KR20200143157A - Apparatus and system for prescribing personal-ustomized drugs or nutraceuticals using artificial intelligence - Google Patents

Abstract

The present invention provides a method for prescribing patient-personalized medicine using artificial intelligence. The method comprises: a step of periodically acquiring treatment data including a gene expression distribution of a patient to whom medicine manufactured to treat a first disease is administered; a step of determining a second disease which can occur in a patient who underwent a treatment of the first disease from the gene expression distribution of the patient; a step of receiving bioinformation of the patient from a terminal of the patient; a step of determining an occurrence possibility of the second disease based on the bioinformation of the patient; a step of generating prevention information of the second disease corresponding to the current condition of the patient by using a first artificial intelligence learning model which has learned the correlation of a prevention method of the second disease in accordance with a numerical value of the occurrence possibility of the second disease; a step of reflecting a past health condition, a current health condition and a future health condition as time passes to express the prevention information of the second disease in a character of the patient to generate the character of the patient; and a step of supplying the character of the patient in which the prevention information of the second disease is reflected to the terminal of the patient.

Description

{Apparatus and system for prescribing personal-ustomized drugs or nutraceuticals using artificial intelligence}

By way of disclosure, the present invention relates to a method and an apparatus for predicting a possible disease in a patient by reflecting the current state of the patient using artificial intelligence, and providing a customized drug or exercise prescription for preventing the disease by period.

Due to the rapid increase in population, medical staff can quickly and accurately provide customized medical services for each patient by individually diagnosing their own health status, so the development of related technologies is demanding.

With the advancement of artificial intelligence and digital technology, healthcare technology is undergoing major changes, and it has become an important task to effectively manage new data by creating, collecting and tracking new data. In particular, in the field of medical technology, as the importance of not only personal health but also various industrial scalability and health welfare management is increasing, interest in digital medical technology that can manage medical data and derive appropriate medical diagnosis is increasing. Are doing.

Artificial Intelligence (AI) system is a computer system that realizes human-level intelligence, and unlike existing rule-based smart systems, machines learn, judge, and become smarter. As the artificial intelligence system is used, the recognition rate improves and the user's taste can be understood more accurately, and the existing rule-based smart system is gradually being replaced by a deep learning-based artificial intelligence system.

Artificial intelligence technology consists of machine learning (deep learning) and component technologies using machine learning. Machine learning is an algorithm technology that classifies/learns the characteristics of input data by itself, and element technology is a technology that utilizes machine learning algorithms such as deep learning, such as linguistic understanding, visual understanding, reasoning/prediction, knowledge expression, motion control, etc. Consists of the technical fields of.

With the advancement of artificial intelligence and digital technology, healthcare technology is undergoing major changes, and it has become an important task to effectively manage new data by creating, collecting and tracking new data. In particular, in the field of medical technology, as the importance of not only personal health but also various industrial scalability and health welfare management is increasing, interest in digital medical technology that can manage medical data and derive appropriate medical diagnosis is increasing. I'm doing...

Korean Patent Publication No. 10-2015-0112423 (published on October 7, 2015) Korean Patent Publication No. 10-2009-0003748 (published on January 12, 2009)

An object of the present invention is to provide an apparatus and method for monitoring a disease healing process of a patient who has been administered a specific drug and generating customized prescription information that can prevent diseases that may occur in the patient.

According to a first embodiment, a method for prescribing a patient-tailored drug using artificial intelligence is provided, and the method provides treatment data including gene expression distribution of a patient who has received a drug manufactured to treat a first disease. Periodically acquiring, determining a second disease that may develop in the patient from the gene expression distribution of the patient who has completed treatment of the first disease, receiving the patient's biometric information from the patient's terminal, and receiving the patient's biometric information Based on the step of determining the likelihood of developing a second disease, using the first artificial intelligence learning model that has learned the association of the prevention method of the second disease according to the value of the likelihood of developing the second disease, Generating the prevention information of the second disease, creating the second disease prevention information by expressing the patient's character by reflecting the past health state, the current health state, and the future health state over time, the second disease It may include the step of providing the patient's character on which the prevention information of is reflected to the patient's terminal.

According to a second embodiment, a device for prescribing a patient-specific drug using artificial intelligence is provided, the device comprising a processor and a memory storing executable instructions, and the processor executes the instructions, thereby preventing a first disease. Periodically acquiring medical treatment data including the gene expression distribution of the patient who received the drug prepared for treatment, and determining a second disease that can affect the patient from the gene expression distribution of the patient who has completed treatment of the first disease, Receiving the patient's biometric information from the patient's terminal, determining the likelihood of developing a second disease based on the patient's biometric information, and learning the association of the second disease prevention method according to the value of the likelihood of developing the second disease. The first AI learning model is used to generate prevention information for the second disease corresponding to the current patient's condition, and the second disease is prevented by reflecting the past health status, current health status, and future health status over time. The information may be expressed as a patient's character and generated, and the patient's character reflecting the second disease prevention information may be provided to the patient's terminal.

According to the third embodiment, it is possible to provide a computer-readable non-transitory recording medium in which a program for implementing a method for prescribing a drug customized to a patient using artificial intelligence is recorded.

By periodically transmitting information on prescriptions to prevent diseases that may occur to the patient by the start of work, the patient's health can be regularly managed, and by immediately transmitting prescription information reflecting the patient's current condition. The patient's health can be managed more closely.

By initiating, by tracking whether patients with the same disease using the same drug have cured the disease, it is possible to predict other diseases that may occur in the patient group with the same genome, and provide information on preventive measures that can cope with other diseases. I can.

Providing information on disease information and periodic prediction results by predicting possible diseases in a specific patient using an artificial intelligence model created by performing repetitive learning using disease data of a patient group having the same genome at the beginning of the day. can do.

1 is a view for explaining the flow of a method for prescribing a patient-tailored drug using artificial intelligence according to one disclosure.
FIG. 2 is a diagram for describing a feature of generating a first artificial intelligence learning model according to one disclosure.
FIG. 3 is a diagram for describing a feature of generating a second artificial intelligence learning model according to one disclosure.
4 is a diagram for describing a feature of generating a third artificial intelligence learning model according to one disclosure.
5 is a diagram for explaining a pathway for a drug to act on a gene according to one initiation.
6 is a diagram illustrating a screen for providing medical information to a terminal of a patient according to an exemplary embodiment.
7 is a diagram for explaining the configuration of a device for prescribing a patient-customized drug using artificial intelligence according to one disclosure.
FIG. 8 is a diagram illustrating a configuration of a processor that generates an artificial intelligence learning model through data learning according to one disclosure.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings so that those of ordinary skill in the art can easily implement the present invention. However, the present invention may be implemented in various different forms and is not limited to the embodiments disclosed below. In addition, parts irrelevant to the present invention are omitted in the drawings in order to clearly disclose the present invention, and the same or similar reference numerals denote the same or similar components in the drawings.

Objects and effects of the present invention may be naturally understood or more clearly understood by the following description, and the objects and effects of the present invention are not limited only by the following description.

Objects, features and advantages of the present invention will become more apparent through the following detailed description. In addition, in describing the present invention, when it is determined that a detailed description of a known technology related to the present invention may unnecessarily obscure the subject matter of the present invention, a detailed description thereof will be omitted. Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings.

