WO2019074191A1 - Procédé et système permettant de fournir un résultat de prédiction de traitement de cancer, procédé et système permettant de fournir un résultat de prédiction de traitement sur la base d'un réseau d'intelligence artificielle, et procédé et système permettant de fournir collectivement un résultat de prédiction de traitement et des données de preuve - Google Patents

Procédé et système permettant de fournir un résultat de prédiction de traitement de cancer, procédé et système permettant de fournir un résultat de prédiction de traitement sur la base d'un réseau d'intelligence artificielle, et procédé et système permettant de fournir collectivement un résultat de prédiction de traitement et des données de preuve Download PDF

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WO2019074191A1
WO2019074191A1 PCT/KR2018/007865 KR2018007865W WO2019074191A1 WO 2019074191 A1 WO2019074191 A1 WO 2019074191A1 KR 2018007865 W KR2018007865 W KR 2018007865W WO 2019074191 A1 WO2019074191 A1 WO 2019074191A1
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treatment
information
data
result
input information
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PCT/KR2018/007865
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English (en)
Korean (ko)
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이석
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고려대학교 산학협력단
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Priority claimed from KR1020170137212A external-priority patent/KR101947725B1/ko
Priority claimed from KR1020170143559A external-priority patent/KR101943222B1/ko
Priority claimed from KR1020170143558A external-priority patent/KR101946402B1/ko
Application filed by 고려대학교 산학협력단 filed Critical 고려대학교 산학협력단
Publication of WO2019074191A1 publication Critical patent/WO2019074191A1/fr

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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
    • A61N5/00—Radiation therapy
    • A61N5/10—X-ray therapy; Gamma-ray therapy; Particle-irradiation therapy
    • 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
    • G16H10/20—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for electronic clinical trials or questionnaires
    • 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
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • 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

Definitions

  • the present invention relates to a method and system for providing a cancer treatment prediction result, and more particularly, to a method and system for providing a cancer treatment prediction result using a data mining technique, And to a method and system for providing a cancer treatment prediction result which can identify the most appropriate cancer treatment method.
  • one embodiment of the present invention relates to a method and system for providing an artificial intelligence network-based therapeutic prediction result, which enables an artificial intelligence network to be learned using a library information data mining result and to promptly and accurately predict a patient treatment prediction result based thereon will be.
  • the present invention relates to a method and system for providing a therapeutic prediction result, and more particularly, to data mining a plurality of bibliographic information to provide a treatment prognosis according to a condition required by a user, And a method and system for collectively providing evidence data.
  • the optimal combination of treatments suited to cancer and patient characteristics is dependent on the experience and knowledge of the medical staff, and there is a difficulty in finding the literature directly. Further, there is a problem that it is difficult to find out the desired chemotherapy result because of the inconsistent experience of the multiple medical staff, in order to find the optimal chemotherapy method between the medical staff specializing in radiotherapy and the medical specialist .
  • Korean Patent No. 10-0794516 discloses a configuration in which a correlation between a patient case database and a disease name critical inspection item is machine-learned, and a disease corresponding to patient examination information is determined using a machine learning result, A method of artificial intelligence to provide comprehensive treatment methods for different types of treatments that have different treatments or interactions is not yet disclosed.
  • a problem to be solved by the present invention is to provide a method and system for providing a user with a prognosis for cancer treatment from data mined information in a deep learning manner.
  • a problem to be solved by the present invention is to provide a new artificial intelligence network-based therapeutic prediction result providing method and system that learns through artificial intelligence network using literature information data mining results and provides patient treatment prediction results based thereon .
  • a problem to be solved by the present invention is to provide a treatment prognosis according to a condition required by a user by data mining a plurality of bibliographic information, and to provide a treatment prediction result And a method and system for collectively providing evidence data.
  • a cancer treatment prediction result providing method comprising: constructing a semantic database dictionary by collecting and data mining documents or case information related to cancer treatment; Constructing a treatment prediction model based on the input information and the semantic database dictionary when at least one of a cancer type, a treatment method of interest, a side effect type, and patient condition information is input as input information; And providing result information corresponding to the input information based on the treatment prediction model.
  • the result information is at least one of a side effect level, a survival rate according to a treatment method of interest, toxicity, a response, a treatment condition, and a combination of treatment methods.
  • the step of constructing the treatment prediction model includes determining a treatment method of interest according to at least one or a combination of two or more of surgery, chemotherapy, radiation therapy, immunotherapy, hyperthermia treatment, and bone marrow transplantation.
  • the step of constructing the semantic database dictionary may include extracting input information parameter data for input information from literature or case information related to cancer treatment; Extracting an index for an input information factor through semantic analysis on the prediction factor data; And creating and storing the semantic database dictionary based on the index.
  • the cancer treatment prediction result providing method may include determining a treatment site; Selecting input information including at least one treatment method and output information type for the determined treatment site; Inputting patient information to be treated; Constructing a treatment prediction model according to the input information input from the semantic database dictionary and the patient information; And outputting the selected kind of output information from the configured therapeutic prediction model.
  • the treatment method may include at least one of radiation therapy, drug therapy, immunotherapy, hormone therapy, and gene therapy.
  • the output information includes at least one of a side effect level, a survival rate according to a treatment method of interest, toxicity, a response, a treatment condition, and a combination of treatment methods.
  • a cancer treatment prediction result providing system includes a semantic database dictionary storing a result of collecting and data mining documents or case information related to cancer treatment;
  • An input information receiving unit for receiving at least one input information of a type of cancer, a treatment method of interest, a side effect type, and patient condition information from a user terminal;
  • a prediction model constructing unit for constructing a prediction model related to cancer treatment based on the input information received from the input information receiving unit and the semantic database dictionary;
  • a result information transmitter for providing result information corresponding to the input information to the user terminal based on the prediction model.
  • the result information is at least one of a side effect level, a survival rate according to a treatment method of interest, toxicity, a response, a treatment condition, and a combination of treatment methods.
