System for providing genetic diagnostic test service based on demand prediction using artificial intelligence and method thereof

Abstract

The present invention relates to a system and method for providing a demand-prediction-based genetic diagnostic test service utilizing artificial intelligence. It collects and analyzes genetic diagnostic test request information requested by clients, constructs an artificial intelligence model that predicts the demand for genetic diagnostic tests for each client by referring to the analysis results, and can provide a customized genetic diagnostic test service for each client by utilizing the artificial intelligence model.

Description

Technology Field

[0001] The present invention relates to a system and method for providing a demand-prediction-based genetic diagnostic test service utilizing artificial intelligence. More specifically, the invention relates to a system and method for providing a demand-prediction-based genetic diagnostic test service utilizing artificial intelligence that collects and analyzes genetic diagnostic test request information requested by a client, constructs an artificial intelligence model that predicts the demand for genetic diagnostic tests for each client by referring to the analysis results, and provides a customized genetic diagnostic test service for each client by utilizing the artificial intelligence model. Background Technology

[0002] Recently, public interest in health and disease prevention has been increasing, and along with this, genetic diagnostic testing, a useful testing tool that plays an important role in personal health management, disease treatment and prevention, and the development of new treatments, is being actively utilized.

[0003] Genetic diagnostic testing is widely used for disease diagnosis and prediction, treatment and management, research purposes, couples' childbirth planning, and confirming blood relationships.

[0004] For example, in the case of patients who have already exhibited symptoms such as cancer or rare genetic diseases, genetic diagnostic testing can accurately diagnose the cause of the disease, and the diagnostic results can be used to determine the appropriate direction of treatment.

[0005] In addition, for healthy people who have not yet developed symptoms, genetic diagnostic testing can predict the risk of developing specific diseases, enabling early detection and prevention of conditions such as breast cancer, Alzheimer's disease, and hemophilia. It can also be utilized to identify the causes of diseases, as well as for gene therapy and the development of personalized drugs.

[0006] In addition, for couples with a family history of genetic diseases, genetic diagnostic testing can be used to determine if their children are at risk of developing the disease and to formulate reproductive plans; it can also be utilized for paternity testing and verifying family relationships.

[0007] However, since current genetic diagnostic tests are conducted by collecting multiple samples and processing them simultaneously using a single test chip, there were problems such as incurring excessive costs for diagnosis and failing to utilize resources efficiently when the demand for accurate genetic diagnostic testing was not predicted.

[0008] Therefore, the present invention aims to propose a method to optimize the costs associated with genetic diagnostic testing and achieve product planning and resource conservation related to genetic diagnostic testing by constructing an artificial intelligence model for predicting demand for genetic diagnostic testing by referring to genetic diagnostic test request information requested by each client, and by utilizing the constructed artificial intelligence model to provide customized genetic diagnostic testing services for each client.

[0009] Next, we will briefly explain the prior art existing in the technical field of the present invention, and then describe the technical details that the present invention aims to achieve differently from the said prior art.

[0010] Korean Published Patent No. 10-2022-0124483 (September 14, 2022) is a prior art invention relating to a disease prediction method comprising the steps of: collecting phenotype data and genotype data; inputting the collected phenotype data and genotype data into a first model to generate low-dimensional data; inputting the generated low-dimensional data into a second model to generate high-dimensional data; forming complex-dimensional data based on the generated low-dimensional data and inputting the complex-dimensional data into a third model to diagnose and predict a genetic disease; forming complex-dimensional data based on the generated high-dimensional data and inputting the complex-dimensional data into a fourth model to diagnose and predict a genetic disease; and evaluating data generation performance by comparing the genetic disease diagnosis result diagnosed through the third model with the genetic disease diagnosis result diagnosed through the fourth model.

[0011] However, the present invention aims to optimize costs and save resources by providing customized genetic diagnostic testing services for each client through the forecasting of demand for genetic diagnostic testing, and there are significant structural differences when compared to the aforementioned Korean Published Patent No. 10-2022-0124483, which describes a disease prediction method for predicting diseases through the genome using machine learning and artificial intelligence models. The problem to be solved

[0012] The present invention was created to solve the aforementioned problems and aims to provide a system and a method capable of constructing an artificial intelligence model for predicting the demand for genetic diagnostic tests for each client based on genetic diagnostic test request information requested by the client, and providing customized genetic diagnostic test services for each client by utilizing the artificial intelligence model.

[0013] In addition, another objective of the present invention is to provide a system and a method capable of optimizing the cost of requesting genetic diagnostic tests for each client by predicting the demand for genetic diagnostic tests for each client using an artificial intelligence model.

