WO2023007691A1 - Animal health management system - Google Patents

Animal health management system Download PDF

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Publication number
WO2023007691A1
WO2023007691A1 PCT/JP2021/028250 JP2021028250W WO2023007691A1 WO 2023007691 A1 WO2023007691 A1 WO 2023007691A1 JP 2021028250 W JP2021028250 W JP 2021028250W WO 2023007691 A1 WO2023007691 A1 WO 2023007691A1
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Prior art keywords
animal
disease
storage unit
learning model
injury
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PCT/JP2021/028250
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French (fr)
Japanese (ja)
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純 岡崎
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株式会社Peco
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Priority to PCT/JP2021/028250 priority Critical patent/WO2023007691A1/en
Priority to JP2022577783A priority patent/JP7260943B1/en
Publication of WO2023007691A1 publication Critical patent/WO2023007691A1/en
Priority to JP2023057339A priority patent/JP2023082130A/en

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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
    • 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
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A40/00—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production
    • Y02A40/70—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production in livestock or poultry

Definitions

  • the present invention relates to an animal health management system.
  • the present invention was made in view of such a background, and aims to provide a technology that allows breeders to quickly ascertain the health condition of animals.
  • the main invention of the present invention for solving the above problems is a system for managing the health of animals, comprising a medical record information storage unit for storing medical record information including diseases or injuries diagnosed for the animal; A medical inquiry storage unit that stores questions related to the health condition of the animal in association with each other, and learning created by machine learning using answers to the questions as input data and the disease or injury included in the medical record information as teacher data. a learning model storage unit that stores a model; an inquiry transmission unit that periodically transmits the question corresponding to the animal species of the animal to an owner terminal; an answer reception unit that receives an answer to the question from the owner terminal; an estimating unit that supplies the received answer to the learning model to estimate the disease or injury that the animal may suffer.
  • the breeder can grasp the health condition of the animal at an early stage.
  • FIG. 3 is a diagram showing an example hardware configuration of a management server 20;
  • FIG. 3 is a diagram showing an example of software configuration of a management server 20;
  • FIG. It is a figure explaining operation
  • a system for managing animal health comprising: a chart information storage unit that stores chart information including diseases or injuries diagnosed for the animal; an inquiry storage unit that stores questions related to the health condition of the animal in association with animal species; a learning model storage unit that stores a learning model created by machine learning using answers to the questions as input data and the disease or injury included in the medical record information as teacher data; an inquiry transmission unit that periodically transmits the question corresponding to the animal species of the animal to an owner terminal; an answer receiving unit that receives an answer to the question from the owner terminal; an estimating unit that feeds the received responses to the learning model to estimate the disease or injury that the animal may suffer;
  • An animal health management system comprising: [Item 2] The animal health management system according to item 1, The learning model storage unit stores the learning model for estimating the disease or injury to be afflicted by the time lag, in association with the time lag;
  • a periodic medical examination system according to an embodiment of the present invention will be described below.
  • the regular check-up system of this embodiment is intended to support regular check-ups of animals.
  • the veterinary institution side periodically asks the owner of the animal such as a pet to determine the health condition of the animal, especially the necessity of visiting the hospital (triage).
  • AI can be used for triage.
  • the AI can be a classifier that classifies whether a visit is necessary.
  • the AI can learn by using diagnoses of diseases (including injuries; the same shall apply hereinafter) included in medical record information as teacher data and answers to medical interviews as input data. As a result, it is possible to learn the relationship between the answers to medical interviews and diseases, and it is possible to determine possible diseases using the learning model of the learning results, and to determine the necessity of visiting the hospital accordingly.
  • animal species can be given as input data for AI learning.
  • an owner can give an image of a specific part of an animal as input data.
  • FIG. 1 is a diagram showing an example of the overall configuration of the regular medical examination system of this embodiment.
  • the regular check-up system of this embodiment includes a management server 20 .
  • the management server 20 is communicably connected to each of the owner terminal 10 and the medical institution terminal 30 via the communication network 40 .
  • the communication network 40 is, for example, the Internet, and is constructed by a public telephone line network, a mobile telephone line network, a wireless communication path, Ethernet (registered trademark), or the like.
  • the owner terminal 10 is a computer operated by an animal caregiver (owner or person taking care of the animal).
  • Owner terminal 10 is, for example, a smart phone, a tablet computer, a personal computer, or the like. It is assumed that the owner terminal 10 has a camera (not shown) and is capable of taking pictures.
  • the medical institution terminal 30 is a computer operated by veterinary medical personnel (such as nurses and doctors at veterinary hospitals) at veterinary medical institutions.
  • Medical institution terminal 30 is, for example, a tablet computer.
  • the medical institution terminal 30 can be, for example, any computer such as a smart phone or a personal computer.
  • the management server 20 is a computer that conducts regular medical interviews and determines whether animal patients need to visit the hospital.
  • the management server 20 may be a general-purpose computer such as a workstation or personal computer, or a virtual computer logically implemented by cloud computing.
  • FIG. 2 is a diagram showing a hardware configuration example of the management server 20. As shown in FIG. Note that the illustrated configuration is an example, and other configurations may be employed.
  • the management server 2 includes a CPU 201 , a memory 202 , a storage device 203 , a communication interface 204 , an input device 205 and an output device 206 .
  • the storage device 203 is, for example, a hard disk drive, solid state drive, flash memory, etc., which stores various data and programs.
  • the communication interface 204 is an interface for connecting to the communication network 3, and includes, for example, an adapter for connecting to Ethernet (registered trademark), a modem for connecting to a public telephone network, and a wireless communication device for performing wireless communication.
  • the input device 205 is, for example, a keyboard, mouse, touch panel, button, microphone, etc. for inputting data.
  • the output device 206 is, for example, a display, printer, speaker, or the like that outputs data.
  • Each functional unit of the management server 20, which will be described later, is implemented by the CPU 201 reading a program stored in the storage device 203 into the memory 202 and executing it. is implemented as part of the storage provided by
  • FIG. 3 is a diagram showing a software configuration example of the management server 20.
  • the management server 20 includes an inquiry transmission unit 211, an answer reception unit 212, a disease estimation unit 213, a hospital visit necessity determination unit 214, a hospital visit necessity transmission unit 215, a hospital visit necessity acquisition unit 215, a learning processing unit 216, and an owner information storage unit. 231 , an animal information storage unit 232 , a question storage unit 233 , an interview storage unit 234 , an answer history storage unit 235 , a learning model storage unit 237 , a chart information storage unit 238 and an intervention information storage unit 239 .
  • the owner information storage unit 231 stores information (hereinafter referred to as owner information) on the owner of an animal (patient animal) that undergoes medical examination at a veterinary medical institution.
  • the owner information includes an owner ID that identifies the owner, name, address, and contact information. Information about the owner other than name, address and contact information may be included.
  • the animal information storage unit 232 stores information about patient animals (hereinafter referred to as animal information).
  • the animal information can include various attributes of the patient animal, such as the name and species of the patient animal, in association with the owner ID indicating the owner of the patient animal and the animal ID identifying the patient animal.
  • the question storage unit 233 stores information related to questions about the animal's health condition (hereinafter referred to as question information).
  • the question information can include a question and presentation conditions in association with a question ID specifying a question and an animal species to which the question corresponds. Questions include questions that are expected to be answered in free text, questions that are expected to be answered by selecting one or more from 2 or 3 or more options, and answers with values (real numbers or integers) within a predetermined range. It can include scheduled questions and the like.
  • the questions can be text data, for example, and can include images and sounds.
  • the question includes a question to be answered with an image of a specific site.
  • the presentation condition can set a condition for including the question in the medical interview.
  • the presentation conditions are that the question must be the first question asked, and that the answer to the question before the question has specific content (for example, the answer to the question "Do you have an appetite?" Any judging condition can be set, such as the fact that the date falls within a specific period (for example, the season at the time of the question is winter).
  • the medical interview storage unit 234 stores information about one medical interview (hereinafter referred to as medical interview information).
  • the inquiry information includes an animal ID indicating an animal to be interviewed and a question ID indicating a question to be included in the inquiry, in association with an inquiry ID identifying an inquiry.
  • a plurality of question IDs may be set for one inquiry ID.
  • the inquiry information may include an owner ID indicating an owner or an animal ID indicating a patient animal, and an inquiry may be set for a specific animal patient.
  • the answer history storage unit 235 stores the history of answers from the owner to questions related to medical interviews.
  • the answer history stored by the answer history storage unit 235 includes a question ID indicating a question and an answer to the question in association with an interview ID, an animal ID, and the date and time when the interview was conducted (or the answer was received). be It should be noted that sets of question IDs and answers corresponding to the number of questions can be registered for one inquiry ID.
  • the learning model storage unit 237 stores a learning model for estimating possible diseases based on the medical interview results.
  • the learning model storage unit 237 can store learning models in association with animal species.
  • the learning model storage unit 237 can also store learning models in association with time lags.
  • the time lag indicates the time it takes for the disease to develop and can be, for example, a period of time such as 1 day, 3 days, 1 week, 1 month, quarter, 6 months, 1 year.