  • the treatment prediction model includes a treatment method of interest according to at least one or two or more combinations of surgery, chemotherapy, radiation therapy, immunotherapy, hyperthermia treatment, and bone marrow transplantation for a patient to be treated.
  • a cancer treatment prediction result providing method collects and data minings a plurality of bibliographic information, and obtains a treatment site, a treatment method, patient status information, Obtaining a plurality of learning data representing at least one correlation; Learning the artificial intelligence network based on the plurality of learning data; And providing the result information corresponding to the input information through the artificial intelligence network when at least one of the treatment site, the treatment method, the patient state information, and the side effect type is input as the input information.
  • the result information is at least one of survival rate, toxicity, response rate, combination of treatment methods, and treatment conditions according to at least one of the side effect type and the treatment method.
  • the method may further include the step of acquiring actual clinical data while monitoring and monitoring the patient's treatment history and treatment result, and re-learning the artificial intelligence network through the actual clinical data.
  • the step of acquiring the learning data may include constructing a semantic database dictionary by collecting and mining a plurality of document information, Wherein when at least one of a treatment site, a treatment method, a patient state information, and a side effect type is input as data mining input information, a treatment prediction model is constructed based on the input information and the semantic database dictionary, Providing data mining result information corresponding to the data mining input information; And generating and providing learning data including the data mining input information and the data mining result information.
  • the data mining result information may be at least one of a survival rate, a toxicity, a response, a combination of treatment methods, and a treatment method according to at least one of a side effect type and a treatment method.
  • the step of constructing the treatment prediction model further comprises determining an integrated treatment method according to at least one or more combinations of surgery, chemotherapy, radiation therapy, immunotherapy, hyperthermia treatment, and bone marrow transplantation .
  • the step of constructing the semantic database dictionary may include extracting prognostic factor data corresponding to cancer treatment result predictive factors and output results from literature or case information related to cancer treatment; Extracting a semantic keyword through semantic analysis on the prognostic factor data; And creating and storing the semantic database dictionary based on the semantic keyword.
  • an artificial intelligence network-based therapeutic and / or therapeutic result providing system for collecting and data mining a plurality of bibliographic information to provide a treatment site, a treatment method, A learning data obtaining unit that obtains a plurality of pieces of learning data representing at least one correlation; A learning unit for learning an artificial intelligence network based on the plurality of learning data; And a prediction unit for providing result information corresponding to the input information through the artificial intelligence network when at least one of the treatment region, the treatment method, the patient state information, and the side effect type is input as input information.
  • the result information is at least one of survival rate, toxicity, response rate, combination of treatment methods, and treatment conditions according to at least one of the side effect type and the treatment method.
  • the system further comprises a clinical data acquiring unit for acquiring actual clinical data while monitoring and tracking the patient's treatment history and treatment result and for allowing the learning unit to re-learn the authentication intelligent network through the actual clinical data .
  • the learning data obtaining unit comprises: a semantic database dictionary storing a result of collecting and mining document information; An input information receiving unit for receiving input information of at least one of a treatment region, a treatment method, patient state information, and a side effect type from a user terminal; A prediction model constructing unit for constructing a prediction model related to cancer treatment based on the input information received from the input information receiving unit and the semantic database dictionary; And a result information providing unit for obtaining and providing result information corresponding to the input information based on the prediction model.
  • the result information is at least one of survival rate, toxicity, response rate, combination of treatment methods, and treatment conditions according to at least one of the side effect type and the treatment method.
  • the treatment prediction model is characterized in that it includes a treatment method and a treatment side effect according to at least one or two or more combinations of surgery, chemotherapy, radiation therapy, immunotherapy, hyperthermia treatment, and bone marrow transplantation for a patient to be treated .
  • a method for providing a treatment prediction result and a data base in accordance with an embodiment of the present invention includes: constructing a semantic database dictionary by collecting and mining document information; Constructing a treatment prediction model based on the input information and the semantic database dictionary when at least one of a treatment site, a treatment method, patient state information, and a side effect type is input as input information; Obtaining result information corresponding to the input information based on the treatment prediction model; Collecting and cataloging evidence data of the result information; And displaying the result data on the same screen together with the result information.
  • the step of collecting and cataloging the evidence data may include prioritizing and cataloging the collected evidence data based on at least one of a document information start date, information relevance, and author credibility.
  • the step of collecting and cataloging the evidence data can arbitrarily adjust the ratio of consideration of each of the document information start date, the information relevance, and the author credibility under a user request.
  • the method may further include the step of, when the selection and the viewing of one piece of the base material are requested through the base material list, calling and displaying detailed information of the base material requested to be viewed.
  • the step of further displaying and displaying the detailed information of the evidence data may further include a function of bolding or box processing the semantic keyword used in the structure of the treatment prediction model and then displaying the screen.
  • step of further displaying and displaying detailed information of the evidence data may further include extracting only the sentences in which the semantic keywords used in the treatment prediction model construction are selected and selectively providing the extracted sentences.
  • the result information is at least one of survival rate, toxicity, response rate, combination of treatment methods, and treatment conditions according to at least one of the side effect type and the treatment method.
  • the step of constructing the treatment prediction model may further comprise determining an integrated treatment method according to at least one or more combinations of surgery, chemotherapy, radiotherapy, immunotherapy, hyperthermia treatment, and bone marrow transplantation.
  • the step of constructing the semantic database dictionary includes extracting prognostic factor data corresponding to cancer treatment outcome prognostic factors and output results from literature or case information related to cancer treatment; Extracting a semantic keyword through semantic analysis on the prognostic factor data; And creating and storing the semantic database dictionary based on the semantic keyword.