[0014] In addition, another objective of the present invention is to provide a system and a method that can reduce costs by optimizing resource allocation for genetic diagnostic tests on the part of the system operator through the prediction of demand for genetic diagnostic tests for each client using an artificial intelligence model, and improve the product planning and sales efficiency of genetic diagnostic tests.

[0015] However, the technical problems that this embodiment aims to solve are not limited to the technical problems described above, and other technical problems may exist. means of solving the problem

[0016] A genetic diagnostic test service provision system based on demand forecasting using artificial intelligence according to an embodiment of the present invention comprises: a data collection unit that collects genetic diagnostic test request information from a client; a request pattern analysis unit that analyzes genetic diagnostic test request patterns for each client regarding diagnostic test needs, timing, frequency, gender and age of the requester by referring to the collected genetic diagnostic test request information; an artificial intelligence model generation unit that generates an artificial intelligence model for genetic diagnostic test demand forecasting by referring to the analyzed genetic diagnostic test request patterns; a genetic diagnostic test demand forecasting unit that forecasts the demand for genetic diagnostic tests for each client through the generated artificial intelligence model for genetic diagnostic test demand forecasting; a genetic diagnostic test cost calculation unit that calculates the genetic diagnostic test request cost for each client by referring to the predicted result; and a genetic diagnostic test cost calculation result providing unit that provides information regarding the calculated genetic diagnostic test request cost to the client.

[0017] At this time, the genetic diagnostic test request information is characterized by including: information on the requesting institution, including the name of the requesting institution, the name of the attending physician, the name of the requesting department, the address and contact information of the requesting institution, the date and time of the request, and the number of requests by the requesting institution by week and month; and information on the test subject, including the age, gender, height and weight, clinical findings, test request items, type of specimen, date and time of specimen collection, and method of specimen collection.

[0018] In addition, the artificial intelligence model for predicting the demand for genetic diagnostic tests is characterized as a deep learning model trained to output the demand for genetic diagnostic tests for each client by using the genetic diagnostic test request patterns for each client regarding diagnostic test needs, timing, frequency, gender and age of the test subjects analyzed by the request pattern analysis unit, and the number of patients treated for each client additionally collected through the data collection unit as training data.

[0019] In addition, the artificial intelligence model generation unit comprises: a preprocessing unit that performs data cleaning to correct information errors or abnormalities in the genetic diagnostic test request patterns analyzed by the request pattern analysis unit, data transformation for converting text data into numerical vectors or image data into sizes or normalization, and data standardization for learning stabilization through the adjustment of feature ranges; a training data generation unit that generates training data through the division, sampling, and augmentation of the data that has completed the preprocessing; and a learning unit that generates an artificial intelligence model for predicting genetic diagnostic test demand by learning the generated training data, verifies the performance of the generated artificial intelligence model for predicting genetic diagnostic test demand, and stores it in a database.

[0020] In addition, the artificial intelligence model generation unit further includes an update unit that updates the artificial intelligence model for predicting demand for genetic diagnostic tests through fine-tuning or retraining when the number of genetic diagnostic test request patterns analyzed from genetic diagnostic test request information continuously added and collected from each client satisfies a preset standard.

[0021] In addition, the genetic diagnostic test cost calculation unit determines the genetic diagnostic test request cost for each client corresponding to the predicted demand based on the processing cost according to the amount of sample when performing the genetic diagnostic test using a single test chip, when the demand for genetic diagnostic testing for each client is predicted through the artificial intelligence model for predicting the demand for genetic diagnostic testing; the genetic diagnostic test request cost for each client is characterized by being able to be determined differently depending on the selection of daily, weekly, monthly, quarterly, or yearly.

[0022] In addition, the system further comprises a genetic diagnostic test product development unit that develops and plans genetic diagnostic test products based on the type, timing, and frequency of diagnostic tests by referring to the demand for genetic diagnostic tests for each client predicted through the artificial intelligence model for predicting demand for genetic diagnostic tests and the request cost for genetic diagnostic tests determined according to the demand prediction, and performs sales and promotion by providing information about the genetic diagnostic test products to each client.

[0023] In addition, a method for providing a genetic diagnostic test service based on demand forecasting using artificial intelligence according to an embodiment of the present invention is performed in a system for providing a genetic diagnostic test service based on demand forecasting using artificial intelligence, and comprises: a data collection step of collecting genetic diagnostic test request information from a client; a request pattern analysis step of analyzing genetic diagnostic test request patterns for each client regarding diagnostic test needs, timing, frequency, gender and age of the requester by referring to the collected genetic diagnostic test request information; an artificial intelligence model generation step of generating an artificial intelligence model for predicting genetic diagnostic test demand by referring to the analyzed genetic diagnostic test request pattern; a genetic diagnostic test demand prediction step of predicting the demand for genetic diagnostic tests for each client through the generated artificial intelligence model for predicting genetic diagnostic test demand; a genetic diagnostic test cost calculation step of calculating the genetic diagnostic test request cost for each client by referring to the predicted result; and a genetic diagnostic test cost calculation result provision step of providing information on the calculated genetic diagnostic test request cost to the client.

