KR102625111B1 - Multilevel object segmentation-based online automated system for clinical trials of pet medications and operating method thereof - Google Patents

Abstract

The method of operating the online automation system for pharmaceutical clinical trials according to various embodiments of the present application includes, by a test institute server, in response to receiving a test request from a pharmaceutical company server, registering a test agency corresponding to the test request and requesting a test on the server. performing registration; transmitting, by the server, a clinical trial approval request corresponding to the test institution registration and test request registration to the clinical trial approval institution server; transmitting, by the approval agency server, a clinical trial approval confirmation to the server; Upon receiving confirmation of clinical trial approval by the server, uploading and posting the clinical trial notice on a website accessible to servers of a plurality of clinical trial participating institutions; Receiving, by a server, data and supporting materials representing clinical trial results from at least one clinical trial participating institution server among a plurality of clinical trial participating institution servers; Generating, by a server, a result report on clinical trial results using the received data and supporting materials; It may include transmitting the generated result report by the server to the test institute server.

Description

Multi-level individualized pet drug clinical trial online automation system and its operation method {MULTILEVEL OBJECT SEGMENTATION-BASED ONLINE AUTOMATED SYSTEM FOR CLINICAL TRIALS OF PET MEDICATIONS AND OPERATING METHOD THEREOF}

The present invention relates to an online automation system for clinical trials, and specifically relates to a system that improves service efficiency by developing a platform that can automate clinical trials for pet medicines.

In existing clinical trials of pharmaceuticals for pets, testing institutions contact individual animal hospitals, and clinical trials on animals are conducted offline by veterinarians. In the case of offline clinical trials like the above, most of the time, there is no data-based data on the test results, so it is determined based on the pet's weight of 5kg, 10kg, or 20kg depending on the drug, and this is also often determined by the veterinarian's experience, so there is no clear information. There are times when there are no standards.

Therefore, there was no systematic data management of test results, making it difficult to apply consistent standards to various entities (animals). In addition, there were disadvantages in that it was difficult to monitor the clinical progress of individual hospitals due to difficulty in scheduling, and it was difficult to collect real-time clinical data together.

Lastly, there were inconveniences as there was no technology or system to conduct various clinical trials based on the data necessary for clinical trials of drugs on pets' weight, breed, age, and various other factors.

In order to solve the above-mentioned problems, our institute seeks to provide a system that collects and analyzes data through a multi-level analysis model to quantify pet drug testing through online automation.

In response to receiving a test request from a pharmaceutical company server, by a test center server according to various embodiments of the present application, registering a test center corresponding to the test request and registering a test request on the server; transmitting, by the server, a clinical trial approval request corresponding to the test institution registration and test request registration to the clinical trial approval institution server; transmitting, by the approval agency server, a clinical trial approval confirmation to the server; Upon receiving confirmation of clinical trial approval by the server, uploading and posting the clinical trial notice on a website accessible to servers of a plurality of clinical trial participating organizations; Receiving, by a server, data and supporting materials representing clinical trial results from at least one clinical trial participating institution server among a plurality of clinical trial participating institution servers; Generating, by a server, a result report on clinical trial results using the received data and supporting materials; It may include transmitting the generated result report by the server to the test institute server.

In the method according to various embodiments of the present application, the clinical trial is performed for a plurality of items by a server, and the server converts the data received from the server of the at least one clinical trial participating organization into content for the plurality of items. Classifying and generating a survey table; Generating a clinical trial result report based on the contents of the questionnaire using a model learned using machine learning techniques; It may include transmitting the generated result report to at least one participating organization server.

According to various embodiments of the present application, according to clinical trials, data may be collected separately by item according to the pet's condition, weight, breed, and age.