In the following, the device 100 for prescribing a patient-tailored drug using artificial intelligence is abbreviated as the customized drug prescribing device 100 and used.

The disease described in the present invention by one disclosure is, for example, a cancer, a neoplasm, a tumor, and/or a metastatic form therein, a metabolic disorder, an inflammatory disease, or an infectious disease. In some embodiments, the cancer, neoplasm, tumor, or metastatic form therein is, for example, leukemia, lymphoma, prostate cancer, lung cancer, breast cancer, liver cancer, colorectal cancer, or kidney cancer. All other diseases that occur in the human body may be included.

According to one initiation, the drug is a drug formulated with components suitable for curing the onset of the patient, and acts effectively on the target gene of the disease.

1 is a view for explaining the flow of a method for prescribing a patient-tailored drug using artificial intelligence according to one disclosure.

According to one initiation, in block 101, the customized drug prescription device 100 may periodically acquire treatment data including gene expression distribution of a patient who has received a drug manufactured to treat a first disease.

According to one initiation, in block 102, the customized drug prescription device 100 may determine a second disease that may be onset to the patient from the gene expression distribution of the patient who has completed the treatment of the first disease.

According to one initiation, in block 103, the customized drug prescription device 100 may receive biometric information of the patient from the terminal of the patient.

According to an initiation, in block 104, the customized drug prescription device 100 may determine a possibility of developing a second disease based on the patient's biometric information.

By the start of the day, in block 105, the customized drug prescription device 100 uses the first artificial intelligence learning model that has learned the association of the second disease prevention method according to the value of the probability of the second disease. Prevention information of the corresponding second disease may be generated.

By the start of the day, the first AI learning model corresponds to medical identification information related to the drug prescribed by the patient, information on the second disease, medical history information, medical treatment history information, information on the prescribed drug, and medical identification information. It may be generated based on a result of learning drug medication information.

In addition, the first artificial intelligence learning model may generate time-based drug medication information according to the patient's current state information by setting a weight to at least one of the patient's current state information, the patient's gene expression state information, and the surrounding environment information. .

According to the start of the day, the prevention information of the second disease includes information on the second disease, the possibility of developing the second disease, changes in health status according to the progression of the second disease, information on necessary drugs according to the current state information of the patient, and It is characterized by including medication information, precautions, the number of times the drug is properly ingested and the timing of the appropriate intake.

In addition, the prevention information for the second disease includes whether a visit to the hospital is necessary (first screening), stress/fatigue, health age, skin condition, obesity, body balance, heart health, digestive system health, and respiratory system health. It includes at least any one, and the parameters are facial muscle change rate, facial skin color change rate, facial wrinkle change rate, face width change rate, heart size change rate, heart color change rate, digestive tract abnormal region color change rate, respiratory tract abnormal region color change rate, body balance It is preferable to include at least one of the rate of change in size for each body part according to.

By the start of the day, in block 106, the customized drug prescription device 100 may generate the second disease prevention information by expressing the patient's character by reflecting the past health state, the current health state, and the future health state over time. have.

According to one disclosure, the patient's character may express the patient's health status as a body part balance, heart health, digestive organ health, respiratory organ health, and the like.

According to an initiation, in block 107, the customized drug prescription device 100 may provide the patient's character reflecting the second disease prevention information to the patient's terminal.

In addition, the customized drug prescription device 100 may generate question information related to patient-customized medication information based on the second disease prevention information, and provide the generated question information to the patient's terminal.

In addition, the customized drug prescription device 100 may modify the second disease prevention information by using the answer information to the question information from the patient's terminal and provide it to the patient's terminal.

According to an initiation, the customized drug prescription device 100 may periodically acquire treatment data of patients who have been administered a drug manufactured to cure a disease.

According to one disclosure, the customized drug prescription device 100 may classify treatment data of patients whose disease has been cured after drug administration among the treatment data into a first category.

Patients included in the first category by one disclosure refer to patients whose disease has been cured by administering a drug prepared with a predetermined component. Here, the patients included in the first category may not have been administered drugs by the same dosage method, and each drug may have been administered by different dosage methods according to each patient's condition. Patients included in the first category indicate that the disease was cured by the drug, and the drug effectively acted on a specific gene of the patients included in the first category.

According to one disclosure, the customized drug prescription apparatus 100 may classify treatment data of patients including genes commonly expressed among patients included in the first category into the second category.

According to one disclosure, patients included in the second category may include diseases other than diseases, and patients included in the category may include at least some of the same genetic information.

According to the start of the day, the customized drug prescription device 100 uses a second artificial intelligence learning model that has learned the relationship between the distribution of gene expression due to disease and the distribution of gene expression due to the action of the drug. Target genes can be derived from genes.

According to one initiation, the customized drug prescription device 100 uses at least one of the third artificial intelligence learning models that learn the relationship between the expression status of the genes of the patients included in the second category according to the disease and the healing of a plurality of diseases. It is possible to determine the likelihood of developing other diseases.

By the start of the day, the third artificial intelligence learning model is predictable disease rate due to expression of genes related to other diseases according to Equation 1 below based on gene expression information of patients included in the second category. PDR), and if the value of PDR is more than the reference value, it can be determined that other diseases may develop.

Here, h(t) is a function that predicts the onset of a disease, T is a random variable for survival time, and t is a specific value for random variable T And h0 is characterized by being a reference disease onset function (X1=0, X2=0).

According to an initiation, the customized drug prescription device 100 may provide information on a disease prevention method corresponding to a possibility of occurrence of at least one other disease.

According to the start of the day, when another disease that may occur in the patient is determined by the third artificial intelligence learning model, the detailed information on the disease, disease prevention information, and At least one of drug recommendation information and hospital recommendation information may be generated. In addition, the customized drug prescription device 100 may transmit the generated at least one piece of information to the patient's terminal.

According to the start of the day, the customized drug prescription device 100 is a terminal of a patient who has at least one other disease, and whether the body changes according to the disease, whether pain occurs due to the disease, whether or not to take medicine for pain, pain intensity, pain area And a question message about the duration of the pain.

By the start of the day, the customized drug prescription device 100 receives an answer message to the question message from the terminal of the patient, and based on the information included in the answer message, changes in the region and intensity of pain for each activity over time, Pain information of the patient, including whether or not pain relief according to whether or not to take medicine for pain, can be graphed.

The customized drug prescription device 100 may obtain biometric information including body temperature information, sweat emission information, skin characteristic information, heart rate information, blood pressure information, and activity information of the patient from the patient's terminal. Biometric information of a patient may be obtained through a sensor unit included in the customized drug prescription device 100 by one start.

According to one initiation, the biometric information includes at least one of an electrocardiogram signal, an ultrasonic signal, an electrodermal activity (EDA), a body temperature, and a body fat percentage. It is preferable that the questionnaire information includes at least one of information on lifestyle, symptoms, height, weight, and waist circumference.

In addition, the customized drug prescription device 100 may generate management information on the patient's pain in consideration of at least one of the patient's body temperature information, sweat discharge information, skin characteristic information, heart rate information, blood pressure information, and activity information. have.

Based on the patient's pain information, the customized drug prescription device 100 is an exercise application that provides an exercise that can improve pain, a medical management application that helps relieve pain, a hospital related to pain, and is effective for pain relief. At least one of medicines and medical devices that help improve pain can be recommended. At this time, the recommended information may be transmitted to the patient's terminal.