[0024] In addition, the artificial intelligence model generation step further comprises: a preprocessing step for performing data cleaning to correct information errors or abnormalities in the genetic diagnostic test request patterns analyzed in the request pattern analysis step, data transformation for converting text data into numerical vectors or image data into sizes or normalization, and data standardization for learning stabilization through adjustment of feature ranges; a training data generation step for generating training data through partitioning, sampling, and augmentation of the data that has completed the preprocessing; a learning step for generating an artificial intelligence model for predicting genetic diagnostic test demand by learning the generated training data, verifying the performance of the generated artificial intelligence model for predicting genetic diagnostic test demand, and storing it in a database; and an update step for updating the artificial intelligence model for predicting genetic diagnostic test demand through fine-tuning or retraining when the number of genetic diagnostic test request patterns analyzed from genetic diagnostic test request information continuously added and collected from each client satisfies a preset standard.

[0025] In addition, the above method further comprises a genetic diagnostic test product development step, which involves developing and planning a genetic diagnostic test product based on the type, timing, and frequency of the test by referring to the demand for genetic diagnostic tests for each client predicted through the artificial intelligence model for predicting demand for genetic diagnostic tests and the request cost for genetic diagnostic tests determined according to the demand prediction, and providing information about the genetic diagnostic test product to each client to perform sales and promotion. Effects of the invention

[0026] As described above, according to the system and method for providing a demand-prediction-based genetic diagnostic test service utilizing artificial intelligence of the present invention, by utilizing an artificial intelligence model for predicting demand for genetic diagnostic tests built based on genetic diagnostic test request information requested by each client to provide customized genetic diagnostic test services for each client, it is possible to help optimize the genetic diagnostic test request costs for each client, and to achieve cost reduction through the efficient allocation of resources related to genetic diagnostic tests, as well as improve the product planning and sales efficiency of genetic diagnostic tests.

[0027] However, the effects of the present invention are not limited to the effects described above, and unmentioned effects will be clearly understood by those skilled in the art from this specification and the attached drawings. Brief explanation of the drawing

[0028] FIG. 1 is a schematic diagram showing the overall configuration including a demand forecasting-based genetic diagnostic test service provision system utilizing artificial intelligence according to one embodiment of the present invention. FIG. 2 is a diagram showing the configuration of a genetic diagnostic test service provision system according to one embodiment of the present invention in more detail. FIG. 3 is a diagram showing the hardware structure of a genetic diagnostic test service providing system and method according to one embodiment of the present invention. FIG. 4 is a flowchart illustrating in detail the operation process of a method for providing a demand forecast-based genetic diagnostic test service using artificial intelligence according to an embodiment of the present invention. Specific details for implementing the invention

[0029] Specific embodiments of the present invention will be described in detail below with reference to the drawings. However, the concept of the present invention is not limited to the presented embodiments. Those skilled in the art who understand the concept of the present invention may easily propose other inventions that are inferior or other embodiments included within the scope of the concept of the present invention by adding, changing, or deleting other components within the same scope of the concept, and such are also to be considered to be included within the scope of the concept of the present invention.

[0030] Additionally, components with the same function within the scope of the same concept appearing in the drawings of each embodiment are described using the same reference numeral.

[0031] FIG. 1 is a schematic diagram showing the overall configuration including a demand forecasting-based genetic diagnostic test service provision system utilizing artificial intelligence according to one embodiment of the present invention.

[0032] As illustrated in FIG. 1, the present invention comprises a demand forecasting-based genetic diagnostic test service providing system utilizing artificial intelligence (100, hereinafter referred to as the genetic diagnostic test service providing system), a plurality of client terminals (200), a database (300), etc.

[0033] The above-mentioned genetic diagnostic test service providing system (100) receives a genetic diagnostic test request form, patient consent form, and specimen of a specific patient in accordance with a genetic diagnostic test request from the client terminal (200) that has established a communication connection via an application program or the web, performs the requested genetic diagnostic test, and then provides the genetic diagnostic test results to the client terminal (200) in the form of a report.

[0034] In this process, the genetic diagnostic test service providing system (100) can analyze the genetic diagnostic test request patterns for each client (i.e., each medical institution) based on the genetic diagnostic test request information received from the client terminal (200).

[0035] For example, the genetic diagnostic test request information received from each client terminal (200) in the genetic diagnostic test service providing system (100) may include personal information, physical information, and clinical information of the test subject, as well as the type of genetic diagnostic test, medical institution information, and treatment information, and by analyzing the genetic diagnostic test request information collected in this way, detailed analysis such as by medical institution, by patient, and by day of the week can be performed.