  • two kinds of time lags are prepared: short term of one week and medium and long term of one year. That is, the learning model can estimate diseases that are likely to be afflicted within a short, medium or long term for a particular animal species.
  • the learning model is a classifier that indicates to which of a plurality of given diseases the animal of the animal species corresponds after the time lag. It is assumed that it is possible to ask for the possibility.
  • the chart information storage unit 238 stores chart information related to animal examinations.
  • the medical record information can include a diagnosis result in association with a medical record ID that identifies a medical record, an animal ID that identifies an animal, and a date and time.
  • the diagnosis result is information about an animal that indicates whether or not the animal is afflicted with a disease.
  • the medical chart information can also include information such as results of animal biopsy performed at a veterinary institution, diagnosis by a veterinarian, treatment or surgery by a veterinarian, prescribed medications, and the like.
  • the chart information storage unit 238 can arbitrarily contain information managed as a chart in a veterinary medical institution.
  • the medical chart information storage unit 238 may access a database managed by an external server, such as a medical chart database, instead of being provided by the management server 20 .
  • the intervention information storage unit 239 stores intervention information about diseases.
  • the intervention information storage unit 239 can store intervention information in association with animal species and diseases.
  • Intervention information may include actions to be taken by the animal and advice from veterinarians. Actions can also include the need for hospital visits. Actions that should be made to the animal can be set, for example, "Let's take a walk" and "Let's eat some more rice”. Action options can be preset.
  • the advice may be text data, images (still images, moving images), audio, or any combination thereof.
  • the inquiry transmission unit 211 periodically transmits an inquiry to the owner terminal 10 .
  • the inquiry sending unit 211 can create one inquiry by selecting one or a plurality of questions stored in the question storage unit 233 corresponding to the animal species of the patient animal.
  • the medical inquiry sending unit 211 can, for example, randomly select a predetermined number of questions that satisfy the presentation conditions from among the questions corresponding to the animal species of the patient animal.
  • the medical inquiry sending unit 211 may select a question according to a predetermined rule set in advance, for example.
  • the answer receiving unit 212 receives answers to each medical interview question from the owner terminal 10 .
  • the answer receiving unit 212 can accept uploading of an image when the question includes a question to be answered with an image of a specific part.
  • the reply receiving section 212 can register the received reply in the reply history storage section 235 .
  • the disease estimation unit 213 estimates diseases that the patient animal can contract.
  • the disease estimating unit 213 can estimate the disease that the patient animal can contract by providing the results of the medical interview (answers to each question of the medical interview) to the learning model.
  • the disease estimator 213 can use a learning model corresponding to the animal species of the patient animal.
  • the disease estimation unit 213 can use a learning model corresponding to short-term (eg, one week) and long-term (eg, one year) time lags.
  • the disease estimating unit 213 identifies learning models corresponding to each of the short-term and long-term time lags and the animal species of the patient animal from the learning model storage unit 237, and uses the identified short-term and long-term learning models.
  • the disease estimating unit 213 can calculate the probability of contracting each of a plurality of given diseases after the corresponding time lag by providing the interview results to the learning model.
  • the hospital visit necessity determination unit 214 determines whether the patient animal needs to visit the hospital.
  • the hospital visit necessity determination unit 214 can determine the necessity of a hospital visit according to whether or not the disease estimated by the disease estimation unit 213 is present.
  • the hospital visit necessity determination unit 214 can determine that a hospital visit is necessary when there is a disease whose morbidity (probability) estimated by the disease estimation unit 213 is equal to or greater than a predetermined threshold.
  • the hospital visit necessity determination unit 214 can determine that a hospital visit is necessary when there is a disease estimated using a short-term learning model (that is, a disease that can be contracted in a short period of time, such as one week).
  • the hospital visit necessity determination unit 214 can determine that a hospital visit is necessary when there is a disease whose morbidity probability estimated using a short-term learning model is equal to or higher than a predetermined threshold.
  • the hospital visit necessity determination unit 214 can determine that a hospital visit is necessary when a disease different from the diagnosis result included in the medical record information is estimated.
  • the hospital visit necessity transmission unit 215 transmits the hospital visit necessity to the owner terminal 10 .
  • the hospital visit necessity transmission unit 215 can transmit the necessity of the hospital visit together with the estimated disease.
  • the hospital visit necessity transmitting unit 215 may transmit the disease estimated by the learning model corresponding to the long-term time lag to the owner terminal 10 as the disease requiring attention.
  • the hospital visit necessity transmission unit 215 may transmit intervention information (action and/or advice) corresponding to the estimated disease to the owner terminal 10 .
  • the hospital visit necessity transmission unit 215 may transmit the necessity of a hospital visit to the medical institution terminal 30 together with the animal information corresponding to the animal patient and the estimated disease.
  • the learning processing unit 216 learns the diagnostic results of medical chart information and creates and updates a learning model. For each animal species, the learning processing unit 216 uses the interview content corresponding to the animal species as input data, performs machine learning using the diagnosis result (diagnosed disease) of the corresponding medical record information as teacher data, and creates by machine learning
  • the learned model can be registered in the learning model storage unit 237 .
  • a predetermined time lag short-term and long-term
  • Machine learning can be performed by using the answer information of the medical record information as input data and the disease in the diagnosis result of the medical record information as teacher data.
  • the learning processing unit 216 selects only the diagnosed disease contained in the diagnosis result (included in the medical record information corresponding to the date and time from the response history date and time to after the time lag and the animal ID in the response history). can be extracted.) may be used as teacher data for learning.
  • the learning processing unit 216 performs machine learning using the response history corresponding to the animal ID in the medical chart information and the diagnosis result of the medical chart information, A learning model corresponding to the animal species of the animal information can be updated.
  • learning can be performed using reply information from the date and time of the medical record information to the time before the time lag as input data.
  • FIG. 4 is a diagram for explaining the operation of the periodic medical examination system of this embodiment.
  • the management server 20 creates an inquiry (S301).
  • the medical interview can be prepared by reading out a predetermined number of questions that correspond to the animal species of the patient animal and satisfy the provision conditions among the questions stored in the question storage unit 233 .
  • the management server 20 transmits the created medical interview to the owner terminal 10 (S302).
  • An interview is displayed on the owner terminal 10.
  • the medical interview may be conducted, for example, in a chat format, or may be conducted using a form on a web page.
  • a camera included in the owner terminal 10 can be activated to obtain an image captured by the camera as an answer.
  • the owner terminal 10 transmits the inputted answer to the management server 20 .
  • the management server 20 can receive an answer from the owner terminal 10 (S303) and register the received answer in the answer history storage unit 235.
  • the management server 20 reads the learning model corresponding to the animal species of the patient animal and the short-term time lag from the learning model storage unit 237, gives an answer to the read learning model, and estimates the short-term morbidity of each disease (S304). . If the estimated disease probability is equal to or greater than the threshold (S305: YES), the management server 20 determines that a hospital visit is necessary (S306), and if the disease probability is less than the threshold (S306: NO), no hospital visit is required. (S307).
  • the management server 20 transmits to the owner terminal 10 whether or not it is necessary to visit the hospital (S308).
  • the management server 20 transmits a message to the owner terminal 10 to call attention to diseases in which the likelihood of contracting the disease estimated by giving a response to a learning model corresponding to a long time lag is equal to or greater than a predetermined threshold. You may make it Further, intervention information (actions and advice) corresponding to a disease with a short-term and/or long-term morbidity probability equal to or greater than a predetermined value may be transmitted to the owner terminal 10 .
  • the management server 20 periodically repeats the processing from S301 until the period for sending periodic medical interviews ends (S309: NO). In step S301 from the second time onward, the contents of the medical interview can be changed.
  • the veterinary institution can proactively send medical interviews to the breeders of patient animals periodically to confirm the health condition of the patient animals.
  • AI learning model
  • AI can be used to estimate the possibility of a patient animal contracting a disease based on the results of an interview, and the necessity of visiting a hospital can be determined accordingly.
  • a learning model is created for each animal species, but one or more learning models may be used as a whole, and the animal species may be given as a feature amount.
  • the learning model can include an image captured by the owner terminal 10 in the feature amount of the learning model.
  • the learning model can include an image captured by the owner terminal 10 in the feature amount of the learning model.
  • the degree of good health of a patient animal may be estimated, not limited to the possibility of contracting a disease.
  • the learning model can be a predictor that predicts the degree of good health, and the input of the degree of good health is received from the medical institution terminal 30 and used as teacher data to perform machine learning. can be done.
  • a learning model that estimates the possibility of affliction of an animal's disease based on the interview results.
  • a learning model that estimates an action may be used.
  • the action model can be created in advance by machine learning using the interview results as input data and actions to be taken as teacher data. Items included in medical record information may be included as feature amounts to be given to the action model. It is also possible to include the results of medical interviews and medical record information for a predetermined period in the past in the medical interview results given when learning the action model.
  • the hospital visit necessity determination unit 214 can provide the medical interview results (and medical chart information, and past data thereof) to the disease model and action model to infer the disease morbidity and actions.

