WO2019132067A1 - Système de fourniture d'informations médicales - Google Patents

Système de fourniture d'informations médicales Download PDF

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Publication number
WO2019132067A1
WO2019132067A1 PCT/KR2017/015672 KR2017015672W WO2019132067A1 WO 2019132067 A1 WO2019132067 A1 WO 2019132067A1 KR 2017015672 W KR2017015672 W KR 2017015672W WO 2019132067 A1 WO2019132067 A1 WO 2019132067A1
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Prior art keywords
medical data
medical
modeling
patient
reference group
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PCT/KR2017/015672
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English (en)
Korean (ko)
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박준후
박동휘
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(재)대구포교성베네딕도수녀회
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Publication of WO2019132067A1 publication Critical patent/WO2019132067A1/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
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
    • 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
    • G16H30/00—ICT specially adapted for the handling or processing of medical images
    • 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
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
    • 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
    • G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
    • 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
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
    • 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

Definitions

  • the present invention relates to a medical information providing system, and more particularly, to a medical information providing system for providing medical data that has not been collected through machine learning.
  • the present applicant has invented a medical information providing system capable of providing a large amount of learning by newly modeling a body model that was not existing in the past based on a machine learning basis.
  • Another object of the present invention is to provide a medical information providing system that provides information on changes in the body when the geometric shape of the body is arbitrarily changed.
  • the technical problem to be solved by the present invention is not limited to the above.
  • the medical information providing system may include a medical data collecting unit collecting reference parameter information and medical data of each patient from a plurality of patients, And a body modeling unit for modeling the body based on the medical data classified in correspondence with the reference group to be grouped based on the reference factor information and the medical data classified corresponding to the reference group.
  • the body modeling unit reflects, on the basis of the medical data collected from other patients corresponding to the same reference group, the non-collected medical data among the medical data collected from the patients corresponding to the specific reference group, The body can be modeled.
  • the body modeling unit may be configured to classify, into medical data of a first view of a specific body part collected from a patient corresponding to a specific reference group, medical data of the same body collected from other patients corresponding to the same reference group
  • the body can be modeled by reflecting the medical data of the second view (view) on the part.
  • the modeled body may provide medical data of the moving image type of the first viewpoint and medical image data of the moving image type of the second viewpoint.
  • the medical data collection unit further collects time information, and the body modeling unit can provide disease progress prediction information in consideration of the time information.
  • the apparatus further includes an interface unit, and the interface unit may output the modeled body for education.
  • the body modeling unit may extract, from among the medical data corresponding to the reference group of the modeled body in the medical database, The body modeling reflecting a specific disease can be output through the interface by reflecting the medical data related to the disease.
  • the body modeling unit when the body modeling unit is requested to change the geometric shape of the body with respect to the modeled body through the interface unit, the body modeling unit may select, from among the medical data corresponding to the reference group of the modeled body in the medical database, The body modeling in which the geometric shape of the body is deformed through the interface can be outputted by reflecting the medical data related to the geometric shape.
  • the reference factor information may include at least one of sex, height, age, weight, age, pulse, blood sugar, race, and disease.
  • a medical information providing system includes a medical data collecting unit for collecting reference parameter information and medical data of each patient from a plurality of patients, a medical data collecting unit for collecting the medical data collected from the medical data collecting unit, And a body modeling unit for modeling the body based on the medical data classified according to the reference group to be grouped based on the factor information and the medical data classified corresponding to the reference group.
  • the collected medical data are classified according to the reference group, and when there is a body modeling request, the medical data in the corresponding reference group can be provided. Therefore, even if the reference factor information and the request factor information are not exactly matched, that is, even if there is no actual medical data, high-similarity medical data can be provided.
  • the learner can directly participate in the body model that the learner can learn by providing the calculated result in real time rather than merely providing the previously stored medical data. This can reflect various medical conditions.
  • FIG. 1 is a view for explaining a medical information providing system according to an embodiment of the present invention.