[0036] That is, the above-mentioned genetic diagnostic test service providing system (100) can identify the genetic diagnostic test needs of patients using the above-mentioned genetic diagnostic test request information, as well as analyze various genetic diagnostic test request patterns according to time, frequency, age, gender, etc.

[0037] In addition, the genetic diagnostic test service providing system (100) can analyze the request pattern of genetic diagnostic tests by referring to genetic diagnostic test request information collected from each client, and then build an artificial intelligence model for predicting the demand for genetic diagnostic tests for each client using the analyzed genetic diagnostic test request pattern as training data.

[0038] In addition, the above-mentioned genetic diagnostic test service provision system (100) can predict the demand for genetic diagnostic tests for each client in advance based on various conditions such as season and age using an artificial intelligence model for predicting demand for genetic diagnostic tests, and it is possible to optimize the genetic diagnostic test request cost for each client according to the demand predicted in advance.

[0039] In addition, the above-mentioned genetic diagnostic test service provision system (100) can reduce costs by performing resource allocation according to genetic diagnostic tests through the prediction of demand for genetic diagnostic tests of specific medical institutions predicted by an artificial intelligence model for predicting demand for genetic diagnostic tests, and can also perform product or content development to improve product planning and sales efficiency of genetic diagnostic tests.

[0040] The above client terminal (200) is a communication terminal, such as a PC or smartphone, used by a medical institution representative who requests a genetic diagnostic test of a patient or client from the above genetic diagnostic test service providing system (100).

[0041] The above client terminal (200) performs communication connection to the above genetic diagnostic test service providing system (100) through an application program or the web, then transmits the patient's sample to the above genetic diagnostic test service providing system (100) along with a genetic diagnostic test request form and a patient consent form written by the patient or client who is the subject of the genetic diagnostic test, and receives the genetic diagnostic test results from the above genetic diagnostic test service providing system (100).

[0042] The above database (300) stores and manages various operation programs used in the above genetic diagnostic test service providing system (100) (e.g., tools for collecting and analyzing genetic diagnostic test request information, creating, managing, and updating artificial intelligence models, application programs or web access programs used on each client terminal, etc.).

[0043] In addition, the database (300) stores and manages an artificial intelligence model for predicting demand for genetic diagnostic tests that is generated based on genetic diagnostic test request information collected from clients in the genetic diagnostic test service providing system (100) and is periodically updated.

[0044] FIG. 2 is a diagram showing the configuration of a genetic diagnostic test service provision system according to one embodiment of the present invention in more detail.

[0045] As illustrated in FIG. 2, the genetic diagnostic test service providing system (100) is configured to include a data collection unit (110), a request pattern analysis unit (120), an artificial intelligence model generation unit (130), a genetic diagnostic test demand forecasting unit (140), a genetic diagnostic test cost calculation unit (150), a genetic diagnostic test cost calculation result providing unit (160), a genetic diagnostic test product development unit (170), etc.

[0046] The data collection unit (110) collects genetic diagnostic test request information from the client terminal (200) connected via the network. That is, it receives genetic diagnostic test request information from medical institutions of various levels requesting genetic diagnostic tests.

[0047] At this time, the above genetic diagnostic test request information includes information on the requesting institution and information on the test subject.

[0048] The above requesting agency information includes the name of the requesting agency, the name of the attending physician, the name of the requesting department, the address and contact information of the requesting agency, the date and time of the request, and the number of requests by the agency per week / month.

[0049] The above information regarding the test subject includes the subject's age, gender, height and weight, clinical findings (e.g., gestational age and number of fetuses in the case of pregnant women), test request items, type of specimen (e.g., blood, plasma, etc.), date and time of specimen collection, chart number, and specimen collection method.

[0050] In addition, the data collection unit (110) may receive additional information regarding the number of patients treated per day at the medical institution from each client terminal (200), and the number of patients treated per day is used when generating an artificial intelligence model for predicting the demand for genetic diagnostic tests through the artificial intelligence model generation unit (130).

[0051] The above request pattern analysis unit (120) refers to the genetic diagnostic test request information collected from each client through the above data collection unit (110), analyzes the genetic diagnostic test request patterns for each client regarding diagnostic test needs, timing, frequency, requester gender and age, etc., and provides the analyzed results to the above artificial intelligence model generation unit (130).

[0052] The artificial intelligence model generation unit (130) generates an artificial intelligence model for predicting demand for genetic diagnostic tests using the genetic diagnostic test request pattern analyzed by the request pattern analysis unit (120), and stores and manages the generated artificial intelligence model for predicting demand for genetic diagnostic tests in the database (300).