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Abstract

[Problem] To make it possible for an animal caretaker to ascertain the state of health of an animal at an early stage. [Solution] A system for managing the health of an animal, said system being characterized by being equipped with: a medical record information storage unit for storing medical record information which includes the disease or injury with which an animal was diagnosed; a medical interview storage unit for storing questions pertaining to the state of health of an animal in association with the animal type; a training model storage unit for storing a training model created by machine learning with responses to questions as input data and diseases or injuries included in the medical chart information as training data; a medical interview transmission unit for periodically transmitting a question which corresponds to the type of animal to the terminal of the owner; a response receiving unit for receiving a response to the question from the owner terminal; and a prediction unit for predicting the disease or injury which an animal may have by applying the received response to the training model.

Description

動物健康管理システムanimal health management system
　本発明は、動物健康管理システムに関する。 The present invention relates to an animal health management system.
　ネットワークを介して問診を行うことが行われている（特許文献１参照）。 Medical interviews are conducted via networks (see Patent Document 1).
国際公開２００４／１０４８８０号WO 2004/104880
　一般に問診は患者が自覚症状を感じたときに行われる。しかしながら、獣医療においては飼育者が健康状態を把握する必要がある。 In general, an interview is conducted when the patient feels subjective symptoms. However, in veterinary medicine, it is necessary for breeders to grasp their health conditions.
　本発明はこのような背景を鑑みてなされたものであり、飼育者が動物の健康状態を早期に把握することができる技術を提供することを目的とする。 The present invention was made in view of such a background, and aims to provide a technology that allows breeders to quickly ascertain the health condition of animals.
　上記課題を解決するための本発明の主たる発明は、動物の健康を管理するシステムであって、前記動物について診断された疾病又は怪我を含むカルテ情報を記憶するカルテ情報記憶部と、動物種に対応付けて前記動物の健康状態に関連する質問を記憶する問診記憶部と、前記質問に対する回答を入力データとし、前記カルテ情報に含まれる前記疾病又は怪我を教師データとして機械学習により作成された学習モデルを記憶する学習モデル記憶部と、前記動物の動物種に対応する前記質問を定期的に飼い主端末に送信する問診送信部と、前記飼い主端末から前記質問に対する回答を受信する回答受信部と、受信した前記回答を前記学習モデルに与えて、前記動物が罹りうる前記疾病又は怪我を推定する推定部と、を備えることを特徴とする。 The main invention of the present invention for solving the above problems is a system for managing the health of animals, comprising a medical record information storage unit for storing medical record information including diseases or injuries diagnosed for the animal; A medical inquiry storage unit that stores questions related to the health condition of the animal in association with each other, and learning created by machine learning using answers to the questions as input data and the disease or injury included in the medical record information as teacher data. a learning model storage unit that stores a model; an inquiry transmission unit that periodically transmits the question corresponding to the animal species of the animal to an owner terminal; an answer reception unit that receives an answer to the question from the owner terminal; an estimating unit that supplies the received answer to the learning model to estimate the disease or injury that the animal may suffer.
　その他本願が開示する課題やその解決方法については、発明の実施形態の欄及び図面により明らかにされる。 Other problems disclosed by the present application and their solutions will be clarified in the section of the embodiment of the invention and the drawings.
　本発明によれば、飼育者が動物の健康状態を早期に把握することができる。 According to the present invention, the breeder can grasp the health condition of the animal at an early stage.
本実施形態の定期検診システムの全体構成例を示す図である。BRIEF DESCRIPTION OF THE DRAWINGS It is a figure which shows the whole structural example of the periodical medical examination system of this embodiment. 管理サーバ２０のハードウェア構成例を示す図である。3 is a diagram showing an example hardware configuration of a management server 20; FIG. 管理サーバ２０のソフトウェア構成例を示す図である。3 is a diagram showing an example of software configuration of a management server 20; FIG. 本実施形態の定期検診システムの動作を説明する図である。It is a figure explaining operation|movement of the periodical medical examination system of this embodiment.
＜発明の概要＞
　本発明の実施形態の内容を列記して説明する。本発明は、たとえば、以下のような構成を備える。
［項目１］
　動物の健康を管理するシステムであって、
　前記動物について診断された疾病又は怪我を含むカルテ情報を記憶するカルテ情報記憶部と、
　動物種に対応付けて前記動物の健康状態に関連する質問を記憶する問診記憶部と、
　前記質問に対する回答を入力データとし、前記カルテ情報に含まれる前記疾病又は怪我を教師データとして機械学習により作成された学習モデルを記憶する学習モデル記憶部と、
　前記動物の動物種に対応する前記質問を定期的に飼い主端末に送信する問診送信部と、
　前記飼い主端末から前記質問に対する回答を受信する回答受信部と、
　受信した前記回答を前記学習モデルに与えて、前記動物が罹りうる前記疾病又は怪我を推定する推定部と、
　を備えることを特徴とする動物健康管理システム。
［項目２］
　項目１に記載の動物健康管理システムであって、
　前記学習モデル記憶部は、タイムラグに対応付けて、前記タイムラグ後までに罹患する前記疾病又は怪我を推定する前記学習モデルを記憶すること、
　を特徴とする動物健康管理システム。
［項目３］
　項目２に記載の動物健康管理システムであって、
　前記学習モデル記憶部は、複数の前記タイムラグのそれぞれについて、前記学習モデルを記憶しており、
　前記推定部は、最も短い前記タイムラグに対応する前記学習モデルを用いること、
　を特徴とする動物健康管理システム。
［項目４］
　項目３に記載の動物健康管理システムであって、
　前記推定部は、前記タイムラグのそれぞれに対応する前記学習モデルに前記回答を与えて前記疾病又は怪我を推定し、
　最も短い前記タイムラグに対応する前記疾病又は怪我が存在する場合に、来院が必要と判断する来院要否判定部と、
　前記来院要否とともに、他の前記タイムラフに対応する前記疾病又は怪我を飼い主端末に送信する送信部と、
　をさらに備えることを特徴とする動物健康管理システム。
［項目５］
　項目１乃至４のいずれか１項に記載の動物健康管理システムであって、
　複数の前記質問の少なくとも一部には、前記動物を撮影した画像により回答をするべきものが含まれること、
　を特徴とする動物健康管理システム。
［項目６］
　項目１乃至５のいずれか１項に記載の動物健康管理システムであって、
　前記疾病又は怪我に対応付けて介入情報を記憶する介入情報記憶部と、
　推定された前記疾病又は怪我とともに、当該疾病又は怪我に対応する前記介入情報を飼い主端末に送信する送信部と、
　をさらに備えることを特徴とする動物健康管理システム。
［項目７］
　動物の健康を管理する方法であって、
　前記動物について診断された疾病又は怪我を含むカルテ情報を記憶するカルテ情報記憶部と、
　動物種に対応付けて前記動物の健康状態に関連する質問を記憶する問診記憶部と、
　前記質問に対する回答を入力データとし、前記カルテ情報に含まれる前記疾病又は怪我を教師データとして機械学習により作成された学習モデルを記憶する学習モデル記憶部と、を備える情報処理装置が、
　前記動物の動物種に対応する前記質問を定期的に飼い主端末に送信するステップと、
　前記飼い主端末から前記質問に対する回答を受信するステップと、
　受信した前記回答を前記学習モデルに与えて、前記動物が罹りうる前記疾病又は怪我を推定するステップと、
　を実行することを特徴とする動物健康管理方法。
［項目８］
　動物の健康を管理する方法であって、
　前記動物について診断された疾病又は怪我を含むカルテ情報を記憶するカルテ情報記憶部と、
　動物種に対応付けて前記動物の健康状態に関連する質問を記憶する問診記憶部と、
　前記質問に対する回答を入力データとし、前記カルテ情報に含まれる前記疾病又は怪我を教師データとして機械学習により作成された学習モデルを記憶する学習モデル記憶部と、を備える情報処理装置に、
　前記動物の動物種に対応する前記質問を定期的に飼い主端末に送信するステップと、
　前記飼い主端末から前記質問に対する回答を受信するステップと、
　受信した前記回答を前記学習モデルに与えて、前記動物が罹りうる前記疾病又は怪我を推定するステップと、
　を実行させるためのプログラム。 <Overview of the invention>
The contents of the embodiments of the present invention are listed and explained. The present invention has, for example, the following configurations.