According to one disclosure, the customized drug prescription device 100 may determine an interaction relationship between a disease and a drug by using a correlation between a human metabolite and an enzyme that causes disease in patients included in the second category.

In addition, the customized drug prescription device 100 may determine a plurality of drug candidates capable of curing a specific disease based on an interaction relationship between the disease and the drug, according to the degree of onset of a specific disease that occurs in a specific patient. In other words, appropriate drugs can be recommended depending on whether the onset of the disease is early or worse.

Furthermore, the customized drug prescription device 100 may determine a specific drug based on a plurality of drug candidates and a disease progression of a specific patient.

In addition, the customized drug prescription device 100 may find the most suitable method for curing other diseases by adjusting the components and contents of the drug. That is, the customized drug prescription device 100 may provide a more accurate customized dosage method for each disease by reflecting the feedback from the patient and performing learning again based on this.

The customized drug prescription device 100 analyzes the expression state of the target gene of each of the patients included in the second category according to the component ratio of the drug, the dose per dose, the number of doses, the dose time, and the dose method, so that the drug can be cured. A utility value can be calculated for each of at least one disease.

The customized drug prescription device 100 may determine a customized drug administration method for each of the patients included in the second category according to the drug utility value.

Diseases that can be detected by the method of the present invention include cancer, and cancer is expressed by cancer cells, and non-limiting examples of cancer cells include adenocarcinoma cells, adrenal gland tumor cells, and enamel epithelium. Ameloblastoma cells, anaplastic cells, anaplastic carcinoma of thyroid cells, angiofibroma cells, angioma cells, angiosarcoma cells, afudoma cells (apudoma cell), argentaffinoma cell, arrhenoblastoma cell, ascites tumor cell, ascitic tumor cell, astroblastoma cell, appearance Astrocytoma cells, ataxia-telangiectasia cells, atrial myxoma cells, basal cell carcinoma cells, benign tumor cells, osteosarcoma cells (bone cancer cell), bone tumor cell, brain stem glioma cell, brain tumor cell, breast cancer cell, Burkitt's lymphoma cell, Cancerous cells, carcinoid cells, carcinoma cells, cerebellar astrocytoma cells, cerebellar astrocytoma cells, cervical cancer cells, cherry angioma cells , Cholangiocarcinoma cells, cholangioma cells, chondroblastoma cells, chondroma cells, chondrosarcoma cells, chorioblastoma cells, chorioblastoma cells ( choriocarcinoma cells), colon cancer cells, common acute lymphoblastic leukemia cells, craniopharyngioma cells, cystocarcinoma cells, cystocarcinoma cells, and cystofbroma cells. cell), cystoma cell, cytoma cell, ductal carcinoma in situ cell, ductal papilloma cell, dysgerminoma cell, brain tumor Cells (encephaloma cells), endometrial carcinoma cells (endometrial carcinoma cells), endothelioma cells (endothelioma cells), epithelioma cells (ependymoma cells), epithelioma cells (epithelioma cells), erythroleukemia cells (erythroleukemia cells), Ewing's sarcoma Ewing's sarcoma cells, extra nodal lymphoma cells, feline sarcoma cells, fibro adenoma cells, fibro sarcoma cells, follicular adenocarcinoma cells of the thyroid gland (follicular cancer of the thyroid cell), ganglioglioma cell, gastrinoma cell, polymorphic glioblastoma cell (g lioblastoma multiform cells), glioma cells, gonadoblastoma cells, hemangioblastoma cells, haemangioendothelioblastoma cells, haemangioendothelioma cells, perivascular Hemangiopericytoma cells, hematolymphangioma cells, haemocytoblastoma cells, haemocytoma cells, hairy cell leukemia cells, Hamartoma cells, hepatocarcinoma cells, hepatocellular carcinoma cells, hepatoma cells, histoma cells, Hodgkin's disease cells, adrenal tumors Cells (hypernephroma cells), infiltrating cancer cells (infiltrating cancer cells), infiltrating ductal cell carcinoma cells (infiltrating ductal cell carcinoma cells), insulinoma cells (insulinoma cells), juvenile angioforoma cells (juvenile angioforoma cells), Kaposi's sarcoma Cells (Kaposi sarcoma cells), kidney tumor cells (kidney tumor cells), large cell lymphoma cells (large cell lymphoma cells), leukemia cells (leukemia cells), chronic leukemia cells (chronic leukemia cells), acute leukemia cells (acute leukemia cells) ), lipoma cells, liver cancer cells, liver metastases cell), Luke carcinoma cells, lymphadenoma cells, lymphangioma cells, lymphocytic leukemia cells, lymphocytic lymphoma cells, lymphoid cucumbers Lymphoeytoma cells, lymphoedema cells, lymphoma cells, lung cancer cells, malignant mesothelioma cells, malignant teratoma cells, Mastocytoma cells, medulloblastome cells, melanoma cells, meningioma cells, mesothelioma cells, metastatic cells, metastatic cells ( metastasis cell), metastatic spread cell, Morton's neuroma cell, multiple myeloma cell, myeloblastoma cell, myeloid leukemia cell, bone marrow Myelolipoma cells, myeloma cells, myoblastoma cells, myxoma cells, nasopharyngeal carcinoma cells, neoplastic cells, nephroblastoma cells cell), neuroblastoma cell, neurofibroma cell, neurofibromatosis cell, Glioma cells, neuroma cells, non-Hodgkin's lymphoma cells, oligodendroglioma cells, optic glioma cells, osteochondroma cells ( osteochondroma cells, osteosarcoma cells, osteosarcoma cells, ovarian cancer cells, Paget's disease of the nipple cells, pancoast tumor cells cell), pancreatic cancer cell, pheochromocytoma cell, pheoehromocytoma cell, plasmacytoma cell, primary brain tumor cell, fetal potential Progonoma cells, prolactinoma cells, renal cell carcinoma cells, retinoblastoma cells, rhabdomyosarcoma cells, rhabdosarcoma cells , Solid tumor cell, sarcoma cell, secondary tumor cell, semi-noma cell, skin cancer cell, small cell carcinoma cell ), squamous cell carcinoma cell, strawberry haemangioma cell, T-cell lymphoma cell, teratoma Cells (teratoma cells), testicular cancer cells (testicular cancer cells), thymoma cells (thymoma cells), trophoblastic tumor cells (trophoblastic tumor cells), tumorigenic cells (tumorigenic cells), tumor initiation cells (tumor initiation cells) , Tumor progression cells, vestibular schwannoma cells, Wilm's tumor cells, or a combination thereof.

FIG. 2 is a diagram for describing a feature of generating a first artificial intelligence learning model according to one disclosure.

By the start of the day, the first AI learning model corresponds to medical identification information related to the drug prescribed by the patient, information on the second disease, medical history information, medical treatment history information, information on the prescribed drug, and medical identification information. It may be generated based on a result of learning drug medication information.

The customized drug prescription device 100 according to the start of the day relearns the first artificial intelligence learning model so that the effect of the patient's drug intake is maximized based on the surrounding environment information, and regenerates the second disease prevention information. I can.