According to various embodiments of the hospital, the plurality of items consists of items of general symptoms, general blood test (CBC), blood chemistry test, video data, photo data, and sample data, and data on which symptoms for each item occurred. Comparing the average value of the received data and calculating cosine similarity; Setting a threshold value of cosine similarity for each item based on pre-stored data; If the cosine similarity for each item is confirmed to be within the threshold for each item, determining that the item corresponds to a symptom; A server setting a weight for each of a plurality of items; Calculating a score for the result value of the clinical result based on whether each item is satisfied and the weight of the plurality of items; It may include the step of sorting the result values in order of highest score and displaying the result values through a display.

According to various embodiments of the present application, clustering data of a plurality of pets stored in the memory by item; For each of the plurality of clustered items, calculating average value information on a normal distribution of data corresponding to the item; setting a weight for correcting a threshold for each item based on the average value calculated for each item; setting a threshold range for the average value using a weight for correcting the threshold value for each item; The data value corresponding to the item may include selecting a data value that satisfies the threshold range.

According to various embodiments of the present application, when data and supporting materials are received from a server of a participating organization, the data and supporting materials are automatically classified by a plurality of items using a survey mapping method by the server, the data is converted, and stored in memory. It may include steps.

In the method according to various embodiments of the present application, the step of confirming, by a server, that each step of claim 1 is completed when each step is performed; When the server confirms that each step has been completed, transmitting a notification message indicating that each step has been completed to the participating organization server; It may include displaying a notification message through a display connected wired or wirelessly to the participating organization server.

In our hospital's online automation system for clinical trials of various drugs, a test institution server; Pharmaceutical company servers; server; and a plurality of clinical trial participating institution servers, wherein the trial institution server, in response to receiving a test request from the pharmaceutical company server, registers a test institution corresponding to the test request and registers a test request on the server; The server transmits a clinical trial approval request corresponding to the test site registration and test request registration to the clinical trial approval agency server; The approval agency server transmits the clinical trial approval confirmation to the server; Upon receiving the clinical trial approval confirmation, the server uploads and posts the clinical trial notice on a website accessible to multiple clinical trial participating institution servers; The server receives data and supporting materials representing clinical trial results from at least one clinical trial participating institution server among the plurality of clinical trial participating institution servers; The server uses the received data and supporting materials to generate a result report on the clinical trial results; And the server may transmit the generated result report to the test agency server.

1 is a configuration diagram of an online automated drug clinical trial system according to various embodiments of the present invention.
Figure 2 is a block diagram of a server according to various embodiments of the present disclosure.
Figure 3 is a flowchart of the operation method of the online automated drug clinical trial system according to various embodiments of the present application.
4 to 5 illustrate mutual operations between a test approval organization, a testing organization, and a clinical trial participating organization according to various embodiments of the present application.
The above drawings are provided as examples so that the idea of the present invention can be sufficiently conveyed to those skilled in the art.
Accordingly, the present invention is not limited to the drawings presented below and may be embodied in other forms.
Additionally, like reference numerals refer to like elements throughout the specification.
In addition, it should be noted that in the above drawings, to facilitate understanding, certain parts are enlarged or reduced not in proportion to the scale.

Various embodiments are now described with reference to the drawings. In this specification, various descriptions are presented to provide a better understanding of the disclosure. However, it is clear that these embodiments may be practiced without these specific descriptions.

As used herein, the terms “component,” “module,” “system,” and the like refer to a computer-related entity, hardware, firmware, software, a combination of software and hardware, or an implementation of software. For example, a component may be, but is not limited to, a process running on a processor, a processor, an object, a thread of execution, a program, and/or a computer. For example, both an application running on an electronic device and the electronic device can be a component. One or more components may reside within a processor and/or thread of execution. A component can be localized within one computer. A component may be distributed between two or more computers. Additionally, these components can execute from various computer-readable media having various data structures stored thereon. Components can communicate, for example, with one or more data packets (e.g., data and/or signals from one component interacting with other components in a local system, distributed system, other systems and networks such as the Internet). Depending on the data being transmitted, the communication may be carried out locally and/or remotely.