[Item 1]
A system for managing animal health, comprising:
a chart information storage unit that stores chart information including diseases or injuries diagnosed for the animal;
an inquiry storage unit that stores questions related to the health condition of the animal in association with animal species;
a learning model storage unit that stores a learning model created by machine learning using answers to the questions as input data and the disease or injury included in the medical record information as teacher data;
an inquiry transmission unit that periodically transmits the question corresponding to the animal species of the animal to an owner terminal;
an answer receiving unit that receives an answer to the question from the owner terminal;
an estimating unit that feeds the received responses to the learning model to estimate the disease or injury that the animal may suffer;
An animal health management system comprising:
[Item 2]
The animal health management system according to item 1,
The learning model storage unit stores the learning model for estimating the disease or injury to be afflicted by the time lag, in association with the time lag;
An animal health management system characterized by:
[Item 3]
The animal health management system according to item 2,
The learning model storage unit stores the learning model for each of the plurality of time lags,
The estimation unit uses the learning model corresponding to the shortest time lag;
An animal health management system characterized by:
[Item 4]
The animal health management system according to item 3,
The estimating unit estimates the disease or injury by giving the answer to the learning model corresponding to each of the time lags;
a hospital visit necessity determination unit that determines that a hospital visit is necessary when the disease or injury corresponding to the shortest time lag exists;
a transmission unit that transmits the disease or injury corresponding to the other time-roughness to the owner terminal together with the necessity of visiting the hospital;
An animal health management system, further comprising:
[Item 5]
The animal health management system according to any one of items 1 to 4,
at least some of the plurality of questions include those to be answered by images of the animal;
An animal health management system characterized by:
[Item 6]
The animal health management system according to any one of items 1 to 5,
an intervention information storage unit that stores intervention information in association with the disease or injury;
a transmitting unit that transmits the intervention information corresponding to the disease or injury together with the estimated disease or injury to the owner terminal;
An animal health management system, further comprising:
[Item 7]
A method of managing animal health, comprising:
a chart information storage unit that stores chart information including diseases or injuries diagnosed for the animal;
an inquiry storage unit that stores questions related to the health condition of the animal in association with animal species;
an information processing apparatus comprising a learning model storage unit that stores a learning model created by machine learning using answers to the questions as input data and the disease or injury included in the medical record information as teacher data;
periodically sending the question corresponding to the species of the animal to an owner terminal;
receiving an answer to the question from the owner terminal;
feeding the received responses to the learning model to estimate the disease or injury that the animal may suffer;
An animal health management method characterized by performing
[Item 8]
A method of managing animal health, comprising:
a chart information storage unit that stores chart information including diseases or injuries diagnosed for the animal;
an inquiry storage unit that stores questions related to the health condition of the animal in association with animal species;
a learning model storage unit that stores a learning model created by machine learning using answers to the questions as input data and the disease or injury included in the medical record information as teacher data;
periodically sending the question corresponding to the species of the animal to an owner terminal;
receiving an answer to the question from the owner terminal;
feeding the received responses to the learning model to estimate the disease or injury that the animal may suffer;
program to run the
＜システムの概要＞
　以下、本発明の一実施形態に係る定期検診システムについて説明する。本実施形態の定期検診システムは、動物の定期検診を支援しようとするものである。 <Overview of the system>
A periodic medical examination system according to an embodiment of the present invention will be described below. The regular check-up system of this embodiment is intended to support regular check-ups of animals.
　本実施形態の定期検診システムは、獣医療機関側からペット等の動物の飼い主に対して定期的な問診を行うことにより、動物の健康状態、とくに来院の要否（トリアージ）を判断しようとするものである。本実施形態では、ＡＩを用いてトリアージを行うことができる。ＡＩは、来院の要否を分類する分類器とすることができる。本実施形態では、ＡＩは、カルテ情報に含まれている疾病（怪我を含む。以下同じ。）の診断を教師データとし、問診の回答を入力データとして学習を行うことができる。これにより、問診の回答と疾病との関係を学習することができ、当該学習結果の学習モデルを用いて罹りうる疾病を判定することが可能となり、これに応じて来院要否を判定することができる。また、本実施形態の定期検診システムでは、ＡＩの学習にあたり動物種を入力データとして与えることができる。また、ＡＩの学習にあたり、飼い主が動物の特定部位を撮影した画像を入力データとして与えることができる。 In the regular medical examination system of this embodiment, the veterinary institution side periodically asks the owner of the animal such as a pet to determine the health condition of the animal, especially the necessity of visiting the hospital (triage). It is. In this embodiment, AI can be used for triage. The AI can be a classifier that classifies whether a visit is necessary. In the present embodiment, the AI can learn by using diagnoses of diseases (including injuries; the same shall apply hereinafter) included in medical record information as teacher data and answers to medical interviews as input data. As a result, it is possible to learn the relationship between the answers to medical interviews and diseases, and it is possible to determine possible diseases using the learning model of the learning results, and to determine the necessity of visiting the hospital accordingly. can. In addition, in the periodic medical examination system of this embodiment, animal species can be given as input data for AI learning. Also, when AI learns, an owner can give an image of a specific part of an animal as input data.
　図１は、本実施形態の定期検診システムの全体構成例を示す図である。本実施形態の定期検診システムは、管理サーバ２０を含んで構成される。管理サーバ２０は、飼い主端末１０および医療機関端末３０とのそれぞれに対して通信ネットワーク４０を介して通信可能に接続される。通信ネットワーク４０は、たとえばインターネットであり、公衆電話回線網や携帯電話回線網、無線通信路、イーサネット（登録商標）などにより構築される。 FIG. 1 is a diagram showing an example of the overall configuration of the regular medical examination system of this embodiment. The regular check-up system of this embodiment includes a management server 20 . The management server 20 is communicably connected to each of the owner terminal 10 and the medical institution terminal 30 via the communication network 40 . The communication network 40 is, for example, the Internet, and is constructed by a public telephone line network, a mobile telephone line network, a wireless communication path, Ethernet (registered trademark), or the like.
　飼い主端末１０は、動物の介護者（飼い主または世話をしている人）が操作するコンピュータである。飼い主端末１０は、たとえば、スマートフォンやタブレットコンピュータ、パーソナルコンピュータなどである。飼い主端末１０は、カメラ（不図示）を備え、撮影が可能となっていることを想定する。 The owner terminal 10 is a computer operated by an animal caregiver (owner or person taking care of the animal). Owner terminal 10 is, for example, a smart phone, a tablet computer, a personal computer, or the like. It is assumed that the owner terminal 10 has a camera (not shown) and is capable of taking pictures.
　医療機関端末３０は、獣医療機関での獣医療関係者（動物病院の看護師や医師などが）が操作するコンピュータである。医療機関端末３０は、たとえば、タブレットコンピュータである。医療機関端末３０は、たとえば、スマートフォンやパーソナルコンピュータなど、任意のコンピュータとすることができる。 The medical institution terminal 30 is a computer operated by veterinary medical personnel (such as nurses and doctors at veterinary hospitals) at veterinary medical institutions. Medical institution terminal 30 is, for example, a tablet computer. The medical institution terminal 30 can be, for example, any computer such as a smart phone or a personal computer.
　管理サーバ２０は、定期的な問診を行い、動物患者の来院要否を判断するコンピュータである。管理サーバ２０は、たとえばワークステーションやパーソナルコンピュータのような汎用コンピュータとしてもよいし、あるいはクラウド・コンピューティングによって論理的に実現される仮想コンピュータとしてもよい。 The management server 20 is a computer that conducts regular medical interviews and determines whether animal patients need to visit the hospital. The management server 20 may be a general-purpose computer such as a workstation or personal computer, or a virtual computer logically implemented by cloud computing.
　図２は、管理サーバ２０のハードウェア構成例を示す図である。なお、図示された構成は一例であり、これ以外の構成を有していてもよい。管理サーバ２は、ＣＰＵ２０１、メモリ２０２、記憶装置２０３、通信インタフェース２０４、入力装置２０５、出力装置２０６を備える。記憶装置２０３は、各種のデータやプログラムを記憶する、例えばハードディスクドライブやソリッドステートドライブ、フラッシュメモリなどである。通信インタフェース２０４は、通信ネットワーク３に接続するためのインタフェースであり、例えばイーサネット（登録商標）に接続するためのアダプタ、公衆電話回線網に接続するためのモデム、無線通信を行うための無線通信機、シリアル通信のためのＵＳＢ（Universal Serial Bus）コネクタやＲＳ２３２Ｃコネクタなどである。入力装置２０５は、データを入力する、例えばキーボードやマウス、タッチパネル、ボタン、マイクロフォンなどである。出力装置２０６は、データを出力する、例えばディスプレイやプリンタ、スピーカなどである。後述する管理サーバ２０の各機能部は、ＣＰＵ２０１が記憶装置２０３に記憶されているプログラムをメモリ２０２に読み出して実行することにより実現され、管理サーバ２０の各記憶部は、メモリ２０２及び記憶装置２０３が提供する記憶領域の一部として実現される。 FIG. 2 is a diagram showing a hardware configuration example of the management server 20. As shown in FIG. Note that the illustrated configuration is an example, and other configurations may be employed. The management server 2 includes a CPU 201 , a memory 202 , a storage device 203 , a communication interface 204 , an input device 205 and an output device 206 . The storage device 203 is, for example, a hard disk drive, solid state drive, flash memory, etc., which stores various data and programs. The communication interface 204 is an interface for connecting to the communication network 3, and includes, for example, an adapter for connecting to Ethernet (registered trademark), a modem for connecting to a public telephone network, and a wireless communication device for performing wireless communication. , USB (Universal Serial Bus) connector and RS232C connector for serial communication. The input device 205 is, for example, a keyboard, mouse, touch panel, button, microphone, etc. for inputting data. The output device 206 is, for example, a display, printer, speaker, or the like that outputs data. Each functional unit of the management server 20, which will be described later, is implemented by the CPU 201 reading a program stored in the storage device 203 into the memory 202 and executing it. is implemented as part of the storage provided by
＜管理サーバ２０のソフトウェア構成＞
　図３は、管理サーバ２０のソフトウェア構成例を示す図である。管理サーバ２０は、問診送信部２１１、回答受信部２１２、疾病推定部２１３、来院要否判定部２１４、来院要否送信部２１５、来院要否取得部２１５、学習処理部２１６、飼い主情報記憶部２３１、動物情報記憶部２３２、質問記憶部２３３、問診記憶部２３４、回答履歴記憶部２３５、学習モデル記憶部２３７、カルテ情報記憶部２３８、介入情報記憶部２３９を備える。 <Software Configuration of Management Server 20>
FIG. 3 is a diagram showing a software configuration example of the management server 20. As shown in FIG. The management server 20 includes an inquiry transmission unit 211, an answer reception unit 212, a disease estimation unit 213, a hospital visit necessity determination unit 214, a hospital visit necessity transmission unit 215, a hospital visit necessity acquisition unit 215, a learning processing unit 216, and an owner information storage unit. 231 , an animal information storage unit 232 , a question storage unit 233 , an interview storage unit 234 , an answer history storage unit 235 , a learning model storage unit 237 , a chart information storage unit 238 and an intervention information storage unit 239 .