  • a treatment prediction result and a data base providing system include a semantic database dictionary storing a result of collecting and data mining treatment-related documents or case information; At least one of a treatment part, a treatment method, patient condition information, and a side effect type is inputted from a user terminal; A prediction model constructing unit for constructing a prediction model for treatment based on the input information received from the input information receiving unit and the semantic database dictionary; A result information obtaining unit for providing result information corresponding to the input information to the user terminal based on the prediction model; A base data acquisition unit for collecting and cataloging base data of the result information; And a UI constructing unit for constructing and providing a UI (User Interface) for receiving the input information or simultaneously guiding the result information and the supporting data.
  • a semantic database dictionary storing a result of collecting and data mining treatment-related documents or case information
  • At least one of a treatment part, a treatment method, patient condition information, and a side effect type is inputted from a user terminal
  • a prediction model constructing unit for
  • the evidence data acquiring unit may determine and catalog the priorities of the collected evidence data based on at least one of the date of publication of the information, the information relevance, and the author credibility.
  • the proof data acquiring unit can arbitrarily adjust the consideration of each of the document information start date, the information relevance, and the author credibility under a user request.
  • the base data acquiring unit may further include a function of retrieving detailed information of the base data requested to be browsed and providing the selected base data to the UI unit when the base data is selected and viewed through the base data list.
  • a treatment method for cancer in which various non-surgical treatment methods are present, and toxicity and side effects of each method can be simultaneously displayed on one platform.
  • a complex treatment method such as radiation or a combination of medicines, the result of toxicity and survival rate can be displayed on the same platform according to the user's selection.
  • learning through the artificial intelligence network using the results of the library information data mining can predict the patient treatment prediction result more quickly and accurately.
  • a variety of non-surgical treatments such as cancer can show the results of treatments for the disease and the side effects of each method on a single platform at the same time.
  • a complex treatment method such as radiation or a combination of medicines, the result of toxicity and survival rate can be displayed on the same platform according to the user's selection.
  • FIG. 1 is a diagram illustrating a method of providing a cancer treatment prediction result according to an embodiment of the present invention.
  • FIG. 2 is a diagram for explaining a data mining method according to an embodiment of the present invention.
  • FIG. 3 is a block diagram of a method for constructing a semantic database dictionary according to an embodiment of the present invention.
  • 4 to 7 are diagrams for explaining an example of evaluating cancer treatment prognosis according to the above-described method.
  • FIG. 8 is a block diagram of a cancer treatment prediction result providing system according to an embodiment of the present invention.
  • FIG. 9 is a flowchart illustrating a method of providing an artificial intelligence network-based therapeutic prediction result according to an embodiment of the present invention.
  • FIG. 10 is a conceptual diagram of a method of providing an artificial intelligence network-based therapeutic prediction result according to another embodiment of the present invention.
  • FIG. 11 is a diagram for explaining a learning data acquisition method according to an embodiment of the present invention.
  • 12 to 16 are diagrams for explaining an example of providing a document information data mining method according to the above-described method.
  • FIG. 17 is a block diagram of a treatment prediction result providing system according to an embodiment of the present invention.
  • FIG. 18 is a diagram illustrating a method of collectively providing a result of a treatment and a data base according to an embodiment of the present invention.
  • 19 is a diagram for explaining a data mining method according to an embodiment of the present invention.
  • 20 and 21 are views for explaining a method of providing evidence data for predicting a treatment prognosis according to the above-described method.
  • FIG. 22 is a block diagram of a system for providing a treatment prediction result and a data base in accordance with an embodiment of the present invention.
  • FIG. 23 is a diagram illustrating an exemplary computing environment in which one or more embodiments disclosed herein may be implemented.
  • &quot when an element is referred to as " including " an element, it is understood that the element may include other elements as well, without departing from the other elements unless specifically stated otherwise. Also, throughout the specification, the term " on " means located above or below a target portion, and does not necessarily mean that the target portion is located on the upper side with respect to the gravitational direction.
  • FIG. 1 is a diagram illustrating a method of providing a cancer treatment prediction result according to an embodiment of the present invention.
  • an evaluation method includes constructing a semantic database dictionary by collecting and data mining documents or case information related to cancer treatment (S100); Constructing a treatment prediction model based on the input information and the semantic database dictionary when at least one of a type of cancer, a treatment method of interest, a side effect type, and patient condition information is input as input information (S200); And providing result information corresponding to the input information based on the treatment prediction model (S300).
  • the method of providing data mining and result information for constructing a semantic database dictionary according to an embodiment of the present invention is as follows, but the scope of the present invention is not limited thereto.
  • FIG. 2 is a diagram for explaining a data mining method according to an embodiment of the present invention.
  • the treatment area is treated as the lung, the treatment method is radiation, and the pneumonia as a side effect is inputted as the type of the output information.
  • the text corresponding to the cancer treatment result prediction (prognosis) factors and the results is extracted from the clinical study documents (PDF document) (S310) (S320).
  • PDF document PDF document
  • the extracted semantic keyword is converted into a database suitable for retrieval (query), and a database dictionary is constructed (S340).
  • the present invention utilizes a database dictionary constructed by classifying cancer treatment types and various kinds of treatment results into a semantic keyword form, thereby constructing a single treatment prediction model by taking only desired data according to cancer type and patient information .
  • the treatment is radiation and the side effects are pneumonia
  • the treatment prediction model according to the present invention finds the most suitable condition according to the information (patient information, tumor type, treatment method) inputted from the constructed database dictionary, and becomes a reconstituted model.
  • step S360 the semantic table included in the range of age and V20 is searched from the configured treatment prediction model, and the user obtains a side effect (toxicity) grade value, which is output information desired by the user (S370).
  • a visualization step which allows the user to intuitively grasp the toxicity grade in the UI concept, which can be in various ways.
  • the result information may be at least one of a side effect level, a survival rate according to a treatment method of interest, toxicity, a response, a treatment condition, and a combination of treatment methods.
  • the user can determine the type in the input step.