Additionally, the term “or” is intended to mean an inclusive “or” and not an exclusive “or.” That is, unless otherwise specified or clear from context, “X utilizes A or B” is intended to mean one of the natural implicit substitutions. That is, either X uses A; X uses B; Or, if X uses both A and B, “X uses A or B” can apply to either of these cases. Additionally, the term “and/or” as used herein should be understood to refer to and include all possible combinations of one or more of the related listed items.

Additionally, the terms “comprise” and/or “comprising” should be understood to mean that the corresponding feature and/or element is present. However, the terms “comprise” and/or “comprising” should be understood as not excluding the presence or addition of one or more other features, elements and/or groups thereof. Additionally, unless otherwise specified or the context is clear to indicate a singular form, the singular terms herein and in the claims should generally be construed to mean “one or more.”

And, the term “at least one of A or B” should be interpreted to mean “a case containing only A,” “a case containing only B,” and “a case of combining A and B.”

Those skilled in the art will additionally recognize that the various illustrative logical blocks, components, modules, circuits, means, logic, and algorithm steps described in connection with the embodiments disclosed herein may be implemented using electronic hardware, computer software, or a combination of both. It must be recognized that it can be implemented with To clearly illustrate the interchangeability of hardware and software, various illustrative components, blocks, configurations, means, logics, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented in hardware or software will depend on the specific application and design constraints imposed on the overall system. A skilled technician can implement the described functionality in a variety of ways for each specific application. However, such implementation decisions should not be construed as taking the scope beyond the scope of this disclosure.

The description of the presented embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be apparent to those skilled in the art. The general principles defined herein may be applied to other embodiments without departing from the scope of the disclosure. Therefore, the present invention is not limited to the embodiments presented herein. The present invention is to be interpreted in the broadest scope consistent with the principles and novel features presented herein.

Network function, artificial neural network, and neural network may be used interchangeably herein.

Various embodiments described herein can be implemented, for example, in recording and storage media readable by a computer or similar device using software, hardware, or a combination thereof.

According to hardware implementation, the embodiments described herein include application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), and field programmable gate arrays (FPGAs). It may be implemented using at least one of processors, controllers, micro-controllers, microprocessors, and other electrical units for performing functions. In some cases, as described herein, The described embodiments may be implemented in the processor of the electronic device itself.

1 is a configuration diagram of an online automated drug clinical trial system according to various embodiments of the present invention.

According to various embodiments herein, an online automation system for pharmaceutical clinical trials may be provided. The pharmaceutical clinical trial online automation system is composed of a server 100, a pharmaceutical company server 110, a testing agency server 120, an approval agency server 130, a participating agency server 140, and a clinical entity 150. You can.

The server 100 is a server that communicates with the pharmaceutical company server 110, the testing agency server 120, the approval agency server 130, and the participating agency server 140 and controls the overall operation of the online automated pharmaceutical clinical trial system. It can be referred to collectively. The pharmaceutical company server 110 may refer to the servers of pharmaceutical companies applying for clinical trials. The test institution server 120 may refer to the server of the institution conducting the clinical trial. The approval agency server 130 may refer to the server of the agency that receives the clinical trial results and notifies them of approval. Participating institution server 140 may collectively refer to servers of companies, such as hospitals, that wish to receive information about clinical trial results.

Figure 2 is a block diagram of the internal configuration of a server according to various embodiments of the present disclosure.

The configuration of the server 120 shown in Figure 2 is only a simplified example. In one embodiment of the present disclosure, server 120 may include different configurations for performing the computing environment of server 120, and only some of the disclosed configurations may constitute server 120. There is also a possibility.

Server 120 may include a processor 210, memory 220, and communication module 230. The processor 210 may be composed of one or more cores, including a central processing unit (CPU) and a general purpose graphics processing unit (GPGPU) of the server 120. unit), tensor processing unit (TPU: It can include a processor for data analysis and deep learning, such as a tensor processing unit. The processor 210 can read a computer program stored in a memory and perform data processing for machine learning according to an embodiment of the present application. Additionally, the processor 210 controls the configuration of the server 120 to operate and can implement the overall operation of the system 100.

