relative ratio of the contents of the ingredients included in the target food to the recommended intake
  • the ingredient information further includes the recommended intake of the ingredients
  • the ingredient score calculation step may further include calculating the recommended score for each of the ingredients based on the content and recommended intake of the ingredients.
  • the ranking score can be calculated based on Equation 1 described above.
  • the recommended score can be calculated based on Equation 2 described above.
  • the efficacy score calculation step includes calculating a first efficacy score for each group of the target food based on the ranking score calculated for each of the ingredients; And it may include calculating a second efficacy score for each group of the target food based on the recommended score calculated for each of the ingredients.
  • it may further include calculating a final efficacy score based on at least one of the first efficacy score and the second efficacy score.
  • the group weight is set based on evidence elements regarding the influence of each of the ingredients on the group, and the evidence elements are at least one of the following: presence or absence of a scientific mechanism, presence or absence of clinical evidence, use as a treatment, and presence or absence of alternative ingredients. It can contain one.
  • the group weight may be set based on a first weight set based on the presence or absence of the scientific mechanism, the presence or absence of the clinical evidence, and whether or not it is used as a therapeutic agent, and a second weight given depending on the presence or absence of the substitute ingredient. there is.
  • the group may refer to a group of people/animals with at least one of a specific disease, symptom, sign, or sign, or a group of people classified by a specific standard.
  • the specific criteria include gender, age, life cycle, and status information ( (e.g., pregnancy, etc.) may be applied.
  • the device 100 for calculating the medical efficacy of food may be configured to determine which of a plurality of groups the analysis subject determines when a request for providing customized food information for an analysis subject is received. It is possible to provide customized food information by determining whether a food belongs to a group and calculating the final efficacy score based on the group information to which the analysis subject belongs.
  • the device 100 for calculating the medical efficacy of food may provide customized food information by combining efficacy scores for each group when the analysis target belongs to multiple groups.
  • the method according to an embodiment of the present disclosure described above may be implemented as a program (or application) and stored in a medium in order to be executed in combination with a server, which is hardware.
  • the above-mentioned program is C, C++, JAVA, machine language, etc. that can be read by the processor (CPU) of the computer through the device interface of the computer in order for the computer to read the program and execute the methods implemented in the program.
  • It may include code coded in a computer language. These codes may include functional codes related to functions that define the necessary functions for executing the methods, and include control codes related to execution procedures necessary for the computer's processor to execute the functions according to predetermined procedures. can do.
  • these codes may further include memory reference-related codes that indicate at which location (address address) in the computer's internal or external memory additional information or media required for the computer's processor to execute the above functions should be referenced. there is.
  • the code uses the computer's communication module to determine how to communicate with any other remote computer or server. It may further include communication-related codes regarding whether communication should be performed and what information or media should be transmitted and received during communication.
  • the storage medium refers to a medium that stores data semi-permanently and can be read by a device, rather than a medium that stores data for a short period of time, such as a register, cache, or memory.
  • examples of the storage medium include ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical data storage device, etc., but are not limited thereto. That is, the program may be stored in various recording media on various servers that the computer can access or on various recording media on the user's computer. Additionally, the medium may be distributed to computer systems connected to a network, and computer-readable code may be stored in a distributed manner.
  • the steps of the method or algorithm described in connection with the embodiments of the present disclosure may be implemented directly in hardware, implemented as a software module executed by hardware, or a combination thereof.
  • the software module may be RAM (Random Access Memory), ROM (Read Only Memory), EPROM (Erasable Programmable ROM), EEPROM (Electrically Erasable Programmable ROM), Flash Memory, hard disk, removable disk, CD-ROM, or It may reside on any type of computer-readable recording medium well known in the art to which this disclosure pertains.

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Abstract

La présente divulgation concerne un procédé de calcul de l'efficacité médicale d'aliments à l'aide d'une analyse de composants basée sur l'intelligence artificielle et de preuves médicales. Dans le procédé, un score de composant peut être calculé pour chacun des composants contenus dans des aliments sur la base des informations de composants et un score d'efficacité peut être calculé pour chaque aliment pour des symptômes sur la base des scores de composant et de poids de maladie attribués à chaque composant pour chaque maladie.
PCT/KR2023/017724 2022-11-08 2023-11-07 Dispositif, procédé et programme pour calculer l'efficacité médicale d'un aliment à l'aide d'une analyse de composants basée sur l'intelligence artificielle et de preuves médicales WO2024101840A1 (fr)

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