In addition, the customized drug prescription device 100 is the current state information of the patient. By setting a weight to at least one of the patient's genetic characteristic information and the surrounding environment information, it may be characterized in that time-based drug medication information according to the patient's current state information is generated.

For example, the first artificial intelligence learning model may set a weight on at least one of information on a patient's current state, information on a patient's genetic characteristics, and information on a surrounding environment according to a patient's request. For example, when receiving a request to generate new drug medication information along with information that the patient's current blood pressure has risen from the patient terminal, the first learning model weights the blood pressure increase information among the patient's current state information. New drug medication information can be created. At this time, the first artificial intelligence learning model is an artificial intelligence learning model, which is based on big data in consideration of patient status information, risk information due to blood pressure increase, blood pressure increase factor according to environmental information, environmental information due to genetic factors, etc. Drug medication information can be generated.

According to one disclosure, the first artificial intelligence learning model may generate drug medication information by setting weights differently according to each piece of information. For example, the first learning model may generate drug medication information by focusing on the patient's current state information by assigning a weight of 0.7 to the patient's current state information and applying a weight of 0.3 to the surrounding environment information. In this case, whether to apply the weight to which information and how much weight to apply the weight to a number of pieces of information may be automatically set based on a patient's selection or by artificial intelligence.

By the start, the customized drug prescription device 100 includes at least one of a patient's psychological state, drinking state, pain, meal information, allergy information, family history information, and sleep state information from the patient terminal. It can provide a step of obtaining.

By the start of the day, the customized drug prescription device 100 generates question information related to patient-customized medication information by using the first artificial intelligence learning model in which medication information suitable for each situation is learned using health status information. Steps can be provided.

For example, if there is hair loss in the patient's family history, question information about whether the hair loss effect occurs, improves, or newly occurs according to the medication effect may be generated.

According to one disclosure, the customized drug prescription device 100 may provide a step of regenerating patient customized medication information based on the response information periodically obtained from the patient terminal. For example, in the case of answering that hair loss has occurred, information including information on whether to reduce or stop the drug being taken may be generated and provided.

FIG. 3 is a diagram for describing a feature of generating a second artificial intelligence learning model according to one disclosure.

By the start of the day, the second artificial intelligence learning model was learned to derive the target gene of a disease, and the change in the expression state of the gene due to the onset of the disease, the cell function, the change in the expression state of the gene due to the drug, and the drug It is characterized in that it is obtained based on the result of unsupervised learning of the relationship between changes in the expression state of other genes.

The customized drug prescription device 100 targets a disease based on a change in the expression state of a gene due to a disease, a change in the expression state of a target gene due to a drug, a change in the expression state of another gene due to a drug, and the function of the cell and the type of disease. Iterative learning to derive genes can be performed. The customized drug prescription device 100 may generate a second artificial intelligence learning model for deriving a target gene of a disease after repeated learning.

The second artificial intelligence learning model may be generated as a result of unsupervised learning of the initial deep neural network by inputting training data into the input layer of the initial deep neural network, and an intermediate deep neural network with an updated initial connection strength. And the learning data may be information on expression change genes for each of a plurality of gene datasets.

By the start of the day, the second artificial intelligence learning model was learned to derive the target gene of a disease, and the change in the expression state of the gene due to the onset of the disease, the cell function, the change in the expression state of the gene due to the drug, and the drug It is characterized in that it is obtained based on the result of unsupervised learning of the relationship between changes in the expression state of other genes.

In order to derive the target gene, the customized drug prescription device 100 measures the weighted mutual information by assigning a weight to the rarity of the domain in order to analyze the similarity of the domain profile between the proteins of the genome of the second category patients. can do.

In addition, the customized drug prescription device 100 may acquire an interaction pathway between proteins after the drug is injected, based on the similarity of the domain profiles between proteins.

According to one initiation, the customized drug prescription device 100 may determine a second other disease that may occur from the genes of patients included in the second category using the target gene.

Using a Deep Belief Network method of learning the current layer by performing propagation and backpropagation in the current layer by one start, and learning the next layer of the current layer when learning of the current layer is completed. The initial connection strength may be updated.

The customized drug prescription device 100 sets training data to the input layer of the intermediate deep neural network, sets the output value specified for the type of training data input to the input layer to the output layer of the intermediate deep neural network, and sets the input layer of the intermediate deep neural network A final deep neural network may be generated by supervised learning the intermediate deep neural network based on a value set in the and output layers.

The type of learning data by the customized drug prescription device 100 includes any one of a first type representing a normal gene dataset extracted from a normal population and a second type representing a disease gene dataset extracted from a patient group, and the learning unit When the training data corresponding to the first type is input to the input layer of the intermediate deep neural network, the intermediate deep neural network is trained by setting the first output value to the output layer of the intermediate deep neural network, and the training data corresponding to the second type is stored in the intermediate deep layer. When inputting to the input layer of the neural network, the intermediate deep neural network may be trained by setting a second output value in the output layer of the intermediate deep neural network.

The customized drug prescription device 100 compares the predicted value obtained by calculating propagation in the forward direction from the input layer of the intermediate deep neural network and the output value set in the output layer of the intermediate deep neural network to obtain an error, and then backpropagates in a direction to minimize the error. Thus, each connection strength of the intermediate deep neural network can be updated.

In addition, the customized drug prescription device 100 can obtain the association score of the drug-protein action pathway according to Equation 2 based on the association of the interaction pathways between proteins, and is specific according to the association score of the drug-protein action pathway. Enemy domains can be selected.

)은 작용경로 상의 양성/음성 유전자 쌍에 대한 전체 빈도수를 의미하며, P(L|E) 및

는 미리 예측된 양성(L) 및 음성(L)의 작용경로 상의 유전자 연결 빈도수를 의미하는 것을 특징으로 한다.Where P(L) and

) Means the total frequency for the positive/negative gene pair on the pathway of action, P(L|E) and

Is characterized in that it means the frequency of gene linkage on the pathway of action of positive (L) and negative (L) predicted in advance.

The customized drug prescription device 100 may determine a target gene by analyzing a correlation with a disease phenotype based on a specific domain selection result.

Genes that are targets for analysis in DNA by one initiation may include MAM domain containing glycosylphosphatidylinositol anchor 1 (MDGA1). The MDGA1 may be composed of a polynucleotide including bases from 37617785 to 37618223 on chromosome 6 or a complementary polynucleotide thereof.

In addition, the target gene may further include CYP4V2 (cytochrome P450 family 4 subfamily V member 2). The CYP4V2 may be composed of a polynucleotide including a base 187124479 to a base 187125005 on chromosome 4 or a complementary polynucleotide thereof. The target gene may further include one or more of ADAP1 (Arf-GAP with dual PH domain-containing protein 1), LHX6 (LIM homeobox 6), and RASA3 (Ras GTPase-activating protein 3). The ADAP1 may be composed of a polynucleotide including bases 948157 to 949189 bases on chromosome 7 or a complementary polynucleotide thereof. The LHX6 may be composed of a polynucleotide including bases from 124989781 th to 124989950 th base on chromosome 9 or a complementary polynucleotide thereof. The RASA3 may be composed of a polynucleotide including bases from 114813885 to 114814341 on chromosome 13 or a complementary polynucleotide thereof.