For example, the processor 210 may typically control the overall operation of the server 120. The processor 210 can provide or process appropriate information or functions to the user by processing signals, data, information, etc. input or output through the components discussed above, or by running an application program stored in the memory 220.

Additionally, the processor 210 may control at least some of the components of the server 120 to run an application program stored in the memory 220. Furthermore, the processor 210 may operate at least two of the components included in the server 120 in combination with each other in order to run the application program.

The processor 210 is a central processing unit that manages all modules centrally to operate the system optimally. It can perform thread functions that can handle various processors, interrupt processing, storage management, and backup functions.

In addition, when real-time clinical information about a clinical entity (pet) is delivered, the processor 210 separates and stores the data into multiple stages for each plurality of items and runs a process appropriate for each stage to provide the basis for determining the standard indicator. can be provided. If other pharmaceutical clinical trials are conducted in the future, the output report can be used as a base database to refine the clinical trial.

In addition, the processor 210 is in charge of the subscription registration process of test institutions and trial participating institutions, and can perform subscriber management to proceed with the corresponding approval and start procedures when a clinical trial project is requested by a registered test institution. . The test agency server is a server operated by an agency that conducts the test upon request from a pharmaceutical company, and the test participating agency can be an institution such as a veterinary hospital that manages pets or an animal breeding institution.

The processor 210 can perform the function of collecting the data when the trial participating institution server transmits the clinical results to the server by uploading them online, converting the data into a form that can be processed by the multi-step analysis module, and delivering it. there is.

The processor 210 can perform communication to transmit and receive all data in the system through a communication module. The processor 210 can always run a background TCP server so that wired and wireless communication modules can be transmitted through TCP/IP communication and maintain constancy of communication.

The processor 210 requests the test agency server to start the project, so that the drug clinical trial is approved by the approval agency server. In the execution stage, the processor 210 announces and recruits clinical trial participating agency servers and then assigns a project account to participate in the trial. This allows data collection according to the schedule. Additionally, the processor 210 may deliver a report to the testing agency server based on all collected multi-step analysis data at the end of the project.

Other servers shown in FIG. 1 may include a similar configuration to server 100.

The communication module 230 may collectively refer to all hardware components capable of wireless communication. Since the communication module 230 is self-evident to those skilled in the art, detailed description will be omitted.

Figure 3 is a flowchart of the operation method of the online automated drug clinical trial system according to various embodiments of the present application.

In step 310, in response to receiving a test request from the pharmaceutical company server, the test institute server may register a test institute corresponding to the test request and register a test request on the server. A test request may mean a request for a clinical trial of a pharmaceutical product. Registration as a testing agency may mean registration as an organization that can supervise testing for pharmaceutical products. Registering a trial request may refer to a request containing a message indicating a plan or intention to host a clinical trial.

In step 320, a clinical trial approval request corresponding to the test institution registration and trial request registration may be transmitted by the server to the clinical trial approval institution server.

At step 330, a clinical trial approval confirmation may be transmitted to the server by the approval agency server. Through the above procedures, clinical trial approval can be achieved.

In step 340, upon receipt of clinical trial approval confirmation by the server, the clinical trial notice may be uploaded and posted on a website accessible to a plurality of clinical trial participating institution servers. In step 340, it is described that a clinical trial notice notifying that a clinical trial is scheduled to be conducted is posted on a website, but this can be done in various ways, such as sending a notification to the servers of multiple clinical trial participating institutions.

In step 350, data representing clinical trial results and supporting materials may be received by the server from at least one clinical trial participating institution server among the plurality of clinical trial participating institution servers. Data and supporting data may refer to the results obtained through clinical trials using pharmaceuticals and the indicators (success or failure) indicated by the results.