＝＝記憶部＝＝
　飼い主情報記憶部２３１は、獣医療機関を受診する動物（患者動物）の飼い主に関する情報（以下、飼い主情報という。）を記憶する。飼い主情報には、飼い主を特定する飼い主ＩＤ、氏名、住所、連絡先を含む。氏名、住所、連絡先以外の飼い主に関する情報を含めてもよい。 == Storage unit ==
The owner information storage unit 231 stores information (hereinafter referred to as owner information) on the owner of an animal (patient animal) that undergoes medical examination at a veterinary medical institution. The owner information includes an owner ID that identifies the owner, name, address, and contact information. Information about the owner other than name, address and contact information may be included.
　動物情報記憶部２３２は、患者動物に関する情報（以下、動物情報という。）を記憶する。動物情報には、患者動物の飼い主を示す飼い主ＩＤおよび患者動物を特定する動物ＩＤに対応付けて、患者動物の名称及び動物種など、患者動物の各種属性を含めることができる。 The animal information storage unit 232 stores information about patient animals (hereinafter referred to as animal information). The animal information can include various attributes of the patient animal, such as the name and species of the patient animal, in association with the owner ID indicating the owner of the patient animal and the animal ID identifying the patient animal.
　質問記憶部２３３は、動物の健康状態についての質問に係る情報（以下、質問情報という。）を記憶する。質問情報には、質問を特定する質問ＩＤと、当該質問が該当する動物種とに対応付けて、質問及び提示条件を含めることができる。質問には、フリーテキストでの回答を予定する質問、２択または３択以上の選択肢から１つ以上を選択する回答を予定する質問、所定の範囲内での値（実数または整数）の回答を予定する質問などを含めることができる。質問は、例えば、テキストデータとすることができ、画像や音声を含めるようにすることもできる。本実施形態では、質問には、特定の部位を撮影した画像により回答するべき質問が含まれるものとする。提示条件は、質問を問診に含める条件を設定することができる。提示条件には、一番最初に行う質問であること、当該質問よりも前の質問に対する回答が特定の内容であったこと（たとえば、「食欲があるか」という質問に対する回答として「ない」と回答した場合など）、日付が特定の期間にあたること（たとえば、質問時点の季節が冬である場合など）など、任意の判定可能な条件を設定することができる。 The question storage unit 233 stores information related to questions about the animal's health condition (hereinafter referred to as question information). The question information can include a question and presentation conditions in association with a question ID specifying a question and an animal species to which the question corresponds. Questions include questions that are expected to be answered in free text, questions that are expected to be answered by selecting one or more from 2 or 3 or more options, and answers with values (real numbers or integers) within a predetermined range. It can include scheduled questions and the like. The questions can be text data, for example, and can include images and sounds. In this embodiment, the question includes a question to be answered with an image of a specific site. The presentation condition can set a condition for including the question in the medical interview. The presentation conditions are that the question must be the first question asked, and that the answer to the question before the question has specific content (for example, the answer to the question "Do you have an appetite?" Any judging condition can be set, such as the fact that the date falls within a specific period (for example, the season at the time of the question is winter).
　問診記憶部２３４は、１回の問診に関する情報（以下、問診情報という。）を記憶する。問診情報には、問診を識別する問診ＩＤに対応付けて、問診対象とした動物を示す動物ＩＤ及び問診に含める質問を示す質問ＩＤを含む。１つの問診ＩＤに対して質問ＩＤは複数設定されてよい。問診情報には、飼い主を示す飼い主ＩＤ又は患者動物を示す動物ＩＤを含めて、特定の動物患者に対する問診を設定するようにしてもよい。 The medical interview storage unit 234 stores information about one medical interview (hereinafter referred to as medical interview information). The inquiry information includes an animal ID indicating an animal to be interviewed and a question ID indicating a question to be included in the inquiry, in association with an inquiry ID identifying an inquiry. A plurality of question IDs may be set for one inquiry ID. The inquiry information may include an owner ID indicating an owner or an animal ID indicating a patient animal, and an inquiry may be set for a specific animal patient.
　回答履歴記憶部２３５は、問診に係る質問に対する飼い主からの回答の履歴を記憶する。回答履歴記憶部２３５が記憶する回答履歴には、問診ＩＤ、動物ＩＤ及び問診を行った（又は回答を受け付けた）日時に対応付けて、質問を示す質問ＩＤと、当該質問に対する回答とが含まれる。なお、１つの問診ＩＤに対して、質問の数だけ質問ＩＤ及び回答のセットが登録されうる。 The answer history storage unit 235 stores the history of answers from the owner to questions related to medical interviews. The answer history stored by the answer history storage unit 235 includes a question ID indicating a question and an answer to the question in association with an interview ID, an animal ID, and the date and time when the interview was conducted (or the answer was received). be It should be noted that sets of question IDs and answers corresponding to the number of questions can be registered for one inquiry ID.
　学習モデル記憶部２３７は、問診結果に基づいて罹りうる疾病を推定する学習モデルを記憶する。学習モデル記憶部２３７は、動物種に対応付けて学習モデルを記憶することができる。また、学習モデル記憶部２３７は、タイムラグにも対応付けて学習モデルを記憶することができる。タイムラグは、疾病が発生するまでの時間を示し、例えば、１日、３日、１週間、１か月、四半期、半年、１年など任期の期間とすることができる。本実施形態では、１週間の短期と１年の中長期と２種類のタイムラグを準備するものとする。すなわち、学習モデルは、特定の動物種について、短期又は中長期の期間内に罹患する可能性のある疾病を推定することができる。また、本実施形態では、学習モデルは、当該動物種の動物が、タイムラグ後において、所与の複数の疾病のいずれに該当するかの分類器であるものとし、分類器によって、各疾病の罹患可能性を求めることもできいるものとする。 The learning model storage unit 237 stores a learning model for estimating possible diseases based on the medical interview results. The learning model storage unit 237 can store learning models in association with animal species. The learning model storage unit 237 can also store learning models in association with time lags. The time lag indicates the time it takes for the disease to develop and can be, for example, a period of time such as 1 day, 3 days, 1 week, 1 month, quarter, 6 months, 1 year. In this embodiment, two kinds of time lags are prepared: short term of one week and medium and long term of one year. That is, the learning model can estimate diseases that are likely to be afflicted within a short, medium or long term for a particular animal species. Further, in the present embodiment, the learning model is a classifier that indicates to which of a plurality of given diseases the animal of the animal species corresponds after the time lag. It is assumed that it is possible to ask for the possibility.
　カルテ情報記憶部２３８は、動物の診察に関するカルテ情報を記憶する。カルテ情報は、カルテを特定するカルテＩＤ、動物を特定する動物ＩＤ、及び日時に対応付けて、診断結果が含むことができる。診断結果は、動物について、当該動物が疾病に罹っているか否かを示す情報とする。カルテ情報には、獣医療機関において行われた動物の生体検査の結果、獣医師による診断、獣医師による処置や手術、処方された薬などの情報が含まれることもできる。カルテ情報記憶部２３８は、獣医療機関においてカルテとして管理される情報を任意に含むことができる。カルテ情報記憶部２３８は、管理サーバ２０が備えずに、カルテデータベースなど外部のサーバに管理されているものにアクセスするようにしてもよい。 The chart information storage unit 238 stores chart information related to animal examinations. The medical record information can include a diagnosis result in association with a medical record ID that identifies a medical record, an animal ID that identifies an animal, and a date and time. The diagnosis result is information about an animal that indicates whether or not the animal is afflicted with a disease. The medical chart information can also include information such as results of animal biopsy performed at a veterinary institution, diagnosis by a veterinarian, treatment or surgery by a veterinarian, prescribed medications, and the like. The chart information storage unit 238 can arbitrarily contain information managed as a chart in a veterinary medical institution. The medical chart information storage unit 238 may access a database managed by an external server, such as a medical chart database, instead of being provided by the management server 20 .