The target gene may further include one or more of a polymeric immunoglobulin receptor (PIGR), disintegrin and metalloproteinase domain-containing protein 12 (ADAM12), and chromosome 21 open reading frame 128 (C21orf128). In addition, the PIGR may be composed of a polynucleotide including bases from 207105468th to 207105762th base on chromosome 1 or a complementary polynucleotide thereof. The ADAM12 may be composed of a polynucleotide including bases from 127822964 th base to 127823174 th base on chromosome 10 or a complementary polynucleotide thereof. The C21orf128 may be composed of a polynucleotide including the 43528602th base to the 43529012th base or a complementary polynucleotide thereof on chromosome 21.

Additionally, the target gene may be a gene present on chromosome 2. Specifically, the target gene may further include at least one of G protein-coupled receptor 35 (GPR35) and stonin 1 (STON1).

GPR35 may be composed of a polynucleotide including bases from 241562679 to 241562995 on chromosome 2 or a complementary polynucleotide thereof. The STON1 may be composed of a polynucleotide including bases from 48983601 to 48983821 on chromosome 2 or a complementary polynucleotide thereof. The target gene may further include a Parkin coregulated gene protein (PACRG) present on chromosome 6.

4 is a diagram for describing a feature of generating a third artificial intelligence learning model according to one disclosure.

By the beginning, the third artificial intelligence learning model includes information on a plurality of diseases, drug information on the expression status of genes according to the onset of the disease, and genes that are commonly expressed when a disease occurs in people containing the same genome. It is characterized in that it is obtained based on a result of learning the association between information about the, cell function, and the degree of disease exacerbation based on the degree of gene expression and the likelihood of disease according to the degree of gene expression.

At the start of the day, the third artificial intelligence learning model determines the activated genetic modification information for each disease based on the data on the diseases that occurred in the patients included in the second category, and uses the genetic modification information for each disease. This allows you to determine other diseases that may affect a particular patient.

In addition, the customized drug prescription device 100 may generate information for alleviating disease symptoms according to the likelihood and progression of other diseases by using the current gene expression state of a specific patient.

In addition, the third artificial intelligence learning model extracts a pathway in which a drug acts on a target gene, analyzes the activation and inhibition reactions of other genes included in the pathway according to the change in the content of the drug. , It is characterized by predicting at least one other disease that may occur in the patient by analyzing the active response and the inhibitory response of other genes included in the pathway according to the change in the drug content.

More specifically, the third artificial intelligence learning model is a drug component, drug content, drug-induced change in the expression state of target gene, drug-induced change in expression state of other genes, gene expression state change due to multiple diseases, and drug use. It may be generated by learning the relationship between a change in the expression state of a gene by a specific drug and whether or not a disease is cured, based on the remission of a plurality of diseases caused by and cell function.

By initiation, the third artificial intelligence learning model extracts the pathway by which the drug acts on the target gene, and analyzes the activation and inhibition of other genes included in the pathway according to the change in the drug content. And, by analyzing the active response and inhibitory response of other genes included in the pathway according to the change in the drug content, it is possible to determine a cure method for other diseases of patients included in the second category. For example, even if the same drug is used, different dosage regimens can be determined that are optimized for each disease.

The third artificial intelligence learning model examines a tissue sample from a patient diagnosed with a disease and compares the state of the protein of the target gene of the first category with AGER, THBS2, CA3, MMP12, and the protein of the target gene of the second category. MMP-1, MMP-7, MMP-9, MMP-13, MMP-8, MMP-10, MMP-2, PIGR, DCN, PGAM1, CD36, FABP, ACP5, CCDC80, PPBP, LYVE1, STC1, SPON1, At least one selected from IL17RC, MMP1, CA1, SERPINC1, TPSB2, CKB/CKBM, NAMPT/PBEF, PPBP/CTAPIII, F9, DCTPP1, F5, SPOCK2, CAT, PF4, MDK, BGN, CKM, POSTN, PGLYRP1 and CXCL12 The altered expression state of the protein of can be analyzed. According to one disclosure, the proteins listed above may be proteins that are targets of drugs.

In addition, according to the fold difference of at least one protein having an altered expression state of the analyzed protein, other diseases that can be cured by the drug can be determined.

As a result of the initiation, the third artificial intelligence learning model, which is repeatedly learned to find other diseases that can be cured by the same drug, occurs in people containing the same genome by studying the gene expression status of people containing the same genome. It is meaningful to find multiple possible diseases.

5 is a diagram for explaining a pathway for a drug to act on a gene according to one initiation.

According to one initiation, the customized drug prescription device 100 extracts a path in which the drug acts on the target gene using a third artificial intelligence learning model, and the active reaction of other genes included in the path according to the change in the drug content ( Activation) and inhibition reactions, and by analyzing the active reactions and inhibitory reactions of other genes included in the pathway according to changes in drug content, treatment for other diseases experienced by patients in the second category You can find a way.

According to an initiation, the customized drug prescription device 100 may recreate a new drug using a biological network including a plurality of gene information, and a biological network is an interaction between biological entities such as genes and proteins ( interaction).

Referring to (a) of FIG. 5, the right end of each path connecting between an acting gene, an intermediate gene, and a disease gene is indicated by an arrow or a circle, wherein the arrow indicates activation, and the circle indicates Means inhibition.

The relationship between the drug 401 and the disease gene or the target disease can be derived based on the shortest path derived from the initiation and the activity/inhibition, and furthermore, the relationship between the drug 401 and the disease gene or the target disease A step of calculating a score by digitizing is performed.

This score can be calculated based on the number of intermediate genes arranged on the shortest path, whether the shortest path is active/inhibited, the relationship between the drug and the acting gene, and the state of the disease gene in the disease.

Referring to (B) of FIG. 5, the disease gene may be two of the first disease gene 403 and the second disease gene 404, which means that the drug 401 to be verified is two disease genes ( 404, 405).

On the other hand, the input drug 401 may act to treat the disease by reacting with the disease gene, or, conversely, may act to make the disease worse. Looking at this in detail using (b) of FIG. 5, the drug 401 ) Acts to treat the disease by reacting with the first disease gene 404 that is suppressed in the disease state, and acts to cure the disease by inhibiting the second disease gene 405 that is activated in the disease state. do.

Extracting the shortest path between the effector genes 402 and 403 and the disease genes 404 and 405 includes extracting all the shortest paths that can link between the effector genes 402 and 403 and the disease genes 404 and 405, And extracting the shortest path between the working genes 402 and 403 and the disease genes 404 and 405 based on the number of at least one intermediate gene arranged on the path.

6 is a diagram illustrating a screen for providing medical information to a terminal of a patient according to an example.

According to one disclosure, when the terminal of the patient is a wearable watch, prevention information on the second disease may be reported on the wearable terminal.

For example, the customized drug prescription device 100 may transmit a notification message 2002 to the wearable watch 2000. In the notification message 2002, the current time is the drug taking time, and in particular, it may include information that one pill should be taken 30 minutes after a meal. In addition, the notification message 2002 may include a notice. For example, the notification message 2002 may include a content to eat at the same time as a vitamin in order to maximize the effect of a medicine to be eaten at present.

In addition, the customized drug prescription device 100 reflects the past health state, the current health state, and the future health state according to the passage of time, and creates the second disease prevention information by expressing the patient's character, and prevents the second disease. The patient character whose information is reflected may be provided to the patient's terminal.