In step 360, a result report on the clinical trial results may be generated by the server using the received data and supporting materials. At step 370, the generated result report may be transmitted by the server to the test laboratory server. The result report may be a report in which data and supporting materials are written in a form generated by a server.

According to one embodiment, clinical trials may be performed for a plurality of items by a server. Depending on the clinical trial, data may be collected separately by information on the pet's condition, weight, breed, age, etc. Multiple items may consist of items such as general symptoms, general blood test (CBC), blood chemistry test, video data, photo data, and sample data.

According to one embodiment, the server may divide data received from the server of at least one clinical trial participating institution into contents of a plurality of items and generate a survey sheet. The server can generate a clinical trial result report based on the contents of the questionnaire using a model learned using machine learning techniques. The generated result report may be transmitted to at least one participating organization server. A survey sheet can refer to a tabular document prepared for the purpose of understanding the results of a clinical trial. The survey sheet can specify the pet you wish to survey and establish standards for how to survey and evaluate it. A clinical trial result report may indicate the results of a clinical trial.

According to one embodiment, the server may calculate cosine similarity by comparing the received data with the average value of data in which symptoms for each item occurred. The server may set a threshold value of cosine similarity for each item based on pre-stored data. If the server determines that the cosine similarity for each item is within the threshold for each item, it may determine that the item corresponds to a symptom. The server may set a weight for each of a plurality of items. The server may calculate a score for the clinical result based on whether each item is satisfied and the weight of the plurality of items. The server can sort the result values in order of highest score and display the result values on a display. In this way, by calculating the similarity with the data of the pet group in which a problem or symptom has occurred for each item, it is possible to determine whether each item is applicable through a quantitative evaluation of the value of the received data.

The server may cluster data on a plurality of pets stored in the memory by item. The server may calculate information on the average value of the normal distribution of data corresponding to the item for each of the plurality of clustered items. The server may set a weight for correcting the threshold for each item based on the average value calculated for each item. The server can set a threshold range for the average value using a weight for correcting the threshold for each item. The server may select a data value corresponding to an item that satisfies the threshold range. In this way, by setting a critical range for each clustered item based on the critical range, more accurate judgment can be made.

When receiving data and supporting materials from a participating organization server, the server can automatically classify the data and supporting materials into a plurality of items using a questionnaire mapping method, convert the data, and store it in memory.

According to one embodiment, when performing each step of clause 1, the server may confirm that the performance of each step is completed. When the server confirms that each step has been completed, it can transmit a notification message indicating that each step has been completed to the participating organization server. The server may display a notification message through a display connected wired or wirelessly to the participating organization server.

4 to 5 illustrate mutual operations between a test approval organization, a testing organization, and a clinical trial participating organization according to various embodiments of the present application.

Referring to Figure 4, when a request for a clinical trial occurs, the participation information of the server of the clinical trial participating institution may be stored on the server and stored on the test institution server. In Figure 4, it is shown as being transmitted directly from the server of the clinical trial participating institution to the test institution server, but it can be transmitted through the server as shown in Figure 1.

After automatic participation review of registered documents or data through the system's registration module, the clinical trial plan can be submitted to the approval agency server in the form of survey mapping based on the registration documents on the test agency server. In the process, participating hospitals corresponding to participating institutions can be registered, and clinical trial plans can be registered and stored through the server. Documents or data registered from the testing agency server to the approval agency server may be transmitted directly or through the server.

If the clinical trial is rejected by the approval agency server, the test agency server can supplement the test plan, re-request the clinical trial plan to the approval agency, and conduct the clinical trial after approval. The server can manage all interfaces so that the above-mentioned procedures for signing up and conducting tests by participating testing institutions and starting tests by testing institutions are automated.