　介入情報記憶部２３９は、疾病についての介入情報を記憶する。介入情報記憶部２３９は、動物種及び疾病に対応付けて介入情報を記憶することができる。介入情報には、動物にさせるべき行動（アクション）と、獣医療従事者からのアドバイスとが含まれうる。アクションには、来院の必要性を含めることもできる。アクションには、例えば、「散歩させましょう」「ご飯をもう少し食べましょう」といった、動物にさせるべき行動を設定することができる。アクションの選択肢は事前に設定することができる。アドバイスはテキストデータ、画像（静止画像、動画像）、音声のいずれか又はこれらの任意の組み合わせであってよい。 The intervention information storage unit 239 stores intervention information about diseases. The intervention information storage unit 239 can store intervention information in association with animal species and diseases. Intervention information may include actions to be taken by the animal and advice from veterinarians. Actions can also include the need for hospital visits. Actions that should be made to the animal can be set, for example, "Let's take a walk" and "Let's eat some more rice". Action options can be preset. The advice may be text data, images (still images, moving images), audio, or any combination thereof.
＝＝機能部＝＝
　問診送信部２１１は、問診を定期的に飼い主端末１０に送信する。問診送信部２１１は、患者動物の動物種に対応する質問記憶部２３３に記憶されている質問事項を１つ又は複数選択して１つの問診を作成することができる。問診送信部２１１は、患者動物の動物種に対応する質問事項のうち、提示条件が満たされているものを、例えば、ランダムに所定数選択することができる。また、問診送信部２１１は、例えば、事前に設定された所定のルールに従って質問を選択するようにしてもよい。 == Function part ==
The inquiry transmission unit 211 periodically transmits an inquiry to the owner terminal 10 . The inquiry sending unit 211 can create one inquiry by selecting one or a plurality of questions stored in the question storage unit 233 corresponding to the animal species of the patient animal. The medical inquiry sending unit 211 can, for example, randomly select a predetermined number of questions that satisfy the presentation conditions from among the questions corresponding to the animal species of the patient animal. In addition, the medical inquiry sending unit 211 may select a question according to a predetermined rule set in advance, for example.
　回答受信部２１２は、問診の各質問に対する回答を飼い主端末１０から受信する。回答受信部２１２は、質問事項に、特定の部位を撮影した画像により回答するべき質問が含まれていた場合には、画像のアップロードを受け付けることができる。回答受信部２１２は、受信した回答を回答履歴記憶部２３５に登録することができる。 The answer receiving unit 212 receives answers to each medical interview question from the owner terminal 10 . The answer receiving unit 212 can accept uploading of an image when the question includes a question to be answered with an image of a specific part. The reply receiving section 212 can register the received reply in the reply history storage section 235 .
　疾病推定部２１３は、患者動物が罹りうる疾病を推定する。疾病推定部２１３は、問診結果（問診の各質問に対する回答）を学習モデルに与えることにより、患者動物が罹りうる疾病を推定することができる。疾病推定部２１３は、患者動物の動物種に対応する学習モデルを用いることができる。また、疾病推定部２１３は、短期（例えば１週間等）及び長期（例えば１年等）のタイムラグに対応する学習モデルを用いることができる。本実施形態では、疾病推定部２１３は、短期及び長期のタイムラグのそれぞれと患者動物の動物種とに対応する、学習モデルを学習モデル記憶部２３７から特定し、特定した短期及び長期の学習モデルのそれぞれに問診結果を与えて、短期に罹りうる疾病と長期に罹りうる疾病とを推定することができる。本実施形態では、疾病推定部２１３は、学習モデルに問診結果を与えることにより、対応するタイムラグ後において、所与の複数の疾病のそれぞれについて罹りうる確率を算出することができる。 The disease estimation unit 213 estimates diseases that the patient animal can contract. The disease estimating unit 213 can estimate the disease that the patient animal can contract by providing the results of the medical interview (answers to each question of the medical interview) to the learning model. The disease estimator 213 can use a learning model corresponding to the animal species of the patient animal. In addition, the disease estimation unit 213 can use a learning model corresponding to short-term (eg, one week) and long-term (eg, one year) time lags. In this embodiment, the disease estimating unit 213 identifies learning models corresponding to each of the short-term and long-term time lags and the animal species of the patient animal from the learning model storage unit 237, and uses the identified short-term and long-term learning models. It is possible to estimate short-term disease and long-term disease by giving an interview result to each. In this embodiment, the disease estimating unit 213 can calculate the probability of contracting each of a plurality of given diseases after the corresponding time lag by providing the interview results to the learning model.
　来院要否判定部２１４は、患者動物の来院要否を判定する。来院要否判定部２１４は、疾病推定部２１３により推定された疾病が存在するか否かに応じて来院要否を判定することができる。また、来院要否判定部２１４は、疾病推定部２１３が推定した疾病の罹患可能性（確率）が、所定の閾値以上の疾病が存在する場合に、来院が必要と判定することができる。また、来院要否判定部２１４は、短期の学習モデルを用いて推定された疾病（すなわち、１週間などの短期間に罹りうる疾病）が存在する場合に、来院が必要と判定することができる。また、来院要否判定部２１４は、短期の学習モデルを用いて推定された各疾病の罹患可能性が所定の閾値以上であるものが存在する場合に来院が必要と判定することができる。 The hospital visit necessity determination unit 214 determines whether the patient animal needs to visit the hospital. The hospital visit necessity determination unit 214 can determine the necessity of a hospital visit according to whether or not the disease estimated by the disease estimation unit 213 is present. In addition, the hospital visit necessity determination unit 214 can determine that a hospital visit is necessary when there is a disease whose morbidity (probability) estimated by the disease estimation unit 213 is equal to or greater than a predetermined threshold. In addition, the hospital visit necessity determination unit 214 can determine that a hospital visit is necessary when there is a disease estimated using a short-term learning model (that is, a disease that can be contracted in a short period of time, such as one week). . In addition, the hospital visit necessity determination unit 214 can determine that a hospital visit is necessary when there is a disease whose morbidity probability estimated using a short-term learning model is equal to or higher than a predetermined threshold.
　また、来院要否判定部２１４は、カルテ情報に含まれている診断結果とは異なる疾病が推定された場合に、来院が必要と判定することができる In addition, the hospital visit necessity determination unit 214 can determine that a hospital visit is necessary when a disease different from the diagnosis result included in the medical record information is estimated.
　来院要否送信部２１５は、来院要否を飼い主端末１０に送信する。来院要否送信部２１５は、推定された疾病とともに来院要否を送信することができる。また、来院要否送信部２１５は、長期のタイムラグに対応する学習モデルにより推定された疾病を、要注意の疾病として飼い主端末１０に送信するようにしてもよい。また、来院要否送信部２１５は、推定された疾病に対応する介入情報（アクション及び／又はアドバイス）を飼い主端末１０に送信するようにしてもよい。 The hospital visit necessity transmission unit 215 transmits the hospital visit necessity to the owner terminal 10 . The hospital visit necessity transmission unit 215 can transmit the necessity of the hospital visit together with the estimated disease. Further, the hospital visit necessity transmitting unit 215 may transmit the disease estimated by the learning model corresponding to the long-term time lag to the owner terminal 10 as the disease requiring attention. Further, the hospital visit necessity transmission unit 215 may transmit intervention information (action and/or advice) corresponding to the estimated disease to the owner terminal 10 .
　また、来院要否送信部２１５は、動物患者に対応する動物情報、推定された疾病とともに、来院要否を医療機関端末３０に送信するようにしてもよい。 In addition, the hospital visit necessity transmission unit 215 may transmit the necessity of a hospital visit to the medical institution terminal 30 together with the animal information corresponding to the animal patient and the estimated disease.
　学習処理部２１６は、カルテ情報の診断結果を学習して学習モデルを作成及び更新する。学習処理部２１６は、動物種ごとに、当該動物種に対応する問診内容を入力データとし、対応するカルテ情報の診断結果（診断された疾病）を教師データとして機械学習を行い、機械学習により作成された学習モデルを学習モデル記憶部２３７に登録することができる。本実施形態では、所定のタイムラグ（短期及び長期）について、診断結果に診断された疾病が含まれているカルテ情報のそれぞれについて、カルテ情報の日時からタイムラグの時間前の日時からカルテ情報の日時までの回答情報を入力データとし、カルテ情報の診断結果の疾病を教師データとして機械学習を行うことができる。あるいは、学習処理部２１６は、回答履歴のそれぞれについて、回答履歴の日時からタイムラグ後までの日時及び回答履歴の動物ＩＤに対応するカルテ情報に含まれる診断結果（に含まれる診断された疾病のみを抽出することができる。）を教師データとして学習を行うようにしてもよい。 The learning processing unit 216 learns the diagnostic results of medical chart information and creates and updates a learning model. For each animal species, the learning processing unit 216 uses the interview content corresponding to the animal species as input data, performs machine learning using the diagnosis result (diagnosed disease) of the corresponding medical record information as teacher data, and creates by machine learning The learned model can be registered in the learning model storage unit 237 . In this embodiment, with respect to a predetermined time lag (short-term and long-term), for each medical chart information that includes a diagnosed disease in the diagnosis result, from the date and time of the medical chart information to the date and time before the time lag Machine learning can be performed by using the answer information of the medical record information as input data and the disease in the diagnosis result of the medical record information as teacher data. Alternatively, for each response history, the learning processing unit 216 selects only the diagnosed disease contained in the diagnosis result (included in the medical record information corresponding to the date and time from the response history date and time to after the time lag and the animal ID in the response history). can be extracted.) may be used as teacher data for learning.