[0053] That is, the artificial intelligence model generation unit (130) generates an artificial intelligence model for predicting genetic diagnostic test demand, which is a deep learning model trained to output the genetic diagnostic test demand for each client, by using the genetic diagnostic test request pattern for each client regarding the diagnostic test needs, timing, frequency, gender and age of the test subject analyzed by the request pattern analysis unit (120) and the number of patients treated for each client additionally collected through the data collection unit (110) as training data.

[0054] At this time, the artificial intelligence model generation unit (130) is composed of a preprocessing unit (131), a training data generation unit (132), a training unit (133), and an update unit (134).

[0055] The above preprocessing unit (131) preprocesses information regarding the genetic diagnostic test request patterns analyzed by the request pattern analysis unit (120) in order to improve model performance during the process of creating an artificial intelligence model for predicting genetic diagnostic test demand.

[0056] For example, data refinement can be performed to improve the quality of data by removing or correcting information errors, outliers, etc. of the genetic diagnostic test request pattern analyzed by the request pattern analysis unit (120).

[0057] In addition, to make the collected and analyzed data related to genetic diagnostic test request patterns into a form that is easy for the model to learn, data transformation can be performed, such as converting text data into numerical vectors or adjusting the size or normalizing image data.

[0058] In addition, data standardization can be performed to stabilize model training by adjusting the range of all features equally.

[0059] The above training data generation unit (132) generates training data by splitting, sampling, and augmenting the data preprocessed through the above preprocessing unit (131).

[0060] Here, the training data generation unit (132) can divide the training data into training data used for model training and test data used for model performance evaluation.

[0061] In addition, the above-mentioned training data generation unit (132) can balance the data set using sampling techniques such as oversampling or undersampling, and can increase the number of training data through data augmentation.

[0062] The above learning unit (133) learns the learning data generated by the above learning data generation unit (132) to generate an artificial intelligence model for predicting the demand for genetic diagnostic tests. That is, it learns the patterns of the learning data and performs predictions on the data.

[0063] Additionally, the learning unit (133) evaluates the model performance using test data after the learning of the artificial intelligence model for predicting the demand for genetic diagnostic tests is completed, and based on the evaluation results, if the evaluation results are unsatisfactory, performs the preprocessing and learning process again, and if the evaluation results are satisfactory, stores the artificial intelligence model for predicting the demand for genetic diagnostic tests in the database (300).

[0064] The above update unit (134) updates the artificial intelligence model for predicting the demand for genetic diagnostic tests when the number of genetic diagnostic test request patterns continuously collected and analyzed from each client satisfies a preset standard (i.e., a number that serves as a standard for performing updates).

[0065] For example, the AI ​​model for predicting demand for genetic diagnostic tests can be updated through fine-tuning to improve learning performance on additional training data while maintaining the performance of the existing model, or through retraining to initialize the existing model and train the model from scratch using additional training data.

[0066] The above-mentioned genetic diagnostic test demand prediction unit (140) predicts the demand for genetic diagnostic tests for each client through the artificial intelligence model for predicting genetic diagnostic test demand generated by the above-mentioned artificial intelligence model generation unit (130).

[0067] The above-mentioned genetic diagnostic test cost calculation unit (150) calculates the genetic diagnostic test request cost for each client by referring to the results predicted by the above-mentioned genetic diagnostic test demand prediction unit (140). That is, it finds the optimal point for the request cost based on the demand predicted for each client.

[0068] In this case, the genetic diagnostic test conducted by the system is performed by collecting multiple samples and processing them at once when using a single test chip. In other words, since the unit cost of the genetic diagnostic test request can be adjusted according to the volume of samples processed on a single test chip, the more a client, such as a medical institution, requests a large volume of genetic diagnostic tests, the lower the cost can be.

[0069] Accordingly, the above-mentioned gene diagnostic test cost calculation unit (150) can determine the final gene diagnostic test request cost for each client corresponding to the predicted demand based on the cost of processing according to the amount of sample when performing the gene diagnostic test using one test chip, when the demand for the gene diagnostic test for each client is predicted through the above-mentioned artificial intelligence model for predicting the demand for the gene diagnostic test. At this time, it is possible to determine the gene diagnostic test request cost for each client differently depending on the selection of daily, weekly, monthly, quarterly, or yearly.

[0070] For example, when the genetic diagnostic test cost calculation unit (150) finally determines the genetic diagnostic test request cost for each client corresponding to the demand predicted by the genetic diagnostic test demand prediction unit (140), a method of determining the genetic diagnostic test request cost on a daily basis may be used. In addition, it may be possible to determine the average value of the daily genetic diagnostic test request costs during the corresponding period as the final genetic diagnostic test request cost for the client, depending on the operator's or client's selection of a weekly, monthly, quarterly, or yearly basis.