Referring to Figure 5, when the server of a clinical trial participating institution submits the results of each multi-step test item to the server, the server can organize the test items so that the data can be mapped to a survey table by the multi-step analysis module. Alternatively, as shown in Figure 5, the results for each test item can be submitted directly from the server of the clinical trial participating institution to the test institution server. Alternatively, when all tests are completed, the test results are automatically delivered to the test approval agency through review by the testing agency, so the clinical trial can be completed. As mentioned above, the items included in the survey table are comprehensive result data of test animals and can include general symptoms, blood tests, chemical tests, imaging data, and sample data. When a clinical trial is completed, compensation can be automatically delivered to the trial participating organizations through the server.

Those skilled in the art will understand that information and signals can be represented using any of a variety of different technologies and techniques. For example, data, instructions, commands, information, signals, bits, symbols and chips that may be referenced in the above description include voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields. It can be expressed by particles or particles, or any combination thereof.

Those of skill in the art will understand that the various illustrative logical blocks, modules, processors, means, circuits and algorithm steps described in connection with the embodiments disclosed herein may be used in electronic hardware, (for convenience) It will be understood that the implementation may be implemented by various forms of program or design code (referred to herein as software) or a combination of both. To clearly illustrate this interoperability of hardware and software, various illustrative components, blocks, modules, circuits and steps have been described above generally with respect to their functionality. Whether this functionality is implemented as hardware or software depends on the specific application and design constraints imposed on the overall system. A person skilled in the art may implement the described functionality in a variety of ways for each specific application, but such implementation decisions should not be construed as departing from the scope of the present application.

The various embodiments presented herein may be implemented as a method, apparatus, or article of manufacture using standard programming and/or engineering techniques. The term article of manufacture includes a computer program, carrier, or media accessible from any computer-readable storage device. For example, computer-readable storage media includes magnetic storage devices (e.g., hard disks, floppy disks, magnetic strips, etc.), optical disks (e.g., CDs, DVDs, etc.), smart cards, and Includes, but is not limited to, flash memory devices (e.g., EEPROM, cards, sticks, key drives, etc.). Additionally, various storage media presented herein include one or more devices and/or other machine-readable media for storing information.

It is to be understood that the specific order or hierarchy of steps in the processes presented is an example of illustrative approaches. It is to be understood that the specific order or hierarchy of steps in processes may be rearranged within the scope of the present disclosure, based on design priorities. The appended method claims present elements of the various steps in a sample order but are not meant to be limited to the particular order or hierarchy presented.

The description of the presented embodiments is provided to enable any person skilled in the art to use or practice the present disclosure. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be applied to other embodiments without departing from the scope of the disclosure. Accordingly, the disclosure is not limited to the embodiments presented herein, but is to be construed in the broadest scope consistent with the principles and novel features presented herein.

Claims (8)