In addition, the customized drug prescription device 100 may receive a patient input for selecting a body part included in the patient's character from the patient's terminal.

As the customized drug prescription device 100 receives the patient input, the patient's character, which is displayed by expanding the health status change for the selected body part, is transmitted to the patient's terminal, and the future health status from the patient's terminal 2000 You can receive a request to check the status.

The customized drug prescription device 100 may regenerate the patient's character by reflecting information on the health state at a future point in time and transmit it to the patient's terminal.

For example, the customized drug prescription device 100 calculates a regression equation by analyzing changes in health status information according to time changes using health status information up to the current time t1, and calculates the health status in the calculated regression equation. If a time t2 to be predicted is input, health state information at the time point t2 can be predicted.

The customized drug prescription device 100 receives the time point at which the patient wants to know the health status, for example, after 1 month, 6 months, or 1 year, and uses the regression equation to predict the health status information at the time point desired by the patient. It is desirable to do.

The customized drug prescription device 100 provides a character reflecting the health state information at a time point desired by the patient to the patient's terminal, so that the patient can easily know the patient's state and the progress of the disease.

According to the start of the day, the customized drug prescription device 100 can provide current prescription details, customer identification information, billed amount, customer specific details, past prescription history, customer notepad, and past prescription dispensing list to the patient's terminal 2000. I can. However, it is obvious that the items displayed on the screen are exemplified and may vary according to the results of artificial intelligence search.

According to the start of the day, the customized drug prescription device 100 predicts the likelihood of occurrence of a disease using the genetically modified state, and then, when it is determined that the disease has occurred in the patient, medical treatment using the patient's biometric information and surrounding environment You can provide information.

According to the start of the day, the customized drug prescription device 100 includes a temperature sensor that measures the patient's body temperature, a humidity sensor that measures the patient's sweat discharge, a reflective optical sensor that measures the patient's skin characteristics, and the heart rate that measures the patient's heart rate. The biometric information of the patient may be obtained by using a sensor unit including at least one of a sensor, a blood pressure sensor that measures the patient's blood pressure, and a motion sensor that measures the patient's activity. In addition, the customized drug prescription device 100 may generate management information on a patient's disease in consideration of at least one of the patient's body temperature information, sweat emission information, skin characteristic information, heart rate information, blood pressure information, and activity information. have.

The surrounding environment information according to the start of the day may include at least one of temperature, atmospheric pressure, humidity, time, weather, and fine dust concentration.

Furthermore, the customized drug prescription device 100 may generate pain notification information including the effect of surrounding environment information on the patient's pain, and patient pain relief and prevention information based on the surrounding environment information. In addition, the customized drug prescription device 100 may generate pain notification information including the effect of surrounding environment information on the patient's pain, pain relief and prevention information of the patient based on the surrounding environment information, and display pain notification information. can do.

According to one disclosure, the customized drug prescription device 100 may receive surrounding environment information from a meteorological service server, a weather forecast server, and the like, and may measure using sensors included in the customized drug prescription device 100.

According to the start of the day, the customized drug prescription device 100 may display weather, temperature, and humidity information, and may display pain notification information along with the patient's today's pain intensity and whether or not to take today's medicine.

For example, as shown in FIG. 12(a), the correlation between temperature and the patient's pain may be checked, and it may be determined whether or not the pain may become more severe according to the current temperature. For example, in general, if the pain can be intensified as the temperature decreases, it may indicate that the pain can intensify as the ambient temperature decreases.

In addition, the customized drug prescription device 100 may notify pain relief information suitable for a current patient state. For example, in the case of a weather condition in which the pain may worsen, the patient may be informed of information to relieve the pain by stretching or prompting to keep the body warm.

Likewise, it can send a message that the pain can intensify if the ambient humidity is high. In addition, pain relief information in humid weather can be generated and displayed.

Accordingly, the patient has the advantage of being able to check pain management information suitable for the weather at once along with the intensity of his or her pain and whether or not to take medicine.

Furthermore, the customized drug prescription device 100 may help the patient to take the drug in time by displaying whether or not the drug is taken when the drug is to be taken multiple times a day.

7 is a diagram for explaining the configuration of a device for prescribing a patient-customized drug using artificial intelligence according to one disclosure.

According to one disclosure, the customized drug prescription apparatus 100 may include a processor 1300 and a memory 1100, but as shown in FIG. 7, it may be implemented by more configurations than essential components.

For example, as shown in FIG. 7, the customized drug prescription device 100 according to an embodiment includes a memory 1100, a display unit 1210, a camera 1610 and a processor 1300, an output unit ( 1200), a communication unit 1500, a sensing unit 1400, an A/V input unit 1600, and a patient input unit 1700 may be further included.

The memory 1100 may store a program for processing and controlling the processor 1300, and may store an image input to the customized drug prescription device 100 or guide information output from the customized drug prescription device 100. . Also, the memory 1100 may store specific information for determining whether to output the guide information.

The memory 1100 is a flash memory type, a hard disk type, a multimedia card micro type, a card type memory (for example, SD or XD memory), and RAM. (RAM, Random Access Memory) SRAM (Static Random Access Memory), ROM (ROM, Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), PROM (Programmable Read-Only Memory), magnetic memory, magnetic disk And at least one type of storage medium among optical disks.

Programs stored in the memory 1100 can be classified into a plurality of modules according to their functions, for example, a UI module 1110, a touch screen module 1120, a notification module 1130, and the like. .

The UI module 1110 may provide a specialized UI, GUI, etc. linked with the customized drug prescription device 100 for each application. The touch screen module 1120 may detect a touch gesture on a patient's touch screen and transmit information on the touch gesture to the processor 1300. The touch screen module 1120 according to an embodiment may recognize and analyze a touch code. The touch screen module 1120 may be configured as separate hardware including a controller.

The notification module 1130 may generate a signal for notifying the occurrence of an event of the customized drug prescription device 100. Examples of events occurring in the customized drug prescription device 100 include call signal reception, message reception, key signal input, schedule notification, and the like. The notification module 1130 may output a notification signal in the form of a video signal through the display unit 1210, may output a notification signal in the form of an audio signal through the sound output unit 1220, and the vibration motor 1230 It is also possible to output a notification signal in the form of a vibration signal through. For example, the notification module 1130 may generate a signal for outputting guide information based on the estimated lane information.

The output unit 1200 may output an audio signal, a video signal, or a vibration signal, and the output unit 1200 may include a display unit 1210, an audio output unit 1220, and a vibration motor 1230. have.

The display 1210 displays and outputs information processed by the customized drug prescription device 100. Specifically, the display unit 1210 may output an image captured by the camera 1610. Also, the display 1210 may synthesize and output the guide information generated by the processor 1300 with the captured image.

In addition, the display 1210 may display a patient interface for executing an operation related to the response in response to the patient's input.

The sound output unit 1220 outputs audio data received from the communication unit 1500 or stored in the memory 1100. In addition, the sound output unit 1220 outputs sound signals related to functions (eg, call signal reception sound, message reception sound, and notification sound) performed by the customized drug prescription device 100. For example, the sound output unit 1220 may output guide information generated as a signal from the notification module 1130 as an sound signal under the control of the processor 1300.