  • the step of constructing the therapeutic prediction model comprises determining the therapeutic method of interest according to at least one or more combinations of surgery, chemotherapy, radiation therapy, immunotherapy, thermal therapy, and bone marrow transplantation
  • the present invention is particularly capable of considering two or more therapies in a single treatment prediction model so that survival rate or toxicity information when two or more therapies are simultaneously used can be provided from the data mined information. Furthermore, it is possible to provide more accurate output information to the user by learning the artificial intelligence module outputting the provided information.
  • FIG. 3 is a block diagram of a method for constructing a semantic database dictionary according to an embodiment of the present invention.
  • the method includes extracting (S101) resultant (i.e., prognostic) factor data for cancer treatment from literature or case data related to cancer therapy; Extracting a result factor index through semantic analysis on the result parameter data (S201); And creating and storing the semantic database dictionary based on the resultant index (S301).
  • resultant i.e., prognostic
  • the result parameter corresponds to a keyword corresponding to the output information desired by the user, and may include a thesaurus for each keyword.
  • FIGS. 4-7 illustrate an example of providing cancer treatment response results according to the method described above.
  • a treatment site is selected as a lung
  • radiation therapy, drug treatment and immunotherapy are selected as at least one treatment method for the determined treatment site
  • survival rate and toxicity are selected as input information do.
  • the treatment prediction model is constructed from the semantic database corresponding to the inputted input information.
  • the treatment prediction model is a model for classifying the input information (for example, patient age, sex, tumor site, other diseases, The model assumes an arbitrary patient with the highest probability of matching best from literature or case information.
  • result information according to the treatment prediction model is provided to the user, and the result information corresponds to the input information input by the user in advance (refer to FIG. 7).
  • the document information used for constructing the treatment prediction model can be displayed as needed. By clicking the document information, the user can directly check the document in one user interface environment.
  • a medical professional having expert knowledge only in a corresponding field such as a radiation therapy or a medication can select a combination of various treatment methods to confirm the treatment result and effect according to the combined treatment and similar document information in the same user interface environment have.
  • the present invention can provide a combination of the treatment methods, order, or conditions (drug treatment timing, type of drug) and the like in the data mining and the deep learning method for the cancer in which various kinds of treatment methods exist through the above-described method . That is, the system according to the present invention learns from the data mining data and case information in the literature, and learns it by the deep learning method, and provides the most optimized treatment result to the user.
  • FIG. 8 is a block diagram of a cancer treatment prediction result providing system according to an embodiment of the present invention.
  • the system includes a semantic database dictionary 100 for storing results of collecting and data mining documents or case information related to cancer treatment;
  • An input information receiving unit (200) for receiving at least one input information of a type of cancer, a treatment method of interest, a side effect type, and patient condition information from a user terminal;
  • a prediction model constructing unit 300 for constructing a prediction model related to cancer treatment based on the input information received from the input information receiving unit 200 and the semantic database dictionary 100;
  • a result information transmitter 400 for providing result information corresponding to the input information to the user terminal based on the prediction model.
  • the result information includes at least one of the degree of side effect, the survival rate according to the treatment method of interest, the toxicity, the response, the treatment condition, and the combination of treatment methods.
  • the treatment prediction model may include information of treatment modalities according to at least one or more combinations of surgery, chemotherapy, radiotherapy, immunotherapy, hyperthermia, and bone marrow transplantation for the patient to be treated .
  • the present invention can provide an optimum treatment method among various kinds of treatment methods especially for a single disease and then re-confirm information such as survival rate by inputting the treatment method provided in the input conditions on the same platform again. Therefore, despite the various combinations of input information in the same platform, a treatment prediction model can be constructed from a pre-built data mining dictionary to provide desired information.
  • FIG. 9 is a flowchart illustrating a method of providing an artificial intelligence network-based therapeutic prediction result according to an embodiment of the present invention.
  • a method collects and data minings a plurality of bibliographic information, thereby generating a plurality of bibliographic information, which indicates a correlation of at least one of a treatment site, a treatment method, (S1200) of learning the artificial intelligence network based on the plurality of pieces of learning data and at least one of the treatment region, the treatment method, the patient state information, and the side effect type is input information And providing result information corresponding to the input information through the artificial intelligence network (S1300).
  • the present invention provides a quick and accurate treatment prediction result to the user by learning the artificial intelligence network based on the result of data mining of a large amount of bibliographic information.
  • the present invention not only learns all the contents included in the document information, but also grasps what information the medical staff wants to receive through the inquiry, and then enables the artificial intelligence network learning to be performed,
  • the primary consideration of healthcare professionals is to enable them to obtain and provide the most accurate treatment prediction results.
  • the present invention further includes the step of acquiring actual clinical data while monitoring and monitoring the patient's treatment history and treatment result as shown in FIG. 10, and re-learning the artificial intelligence network through the actual clinical data (S1400) You may. That is, the learning data having the treatment result as the input information and the treatment result as the output information are generated, and the artificial intelligence network can be learned through the generated data.
  • the present invention utilizes the actual clinical data as well as the literature information to learn the artificial intelligence network, thereby making it possible to uniformly reflect the therapeutic prediction result of the artificial intelligence network from both the academic viewpoint and the clinical viewpoint.
  • the learning data acquisition method according to an embodiment of the present invention is as follows, but the scope of the present invention is not limited thereto.
  • FIG. 11 is a diagram for explaining a learning data acquisition method according to an embodiment of the present invention.
  • step S1110 the text corresponding to the cancer treatment result prognostic factors and the output result is extracted from various types of document information (PDF document) such as academic papers, medical books, and case information, and the extracted text is subjected to stemming analysis and semantic analysis And extracts the extracted semantic keywords with a meaningful semantic keyword (Age, dose-volume histogram, G1, G2, ...) and constructs a semantic database dictionary by converting the extracted semantic keywords into a database suitable for search (query).
  • a meaningful semantic keyword Age, dose-volume histogram, G1, G2, .
  • the method for constructing the semantic database dictionary is the same as that described above with reference to FIG. 3, so that a duplicate description thereof will be omitted.
  • step S1120 at least one of the treatment site, the treatment method, the patient status information, and the side effect type is input as data mining input information.