[0071] More specifically, when determining the genetic diagnostic test request cost for a specific client on a monthly basis, the genetic diagnostic test cost calculation unit (150) can determine the average value of the genetic diagnostic test request costs determined according to the daily demand of each month predicted by the artificial intelligence model for predicting genetic diagnostic test demand as the final genetic diagnostic test request cost for each month for the client.

[0072] The gene diagnostic test cost calculation result providing unit (160) transmits information regarding the gene diagnostic test request cost calculated by the gene diagnostic test cost calculation unit (150) to the corresponding client terminal (200).

[0073] The above-mentioned genetic diagnostic test product development department (170) can plan and develop products or services for genetic diagnostic tests by referring to the demand for genetic diagnostic tests for each client predicted through the above-mentioned artificial intelligence model for predicting demand for genetic diagnostic tests and the genetic diagnostic test request cost determined by the above-mentioned genetic diagnostic test cost calculation department (150) based on the demand prediction for genetic diagnostic tests.

[0074] That is, the above-mentioned genetic diagnostic test product development department (170) can predict demand for each client and, based on this, develop and plan genetic diagnostic test products based on the type, timing, and frequency of diagnostic tests according to the request cost, and provide information about the genetic diagnostic test products to each client to perform sales and promotion.

[0075] FIG. 3 is a diagram showing the hardware structure of a genetic diagnostic test service providing system according to one embodiment of the present invention.

[0076] As illustrated in FIG. 3, the hardware structure of the gene diagnostic test service providing system (100) comprises a central processing unit (1000), memory (2000), user interface (3000), database interface (4000), network interface (5000), web server (6000), etc.

[0077] The above user interface (3000) provides an input and output interface to the user by using a graphical user interface (GUI).

[0078] The above database interface (4000) provides an interface between the database and the hardware structure. The above network interface (5000) provides a network connection between devices owned by the user.

[0079] The above web server (6000) provides a means for a user to access the hardware structure through a network. Most users can use the genetic diagnostic test service providing system (100) by connecting to the web server remotely.

[0080] Each step of the configuration or method described above may be implemented as computer-readable code on a computer-readable recording medium or transmitted through a transmission medium. A computer-readable recording medium is a data storage device capable of storing data that can be read by a computer system.

[0081] Examples of computer-readable recording media include, but are not limited to, databases, ROM, RAM, CD-ROM, DVD, magnetic tape, floppy disk, and optical data storage devices. Transmission media may include carrier waves transmitted via the Internet or various types of communication channels. Additionally, computer-readable recording media may be distributed through networked computer systems so that computer-readable code is stored and executed in a distributed manner.

[0082] In addition, at least one component applied in the present invention may include or be implemented by a processor, such as a central processing unit (CPU) or a microprocessor, that performs a respective function, and two or more of said components may be combined into a single component to perform all operations or functions of the combined two or more components. Furthermore, a part of the at least one component applied in the present invention may be performed by another of these components. Additionally, communication between said components may be performed via a bus (not shown).

[0083] Next, an embodiment of a method for providing a demand forecast-based genetic diagnostic test service utilizing artificial intelligence according to the present invention configured as described above will be explained in detail with reference to FIG. 4. At this time, the order of each step according to the method of the present invention may be changed depending on the usage environment or a person skilled in the art.

[0084] FIG. 4 is a flowchart illustrating in detail the operation process of a method for providing a demand forecast-based genetic diagnostic test service using artificial intelligence according to an embodiment of the present invention.

[0085] As illustrated in FIG. 4, the genetic diagnostic test service providing system (100) collects genetic diagnostic test request information from each client terminal (200) connected via a network (S100).

[0086] Next, the genetic diagnostic test service providing system (100) analyzes the genetic diagnostic test request patterns for each client regarding diagnostic test needs, timing, frequency, gender and age of the requester by referring to the genetic diagnostic test request information collected through step S100 (S200).

[0087] In addition, the genetic diagnostic test service providing system (100) generates an artificial intelligence model for predicting demand for genetic diagnostic tests by referring to the genetic diagnostic test request pattern analyzed through step S200, and stores the generated artificial intelligence model in the database (300) (S300).

[0088] At this time, when generating an artificial intelligence model through the above S300 step, the genetic diagnostic test service providing system (100) first performs preprocessing to correct information errors or abnormalities in the genetic diagnostic test request pattern analyzed in the above S200 step, data conversion for converting text data into numerical vectors or image data into sizes or normalization, and data standardization for learning stabilization through adjustment of feature ranges.