　学習処理部２１６はまた、カルテ情報が登録又は更新されたことを契機として、カルテ情報の動物ＩＤに対応する回答履歴及びカルテ情報の診断結果を用いて機械学習を行い、当該動物ＩＤに対応する動物情報の動物種に対応する学習モデルを更新することができる。ここでも、所定のタイムラグ（短期及び長期）について、カルテ情報の日時からタイムラグの時間前までの回答情報を入力データとして、学習を行うことができる。 Also, when the medical record information is registered or updated, the learning processing unit 216 performs machine learning using the response history corresponding to the animal ID in the medical chart information and the diagnosis result of the medical chart information, A learning model corresponding to the animal species of the animal information can be updated. Here too, for a predetermined time lag (short-term and long-term), learning can be performed using reply information from the date and time of the medical record information to the time before the time lag as input data.
＜動作＞
　図４は、本実施形態の定期検診システムの動作を説明する図である。 <Action>
FIG. 4 is a diagram for explaining the operation of the periodic medical examination system of this embodiment.
　管理サーバ２０は、問診を作成する（Ｓ３０１）。問診は、質問記憶部２３３に記憶されている質問のうち、患者動物の動物種に対応し、提供条件が満たされているものを所定数読み出すことにより作成することができる。管理サーバ２０は、作成した問診を飼い主端末１０に送信する（Ｓ３０２）。 The management server 20 creates an inquiry (S301). The medical interview can be prepared by reading out a predetermined number of questions that correspond to the animal species of the patient animal and satisfy the provision conditions among the questions stored in the question storage unit 233 . The management server 20 transmits the created medical interview to the owner terminal 10 (S302).
　飼い主端末１０では問診が表示される。問診は例えばチャット形式により行うようにしてもよいし、Ｗｅｂページのフォームにより行うようにしてもよい。また、画像により回答することが求められている質問については、飼い主端末１０が備えるカメラ（不図示）を起動してカメラの撮影画像を回答として取得することもできる。飼い主端末１０は、入力された回答を管理サーバ２０に送信する。 An interview is displayed on the owner terminal 10. The medical interview may be conducted, for example, in a chat format, or may be conducted using a form on a web page. In addition, for a question that requires an answer with an image, a camera (not shown) included in the owner terminal 10 can be activated to obtain an image captured by the camera as an answer. The owner terminal 10 transmits the inputted answer to the management server 20 .
　管理サーバ２０は、飼い主端末１０から回答を受信し（Ｓ３０３）、受信した回答を回答履歴記憶部２３５に登録することができる。管理サーバ２０は、患者動物の動物種及び短期のタイムラグに対応する学習モデルを学習モデル記憶部２３７から読み出し、読み出した学習モデルに回答を与えて各疾病に短期罹患可能性を推定する（Ｓ３０４）。管理サーバ２０は、推定された罹患可能性が閾値以上であれば（Ｓ３０５：ＹＥＳ）、来院が必要と判断し（Ｓ３０６）、罹患可能性が閾値未満であれば（Ｓ３０６：ＮＯ）、来院不要と判断する（Ｓ３０７）。 The management server 20 can receive an answer from the owner terminal 10 (S303) and register the received answer in the answer history storage unit 235. The management server 20 reads the learning model corresponding to the animal species of the patient animal and the short-term time lag from the learning model storage unit 237, gives an answer to the read learning model, and estimates the short-term morbidity of each disease (S304). . If the estimated disease probability is equal to or greater than the threshold (S305: YES), the management server 20 determines that a hospital visit is necessary (S306), and if the disease probability is less than the threshold (S306: NO), no hospital visit is required. (S307).
　管理サーバ２０は、来院要否を飼い主端末１０に送信する（Ｓ３０８）。ここで管理サーバ２０は、疾病のうち、長期のタイムラグに対応する学習モデルに回答を与えて推定された罹患可能性が所定の閾値以上であるものについて、注意を促すメッセージを飼い主端末１０に送信するようにしてもよい。また、短期及び／又は長期の罹患可能性が所定値以上の疾病について、対応する介入情報（アクション及びアドバイス）を飼い主端末１０に送信するようにしてもよい。 The management server 20 transmits to the owner terminal 10 whether or not it is necessary to visit the hospital (S308). Here, the management server 20 transmits a message to the owner terminal 10 to call attention to diseases in which the likelihood of contracting the disease estimated by giving a response to a learning model corresponding to a long time lag is equal to or greater than a predetermined threshold. You may make it Further, intervention information (actions and advice) corresponding to a disease with a short-term and/or long-term morbidity probability equal to or greater than a predetermined value may be transmitted to the owner terminal 10 .
　管理サーバ２０は、定期的な問診の送信を行う期間が終了するまで（Ｓ３０９：ＮＯ）、定期的にＳ３０１からの処理を繰り返す。なお、２回目以降のステップＳ３０１では、問診の内容を変更することができる。 The management server 20 periodically repeats the processing from S301 until the period for sending periodic medical interviews ends (S309: NO). In step S301 from the second time onward, the contents of the medical interview can be changed.
　以上のようにして、本実施形態の定期検診システムでは、獣医療機関側が主体的に、定期的に問診を患者動物の飼育者に対して送信し、患者動物の健康状態を確認することができる。また、ＡＩ（学習モデル）を用いて問診結果に基づいて患者動物の疾病の罹患可能性を推定し、これに応じて来院要否を判定することができる。 As described above, in the regular medical examination system of the present embodiment, the veterinary institution can proactively send medical interviews to the breeders of patient animals periodically to confirm the health condition of the patient animals. . In addition, AI (learning model) can be used to estimate the possibility of a patient animal contracting a disease based on the results of an interview, and the necessity of visiting a hospital can be determined accordingly.
　以上、本実施形態について説明したが、上記実施形態は本発明の理解を容易にするためのものであり、本発明を限定して解釈するためのものではない。本発明は、その趣旨を逸脱することなく、変更、改良され得ると共に、本発明にはその等価物も含まれる。 Although the present embodiment has been described above, the above embodiment is intended to facilitate understanding of the present invention, and is not intended to limit and interpret the present invention. The present invention can be modified and improved without departing from its spirit, and the present invention also includes equivalents thereof.
　例えば、本実施形態では、動物種ごとに学習モデルを作成するものとしたが、全体で１つ又は複数の学習モデルを用いるようにして、特徴量として動物種を与えるようにしてもよい。 For example, in this embodiment, a learning model is created for each animal species, but one or more learning models may be used as a whole, and the animal species may be given as a feature amount.
　また、学習モデルには飼い主端末１０において撮影した画像を学習モデルの特徴量に含めることができる。これにより、例えば、皮膚病などの発症部位を撮影した写真や動画等に基づいて皮膚病等の罹患可能性を判定することができ、必要に応じて適切なタイミングで患者動物に医療処置を施すことができる。 In addition, the learning model can include an image captured by the owner terminal 10 in the feature amount of the learning model. As a result, for example, it is possible to determine the possibility of contracting a skin disease based on photographs or videos taken of the site where the disease occurs, and to administer medical treatment to the patient animal at an appropriate timing as necessary. be able to.
　また、疾病の罹患可能性に限らず、患者動物の健康が良好である度合を推定するようにしてもよい。この場合、学習モデルは、健康が良好である度合を予測する予測器とすることができ、医療機関端末３０からは健康が良好である度合の入力を受け付けて教師データとし、機械学習を行うことができる。 In addition, the degree of good health of a patient animal may be estimated, not limited to the possibility of contracting a disease. In this case, the learning model can be a predictor that predicts the degree of good health, and the input of the degree of good health is received from the medical institution terminal 30 and used as teacher data to perform machine learning. can be done.