  • FIG. 2 is a view for explaining a medical data collecting unit according to an embodiment of the present invention.
  • 3 and 4 are views for explaining a medical database according to an embodiment of the present invention.
  • 5 to 8 are views for explaining a body modeling unit according to an embodiment of the present invention.
  • an element when referred to as being on another element, it may be directly formed on another element, or a third element may be interposed therebetween.
  • first, second, third, etc. in the various embodiments of the present disclosure are used to describe various components, these components should not be limited by these terms. These terms have only been used to distinguish one component from another. Thus, what is referred to as a first component in any one embodiment may be referred to as a second component in another embodiment.
  • Each embodiment described and exemplified herein also includes its complementary embodiment. Also, in this specification, 'and / or' are used to include at least one of the front and rear components.
  • connection &quot is used to include both indirectly connecting and directly connecting a plurality of components.
  • FIG. 1 is a view for explaining a medical information providing system according to an embodiment of the present invention.
  • a medical information providing system 1000 may include a medical data collecting unit 100, a medical database 200, and a body modeling unit 300.
  • the medical data collection unit 100 may collect reference parameter information and medical data of each patient from a plurality of patients.
  • the medical database 200 may classify the medical data collected by the medical data collecting unit 100 according to a reference group to be grouped based on the reference factor information.
  • the body modeling unit 300 may model the body based on the classified medical data corresponding to the reference group.
  • the medical information providing system 1000 can predict the body with high accuracy through machine learning, even though the medical data has not been collected previously.
  • the medical data collection unit 100 will be described in detail with reference to FIG.
  • FIG. 2 is a view for explaining a medical data collecting unit according to an embodiment of the present invention.
  • the medical data collection unit 100 may collect information related to each medical care of each patient, for example, reference parameter information and medical data from a plurality of patients.
  • the reference factor information may be information used as one element of reference information for classifying the medical information collected from a plurality of patients.
  • the reference factor information may include at least one of gender, height, age, weight, age, pulse, blood sugar, race and disease.
  • the medical database for men and the medical database for women are managed separately.
  • the medical database for the male is referred to.
  • the medical database for the female is referred to.
  • the database can be referenced.
  • the key is classified according to a predetermined section, and if body modeling is required for a specific key, the medical database corresponding to the specific key can be referred to.
  • the reference factor information is a disease
  • the disease is classified in a predetermined manner, and when a model of a body with a specific disease is modeled, the medical database of the patient in which the specific disease is expressed can be referred to.
  • the reference factor information can be utilized as indexing information related to which patient's medical database is referred to in modeling the body. This can improve the accuracy in modeling the body.
  • the medical data may refer to medical data related to a specific patient. Medical data may include any measurement, imaging, or detection results related to medical care. For example, the medical data may refer to at least one of data on the heart, liver, kidney, large intestine, small intestine, brain, skeleton based on the body part. Medical data is not limited to this, and may include data for various body parts, such as coronary arteries.
  • the medical data collection unit 100 may acquire more time information.
  • the medical data collection unit 100 may acquire more information about the medical data generation time.
  • the time information forms a database, it can be utilized for learning the change over time of a specific disease.
  • the medical data collection unit 100 measures 170 cm of height, 35 years of age, 80 kg of body weight, sex, You can collect information that you are 40 years of age, ethnic asian, pulse 70, blood sugar 90, and who has been infected with disease A.
  • the medical data collection unit 100 may collect data on ultrasound to the heart, CT to the brain, and data on the chest X-ray as medical data for the patient A.
  • the medical data collection unit 100 can confirm that the reference factor information and medical data for the patient A are generated on 2013.01.01.
  • the medical data collection unit 100 can collect reference parameter information and medical data for Patient B, Patient C, Patient n.
  • the reference parameter information, the time information, and the medical data collected in the medical data collecting unit 100 may be managed by the medical database 200, and the medical database 200 may be described in detail with reference to FIGS. 3 and 4 .