[0089] Then, training data is generated through the partitioning, sampling, and augmentation of the preprocessed data, and an artificial intelligence model for predicting the demand for genetic diagnostic tests is created by training the generated training data. Finally, the performance of the generated artificial intelligence model for predicting the demand for genetic diagnostic tests is verified and stored in a database.

[0090] In addition, the genetic diagnostic test service providing system (100) updates the artificial intelligence model for predicting genetic diagnostic test demand through fine-tuning or re-learning when the number of genetic diagnostic test request patterns analyzed from genetic diagnostic test request information continuously added and collected from each client satisfies a preset standard.

[0091] After creating an artificial intelligence model for predicting the demand for genetic diagnostic tests through the above S300 step, the genetic diagnostic test service provision system (100) predicts the demand for genetic diagnostic tests for each client through the artificial intelligence model for predicting the demand for genetic diagnostic tests (S400), and calculates the genetic diagnostic test request cost for each client by referring to the predicted result (S500).

[0092] Next, the genetic diagnostic test service providing system (100) provides information regarding the genetic diagnostic test request cost calculated in step S500 to the corresponding client terminal (200) (S600).

[0093] In addition, the genetic diagnostic test service provision system (100) can develop and plan genetic diagnostic test products or services by referring to the demand for genetic diagnostic tests for each client predicted through the artificial intelligence model for predicting demand for genetic diagnostic tests and the genetic diagnostic test request cost determined according to the demand prediction for genetic diagnostic tests in step S500, and can perform sales and promotion by providing information about the developed and planned genetic diagnostic test products or services to each client terminal (200) (S700).

[0094] As such, the present invention can provide customized genetic diagnostic testing services for each client by utilizing an artificial intelligence model for predicting demand for genetic diagnostic testing, thereby helping to optimize the cost of requesting genetic diagnostic testing for each client, and can reduce costs through the efficient allocation of resources related to genetic diagnostic testing, as well as improve the product planning and sales efficiency of genetic diagnostic testing.

[0095] In order to more clearly express the technical concept of the present invention, the attached drawings briefly depict or omit configurations that are unrelated to or have little relevance to the technical concept of the present invention.

[0096] Although the structure and features of the present invention have been described above based on embodiments according to the present invention, the present invention is not limited thereto, and it is obvious to those skilled in the art that various changes or modifications can be made within the spirit and scope of the present invention; therefore, it is noted that such changes or modifications fall within the scope of the appended claims. Explanation of the symbols

[0097] 100 : Genetic diagnostic test service provision system 110: Data Collection Unit 120 : Request Pattern Analysis Department 130 : Artificial Intelligence Model Generation Unit 131 : Preprocessing section 132 : Training Data Generation Section 133 : Learning Department 134 : Update Section 140 : Genetic Diagnostic Test Demand Forecasting Department 150 : Genetic diagnostic test cost calculation section 160 : Genetic diagnostic test cost calculation result provision section 170 : Genetic Diagnostic Test Product Development Department 200 : Client terminal 300 : Database