  • some of the data mining input information may be classified as a treatment condition, and the remaining part may be classified as a treatment result condition.
  • pneumonia and esophagitis due to the side effects of treatment may be entered as the treatment conditions, that the treatment site is lung, and the treatment method is radiation.
  • the present invention allows the kind of result information that can be obtained through the treatment prediction model to be changed according to the setting environment of the treatment condition and the treatment result condition. For example, if a treatment adverse event is set as a treatment outcome condition (case 1), the treatment prediction model provides survival rate, toxicity, and response according to the side effect type as the result information, and when the treatment method is set as the treatment outcome condition In case 2), the treatment prediction model provides the survival rate, toxicity, and reactivity of the treatment method as the result information, and when the treatment adverse effect is set as the treatment result condition (case 3), the treatment prediction model is classified into the side effect type Survival rate, toxicity, and reactivity may be provided as outcome information.
  • step S1140 the semantic table in the range of the prognostic factors, such as age and V20, extracted from the therapeutic prediction model constructed above is searched to identify pneumonia-related side effects, which are output information desired by the user, Information.
  • step S1150 learning data including data mining input information and data mining result information is generated and stored.
  • the step of constructing the treatment prediction model includes determining a treatment method according to at least one of, or a combination of, surgery, chemotherapy, radiation therapy, immunotherapy, hyperthermia treatment, and bone marrow transplantation ,
  • the present invention particularly allows two or more therapies to be considered in a single treatment prediction model. That is, the present invention can acquire adverse effects when two or more treatment methods are simultaneously used, moreover survival rate, toxicity, and reactivity according to adverse effect types as data mining information, and learns through artificial intelligence network.
  • the artificial intelligent network of the present invention can acquire and provide not only a treatment prediction result according to a single treatment, but also a treatment prediction result according to two or more treatment methods, that is, an integrated treatment.
  • 12 to 16 are diagrams for explaining an example of providing a document information data mining method according to the above-described method.
  • patient state information for example, patient age, sex, treatment site, presence of other diseases, etc.
  • the treatment site is lung
  • the treatment method is radiation
  • drug Is a therapeutic condition
  • pneumonia and esophagitis are therapeutic adverse events.
  • the survival rate and toxicity according to pneumonia are selected as output information.
  • a treatment prediction model is constructed from the semantic database corresponding to the input information.
  • the treatment prediction model is a model that assumes an arbitrary patient matching the input information with the highest probability from the literature or case information.
  • result information according to the treatment prediction model is provided to the user or the user, and the result information corresponds to the input information previously input by the user (refer to FIG. 15).
  • the result information may include survival rate, toxicity, and reactivity according to each of pneumonia and esophagitis, wherein the toxicity includes at least one of a toxicity grade, a probability of occurrence according to the toxicity grade, and a side effect occurrence pattern according to the toxicity grade can do.
  • the odds of a toxicity grade of 1, 2, 3, 4, and 5 are 20.4%, 67.5%, 19.3%, 4.5%, and 0.1%, and a toxicity grade of 1 indicates a dry cough or respiratory disorder exertion occurs. If the toxicity level is 2, a cough or unsteady dyspnea requiring a narcotic anti-inflammatory drug occurs. If the toxicity level is 3, a dyspnea with a cough or a rest period is required. , A toxic grade of 4 requires continuous oxygen supply or drug assistance, and a toxicity rating of 5 may indicate that heat is generated.
  • a treatment prediction model considering the combinations and conditions of a plurality of treatment methods and performing a treatment result prediction operation considering the combination of treatment methods and conditions such as postoperative radiation treatment through a treatment prediction model, Toxicity, and responsiveness to the disease.
  • FIG. 17 is a block diagram of a treatment prediction result providing system according to an embodiment of the present invention.
  • the system collects and data miners a plurality of pieces of bibliographic information to generate a learning data item for acquiring a plurality of learning data indicating a correlation of at least one of a treatment site, a treatment method,
  • a learning unit 1200 for learning an artificial intelligence network based on the plurality of learning data and at least one of a treatment region, a treatment method, a patient state information, and a side effect type is input as input information
  • And a prediction unit 1300 for providing result information corresponding to the input information through the intelligent network.
  • the result information may be at least one of survival rate, toxicity, reactivity, combination of treatment methods, and conditions of treatment according to at least one of the side effect type and the treatment method.
  • the learning data providing unit 1100 inputs at least one of the semantic database dictionary 1110 storing the result of collecting and mining the document information, the treatment site, the treatment method, the patient status information, and the side effect type as data mining input information
  • a prediction model constructing unit 1130 constructing a prediction model related to cancer treatment based on the input information received from the input information receiving unit 1120 and the semantic database dictionary 1110
  • a result information obtaining unit 1140 for obtaining and providing data mining result information corresponding to data mining input information based on a prediction model
  • a learning data generating unit 1140 for generating learning data including data mining input information and data mining result information Section 1150, and the like.
  • the training data providing unit 1100 of the present invention is connected to the external medical record system 1400 and interlocked with the clinical data acquiring unit 1400 to acquire actual clinical data while monitoring and monitoring the patient's treatment history and treatment result 1160).
  • the training data generator 1150 may generate training data having the treatment history of the patient as input information and the treatment result as output information.
  • system of the present invention is preferably implemented as a single hardware device, it may be implemented as an embedded device accommodated in an existing hardware device, or as an application downloaded and installed in software form, if necessary.
  • FIG. 18 is a flowchart illustrating a method of providing a treatment prediction result according to an embodiment of the present invention.
  • an evaluation method includes a step of constructing a semantic database dictionary by collecting and data mining document information (S2100), a treatment site, a treatment method, patient state information, A step S2200 of constructing a treatment prediction model based on the input information and the semantic database dictionary, if the at least one of the input information and the semantic database dictionary is input as input information, and obtaining result information corresponding to the input information based on the treatment prediction model (S2300), collecting and cataloging the result information, and displaying the same on the same screen together with the result information (S2400).