  • 3 and 4 are views for explaining a medical database according to an embodiment of the present invention.
  • the medical database 200 reconstructs the information collected by the medical data collecting unit 100 so that the body modeling unit 300 to be described later transmits back data necessary for modeling a new body and provide back data.
  • the medical database 200 may manage a reference group for classifying patients based on the reference factor information.
  • a reference group can classify patients based on gender, height, age, weight, age, pulse, blood sugar, race and disease corresponding to the reference factor information.
  • the medical database 200 may divide the key into a predetermined section and map the medical data collected for each patient corresponding to the divided key. More specifically, when the reference group is a key, the key is divided into 50 to 55 cm (detailed section 1), 55 to 60 cm (detailed section 2), 60 to 65 cm (detailed section 3), n to n + n) as shown in Fig.
  • the medical database 200 may classify the key into subdivisions and reconfigure the medical data collected from the patients corresponding to the subdivided keys.
  • the patient A, the patient B, and the patient C The medical data for the patient can be mapped.
  • medical data for patients B and C may be utilized when modeling a body having a key corresponding to A have. Accordingly, a body having a key of A having cardiac ultrasound, liver ultrasonography, kidney ultrasound, colonic ultrasonogram, brain CT, brain MRI, and chest X-ray medical data can be modeled.
  • the reference group may reflect at least one of sex, height, age, weight, age, pulse, blood sugar, race and disease corresponding to the reference factor information as described above.
  • the reference group can reflect the respective reference factor information in a hierarchical manner.
  • a disease can be considered as one level, and age as a second level can be considered as a reference group.
  • the disease can be considered as a reference group, with one layer being weight, two layers being key, three layers being age, and four layers. As the reference group becomes more diverse, the similarity with the actual body may increase.
  • the medical database 200 can classify medical data according to disease and age as a reference group.
  • the medical database 200 may set the disease A as a first reference group and the age as a second reference group.
  • the medical database 200 specifies a patient who has suffered from the disease A among the information collected by the medical data collecting section 100, and can classify and map the medical data of the patient with the disease A according to the age. That is, the medical database 200 can identify a patient who has suffered from the disease A and whose age corresponds to the A period.
  • Patient A, Patient B, Patient C may be identified as a patient who is afflicted with disease A and whose age corresponds to period A
  • ultrasound to the heart of patient A, CT to the brain, and chest X-ray medical data can be mapped.
  • ultrasound to the heart of patient B ultrasound to the liver, ultrasound to the kidney, ultrasound to the colon, and chest X-ray medical data can be mapped, and patient X's kidney ultrasound, colonoscopy, .
  • the medical data provided by the other patients can be considered together with the medical data that each patient can not provide.
  • the medical database 200 of the present invention can configure a database by further utilizing the time information collected by the medical data collection unit 100.
  • the time information can be considered in the case of mapping the ultrasound medical data to the heart from the patient A and the patient B in the database shown in FIG. In this case, there is an advantage that it is possible to provide the passage of the echocardiogram according to the time of disease A.
  • the medical database 200 has been described with reference to FIGS. 3 and 4.
  • FIG. 45 to 8 a body modeling unit 300 for providing a newly modeled body based on the medical database 200 will be described.
  • 5 to 8 are views for explaining a body modeling unit according to an embodiment of the present invention.
  • the body modeling unit 300 may model the body based on the classified medical data corresponding to the reference group. That is, the information collected by the medical data collection unit 100 is classified by the medical database 200, and the body modeling unit 300 is capable of modeling the body based on the classified medical database. Further, the body modeling unit 300 may provide the modeled body to the medical learner through an interface, for example, a display.
  • the body modeling unit 300 receives the request characteristic information type of a body characteristic requested by the requestor, for example, a request factor information form, finds a reference group corresponding to the request factor information, Can be output.
  • the medical data corresponding to the reference group may not be provided as it is, but the medical data may be modified and provided based on the machine learning basis so as to correspond to the request factor information. This allows high-reliability body modeling based on the medical database even if the medical data corresponding to the request factor information does not actually exist.