Claims

Claim 1 A genetic diagnostic test service provision system based on demand forecasting utilizing artificial intelligence, characterized by comprising: a data collection unit that collects genetic diagnostic test request information from a client; a request pattern analysis unit that analyzes genetic diagnostic test request patterns for each client regarding diagnostic test needs, timing, frequency, and the gender and age of the requester by referring to the collected genetic diagnostic test request information; an artificial intelligence model generation unit that generates an artificial intelligence model for predicting genetic diagnostic test demand by referring to the analyzed genetic diagnostic test request patterns; a genetic diagnostic test demand prediction unit that predicts the demand for genetic diagnostic tests for each client through the generated artificial intelligence model for predicting genetic diagnostic test demand; a genetic diagnostic test cost calculation unit that calculates the genetic diagnostic test request cost for each client by referring to the predicted results; and a genetic diagnostic test cost calculation result provision unit that provides information on the calculated genetic diagnostic test request cost to the client. Claim 2 A system for providing demand forecast-based genetic diagnostic testing services utilizing artificial intelligence, characterized in that, in claim 1, the genetic diagnostic test request information comprises: requesting institution information including the name of the requesting institution, the name of the attending physician, the name of the requesting department, the address and contact information of the requesting institution, the date and time of the request, and the number of requests by the requesting institution per week and month; and test subject information including the age, gender, height and weight, clinical findings, test request items, type of specimen, date and time of specimen collection, and method of specimen collection. Claim 3 A genetic diagnostic test service provision system based on demand prediction using artificial intelligence, wherein the artificial intelligence model for predicting demand for genetic diagnostic tests according to claim 1 is a deep learning model trained to output the demand for genetic diagnostic tests for each client by using the genetic diagnostic test request pattern for each client regarding diagnostic test needs, timing, frequency, gender and age of the test subject analyzed by the request pattern analysis unit and the number of patients treated for each client additionally collected through the data collection unit as training data. Claim 4 In claim 3, the artificial intelligence model generation unit comprises: a preprocessing unit that performs data refinement to correct information errors or abnormalities in the genetic diagnostic test request pattern analyzed by the request pattern analysis unit, data transformation for numerical vector conversion of text data or size adjustment or normalization of image data, and data standardization for learning stabilization through adjustment of feature ranges; a training data generation unit that generates training data through partitioning, sampling, and augmentation of the data that has completed the preprocessing; and a learning unit that learns the generated training data to generate an artificial intelligence model for predicting genetic diagnostic test demand, verifies the performance of the generated artificial intelligence model for predicting genetic diagnostic test demand, and stores it in a database; characterized by being a genetic diagnostic test service provision system based on demand prediction utilizing artificial intelligence. Claim 5 A system for providing a demand-prediction-based genetic diagnostic test service utilizing artificial intelligence, characterized in that, in claim 4, the artificial intelligence model generation unit further comprises an update unit that updates the artificial intelligence model for predicting genetic diagnostic test demand through fine-tuning or re-learning when the number of genetic diagnostic test request patterns analyzed from genetic diagnostic test request information continuously added and collected from each client satisfies a preset standard. Claim 6 A genetic diagnostic test service provision system based on demand forecasting utilizing artificial intelligence according to claim 1, wherein the genetic diagnostic test cost calculation unit, when the demand for genetic diagnostic tests for each client is predicted through the artificial intelligence model for predicting demand for genetic diagnostic tests, finally determines the genetic diagnostic test request cost for each client corresponding to the predicted demand based on the processing cost according to the amount of sample when performing genetic diagnostic tests through a single test chip, and wherein the genetic diagnostic test request cost for each client can be determined differently depending on the selection of daily, weekly, monthly, quarterly, or yearly. Claim 7 A genetic diagnostic test service provision system based on demand forecasting using artificial intelligence according to claim 1, further comprising: a genetic diagnostic test product development unit that develops and plans genetic diagnostic test products based on the type, timing, and frequency of diagnostic tests by referring to the demand for genetic diagnostic tests for each client predicted through the artificial intelligence model for predicting demand for genetic diagnostic tests and the genetic diagnostic test request cost determined according to the demand forecast for genetic diagnostic tests, and provides information on the genetic diagnostic test products to each client to perform sales and promotion. Claim 8 A method for providing a demand-predicting-based genetic diagnostic test service utilizing artificial intelligence, comprising: a data collection step for collecting genetic diagnostic test request information from a client; a request pattern analysis step for analyzing genetic diagnostic test request patterns for each client regarding diagnostic test needs, timing, frequency, and the gender and age of the requester by referring to the collected genetic diagnostic test request information; an artificial intelligence model generation step for generating an artificial intelligence model for predicting genetic diagnostic test demand by referring to the analyzed genetic diagnostic test request patterns; a genetic diagnostic test demand prediction step for predicting the demand for genetic diagnostic tests for each client through the generated artificial intelligence model for predicting genetic diagnostic test demand; a genetic diagnostic test cost calculation step for calculating the genetic diagnostic test request cost for each client by referring to the predicted results; and a genetic diagnostic test cost calculation result provision step for providing information regarding the calculated genetic diagnostic test request cost to the client. Claim 9 In claim 8, the artificial intelligence model generation step further comprises: a preprocessing step for performing data refinement to correct information errors or abnormalities in the genetic diagnostic test request patterns analyzed in the request pattern analysis step, data transformation for converting text data into numerical vectors or image data into sizes or normalization, and data standardization for learning stabilization through adjustment of feature ranges; a training data generation step for generating training data through partitioning, sampling, and augmentation of the data that has completed the preprocessing; a learning step for generating an artificial intelligence model for predicting genetic diagnostic test demand by learning the generated training data, verifying the performance of the generated artificial intelligence model for predicting genetic diagnostic test demand, and storing it in a database; and an update step for updating the artificial intelligence model for predicting genetic diagnostic test demand through fine-tuning or re-learning when the number of genetic diagnostic test request patterns analyzed from genetic diagnostic test request information continuously added and collected from each client satisfies a preset standard. Claim 10 The method of claim 8 further comprises a genetic diagnostic test product development step, wherein the method refers to the demand for genetic diagnostic tests for each client predicted through the artificial intelligence model for predicting demand for genetic diagnostic tests and the genetic diagnostic test request cost determined according to the demand prediction for genetic diagnostic tests, and develops and plans a genetic diagnostic test product based on the type, timing, and frequency of the diagnostic test, and provides information about the genetic diagnostic test product to each client to perform sales and promotion.

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