In response to receiving a test request from the pharmaceutical company server, by the test agency server, registering a test agency corresponding to the test request and registering a test request on the server;
transmitting, by the server, a clinical trial approval request corresponding to the test institution registration and test request registration to the clinical trial approval institution server;
transmitting, by the approval agency server, a clinical trial approval confirmation to the server;
Upon receiving confirmation of clinical trial approval by the server, uploading and posting the clinical trial notice on a website accessible to servers of a plurality of clinical trial participating organizations;
Receiving, by a server, data and supporting materials representing clinical trial results from at least one clinical trial participating institution server among a plurality of clinical trial participating institution servers;
Generating, by a server, a result report on clinical trial results using the received data and supporting materials;
Transmitting, by the server, the generated result report to the test institute server;
Including,
Clinical trials are performed for multiple items by the server,
generating, by a server, data received from the server of the at least one clinical trial participating organization into contents of a plurality of items to create a questionnaire;
Generating, by a server, a clinical trial result report based on the contents of the questionnaire using a model learned using machine learning techniques;
transmitting the generated result report to at least one participating institution server;
Including,
Multiple items consist of general symptoms, general blood test (CBC), blood chemistry test, video data, photo data, and sample data.
Comparing the received data with the average value of data in which symptoms for each item occurred, and calculating cosine similarity;
Setting a threshold value of cosine similarity for each item based on pre-stored data;
If the cosine similarity for each item is confirmed to be within the threshold for each item, determining that the item corresponds to a symptom;
A server setting a weight for each of a plurality of items;
Calculating a score for the result value of the clinical result based on whether each item is satisfied and the weight of the plurality of items;
Sorting the result values in order of highest score and displaying the result values on a display;
Including,
Clustering data of a plurality of pets stored in memory by item;
For each of the plurality of clustered items, calculating average value information on a normal distribution of data corresponding to the item;
setting a weight for correcting a threshold for each item based on the average value calculated for each item;
setting a threshold range for the average value using a weight for correcting the threshold value for each item;
selecting a data value corresponding to an item satisfying the threshold range;
Including,
Confirming, by the server, that each step has been completed;
When the server confirms that each step has been completed, transmitting a notification message indicating that each step has been completed to the participating organization server;
A method of operating an online automated drug clinical trial system, comprising displaying a notification message through a display connected wired or wirelessly to a participating institution server.
According to paragraph 1,
A method of operating an online automated drug clinical trial system in which data is collected separately by information on the pet's condition, weight, breed, and age, depending on the clinical trial.
According to paragraph 2,
When receiving data and supporting data from a server of a participating institution, the server automatically classifies the data and supporting data into a plurality of items using a questionnaire mapping method, converts the data, and stores it in memory. How to operate the online testing automation system.
In the online automation system for pharmaceutical clinical trials,
Test laboratory server;
Pharmaceutical company servers;
server; and
Contains multiple clinical trial participating institution servers,
The test institute server, in response to receiving a test request from the pharmaceutical company server, performs test agency registration and test request registration corresponding to the test request on the server;
The server transmits a clinical trial approval request corresponding to the test site registration and test request registration to the clinical trial approval agency server;
The approval agency server transmits the clinical trial approval confirmation to the server;
Upon receiving the clinical trial approval confirmation, the server uploads and posts the clinical trial notice on a website accessible to multiple clinical trial participating institution servers;
The server receives data and supporting materials representing clinical trial results from at least one clinical trial participating institution server among the plurality of clinical trial participating institution servers;
The server uses the received data and supporting materials to generate a result report on the clinical trial results; and
The server transmits the generated result report to the test institute server,
Clinical trials are performed for multiple items by the server,
The server divides the data received from the server of the at least one clinical trial participating institution into contents of a plurality of items and generates a questionnaire,
The server uses a model learned using machine learning techniques to generate a report on the results of the clinical trial based on the contents of the questionnaire,
The server transmits the generated result report to at least one participating institution server,
Multiple items consist of general symptoms, general blood test (CBC), blood chemistry test, video data, photo data, and sample data.
The server calculates the cosine similarity by comparing the average value of the data in which the symptom for each item occurred with the received data,
The server sets the threshold of cosine similarity for each item based on pre-stored data,
If the server confirms that the cosine similarity for each item is within the threshold for each item, it determines that the item corresponds to the symptom,
The server sets a weight for each of the plurality of items,
The server calculates a score for the clinical result based on whether each item is satisfied and the weight of the plurality of items,
The server sorts the result values in order of high score, displays the result values on the display, clusters the data of the plurality of pets stored in the memory by item, and corresponds to the item for each of the clustered plurality of items. Calculate information on the average value on the normal distribution of data, set a weight for correcting the threshold for each item based on the average value calculated for each item, and set a weight for correcting the threshold for each item. Using this, set a threshold range for the average value and select a data value whose data value corresponding to the item satisfies the threshold range,
When the server confirms that the performance of each step has been completed, it transmits a notification message indicating that the performance of each step has been completed to the participating organization server, and sends a notification message to the participating organization server by wire or wirelessly. Displaying notification messages through a connected display,
Online automation system for pharmaceutical clinical trials.

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