  • the body modeling unit 300 may provide a variety of organs such as Interest Organic (IO1), liver (IO2), and large intestine (IO3) with reference to the medical database 200.
  • organs such as Interest Organic (IO1), liver (IO2), and large intestine (IO3) with reference to the medical database 200.
  • the body modeling unit 300 reflects, based on the medical data collected from other patients corresponding to the same reference group, the non-collected medical data among the medical data collected from the patients corresponding to the specific reference group, You can do it.
  • the body modeling unit 300 can model a new body based on medical data that is mapped to a reference group defined by a height of 180 cm, an age of 70, a man, a body weight of 60 kg, an Asian, That is, in the medical database 200, a new body can be modeled by collecting medical data mapped with a height of 180 cm, an age of 70, a man, a body weight of 60 kg, an Asian, and disease A as a reference group. If the patient is 1, 3, 4, 79, 99, and 180, the patient is 180 cm tall, 70 years old, male, 60 kg body weight, Asian, Patient 1 has medical data on the heart, liver, and large intestine. Patient 3 has medical data on the heart and colon.
  • Patient 79 has medical data on the heart, patient 99 on the large bowel, and patient 180 on the liver. It can be assumed that there is.
  • the body modeling unit 300 may generate patient medical data for the heart IO1, for example, medical data for patient 1, patient 3, patient 79, patient medical data for liver IO2, Medical data for patient 1, patient 4, patient 180, patient medical data for colon (IO3), for example, medical data for patient 1, patient 3, patient 99 are collected to model the body can do.
  • patient medical data for the heart IO1 for example, medical data for patient 1, patient 3, patient 79, patient medical data for liver IO2, Medical data for patient 1, patient 4, patient 180, patient medical data for colon (IO3), for example, medical data for patient 1, patient 3, patient 99 are collected to model the body can do.
  • IO1 heart
  • IO2 liver
  • IO3 large intestine
  • the learner can select the characteristics of the body to be learned through the interface in the form of request factor information. That is, the learner can input the gender, height, age, weight, age, pulse, blood sugar, race, and disease of the body to be studied in the form of request factor information.
  • the body modeling unit 300 can model the body corresponding to the characteristics of the body requested by the learner, and provide the model to the learner.
  • the body modeling unit 300 may provide a modeled body to a learner, but may provide medical data of a moving picture type, not a photograph, as learning contents. For example, if the medical data of the patients 1, 3, and 79 on the heart IO1 according to the above-described assumption is ultrasound, the body modeling unit 300 may convert the ultrasound waves into motion pictures Similarly, changes over time can be provided to learners with data. This allows the learner to be provided with learning data that is closer to the medical situation.
  • the body modeling unit 300 may classify the medical data of a first body view of a specific body part collected from a patient corresponding to a specific reference group, from other patients corresponding to the same reference group And the body may be modeled by reflecting the medical data of the second view (view) about the same body part collected.
  • ultrasound medical data for an organ of the same heart can provide an ultrasound image to the other side of the heart according to the ultrasound acquisition view. That is, it is necessary to provide ultrasound images of various perspectives about the heart 180 cm, the age 70, the man, the body 60 kg, the Orient, and the heart of the body corresponding to disease A.
  • the learner is provided with echocardiogram learning contents corresponding to a height of 180 cm, an age of 70, a man, a body weight of 60 kg, an Oriental person, and disease A and the learner selects a view of the echocardiogram , You can choose exactly what you want to learn.
  • the body modeling unit 300 may further provide time information, thereby providing past and future prediction information of the disease.
  • the medical data collection unit 100 can acquire more time information in collecting the reference factor information and the medical data from each patient, and the medical database 200 can reflect the time information in the database construction .
  • the body modeling unit 300 can provide a viewpoint selection interface.
  • IT1 Interest Time 1
  • IT2 may refer to past cardiac medical data
  • IT3 may refer to future cardiac medical data .