The processor 1300 typically controls the overall operation of the customized drug prescription device 100. For example, the processor 1300 executes programs stored in the memory 1100, so that the patient input unit 1700, the output unit 1200, the sensing unit 1400, the communication unit 1500, the A/V input unit 1700 ) And so on. Also, the processor 1300 may perform a function of the customized drug prescription device 100 by executing programs stored in the memory 1100.

The sensing unit 1400 may detect a state of the customized drug prescription device 100 or a state around the customized drug prescription device 100 and transmit the detected information to the processor 1300.

The sensing unit 1400 includes a magnetic sensor 1410, an acceleration sensor 1420, a temperature/humidity sensor 1430, an infrared sensor 1440, a gyroscope sensor 1450, and a position sensor. (For example, a GPS) 1460, an atmospheric pressure sensor 1470, a proximity sensor 1480, and an RGB sensor 1490 may be included, but the present invention is not limited thereto. Since the function of each sensor can be intuitively inferred by a person skilled in the art from its name, a detailed description will be omitted.

According to an embodiment, the sensing unit 1400 may measure a distance between the vehicle and at least one object determined from the captured image.

The communication unit 1500 may include one or more components that allow the customized drug prescription device 100 to communicate with other devices (not shown) and servers (not shown). Another device (not shown) may be a computing device such as the customized drug prescription device 100 or a sensing device, but is not limited thereto. For example, the communication unit 1500 may include a short range communication unit 1510, a mobile communication unit 1520, and a broadcast reception unit 1530.

The short-range wireless communication unit 1510 includes a Bluetooth communication unit, a Bluetooth Low Energy (BLE) communication unit, a near field communication unit, a WLAN (Wi-Fi) communication unit, a Zigbee communication unit, and an infrared ( IrDA, infrared data association) communication unit, WFD (Wi-Fi Direct) communication unit, UWB (ultra wideband) communication unit, Ant+ communication unit, etc. may be included, but is not limited thereto.

The mobile communication unit 1520 transmits and receives a radio signal with at least one of a base station, an external terminal, and a server on a mobile communication network. Here, the wireless signal may include a voice call signal, a video call signal, or various types of data according to transmission/reception of text/multimedia messages.

The broadcast receiver 1530 receives a broadcast signal and/or broadcast-related information from outside through a broadcast channel. Broadcast channels may include satellite channels and terrestrial channels. Depending on the implementation, the customized drug prescription device 100 may not include the broadcast reception unit 1530.

The A/V (Audio/Video) input unit 1600 is for inputting an audio signal or a video signal, and may include a camera 1610 and a microphone 1620. The camera 1610 may obtain an image frame such as a still image or a moving image through an image sensor in a video call mode or a photographing mode. The image captured through the image sensor may be processed through the processor 1300 or a separate image processing unit (not shown).

The microphone 1620 receives an external sound signal and processes it as electrical voice data. For example, the microphone 1620 may receive an acoustic signal from an external device or a patient. The microphone 1620 may receive a patient's voice input. The microphone 1620 may use various noise removal algorithms to remove noise generated in the process of receiving an external sound signal.

The patient input unit 1700 refers to a means for a patient to input data for controlling the customized drug prescription device 100. For example, the patient input unit 1700 includes a key pad, a dome switch, and a touch pad (contact capacitive method, pressure resistive film method, infrared sensing method, surface ultrasonic conduction method, integral type). Tension measurement method, piezo effect method, etc.), a jog wheel, a jog switch, and the like, but are not limited thereto.

FIG. 8 is a diagram illustrating a configuration of a processor that generates an artificial intelligence learning model through data learning according to one disclosure.

The processor 1300 according to an embodiment may include a data learning unit 1310 and a data recognition unit 1320.

The data learning unit 1310 may learn a gene expression state according to an action of a drug. The data learning unit 1310 may learn a criterion regarding which data to use and whether to expand the relationship graph by using the data in order to increase the accuracy of the relationship graph. The data learning unit 1310 acquires data to be used for learning and applies the acquired data to a data recognition model to be described later, thereby learning a criterion for forming an appropriate relationship network according to learning of the relationship graph.

According to an embodiment, when there are a plurality of pre-built data recognition models, the data learning unit 1310 may determine a data recognition model having a high correlation between input training data and basic training data as a data recognition model to be trained. have. In this case, the basic training data may be pre-classified by data type, and the data recognition model may be pre-built for each data type. For example, basic training data is classified based on various criteria such as the region where the training data was created, the time when the training data was created, the size of the training data, the genre of the training data, the creator of the training data, and the type of objects in the training data. Can be.

The data recognition unit 1320 may increase the accuracy of which drugs should be used to treat diseases by studying the expression states of drugs and target genes and other genes. The data recognition unit 1320 may increase the accuracy of recommending a new drug taking method by acquiring predetermined data according to a preset criterion by learning and using the data recognition model using the acquired data as an input value. . Also, a result value output by the data recognition model using the acquired data as an input value may be used to update the data recognition model.

At least one of the data learning unit 1310 and the data recognition unit 1320 may be manufactured in the form of at least one hardware chip and mounted on a device. For example, at least one of the data learning unit 1310 and the data recognition unit 1320 may be manufactured in the form of a dedicated hardware chip for artificial intelligence (AI), or an existing general-purpose processor (eg, a CPU Alternatively, it may be manufactured as a part of an application processor) or a graphics dedicated processor (eg, a GPU) and mounted on the aforementioned various devices.

In this case, the data learning unit 1310 and the data recognition unit 1320 may be mounted on one device or may be mounted on separate devices. For example, one of the data learning unit 1310 and the data recognition unit 1320 may be included in the device, and the other may be included in the server. In addition, the data learning unit 1310 and the data recognition unit 1320 may provide model information built by the data learning unit 1310 to the data recognition unit 1320 through wired or wireless communication, or the data recognition unit ( Data input to 1320 may be provided to the data learning unit 1310 as additional learning data.

Meanwhile, at least one of the data learning unit 1310 and the data recognition unit 1320 may be implemented as a software module. When at least one of the data learning unit 1310 and the data recognition unit 1320 is implemented as a software module (or a program module including an instruction), the software module is a computer-readable non-transitory It may be stored in a non-transitory computer readable media. In addition, in this case, at least one software module may be provided by an operating system (OS) or a predetermined application. Alternatively, some of the at least one software module may be provided by an operating system (OS), and the remaining part may be provided by a predetermined application.

In addition, the data recognition unit 1320 may train a data recognition model used to determine a situation by using the training data. In this case, the data recognition model may be a pre-built model. For example, the data recognition model may be a model built in advance by receiving basic training data (eg, sample images, etc.).

The data recognition model may be constructed in consideration of the application field of the recognition model, the purpose of learning, or the computer performance of the device. The data recognition model may be, for example, a model based on a neural network. For example, a model such as a deep neural network (DNN), a recurrent neural network (RNN), or a bidirectional recurrent deep neural network (BRDNN) may be used as a data recognition model, but is not limited thereto.

According to various embodiments, when there are a plurality of pre-built data recognition models, the data learning unit 1310 may determine a data recognition model having a high correlation between input training data and basic training data as a data recognition model to be trained. have. In this case, the basic training data may be pre-classified by data type, and the data recognition model may be pre-built for each data type. For example, basic training data is classified based on various criteria such as the region where the training data was created, the time when the training data was created, the size of the training data, the genre of the training data, the creator of the training data, and the type of objects in the training data. Can be.