  • the present invention provides data mining of a plurality of document information to provide a treatment prognosis according to a condition required by a user, and at the same time provides data mining basis data collectively through the same platform, thereby enhancing the reliability of information provision, It makes it easier to check and view related bibliographic information.
  • 19 is a diagram for explaining a data mining method according to an embodiment of the present invention.
  • the text corresponding to the treatment outcome prognostic factors and the output result is extracted from various literature information (PDF document) such as academic papers, medical books, case information, and the extracted text is analyzed through stemming analysis, semantic analysis, (S2110 ⁇ S2110), and extracts the extracted semantic keywords in a database in a form suitable for the search (query) to construct a database dictionary (S2110 ⁇ S2140).
  • Each of the semantic keywords at this time may include synonyms and the like.
  • the source information for identifying the source data such as the title and link address of the extracted metadata information, is mapped so that the source data can be identified and collected on the basis of the source information.
  • At least one of the treatment site, the treatment method, the patient status information, and the side effect type is input as input information.
  • some of the input information may be classified as a treatment condition, and the remaining part of the input information may be classified as a treatment result condition.
  • a treatment result condition S2150
  • the treatment site is lung and the treatment method is radiation.
  • the present invention utilizes a database dictionary constructed by classifying treatment types and various types of treatment results into semantic keyword form, thereby obtaining only desired data according to the treatment site and patient information, and constructing a single treatment prediction model (S2160, S2170).
  • a database dictionary constructed by classifying treatment types and various types of treatment results into semantic keyword form, thereby obtaining only desired data according to the treatment site and patient information, and constructing a single treatment prediction model (S2160, S2170).
  • the treatment method is radiation and the side effects are pneumonia
  • clinical information such as ge, COPD, ILD, pulmonary function, DVH, treatment site (eg tumor location)
  • the present invention allows the kind of result information that can be obtained through the treatment prediction model to be changed according to the setting environment of the treatment condition and the treatment result condition.
  • the treatment prediction model provides survival rate, toxicity, and response according to the side effect type as the result information
  • the treatment predictive model provides the survival rate, toxicity, and reactivity of each treatment method as the result information, and the survival rate , Toxicity, and reactivity as outcome information.
  • the semantic table in the range of the prognostic factors, such as age and V20, is searched from the therapeutic prediction model constructed in the above, and the pneumonia-related side effect, which is the output information desired by the user, And provides it to the user (S2180).
  • not only the treatment result condition but also the kind of the output information can be selected in the input step.
  • the user setting information can be selectively provided, not all of the treatment result conditions.
  • the treatment result condition is set as a side effect such as pneumonia and esophagitis, but the output information is selected as the output information for the toxicity due to pneumonia and the survival rate according to the esophagus so that only the two pieces of information can be viewed by the user It is possible.
  • the step of constructing the treatment prediction model includes determining a treatment method according to at least one of, or a combination of, surgery, chemotherapy, radiation therapy, immunotherapy, hyperthermia treatment, and bone marrow transplantation
  • the present invention is particularly useful in the treatment of side effects when two or more treatment modalities are simultaneously used and furthermore the survival rate, toxicity, and reactivity of each side effect type to data mining information As shown in FIG.
  • a treatment prediction model considering the combinations and conditions of a plurality of treatment methods and performing a treatment result prediction operation considering the combination of treatment methods and conditions such as postoperative radiation treatment through a treatment prediction model, Toxicity, and responsiveness to the disease.
  • the present invention provides an optimum treatment method among various kinds of treatment methods, especially for a single disease, and then re-verifies information such as survival rate by inputting a treatment method provided in input conditions on the same platform again.
  • the proportion of consideration of survival rate, toxicity, and reactivity can be arbitrarily adjusted in accordance with the subjective intention of the medical staff, so that subjective intervention in the selection of the treatment method of the medical staff is also made possible.
  • the artificial intelligence module which outputs the provided information.
  • the treatment prediction result can be provided to the user as described above with reference to Figs. 12 to 16.
  • the present invention can display the document information used for constructing the treatment prediction model, and by clicking the document information, the user can directly check the document in one user interface environment.
  • the present invention can select a combination of various treatment methods for a medical professional having expert knowledge only in a corresponding field, such as radiation therapy and medication, to confirm treatment results, effects, and related literature information in the same user interface environment .
  • the present invention provides a combination of the treatment methods (kind and order) or condition (drug treatment timing, type of drug) and the like in the data mining and the deep learning method for the cancer in which various kinds of treatment methods exist through the above- can do. That is, the system according to the present invention learns from the data mining data and case information in the literature, and learns it by the deep learning method, and provides the most optimized treatment result to the user.
  • 20 and 21 are views for explaining a method of providing evidence data according to an embodiment of the present invention.
  • the present invention collects all the related document information based on the source information mapped to each semantic keyword used in constructing the treatment prediction model (S2410).
  • the priority of the collected document information is determined based on at least one of the document information start date, the information relevance (i.e., the number of times the semantic keyword is extracted) and the author reliability (S2420) , And further displays the result information on the screen on which the result information is displayed (S2430).
  • the user when the priority of the document information is determined, it is possible for the user to adjust the consideration of each of the document information start date, information relevance, and author reliability. For example, by setting the percentage of consideration of the date of document information to 100%, setting the document with the highest information relevance to the top priority, setting the document with the fastest starting date of the document information to the top priority, or setting the percentage of consideration of the information relevance to 100% Or 50%, 40%, and 10%, respectively, of the information disclosure date, information relevance, and author reliability, so as to provide the author with a relatively high reliability and information relevance.
  • the baseline data may be subdivided according to the type of the result treatment prognosis if necessary. That is, if the outcome information includes three treatment prognoses, ie, survival rate, toxicity, and response to pneumonia, the data can be provided in a subdivision corresponding to each of the three treatment prognoses.