  • the learner can receive medical data corresponding to the height 180 cm, the age 70, the man, the body weight 60 kg, and the Oriental people based on the current standard (for example, the third stage of disease A manifestation)
  • the medical data corresponding to the height 180cm, age 70, male, weight 60kg, Oriental people can be provided based on the viewpoint (stage 1 and stage 2 of disease A) and stage of future (stages 4 and 5 of disease A).
  • body modeling can be provided not only for the present but also for the past and the future.
  • the medical data of other long-term examples for example, can be provided when there is a change in other organs caused by the disease A.
  • the learner can learn the change of the adjacent organs at the same time according to the progress of the illness.
  • this embodiment can be provided as reference data to the medical staff from the viewpoint of disease progress guide.
  • diseases such as lupus and multiple sclerosis, in which the condition of the patient can be rapidly deteriorated, it is possible to inform the starting point of the acute phase of the patient with high accuracy through machine learning.
  • the doctor may request frequent outings, food control, visits to the hospital, etc., considering the time of onset of acute phase. From another viewpoint, optimal care can be provided by predicting the timing of the onset of complications of various chronic diseases, which are governed by adult diseases.
  • a learner may request a specific disease reflection on the modeled body through an interface.
  • the body modeling unit may reflect the medical data related to the specific disease among the medical data corresponding to the reference group of the modeled body in the medical database, and output the body modeling reflecting the specific disease through the interface have.
  • the learner learns the reflection Can be requested.
  • the body modeling unit 300 can search the medical database 200 for a medical database to be mapped to a height of 180 cm, an age of 70, a man, a body weight of 60 kg, an Asian, a disease A, will be.
  • learners can learn how long - term medical data changes when there is a disease that a learner requires, and can learn how a body changes when multiple diseases are involved, not a single disease.
  • the present invention can be performed when a new body modeling request is made.
  • the learner can preliminarily practice a specific situation by reflecting a specific disease, for example, a benign tumor to the modeling body.
  • the learner may request the geometric shape change of the body for the modeled body through the interface part.
  • the body modeling unit may reflect the medical data related to the geometric shape among the medical data corresponding to the reference group of the modeled body in the medical database, and perform body modeling in which the geometric shape of the body is modified through the interface Can be output.
  • the learner can change the geometric shape of the body, for example, .
  • the body modeling unit 300 can search the medical database 200 for a medical database that maps 180 cm in height, 70 in age, male, 60 kg in weight, Oriental, disease A and right heart, will be.
  • the special information such as the right heart may be classified as special information, collected by the medical data collection unit 100, and managed by the medical database 200.
  • the learner can receive the learning contents about the specific body.
  • the whole body of the body can be provided by modeling the body corresponding thereto.
  • the whole body of the body can be provided by modeling the body corresponding thereto.
  • medical data of other organs organically linked to a specific disease can be learned.
  • they can also provide in-depth learning content by providing medical data on coronary arteries associated with these diseases.

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  • Medical Informatics (AREA)
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  • Epidemiology (AREA)
  • General Health & Medical Sciences (AREA)
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  • Databases & Information Systems (AREA)
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  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
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Abstract

Selon un mode de réalisation, la présente invention concerne un système de fourniture d'informations médicales pouvant comprendre : une unité de collecte de données médicales servant à collecter, auprès d'une pluralité de patients, des informations de facteur de référence et des données médicales de chacun des patients ; une base de données médicales servant à classer les données médicales, collectées dans l'unité de collecte de données médicales, par groupes de référence servant à regrouper les données médicales sur la base des informations de facteur de référence ; et une unité de modélisation de corps humain servant à modéliser un corps humain sur la base des données médicales classées par groupes de référence.
PCT/KR2017/015672 2017-12-28 2017-12-28 Système de fourniture d'informations médicales WO2019132067A1 (fr)

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KR20150076482A (ko) * 2013-12-27 2015-07-07 성승주 패턴을 이용한 의료정보 분석시스템 및 방법

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