　また、本実施形態では、問診結果に基づいて動物の疾病の罹患可能性を推定する学習モデル（疾病モデル）を用いるものとしたが、これに加えて、問診結果に基づいて当該動物の取るべきアクションを推定する学習モデル（アクションモデル）を用いるようにしてもよい。アクションモデルは、問診結果を入力データとし、とるべきアクションを教師データとした機械学習により事前に作成しておくことができる。なお、アクションモデルに与える特徴量としてカルテ情報に含まれる項目を含めるようにしてもよい。アクションモデルの学習時に与える問診結果には、過去所定期間の問診結果やカルテ情報を含めるようにすることもできる。来院要否判定部２１４は、問診結果（及びカルテ情報、ならびにこれらの過去のデータ）を疾病モデル及びアクションモデルに与えて、疾病の罹患可能性とアクションとを推論するようにすることができる。 In addition, in the present embodiment, a learning model (disease model) that estimates the possibility of affliction of an animal's disease based on the interview results is used. A learning model (action model) that estimates an action may be used. The action model can be created in advance by machine learning using the interview results as input data and actions to be taken as teacher data. Items included in medical record information may be included as feature amounts to be given to the action model. It is also possible to include the results of medical interviews and medical record information for a predetermined period in the past in the medical interview results given when learning the action model. The hospital visit necessity determination unit 214 can provide the medical interview results (and medical chart information, and past data thereof) to the disease model and action model to infer the disease morbidity and actions.
　　１０　　飼い主端末
　　２０　　管理サーバ
　　３０　　医療機関端末
　　２１１　問診送信部
　　２１２　回答受信部
　　２１３　疾病推定部
　　２１４　来院要否判定部
　　２１５　来院要否送信部
　　２１６　学習処理部
　　２３１　飼い主情報記憶部
　　２３２　動物情報記憶部
　　２３３　質問記憶部
　　２３４　問診記憶部
　　２３５　回答履歴記憶部
　　２３７　学習モデル記憶部 10 owner terminal 20 management server 30 medical institution terminal 211 inquiry transmission unit 212 answer reception unit 213 disease estimation unit 214 visit necessity determination unit 215 visit necessity transmission unit 216 learning processing unit 231 owner information storage unit 232 animal information storage unit 233 question Storage unit 234 Questionnaire storage unit 235 Answer history storage unit 237 Learning model storage unit

Claims (8)

  1. 　動物の健康を管理するシステムであって、
    　前記動物について診断された疾病又は怪我を含むカルテ情報を記憶するカルテ情報記憶部と、
    　動物種に対応付けて前記動物の健康状態に関連する質問を記憶する問診記憶部と、
    　前記質問に対する回答を入力データとし、前記カルテ情報に含まれる前記疾病又は怪我を教師データとして機械学習により作成された学習モデルを記憶する学習モデル記憶部と、
    　前記動物の動物種に対応する前記質問を定期的に飼い主端末に送信する問診送信部と、
    　前記飼い主端末から前記質問に対する回答を受信する回答受信部と、
    　受信した前記回答を前記学習モデルに与えて、前記動物が罹りうる前記疾病又は怪我を推定する推定部と、
    　を備えることを特徴とする動物健康管理システム。     A system for managing animal health, comprising:
    a chart information storage unit that stores chart information including diseases or injuries diagnosed for the animal;
    an inquiry storage unit that stores questions related to the health condition of the animal in association with animal species;
    a learning model storage unit that stores a learning model created by machine learning using answers to the questions as input data and the disease or injury included in the medical record information as teacher data;
    an inquiry transmission unit that periodically transmits the question corresponding to the animal species of the animal to an owner terminal;
    an answer receiving unit that receives an answer to the question from the owner terminal;
    an estimating unit that feeds the received responses to the learning model to estimate the disease or injury that the animal may suffer;
    An animal health management system comprising:
  2. 　請求項１に記載の動物健康管理システムであって、
    　前記学習モデル記憶部は、タイムラグに対応付けて、前記タイムラグ後までに罹患する前記疾病又は怪我を推定する前記学習モデルを記憶すること、
    　を特徴とする動物健康管理システム。     The animal health management system of claim 1,
    The learning model storage unit stores the learning model for estimating the disease or injury to be afflicted by the time lag, in association with the time lag;
    An animal health management system characterized by:
  3. 　請求項２に記載の動物健康管理システムであって、
    　前記学習モデル記憶部は、複数の前記タイムラグのそれぞれについて、前記学習モデルを記憶しており、
    　前記推定部は、最も短い前記タイムラグに対応する前記学習モデルを用いること、
    　を特徴とする動物健康管理システム。     An animal health management system according to claim 2,
    The learning model storage unit stores the learning model for each of the plurality of time lags,
    The estimation unit uses the learning model corresponding to the shortest time lag;
    An animal health management system characterized by:
  4. 　請求項３に記載の動物健康管理システムであって、
    　前記推定部は、前記タイムラグのそれぞれに対応する前記学習モデルに前記回答を与えて前記疾病又は怪我を推定し、
    　最も短い前記タイムラグに対応する前記疾病又は怪我が存在する場合に、来院が必要と判断する来院要否判定部と、
    　前記来院要否とともに、他の前記タイムラフに対応する前記疾病又は怪我を飼い主端末に送信する送信部と、
    　をさらに備えることを特徴とする動物健康管理システム。     An animal health management system according to claim 3,
    The estimating unit estimates the disease or injury by giving the answer to the learning model corresponding to each of the time lags;
    a hospital visit necessity determination unit that determines that a hospital visit is necessary when the disease or injury corresponding to the shortest time lag exists;
    a transmission unit that transmits the disease or injury corresponding to the other time-roughness to the owner terminal together with the necessity of visiting the hospital;
    An animal health management system, further comprising:
  5. 　請求項１乃至４のいずれか１項に記載の動物健康管理システムであって、
    　複数の前記質問の少なくとも一部には、前記動物を撮影した画像により回答をするべきものが含まれること、
    　を特徴とする動物健康管理システム。     The animal health management system according to any one of claims 1 to 4,
    at least some of the plurality of questions include those to be answered by images of the animal;
    An animal health management system characterized by:
  6. 　請求項１乃至５のいずれか１項に記載の動物健康管理システムであって、
    　前記疾病又は怪我に対応付けて介入情報を記憶する介入情報記憶部と、
    　推定された前記疾病又は怪我とともに、当該疾病又は怪我に対応する前記介入情報を飼い主端末に送信する送信部と、
    　をさらに備えることを特徴とする動物健康管理システム。     The animal health management system according to any one of claims 1 to 5,
    an intervention information storage unit that stores intervention information in association with the disease or injury;
    a transmitting unit that transmits the intervention information corresponding to the disease or injury together with the estimated disease or injury to the owner terminal;
    An animal health management system, further comprising:
  7. 　動物の健康を管理する方法であって、
    　前記動物について診断された疾病又は怪我を含むカルテ情報を記憶するカルテ情報記憶部と、
    　動物種に対応付けて前記動物の健康状態に関連する質問を記憶する問診記憶部と、
    　前記質問に対する回答を入力データとし、前記カルテ情報に含まれる前記疾病又は怪我を教師データとして機械学習により作成された学習モデルを記憶する学習モデル記憶部と、を備える情報処理装置が、
    　前記動物の動物種に対応する前記質問を定期的に飼い主端末に送信するステップと、
    　前記飼い主端末から前記質問に対する回答を受信するステップと、
    　受信した前記回答を前記学習モデルに与えて、前記動物が罹りうる前記疾病又は怪我を推定するステップと、
    　を実行することを特徴とする動物健康管理方法。     A method of managing animal health, comprising:
    a chart information storage unit that stores chart information including diseases or injuries diagnosed for the animal;
    an inquiry storage unit that stores questions related to the health condition of the animal in association with animal species;
    an information processing apparatus comprising a learning model storage unit that stores a learning model created by machine learning using answers to the questions as input data and the disease or injury included in the medical record information as teacher data;
    periodically sending the question corresponding to the species of the animal to an owner terminal;
    receiving an answer to the question from the owner terminal;
    feeding the received responses to the learning model to estimate the disease or injury that the animal may suffer;
    An animal health management method characterized by performing
  8. 　動物の健康を管理する方法であって、
    　前記動物について診断された疾病又は怪我を含むカルテ情報を記憶するカルテ情報記憶部と、
    　動物種に対応付けて前記動物の健康状態に関連する質問を記憶する問診記憶部と、
    　前記質問に対する回答を入力データとし、前記カルテ情報に含まれる前記疾病又は怪我を教師データとして機械学習により作成された学習モデルを記憶する学習モデル記憶部と、を備える情報処理装置に、
    　前記動物の動物種に対応する前記質問を定期的に飼い主端末に送信するステップと、
    　前記飼い主端末から前記質問に対する回答を受信するステップと、
    　受信した前記回答を前記学習モデルに与えて、前記動物が罹りうる前記疾病又は怪我を推定するステップと、
    　を実行させるためのプログラム。     A method of managing animal health, comprising:
    a chart information storage unit that stores chart information including diseases or injuries diagnosed for the animal;
    an inquiry storage unit that stores questions related to the health condition of the animal in association with animal species;
    a learning model storage unit that stores a learning model created by machine learning using answers to the questions as input data and the disease or injury included in the medical record information as teacher data;
    periodically sending the question corresponding to the species of the animal to an owner terminal;
    receiving an answer to the question from the owner terminal;
    feeding the received responses to the learning model to estimate the disease or injury that the animal may suffer;
    program to run the
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