  • the data may be provided in a corresponding subdivision.
  • the toxicity from the treatment of pneumonia is the prognosis of treatment
  • the toxicity grade is divided into 1 to 5
  • the data can be provided in detail according to the toxicity grade according to the treatment of pneumonia. This means that after retrieving at least one document information in which the semantic keyword corresponding to the category (for example, the toxicity grade according to the treatment of pneumonia is 1) is disclosed, the information relevancy of each of the document information based on the keyword extraction count, And selecting and providing only a predetermined number of pieces of document information having high information relevance as the basis data corresponding to the category.
  • the present invention can provide detailed information of a document information requested to be browsed through a pop-up window displayed overlay on a current screen, or by bold processing or box processing a semantic keyword used in constructing a treatment prediction model, So that the contents can be checked and read more easily.
  • the document providing mode may be diversified to diversify the manner of providing the document information. For example, after the document providing mode is subdivided into the whole view mode and the simple view mode, all the pages of the document information are provided in the full view mode, and only the sentences in which the semantic keywords are disclosed in the simple view mode are extracted, It is also possible to provide it as an enemy.
  • 22 is a block diagram of a treatment prediction result providing system according to an embodiment of the present invention.
  • the system includes at least one of a semantic database dictionary 2100 for storing a result of collecting and data mining treatment-related documents or case information, at least one of a treatment site, a treatment method, patient status information, and a side effect type
  • a prediction model constructing unit 2200 for constructing a prediction model for treatment based on the input information received from the input information receiving unit 2200 and the semantic database dictionary 2100
  • a result information acquiring unit 2400 for providing result information corresponding to the input information to the user terminal based on the prediction model, a base data acquiring unit for collecting and cataloging basis data of the result information, 2500), a UI (User Interface) for receiving the input information, or simultaneously guiding the result information and the evidence data, It may include a UI generating unit 2600 to the ball.
  • the evidence data acquisition unit 2500 of the present invention determines the priorities of the collected evidence data based on at least one of a document information start date, information relevance, and author credibility, Information relevance, and author credibility, respectively. That is, the user can preferentially provide the necessary document information.
  • the baseline data acquisition unit 2500 of the present invention allows the user to select and request one baseline data from the baseline data list, and in response thereto, invokes detailed information of the baseline data requested to be retrieved, (2600), so that the user can receive not only the base material list but also the detailed information in a lump without performing a separate information search operation.
  • the result information provided as a result of the treatment prediction of the present invention may include at least one of a survival rate, toxicity, response, a combination of treatment methods, and a condition of a treatment method according to at least one of a side effect type and a treatment method,
  • a therapeutic prediction model according to the clinical information of the patient, it is possible to provide the user with the result information on the treatment method, conditions, and the like.
  • the treatment prediction model may include treatment modalities according to at least one or more combinations of surgery, chemotherapy, radiotherapy, immunotherapy, hyperthermia, and bone marrow transplantation for the patient to be treated,
  • the present invention provides an optimum treatment method among various kinds of treatments for a single disease, and then re-verifies information such as survival rate by inputting the treatment method provided in the input condition on the same platform.
  • a care prediction model can be constructed from a pre-constructed data mining dictionary to provide desired information.
  • system of the present invention is preferably implemented as a single hardware device, it may be implemented as an embedded device accommodated in an existing hardware device, or as an application downloaded and installed in software form, if necessary.
  • FIG. 23 is a diagram illustrating an exemplary computing environment in which one or more embodiments disclosed herein may be implemented, and is illustrative of a system 3000 that includes a computing device 3100 configured to implement one or more of the embodiments described above. / RTI >
  • computing device 3100 may be a personal computer, a server computer, a handheld or laptop device, a mobile device (mobile phone, PDA, media player, etc.), a multiprocessor system, a consumer electronics device, A distributed computing environment including any of the above-described systems or devices, and the like.
  • the computing device 3100 may include at least one processing unit 3110 and memory 3120.
  • the processing unit 3110 may include a central processing unit (CPU), a graphics processing unit (GPU), a microprocessor, an application specific integrated circuit (ASIC), a field programmable gate array And may have a plurality of cores.
  • the memory 1120 can be a volatile memory (e.g., RAM, etc.), a non-volatile memory (e.g., ROM, flash memory, etc.) or a combination thereof.
  • the computing device 3100 may include additional storage 3130.
  • Storage 3130 includes, but is not limited to, magnetic storage, optical storage, and the like.
  • the storage 3130 may store computer readable instructions for implementing one or more embodiments as disclosed herein, and other computer readable instructions for implementing an operating system, application programs, and the like.
  • Computer readable instructions stored in storage 3130 may be loaded into memory 3120 for execution by processing unit 3110.
  • computing device 3100 may include input device (s) 3140 and output device (s) 3150.
  • input device (s) 3140 may include, for example, a keyboard, a mouse, a pen, a voice input device, a touch input device, an infrared camera, a video input device or any other input device.
  • output device (s) 3150 can include, for example, one or more displays, speakers, printers or any other output device.
  • Computing device 3100 may also use an input device or output device included in another computing device as input device (s) 3140 or output device (s) 3150.
  • the computing device 3100 may also include communication connection (s) 3160 that enable the computing device 3100 to communicate with other devices (e.g., computing device 3300).
  • (S) 3160 may include a modem, a network interface card (NIC), an integrated network interface, a radio frequency transmitter / receiver, an infrared port, a USB connection or other Interface.
  • the communication connection (s) 3160 may include a wired connection or a wireless connection.
  • Each of the components of computing device 3100 described above may be connected by various interconnects (e.g., peripheral component interconnect (PCI), USB, firmware (IEEE 1394), optical bus architecture, etc.) And may be interconnected by a network 3200.
  • PCI peripheral component interconnect
  • IEEE 1394 firmware
  • optical bus architecture etc.
  • terms such as "component,” “module,” “system,” “interface,” and the like generally refer to a computer-related entity that is hardware, a combination of hardware and software, software, or software in execution.