An embodiment may also be implemented in the form of a recording medium including instructions executable by a computer, such as a program module executed by a computer. Computer-readable media can be any available media that can be accessed by a computer, and includes both volatile and nonvolatile media, removable and non-removable media. Further, the computer-readable medium may include both computer storage media and communication media. Computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Communication media typically includes computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave, or other transmission mechanism, and includes any information delivery medium.

Further, in the present specification, the "unit" may be a hardware component such as a processor or a circuit, and/or a software component executed by a hardware configuration such as a processor.

The above description of the present invention is for illustrative purposes only, and those of ordinary skill in the art to which the present invention pertains will be able to understand that other specific forms can be easily modified without changing the technical spirit or essential features of the present invention will be. Therefore, it should be understood that the embodiments described above are illustrative in all respects and not limiting. For example, each component described as a single type may be implemented in a distributed manner, and similarly, components described as being distributed may also be implemented in a combined form.

The scope of the present invention is indicated by the claims to be described later rather than the detailed description, and all changes or modified forms derived from the meaning and scope of the claims and their equivalent concepts should be interpreted as being included in the scope of the present invention. do.

Claims (10)

Periodically acquiring treatment data including gene expression distribution of a patient who has received a drug prepared to treat a first disease;
Determining a second disease possible to develop in the patient from the distribution of gene expressions of the patient for whom the treatment of the first disease has been completed;
Receiving biometric information of the patient from the terminal of the patient;
Determining a possibility of developing the second disease based on the patient's biometric information;
Generating prevention information for a second disease corresponding to a current patient's condition by using a first artificial intelligence learning model that has learned the association of a second disease prevention method according to the value of the second disease probability;
Reflecting a past health state, a current health state, and a future health state as time passes, and generating the second disease prevention information by expressing the patient's character;
A method for prescribing a patient-tailored drug using artificial intelligence, comprising the step of providing the patient's character on which the second disease prevention information is reflected to the patient's terminal. The method of claim 1,
Generating question information related to patient-specific medication information based on the second disease prevention information, and providing the generated question information to the patient's terminal; And
Further comprising the step of modifying the prevention information of the second disease using the answer information to the question information from the terminal of the patient and providing it to the terminal of the patient,
The second disease prevention information,
Information on the second disease, the possibility of developing the second disease, changes in health status according to the progression of the second disease, information on necessary drugs according to the patient's current state information, information on taking drugs, precautions, the number of appropriate drug intakes A method for prescribing a patient-tailored drug using artificial intelligence, characterized in that it includes an appropriate intake time. The method of claim 1,
The first artificial intelligence learning model,
Based on the results of learning medical identification information related to the drug prescribed by the patient, information on the second disease, medical history information, medical treatment history information, information on prescribed drugs, and drug medication information corresponding to medical identification information Is created,
The first artificial intelligence learning model is characterized in that by setting a weight to at least one of the patient's current state information, the patient's gene expression state information, and the surrounding environment information, to generate time-based drug medication information according to the patient's current state information. A method for prescribing customized drugs for patients using artificial intelligence. The method of claim 1,
Receiving a patient input from the patient's terminal by selecting a body part included in the patient's character;
Transmitting, to the patient's terminal, a character of the patient, which is displayed by expanding and displaying a change in health status of the selected body part according to the patient input;
Receiving a request from the patient's terminal to confirm a health state at a future point in time; And
Regenerating the patient's character by reflecting the information on the health state at a future point in time and transmitting it to the patient's terminal. The method of claim 1,
Categorizing treatment data of patients whose first disease has been cured into a first category after administering the drug to treat the same first disease as the patient;
Classifying treatment data of patients including genes commonly expressed among patients included in the first category into a second category;
Deriving a target gene from the genes of patients included in the second category by using a second artificial intelligence learning model in which the relationship between the gene expression distribution due to the disease and the gene expression distribution due to the action of the drug is learned;
Determining at least one other disease that may develop from the genes of patients included in the second category by using the target gene;
Determining the likelihood that the at least one other disease will develop using a third artificial intelligence learning model that has learned the relationship between the expression status of the genes of the patients included in the second category according to the disease and the healing of a plurality of diseases. ;And
A method for prescribing a drug tailored to a patient using artificial intelligence, comprising the step of providing information on a disease prevention method corresponding to the possibility of occurrence of the at least one other disease. The method of claim 1,
The second artificial intelligence learning model,
As learned to derive the target gene of the first disease, changes in the expression state of genes due to the onset of the first disease, cell function, changes in the expression state of genes due to drugs, and changes in the expression state of other genes due to drugs A method for prescribing a patient-specific drug using artificial intelligence, characterized in that it is obtained based on a result of unsupervised learning. The method of claim 1,
The third artificial intelligence learning model,
Information on multiple diseases, information on the expression status of genes according to the onset of the disease, information on genes commonly expressed when a disease occurs in people containing the same genome, cell functions, and gene expression levels A method for prescribing a patient-tailored drug using artificial intelligence, characterized in that it is obtained based on the result of learning the association between the occurrence probability of the disease and the degree of exacerbation of the disease based on the degree of gene expression. The method of claim 1,
Transmitting a question message about whether the body has changed due to the disease, whether pain is caused by the disease, whether or not to take medicine for pain, pain intensity, pain area, and pain duration to the terminal of the patient who has at least one other disease. step;
Receiving a response message to the question message from the patient's terminal, based on the information included in the response message, changes in the region and intensity of pain for each activity over time, pain according to whether or not to take medicine for pain Graphing the patient's pain information including whether or not to relieve pain;
Obtaining biometric information including body temperature information, sweat emission information, skin characteristic information, heart rate information, blood pressure information, and activity information of the patient from the patient's terminal;
Generating management information on the patient's pain in consideration of at least one of the patient's body temperature information, sweat discharge information, skin characteristic information, heart rate information, blood pressure information, and activity information; And
Based on the pain information of the patient, an exercise application that provides an exercise that can improve the pain, a medical management application that helps relieve the pain, a hospital related to the pain, a drug effective in reducing the pain, the Recommending at least one of the medical devices that help to improve pain; further comprising, a device for prescribing a patient-tailored drug using artificial intelligence. Processor; and
Including; a memory for storing executable instructions,
The processor,
By executing the above commands,
Periodically acquiring medical treatment data including the gene expression distribution of the patient who received the drug manufactured to treat the first disease,
From the gene expression distribution of the patient who has completed the treatment of the first disease, determining a second disease that is possible to develop in the patient,
Receiving the patient's biometric information from the patient's terminal,
Determining the likelihood of developing the second disease based on the patient's biometric information,
Using the first artificial intelligence learning model that learned the association of the second disease prevention method according to the value of the second disease probability value, the second disease prevention information corresponding to the current patient condition is generated,
By reflecting the past health status, the current health status, and the future health status over time, the second disease prevention information is expressed and generated as the patient's character,
A device for prescribing a patient-tailored drug using artificial intelligence that provides the patient's character on which the second disease prevention information is reflected to the patient's terminal. A computer-readable non-transitory recording medium on which a program for implementing the method of any one of claims 1 to 8 is recorded.

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