  • an element may be, but is not limited to being, a processor, an object, an executable, an executable thread, a program and / or a computer running on a processor.
  • both the application running on the controller and the controller may be components.
  • One or more components may reside within a process and / or thread of execution, and the components may be localized on one computer and distributed among two or more computers.

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Abstract

Un mode de réalisation de la présente invention concerne un procédé et un système permettant de fournir un résultat de prédiction de traitement de cancer, qui fournit, à l'aide d'une technique d'exploration de données, le pronostic de traitement selon une condition requise par un utilisateur par rapport à divers types de cancers et des procédés de traitement, de façon à permettre à l'utilisateur de déterminer un procédé de traitement du cancer le plus approprié. La présente invention comprend les étapes consistant : à construire un dictionnaire de base de données sémantiques par collecte et exploration de données d'un document lié au traitement du cancer ou des informations de cas ; lorsqu'au moins un parmi un type de cancer, un procédé de traitement d'intérêt, un type d'effet secondaire, et des informations de condition de patient est entré en tant qu'informations d'entrée, à configurer un modèle de prédiction de traitement sur la base des informations d'entrée et du dictionnaire de base de données sémantiques ; et à fournir des informations de résultat correspondant aux informations d'entrée sur la base du modèle de prédiction de traitement.
PCT/KR2018/007865 2017-10-13 2018-07-11 Procédé et système permettant de fournir un résultat de prédiction de traitement de cancer, procédé et système permettant de fournir un résultat de prédiction de traitement sur la base d'un réseau d'intelligence artificielle, et procédé et système permettant de fournir collectivement un résultat de prédiction de traitement et des données de preuve WO2019074191A1 (fr)

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KR10-2017-0133282 2017-10-13
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KR10-2017-0137212 2017-10-23
KR1020170143559A KR101943222B1 (ko) 2017-10-31 2017-10-31 치료 예측결과 및 근거 자료 일괄 제공 방법 및 시스템
KR1020170143558A KR101946402B1 (ko) 2017-10-31 2017-10-31 인공 지능망 기반 치료 예측 결과 제공 방법 및 시스템
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112037908A (zh) * 2020-08-05 2020-12-04 复旦大学附属眼耳鼻喉科医院 一种耳源性眩晕诊疗装置、系统及大数据分析平台
US20210398687A1 (en) * 2018-10-01 2021-12-23 Digipharm Ltd Computer system and method for determining efficacy of a medical treatment for a medical condition

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20050055894A (ko) * 2003-12-09 2005-06-14 주식회사 케이티 표준 질병예방 지식데이터베이스 및 개인 진료기록데이터베이스를 이용한 의료정보서비스 시스템 및 그 방법
JP2006087375A (ja) * 2004-09-24 2006-04-06 Natl Inst Of Radiological Sciences 放射線治療における副作用発症予測用ｄｎａオリゴマー、遺伝子マーカー、ｄｎａオリゴマーセット、および副作用発症予測方法
KR100794516B1 (ko) * 2007-12-03 2008-01-14 한국정보통신대학교 산학협력단 사례 기반 기계학습 추론을 이용한 질환 진단 및 검사 항목선정 시스템 및 방법
JP2009533782A (ja) * 2006-04-17 2009-09-17 シーメンス メディカル ソリューションズ ユーエスエー インコーポレイテッド 医療計画における個人的予後モデル化
KR20100084265A (ko) * 2009-01-16 2010-07-26 김서준 사용자 피드백을 이용하여 평가된 컨텐츠로부터 정보를 추출하고 이를 이용하기 위한 방법 및 장치
KR20120054986A (ko) * 2010-11-22 2012-05-31 고려대학교 산학협력단 디지털 콘텐츠 검색 장치 및 방법
KR20140109729A (ko) * 2013-03-06 2014-09-16 한국전자통신연구원 의미기반 검색 시스템 및 이의 검색방법
JP2016018321A (ja) * 2014-07-07 2016-02-01 株式会社日立製作所 薬効分析システム及び薬効分析方法

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20050055894A (ko) * 2003-12-09 2005-06-14 주식회사 케이티 표준 질병예방 지식데이터베이스 및 개인 진료기록데이터베이스를 이용한 의료정보서비스 시스템 및 그 방법
JP2006087375A (ja) * 2004-09-24 2006-04-06 Natl Inst Of Radiological Sciences 放射線治療における副作用発症予測用ｄｎａオリゴマー、遺伝子マーカー、ｄｎａオリゴマーセット、および副作用発症予測方法
JP2009533782A (ja) * 2006-04-17 2009-09-17 シーメンス メディカル ソリューションズ ユーエスエー インコーポレイテッド 医療計画における個人的予後モデル化
KR100794516B1 (ko) * 2007-12-03 2008-01-14 한국정보통신대학교 산학협력단 사례 기반 기계학습 추론을 이용한 질환 진단 및 검사 항목선정 시스템 및 방법
KR20100084265A (ko) * 2009-01-16 2010-07-26 김서준 사용자 피드백을 이용하여 평가된 컨텐츠로부터 정보를 추출하고 이를 이용하기 위한 방법 및 장치
KR20120054986A (ko) * 2010-11-22 2012-05-31 고려대학교 산학협력단 디지털 콘텐츠 검색 장치 및 방법
KR20140109729A (ko) * 2013-03-06 2014-09-16 한국전자통신연구원 의미기반 검색 시스템 및 이의 검색방법
JP2016018321A (ja) * 2014-07-07 2016-02-01 株式会社日立製作所 薬効分析システム及び薬効分析方法

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20210398687A1 (en) * 2018-10-01 2021-12-23 Digipharm Ltd Computer system and method for determining efficacy of a medical treatment for a medical condition
CN112037908A (zh) * 2020-08-05 2020-12-04 复旦大学附属眼耳鼻喉科医院 一种耳源性眩晕诊疗装置、系统及大数据分析平台

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